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Sample records for protein-coding gene prediction

  1. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  2. Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

    International Nuclear Information System (INIS)

    Yu Jia-Feng; Sui Tian-Xiang; Wang Ji-Hua; Wang Hong-Mei; Wang Chun-Ling; Jing Li

    2015-01-01

    Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. (special topic)

  3. Histone modification profiles are predictive for tissue/cell-type specific expression of both protein-coding and microRNA genes

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    Zhang Michael Q

    2011-05-01

    Full Text Available Abstract Background Gene expression is regulated at both the DNA sequence level and through modification of chromatin. However, the effect of chromatin on tissue/cell-type specific gene regulation (TCSR is largely unknown. In this paper, we present a method to elucidate the relationship between histone modification/variation (HMV and TCSR. Results A classifier for differentiating CD4+ T cell-specific genes from housekeeping genes using HMV data was built. We found HMV in both promoter and gene body regions to be predictive of genes which are targets of TCSR. For example, the histone modification types H3K4me3 and H3K27ac were identified as the most predictive for CpG-related promoters, whereas H3K4me3 and H3K79me3 were the most predictive for nonCpG-related promoters. However, genes targeted by TCSR can be predicted using other type of HMVs as well. Such redundancy implies that multiple type of underlying regulatory elements, such as enhancers or intragenic alternative promoters, which can regulate gene expression in a tissue/cell-type specific fashion, may be marked by the HMVs. Finally, we show that the predictive power of HMV for TCSR is not limited to protein-coding genes in CD4+ T cells, as we successfully predicted TCSR targeted genes in muscle cells, as well as microRNA genes with expression specific to CD4+ T cells, by the same classifier which was trained on HMV data of protein-coding genes in CD4+ T cells. Conclusion We have begun to understand the HMV patterns that guide gene expression in both tissue/cell-type specific and ubiquitous manner.

  4. Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

    KAUST Repository

    Alam, Tanvir

    2014-10-02

    Transcriptional regulation of protein-coding genes is increasingly well-understood on a global scale, yet no comparable information exists for long non-coding RNA (lncRNA) genes, which were recently recognized to be as numerous as protein-coding genes in mammalian genomes. We performed a genome-wide comparative analysis of the promoters of human lncRNA and protein-coding genes, finding global differences in specific genetic and epigenetic features relevant to transcriptional regulation. These two groups of genes are hence subject to separate transcriptional regulatory programs, including distinct transcription factor (TF) proteins that significantly favor lncRNA, rather than coding-gene, promoters. We report a specific signature of promoter-proximal transcriptional regulation of lncRNA genes, including several distinct transcription factor binding sites (TFBS). Experimental DNase I hypersensitive site profiles are consistent with active configurations of these lncRNA TFBS sets in diverse human cell types. TFBS ChIP-seq datasets confirm the binding events that we predicted using computational approaches for a subset of factors. For several TFs known to be directly regulated by lncRNAs, we find that their putative TFBSs are enriched at lncRNA promoters, suggesting that the TFs and the lncRNAs may participate in a bidirectional feedback loop regulatory network. Accordingly, cells may be able to modulate lncRNA expression levels independently of mRNA levels via distinct regulatory pathways. Our results also raise the possibility that, given the historical reliance on protein-coding gene catalogs to define the chromatin states of active promoters, a revision of these chromatin signature profiles to incorporate expressed lncRNA genes is warranted in the future.

  5. Hominoid-specific de novo protein-coding genes originating from long non-coding RNAs.

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    Chen Xie

    2012-09-01

    Full Text Available Tinkering with pre-existing genes has long been known as a major way to create new genes. Recently, however, motherless protein-coding genes have been found to have emerged de novo from ancestral non-coding DNAs. How these genes originated is not well addressed to date. Here we identified 24 hominoid-specific de novo protein-coding genes with precise origination timing in vertebrate phylogeny. Strand-specific RNA-Seq analyses were performed in five rhesus macaque tissues (liver, prefrontal cortex, skeletal muscle, adipose, and testis, which were then integrated with public transcriptome data from human, chimpanzee, and rhesus macaque. On the basis of comparing the RNA expression profiles in the three species, we found that most of the hominoid-specific de novo protein-coding genes encoded polyadenylated non-coding RNAs in rhesus macaque or chimpanzee with a similar transcript structure and correlated tissue expression profile. According to the rule of parsimony, the majority of these hominoid-specific de novo protein-coding genes appear to have acquired a regulated transcript structure and expression profile before acquiring coding potential. Interestingly, although the expression profile was largely correlated, the coding genes in human often showed higher transcriptional abundance than their non-coding counterparts in rhesus macaque. The major findings we report in this manuscript are robust and insensitive to the parameters used in the identification and analysis of de novo genes. Our results suggest that at least a portion of long non-coding RNAs, especially those with active and regulated transcription, may serve as a birth pool for protein-coding genes, which are then further optimized at the transcriptional level.

  6. Computational prediction of over-annotated protein-coding genes in the genome of Agrobacterium tumefaciens strain C58

    Science.gov (United States)

    Yu, Jia-Feng; Sui, Tian-Xiang; Wang, Hong-Mei; Wang, Chun-Ling; Jing, Li; Wang, Ji-Hua

    2015-12-01

    Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. Project supported by the National Natural Science Foundation of China (Grant Nos. 61302186 and 61271378) and the Funding from the State Key Laboratory of Bioelectronics of Southeast University.

  7. De novo origin of human protein-coding genes.

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    Dong-Dong Wu

    2011-11-01

    Full Text Available The de novo origin of a new protein-coding gene from non-coding DNA is considered to be a very rare occurrence in genomes. Here we identify 60 new protein-coding genes that originated de novo on the human lineage since divergence from the chimpanzee. The functionality of these genes is supported by both transcriptional and proteomic evidence. RNA-seq data indicate that these genes have their highest expression levels in the cerebral cortex and testes, which might suggest that these genes contribute to phenotypic traits that are unique to humans, such as improved cognitive ability. Our results are inconsistent with the traditional view that the de novo origin of new genes is very rare, thus there should be greater appreciation of the importance of the de novo origination of genes.

  8. De Novo Origin of Human Protein-Coding Genes

    Science.gov (United States)

    Wu, Dong-Dong; Irwin, David M.; Zhang, Ya-Ping

    2011-01-01

    The de novo origin of a new protein-coding gene from non-coding DNA is considered to be a very rare occurrence in genomes. Here we identify 60 new protein-coding genes that originated de novo on the human lineage since divergence from the chimpanzee. The functionality of these genes is supported by both transcriptional and proteomic evidence. RNA–seq data indicate that these genes have their highest expression levels in the cerebral cortex and testes, which might suggest that these genes contribute to phenotypic traits that are unique to humans, such as improved cognitive ability. Our results are inconsistent with the traditional view that the de novo origin of new genes is very rare, thus there should be greater appreciation of the importance of the de novo origination of genes. PMID:22102831

  9. Revisiting the missing protein-coding gene catalog of the domestic dog

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    Galibert Francis

    2009-02-01

    Full Text Available Abstract Background Among mammals for which there is a high sequence coverage, the whole genome assembly of the dog is unique in that it predicts a low number of protein-coding genes, ~19,000, compared to the over 20,000 reported for other mammalian species. Of particular interest are the more than 400 of genes annotated in primates and rodent genomes, but missing in dog. Results Using over 14,000 orthologous genes between human, chimpanzee, mouse rat and dog, we built multiple pairwise synteny maps to infer short orthologous intervals that were targeted for characterizing the canine missing genes. Based on gene prediction and a functionality test using the ratio of replacement to silent nucleotide substitution rates (dN/dS, we provide compelling structural and functional evidence for the identification of 232 new protein-coding genes in the canine genome and 69 gene losses, characterized as undetected gene or pseudogenes. Gene loss phyletic pattern analysis using ten species from chicken to human allowed us to characterize 28 canine-specific gene losses that have functional orthologs continuously from chicken or marsupials through human, and 10 genes that arose specifically in the evolutionary lineage leading to rodent and primates. Conclusion This study demonstrates the central role of comparative genomics for refining gene catalogs and exploring the evolutionary history of gene repertoires, particularly as applied for the characterization of species-specific gene gains and losses.

  10. Vertebrate gene predictions and the problem of large genes

    DEFF Research Database (Denmark)

    Wang, Jun; Li, ShengTing; Zhang, Yong

    2003-01-01

    To find unknown protein-coding genes, annotation pipelines use a combination of ab initio gene prediction and similarity to experimentally confirmed genes or proteins. Here, we show that although the ab initio predictions have an intrinsically high false-positive rate, they also have a consistent...

  11. Proteogenomics of rare taxonomic phyla: A prospective treasure trove of protein coding genes.

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    Kumar, Dhirendra; Mondal, Anupam Kumar; Kutum, Rintu; Dash, Debasis

    2016-01-01

    Sustainable innovations in sequencing technologies have resulted in a torrent of microbial genome sequencing projects. However, the prokaryotic genomes sequenced so far are unequally distributed along their phylogenetic tree; few phyla contain the majority, the rest only a few representatives. Accurate genome annotation lags far behind genome sequencing. While automated computational prediction, aided by comparative genomics, remains a popular choice for genome annotation, substantial fraction of these annotations are erroneous. Proteogenomics utilizes protein level experimental observations to annotate protein coding genes on a genome wide scale. Benefits of proteogenomics include discovery and correction of gene annotations regardless of their phylogenetic conservation. This not only allows detection of common, conserved proteins but also the discovery of protein products of rare genes that may be horizontally transferred or taxonomy specific. Chances of encountering such genes are more in rare phyla that comprise a small number of complete genome sequences. We collated all bacterial and archaeal proteogenomic studies carried out to date and reviewed them in the context of genome sequencing projects. Here, we present a comprehensive list of microbial proteogenomic studies, their taxonomic distribution, and also urge for targeted proteogenomics of underexplored taxa to build an extensive reference of protein coding genes. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Bioinformatics analysis identify novel OB fold protein coding genes in C. elegans.

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    Daryanaz Dargahi

    Full Text Available BACKGROUND: The C. elegans genome has been extensively annotated by the WormBase consortium that uses state of the art bioinformatics pipelines, functional genomics and manual curation approaches. As a result, the identification of novel genes in silico in this model organism is becoming more challenging requiring new approaches. The Oligonucleotide-oligosaccharide binding (OB fold is a highly divergent protein family, in which protein sequences, in spite of having the same fold, share very little sequence identity (5-25%. Therefore, evidence from sequence-based annotation may not be sufficient to identify all the members of this family. In C. elegans, the number of OB-fold proteins reported is remarkably low (n=46 compared to other evolutionary-related eukaryotes, such as yeast S. cerevisiae (n=344 or fruit fly D. melanogaster (n=84. Gene loss during evolution or differences in the level of annotation for this protein family, may explain these discrepancies. METHODOLOGY/PRINCIPAL FINDINGS: This study examines the possibility that novel OB-fold coding genes exist in the worm. We developed a bioinformatics approach that uses the most sensitive sequence-sequence, sequence-profile and profile-profile similarity search methods followed by 3D-structure prediction as a filtering step to eliminate false positive candidate sequences. We have predicted 18 coding genes containing the OB-fold that have remarkably partially been characterized in C. elegans. CONCLUSIONS/SIGNIFICANCE: This study raises the possibility that the annotation of highly divergent protein fold families can be improved in C. elegans. Similar strategies could be implemented for large scale analysis by the WormBase consortium when novel versions of the genome sequence of C. elegans, or other evolutionary related species are being released. This approach is of general interest to the scientific community since it can be used to annotate any genome.

  13. IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction

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    Kiran Sree Pokkuluri

    2014-01-01

    Full Text Available Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000. The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata and MCC (modified clonal classifier to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992 datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006 dataset and nonpromoters from EID (Saxonov et al., 2000 and UTRdb (Pesole et al., 2002 datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

  14. Origins of gene, genetic code, protein and life

    Indian Academy of Sciences (India)

    Unknown

    have concluded that newly-born genes are products of nonstop frames (NSF) ... research to determine tertiary structures of proteins such ... the present earth, is favourable for new genes to arise, if ..... NGG) in the universal genetic code table, cannot satisfy ..... which has been proposed to explain the development of life on.

  15. Evolutionary modeling and prediction of non-coding RNAs in Drosophila.

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    Robert K Bradley

    2009-08-01

    Full Text Available We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3' end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.

  16. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

    Science.gov (United States)

    Zhou, Ke-Ren; Liu, Shun; Sun, Wen-Ju; Zheng, Ling-Ling; Zhou, Hui; Yang, Jian-Hua; Qu, Liang-Hu

    2017-01-04

    The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  17. A dual origin of the Xist gene from a protein-coding gene and a set of transposable elements.

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    Eugeny A Elisaphenko

    2008-06-01

    Full Text Available X-chromosome inactivation, which occurs in female eutherian mammals is controlled by a complex X-linked locus termed the X-inactivation center (XIC. Previously it was proposed that genes of the XIC evolved, at least in part, as a result of pseudogenization of protein-coding genes. In this study we show that the key XIC gene Xist, which displays fragmentary homology to a protein-coding gene Lnx3, emerged de novo in early eutherians by integration of mobile elements which gave rise to simple tandem repeats. The Xist gene promoter region and four out of ten exons found in eutherians retain homology to exons of the Lnx3 gene. The remaining six Xist exons including those with simple tandem repeats detectable in their structure have similarity to different transposable elements. Integration of mobile elements into Xist accompanies the overall evolution of the gene and presumably continues in contemporary eutherian species. Additionally we showed that the combination of remnants of protein-coding sequences and mobile elements is not unique to the Xist gene and is found in other XIC genes producing non-coding nuclear RNA.

  18. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  19. Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

    KAUST Repository

    Alam, Tanvir; Medvedeva, Yulia A.; Jia, Hui; Brown, James B.; Lipovich, Leonard; Bajic, Vladimir B.

    2014-01-01

    raise the possibility that, given the historical reliance on protein-coding gene catalogs to define the chromatin states of active promoters, a revision of these chromatin signature profiles to incorporate expressed lncRNA genes is warranted

  20. Codon usage and expression level of human mitochondrial 13 protein coding genes across six continents.

    Science.gov (United States)

    Chakraborty, Supriyo; Uddin, Arif; Mazumder, Tarikul Huda; Choudhury, Monisha Nath; Malakar, Arup Kumar; Paul, Prosenjit; Halder, Binata; Deka, Himangshu; Mazumder, Gulshana Akthar; Barbhuiya, Riazul Ahmed; Barbhuiya, Masuk Ahmed; Devi, Warepam Jesmi

    2017-12-02

    The study of codon usage coupled with phylogenetic analysis is an important tool to understand the genetic and evolutionary relationship of a gene. The 13 protein coding genes of human mitochondria are involved in electron transport chain for the generation of energy currency (ATP). However, no work has yet been reported on the codon usage of the mitochondrial protein coding genes across six continents. To understand the patterns of codon usage in mitochondrial genes across six different continents, we used bioinformatic analyses to analyze the protein coding genes. The codon usage bias was low as revealed from high ENC value. Correlation between codon usage and GC3 suggested that all the codons ending with G/C were positively correlated with GC3 but vice versa for A/T ending codons with the exception of ND4L and ND5 genes. Neutrality plot revealed that for the genes ATP6, COI, COIII, CYB, ND4 and ND4L, natural selection might have played a major role while mutation pressure might have played a dominant role in the codon usage bias of ATP8, COII, ND1, ND2, ND3, ND5 and ND6 genes. Phylogenetic analysis indicated that evolutionary relationships in each of 13 protein coding genes of human mitochondria were different across six continents and further suggested that geographical distance was an important factor for the origin and evolution of 13 protein coding genes of human mitochondria. Copyright © 2017 Elsevier B.V. and Mitochondria Research Society. All rights reserved.

  1. A human-specific de novo protein-coding gene associated with human brain functions.

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    Chuan-Yun Li

    2010-03-01

    Full Text Available To understand whether any human-specific new genes may be associated with human brain functions, we computationally screened the genetic vulnerable factors identified through Genome-Wide Association Studies and linkage analyses of nicotine addiction and found one human-specific de novo protein-coding gene, FLJ33706 (alternative gene symbol C20orf203. Cross-species analysis revealed interesting evolutionary paths of how this gene had originated from noncoding DNA sequences: insertion of repeat elements especially Alu contributed to the formation of the first coding exon and six standard splice junctions on the branch leading to humans and chimpanzees, and two subsequent substitutions in the human lineage escaped two stop codons and created an open reading frame of 194 amino acids. We experimentally verified FLJ33706's mRNA and protein expression in the brain. Real-Time PCR in multiple tissues demonstrated that FLJ33706 was most abundantly expressed in brain. Human polymorphism data suggested that FLJ33706 encodes a protein under purifying selection. A specifically designed antibody detected its protein expression across human cortex, cerebellum and midbrain. Immunohistochemistry study in normal human brain cortex revealed the localization of FLJ33706 protein in neurons. Elevated expressions of FLJ33706 were detected in Alzheimer's brain samples, suggesting the role of this novel gene in human-specific pathogenesis of Alzheimer's disease. FLJ33706 provided the strongest evidence so far that human-specific de novo genes can have protein-coding potential and differential protein expression, and be involved in human brain functions.

  2. Discovery of rare protein-coding genes in model methylotroph Methylobacterium extorquens AM1.

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    Kumar, Dhirendra; Mondal, Anupam Kumar; Yadav, Amit Kumar; Dash, Debasis

    2014-12-01

    Proteogenomics involves the use of MS to refine annotation of protein-coding genes and discover genes in a genome. We carried out comprehensive proteogenomic analysis of Methylobacterium extorquens AM1 (ME-AM1) from publicly available proteomics data with a motive to improve annotation for methylotrophs; organisms capable of surviving in reduced carbon compounds such as methanol. Besides identifying 2482(50%) proteins, 29 new genes were discovered and 66 annotated gene models were revised in ME-AM1 genome. One such novel gene is identified with 75 peptides, lacks homolog in other methylobacteria but has glycosyl transferase and lipopolysaccharide biosynthesis protein domains, indicating its potential role in outer membrane synthesis. Many novel genes are present only in ME-AM1 among methylobacteria. Distant homologs of these genes in unrelated taxonomic classes and low GC-content of few genes suggest lateral gene transfer as a potential mode of their origin. Annotations of methylotrophy related genes were also improved by the discovery of a short gene in methylotrophy gene island and redefining a gene important for pyrroquinoline quinone synthesis, essential for methylotrophy. The combined use of proteogenomics and rigorous bioinformatics analysis greatly enhanced the annotation of protein-coding genes in model methylotroph ME-AM1 genome. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Non-Protein Coding RNAs

    CERN Document Server

    Walter, Nils G; Batey, Robert T

    2009-01-01

    This book assembles chapters from experts in the Biophysics of RNA to provide a broadly accessible snapshot of the current status of this rapidly expanding field. The 2006 Nobel Prize in Physiology or Medicine was awarded to the discoverers of RNA interference, highlighting just one example of a large number of non-protein coding RNAs. Because non-protein coding RNAs outnumber protein coding genes in mammals and other higher eukaryotes, it is now thought that the complexity of organisms is correlated with the fraction of their genome that encodes non-protein coding RNAs. Essential biological processes as diverse as cell differentiation, suppression of infecting viruses and parasitic transposons, higher-level organization of eukaryotic chromosomes, and gene expression itself are found to largely be directed by non-protein coding RNAs. The biophysical study of these RNAs employs X-ray crystallography, NMR, ensemble and single molecule fluorescence spectroscopy, optical tweezers, cryo-electron microscopy, and ot...

  4. False positive reduction in protein-protein interaction predictions using gene ontology annotations

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    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  5. Emerging putative associations between non-coding RNAs and protein-coding genes in Neuropathic Pain. Added value from re-using microarray data.

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    Enrico Capobianco

    2016-10-01

    Full Text Available Regeneration of injured nerves is likely occurring in the peripheral nervous system, but not in the central nervous system. Although protein-coding gene expression has been assessed during nerve regeneration, little is currently known about the role of non-coding RNAs (ncRNAs. This leaves open questions about the potential effects of ncRNAs at transcriptome level. Due to the limited availability of human neuropathic pain data, we have identified the most comprehensive time-course gene expression profile referred to sciatic nerve injury, and studied in a rat model, using two neuronal tissues, namely dorsal root ganglion (DRG and sciatic nerve (SN. We have developed a methodology to identify differentially expressed bioentities starting from microarray probes, and re-purposing them to annotate ncRNAs, while analyzing the expression profiles of protein-coding genes. The approach is designed to reuse microarray data and perform first profiling and then meta-analysis through three main steps. First, we used contextual analysis to identify what we considered putative or potential protein coding targets for selected ncRNAs. Relevance was therefore assigned to differential expression of neighbor protein-coding genes, with neighborhood defined by a fixed genomic distance from long or antisense ncRNA loci, and of parent genes associated with pseudogenes. Second, connectivity among putative targets was used to build networks, in turn useful to conduct inference at interactomic scale. Last, network paths were annotated to assess relevance to neuropathic pain. We found significant differential expression in long-intergenic ncRNAs (32 lincRNAs in SN, and 8 in DRG, antisense RNA (31 asRNA in SN, and 12 in DRG and pseudogenes (456 in SN, 56 in DRG. In particular, contextual analysis centered on pseudogenes revealed some targets with known association to neurodegeneration and/or neurogenesis processes. While modules of the olfactory receptors were clearly

  6. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

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    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  7. A study on climatic adaptation of dipteran mitochondrial protein coding genes

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    Debajyoti Kabiraj

    2017-10-01

    Full Text Available Diptera, the true flies are frequently found in nature and their habitat is found all over the world including Antarctica and Polar Regions. The number of documented species for order diptera is quite high and thought to be 14% of the total animal present in the earth [1]. Most of the study in diptera has focused on the taxa of economic and medical importance, such as the fruit flies Ceratitis capitata and Bactrocera spp. (Tephritidae, which are serious agricultural pests; the blowflies (Calliphoridae and oestrid flies (Oestridae, which can cause myiasis; the anopheles mosquitoes (Culicidae, are the vectors of malaria; and leaf-miners (Agromyzidae, vegetable and horticultural pests [2]. Insect mitochondrion consists of 13 protein coding genes, 22 tRNAs and 2 rRNAs, are the remnant portion of alpha-proteobacteria is responsible for simultaneous function of energy production and thermoregulation of the cell through the bi-genomic system thus different adaptability in different climatic condition might have compensated by complementary changes is the both genomes [3,4]. In this study we have collected complete mitochondrial genome and occurrence data of one hundred thirteen such dipteran insects from different databases and literature survey. Our understanding of the genetic basis of climatic adaptation in diptera is limited to the basic information on the occurrence location of those species and mito genetic factors underlying changes in conspicuous phenotypes. To examine this hypothesis, we have taken an approach of Nucleotide substitution analysis for 13 protein coding genes of mitochondrial DNA individually and combined by different software for monophyletic group as well as paraphyletic group of dipteran species. Moreover, we have also calculated codon adaptation index for all dipteran mitochondrial protein coding genes. Following this work, we have classified our sample organisms according to their location data from GBIF (https

  8. Natural selection in avian protein-coding genes expressed in brain.

    Science.gov (United States)

    Axelsson, Erik; Hultin-Rosenberg, Lina; Brandström, Mikael; Zwahlén, Martin; Clayton, David F; Ellegren, Hans

    2008-06-01

    The evolution of birds from theropod dinosaurs took place approximately 150 million years ago, and was associated with a number of specific adaptations that are still evident among extant birds, including feathers, song and extravagant secondary sexual characteristics. Knowledge about the molecular evolutionary background to such adaptations is lacking. Here, we analyse the evolution of > 5000 protein-coding gene sequences expressed in zebra finch brain by comparison to orthologous sequences in chicken. Mean d(N)/d(S) is 0.085 and genes with their maximal expression in the eye and central nervous system have the lowest mean d(N)/d(S) value, while those expressed in digestive and reproductive tissues exhibit the highest. We find that fast-evolving genes (those which have higher than expected rate of nonsynonymous substitution, indicative of adaptive evolution) are enriched for biological functions such as fertilization, muscle contraction, defence response, response to stress, wounding and endogenous stimulus, and cell death. After alignment to mammalian orthologues, we identify a catalogue of 228 genes that show a significantly higher rate of protein evolution in the two bird lineages than in mammals. These accelerated bird genes, representing candidates for avian-specific adaptations, include genes implicated in vocal learning and other cognitive processes. Moreover, colouration genes evolve faster in birds than in mammals, which may have been driven by sexual selection for extravagant plumage characteristics.

  9. Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes.

    Directory of Open Access Journals (Sweden)

    Casey R Richardson

    2010-05-01

    Full Text Available MicroRNAs (miRNAs and trans-acting small-interfering RNAs (tasi-RNAs are small (20-22 nt long RNAs (smRNAs generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.We explored rice (Oryza sativa sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other

  10. The small RNA content of human sperm reveals pseudogene-derived piRNAs complementary to protein-coding genes

    DEFF Research Database (Denmark)

    Pantano, Lorena; Jodar, Meritxell; Bak, Mads

    2015-01-01

    -specific genes. The most abundant class of small noncoding RNAs in sperm are PIWI-interacting RNAs (piRNAs). Surprisingly, we found that human sperm cells contain piRNAs processed from pseudogenes. Clusters of piRNAs from human testes contain pseudogenes transcribed in the antisense strand and processed...... into small RNAs. Several human protein-coding genes contain antisense predicted targets of pseudogene-derived piRNAs in the male germline and these piRNAs are still found in mature sperm. Our study provides the most extensive data set and annotation of human sperm small RNAs to date and is a resource...... for further functional studies on the roles of sperm small RNAs. In addition, we propose that some of the pseudogene-derived human piRNAs may regulate expression of their parent gene in the male germline....

  11. Evaluation of the efficacy of twelve mitochondrial protein-coding genes as barcodes for mollusk DNA barcoding.

    Science.gov (United States)

    Yu, Hong; Kong, Lingfeng; Li, Qi

    2016-01-01

    In this study, we evaluated the efficacy of 12 mitochondrial protein-coding genes from 238 mitochondrial genomes of 140 molluscan species as potential DNA barcodes for mollusks. Three barcoding methods (distance, monophyly and character-based methods) were used in species identification. The species recovery rates based on genetic distances for the 12 genes ranged from 70.83 to 83.33%. There were no significant differences in intra- or interspecific variability among the 12 genes. The monophyly and character-based methods provided higher resolution than the distance-based method in species delimitation. Especially in closely related taxa, the character-based method showed some advantages. The results suggested that besides the standard COI barcode, other 11 mitochondrial protein-coding genes could also be potentially used as a molecular diagnostic for molluscan species discrimination. Our results also showed that the combination of mitochondrial genes did not enhance the efficacy for species identification and a single mitochondrial gene would be fully competent.

  12. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

    Science.gov (United States)

    Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

    2017-11-24

    Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

  13. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

  14. Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

    Directory of Open Access Journals (Sweden)

    Assaf Gottlieb

    2017-11-01

    Full Text Available Abstract Background Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects. Methods Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. Results We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Conclusions Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort

  15. Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes.

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    Hsu, Jacob Shujui; Kwan, Johnny S H; Pan, Zhicheng; Garcia-Barcelo, Maria-Mercè; Sham, Pak Chung; Li, Miaoxin

    2016-10-15

    Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information. We examined the characteristics of Mendelian disease genes under different inheritance modes. The results suggest that Mendelian disease genes with autosomal dominant (AD) inheritance mode are more haploinsufficiency and de novo mutation sensitive, whereas those autosomal recessive (AR) genes have significantly more non-synonymous variants and regulatory transcript isoforms. In addition, the X-linked (XL) Mendelian disease genes have fewer non-synonymous and synonymous variants. As a result, we derived a new scoring system for prioritizing candidate genes for Mendelian diseases according to the inheritance mode. Our scoring system assigned to each annotated protein-coding gene (N = 18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve  = 0.84) in XL mode. The inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores. This systematic study suggests that genes manifesting disease inheritance modes tend to have unique characteristics. ISPP is included in KGGSeq v1.0 (http://grass.cgs.hku.hk/limx/kggseq/), and source code is available from (https://github.com/jacobhsu35/ISPP.git). mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online. © The Author

  16. Accurate microRNA target prediction correlates with protein repression levels

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    Simossis Victor A

    2009-09-01

    Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

  17. Annotation of the protein coding regions of the equine genome

    DEFF Research Database (Denmark)

    Hestand, Matthew S.; Kalbfleisch, Theodore S.; Coleman, Stephen J.

    2015-01-01

    Current gene annotation of the horse genome is largely derived from in silico predictions and cross-species alignments. Only a small number of genes are annotated based on equine EST and mRNA sequences. To expand the number of equine genes annotated from equine experimental evidence, we sequenced m...... and appear to be small errors in the equine reference genome, since they are also identified as homozygous variants by genomic DNA resequencing of the reference horse. Taken together, we provide a resource of equine mRNA structures and protein coding variants that will enhance equine and cross...

  18. lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

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    Silu Zhang

    2018-01-01

    Full Text Available Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs, which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

  19. Kinetic models of gene expression including non-coding RNAs

    Energy Technology Data Exchange (ETDEWEB)

    Zhdanov, Vladimir P., E-mail: zhdanov@catalysis.r

    2011-03-15

    In cells, genes are transcribed into mRNAs, and the latter are translated into proteins. Due to the feedbacks between these processes, the kinetics of gene expression may be complex even in the simplest genetic networks. The corresponding models have already been reviewed in the literature. A new avenue in this field is related to the recognition that the conventional scenario of gene expression is fully applicable only to prokaryotes whose genomes consist of tightly packed protein-coding sequences. In eukaryotic cells, in contrast, such sequences are relatively rare, and the rest of the genome includes numerous transcript units representing non-coding RNAs (ncRNAs). During the past decade, it has become clear that such RNAs play a crucial role in gene expression and accordingly influence a multitude of cellular processes both in the normal state and during diseases. The numerous biological functions of ncRNAs are based primarily on their abilities to silence genes via pairing with a target mRNA and subsequently preventing its translation or facilitating degradation of the mRNA-ncRNA complex. Many other abilities of ncRNAs have been discovered as well. Our review is focused on the available kinetic models describing the mRNA, ncRNA and protein interplay. In particular, we systematically present the simplest models without kinetic feedbacks, models containing feedbacks and predicting bistability and oscillations in simple genetic networks, and models describing the effect of ncRNAs on complex genetic networks. Mathematically, the presentation is based primarily on temporal mean-field kinetic equations. The stochastic and spatio-temporal effects are also briefly discussed.

  20. The spatial distribution of fixed mutations within genes coding for proteins

    Science.gov (United States)

    Holmquist, R.; Goodman, M.; Conroy, T.; Czelusniak, J.

    1983-01-01

    An examination has been conducted of the extensive amino acid sequence data now available for five protein families - the alpha crystallin A chain, myoglobin, alpha and beta hemoglobin, and the cytochromes c - with the goal of estimating the true spatial distribution of base substitutions within genes that code for proteins. In every case the commonly used Poisson density failed to even approximate the experimental pattern of base substitution. For the 87 species of beta hemoglobin examined, for example, the probability that the observed results were from a Poisson process was the minuscule 10 to the -44th. Analogous results were obtained for the other functional families. All the data were reasonably, but not perfectly, described by the negative binomial density. In particular, most of the data were described by one of the very simple limiting forms of this density, the geometric density. The implications of this for evolutionary inference are discussed. It is evident that most estimates of total base substitutions between genes are badly in need of revision.

  1. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  2. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Science.gov (United States)

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  3. Structural and functional studies of a family of Dictyostelium discoideum developmentally regulated, prestalk genes coding for small proteins

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    Escalante Ricardo

    2008-01-01

    Full Text Available Abstract Background The social amoeba Dictyostelium discoideum executes a multicellular development program upon starvation. This morphogenetic process requires the differential regulation of a large number of genes and is coordinated by extracellular signals. The MADS-box transcription factor SrfA is required for several stages of development, including slug migration and spore terminal differentiation. Results Subtractive hybridization allowed the isolation of a gene, sigN (SrfA-induced gene N, that was dependent on the transcription factor SrfA for expression at the slug stage of development. Homology searches detected the existence of a large family of sigN-related genes in the Dictyostelium discoideum genome. The 13 most similar genes are grouped in two regions of chromosome 2 and have been named Group1 and Group2 sigN genes. The putative encoded proteins are 87–89 amino acids long. All these genes have a similar structure, composed of a first exon containing a 13 nucleotides long open reading frame and a second exon comprising the remaining of the putative coding region. The expression of these genes is induced at10 hours of development. Analyses of their promoter regions indicate that these genes are expressed in the prestalk region of developing structures. The addition of antibodies raised against SigN Group 2 proteins induced disintegration of multi-cellular structures at the mound stage of development. Conclusion A large family of genes coding for small proteins has been identified in D. discoideum. Two groups of very similar genes from this family have been shown to be specifically expressed in prestalk cells during development. Functional studies using antibodies raised against Group 2 SigN proteins indicate that these genes could play a role during multicellular development.

  4. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

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    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  5. Comparison of protein coding gene contents of the fungal phyla Pezizomycotina and Saccharomycotina

    DEFF Research Database (Denmark)

    Arvas, Mikko; Kivioja, Teemu; Mitchell, Alex

    2007-01-01

    Saccharomycotina are slightly better characterised and predicted to encode mainly enzymes. The genes specific to Saccharomycotina are enriched in transcription and mitochondrion related functions. Especially mitochondrial ribosomal proteins seem to have diverged from those of Pezizomycotina. In addition, we...

  6. Cloud prediction of protein structure and function with PredictProtein for Debian.

    Science.gov (United States)

    Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

    2013-01-01

    We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

  7. Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

    Science.gov (United States)

    Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

    2009-02-01

    Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

  8. Ribosome Profiling Reveals Pervasive Translation Outside of Annotated Protein-Coding Genes

    Directory of Open Access Journals (Sweden)

    Nicholas T. Ingolia

    2014-09-01

    Full Text Available Ribosome profiling suggests that ribosomes occupy many regions of the transcriptome thought to be noncoding, including 5′ UTRs and long noncoding RNAs (lncRNAs. Apparent ribosome footprints outside of protein-coding regions raise the possibility of artifacts unrelated to translation, particularly when they occupy multiple, overlapping open reading frames (ORFs. Here, we show hallmarks of translation in these footprints: copurification with the large ribosomal subunit, response to drugs targeting elongation, trinucleotide periodicity, and initiation at early AUGs. We develop a metric for distinguishing between 80S footprints and nonribosomal sources using footprint size distributions, which validates the vast majority of footprints outside of coding regions. We present evidence for polypeptide production beyond annotated genes, including the induction of immune responses following human cytomegalovirus (HCMV infection. Translation is pervasive on cytosolic transcripts outside of conserved reading frames, and direct detection of this expanded universe of translated products enables efforts at understanding how cells manage and exploit its consequences.

  9. Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

    Science.gov (United States)

    Höps, Wolfram; Jeffryes, Matt; Bateman, Alex

    2018-01-01

    We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

  10. Nonsynonymous substitution rate (Ka is a relatively consistent parameter for defining fast-evolving and slow-evolving protein-coding genes

    Directory of Open Access Journals (Sweden)

    Wang Lei

    2011-02-01

    Full Text Available Abstract Background Mammalian genome sequence data are being acquired in large quantities and at enormous speeds. We now have a tremendous opportunity to better understand which genes are the most variable or conserved, and what their particular functions and evolutionary dynamics are, through comparative genomics. Results We chose human and eleven other high-coverage mammalian genome data–as well as an avian genome as an outgroup–to analyze orthologous protein-coding genes using nonsynonymous (Ka and synonymous (Ks substitution rates. After evaluating eight commonly-used methods of Ka and Ks calculation, we observed that these methods yielded a nearly uniform result when estimating Ka, but not Ks (or Ka/Ks. When sorting genes based on Ka, we noticed that fast-evolving and slow-evolving genes often belonged to different functional classes, with respect to species-specificity and lineage-specificity. In particular, we identified two functional classes of genes in the acquired immune system. Fast-evolving genes coded for signal-transducing proteins, such as receptors, ligands, cytokines, and CDs (cluster of differentiation, mostly surface proteins, whereas the slow-evolving genes were for function-modulating proteins, such as kinases and adaptor proteins. In addition, among slow-evolving genes that had functions related to the central nervous system, neurodegenerative disease-related pathways were enriched significantly in most mammalian species. We also confirmed that gene expression was negatively correlated with evolution rate, i.e. slow-evolving genes were expressed at higher levels than fast-evolving genes. Our results indicated that the functional specializations of the three major mammalian clades were: sensory perception and oncogenesis in primates, reproduction and hormone regulation in large mammals, and immunity and angiotensin in rodents. Conclusion Our study suggests that Ka calculation, which is less biased compared to Ks and Ka

  11. Combining gene prediction methods to improve metagenomic gene annotation

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    Rosen Gail L

    2011-01-01

    Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

  12. Prediction of essential proteins based on subcellular localization and gene expression correlation.

    Science.gov (United States)

    Fan, Yetian; Tang, Xiwei; Hu, Xiaohua; Wu, Wei; Ping, Qing

    2017-12-01

    Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction. The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction. In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.

  13. The small RNA content of human sperm reveals pseudogene-derived piRNAs complementary to protein-coding genes

    Science.gov (United States)

    Pantano, Lorena; Jodar, Meritxell; Bak, Mads; Ballescà, Josep Lluís; Tommerup, Niels; Oliva, Rafael; Vavouri, Tanya

    2015-01-01

    At the end of mammalian sperm development, sperm cells expel most of their cytoplasm and dispose of the majority of their RNA. Yet, hundreds of RNA molecules remain in mature sperm. The biological significance of the vast majority of these molecules is unclear. To better understand the processes that generate sperm small RNAs and what roles they may have, we sequenced and characterized the small RNA content of sperm samples from two human fertile individuals. We detected 182 microRNAs, some of which are highly abundant. The most abundant microRNA in sperm is miR-1246 with predicted targets among sperm-specific genes. The most abundant class of small noncoding RNAs in sperm are PIWI-interacting RNAs (piRNAs). Surprisingly, we found that human sperm cells contain piRNAs processed from pseudogenes. Clusters of piRNAs from human testes contain pseudogenes transcribed in the antisense strand and processed into small RNAs. Several human protein-coding genes contain antisense predicted targets of pseudogene-derived piRNAs in the male germline and these piRNAs are still found in mature sperm. Our study provides the most extensive data set and annotation of human sperm small RNAs to date and is a resource for further functional studies on the roles of sperm small RNAs. In addition, we propose that some of the pseudogene-derived human piRNAs may regulate expression of their parent gene in the male germline. PMID:25904136

  14. Revised Mimivirus major capsid protein sequence reveals intron-containing gene structure and extra domain

    Directory of Open Access Journals (Sweden)

    Suzan-Monti Marie

    2009-05-01

    Full Text Available Abstract Background Acanthamoebae polyphaga Mimivirus (APM is the largest known dsDNA virus. The viral particle has a nearly icosahedral structure with an internal capsid shell surrounded with a dense layer of fibrils. A Capsid protein sequence, D13L, was deduced from the APM L425 coding gene and was shown to be the most abundant protein found within the viral particle. However this protein remained poorly characterised until now. A revised protein sequence deposited in a database suggested an additional N-terminal stretch of 142 amino acids missing from the original deduced sequence. This result led us to investigate the L425 gene structure and the biochemical properties of the complete APM major Capsid protein. Results This study describes the full length 3430 bp Capsid coding gene and characterises the 593 amino acids long corresponding Capsid protein 1. The recombinant full length protein allowed the production of a specific monoclonal antibody able to detect the Capsid protein 1 within the viral particle. This protein appeared to be post-translationnally modified by glycosylation and phosphorylation. We proposed a secondary structure prediction of APM Capsid protein 1 compared to the Capsid protein structure of Paramecium Bursaria Chlorella Virus 1, another member of the Nucleo-Cytoplasmic Large DNA virus family. Conclusion The characterisation of the full length L425 Capsid coding gene of Acanthamoebae polyphaga Mimivirus provides new insights into the structure of the main Capsid protein. The production of a full length recombinant protein will be useful for further structural studies.

  15. Both noncoding and protein-coding RNAs contribute to gene expression evolution in the primate brain.

    Science.gov (United States)

    Babbitt, Courtney C; Fedrigo, Olivier; Pfefferle, Adam D; Boyle, Alan P; Horvath, Julie E; Furey, Terrence S; Wray, Gregory A

    2010-01-18

    Despite striking differences in cognition and behavior between humans and our closest primate relatives, several studies have found little evidence for adaptive change in protein-coding regions of genes expressed primarily in the brain. Instead, changes in gene expression may underlie many cognitive and behavioral differences. Here, we used digital gene expression: tag profiling (here called Tag-Seq, also called DGE:tag profiling) to assess changes in global transcript abundance in the frontal cortex of the brains of 3 humans, 3 chimpanzees, and 3 rhesus macaques. A substantial fraction of transcripts we identified as differentially transcribed among species were not assayed in previous studies based on microarrays. Differentially expressed tags within coding regions are enriched for gene functions involved in synaptic transmission, transport, oxidative phosphorylation, and lipid metabolism. Importantly, because Tag-Seq technology provides strand-specific information about all polyadenlyated transcripts, we were able to assay expression in noncoding intragenic regions, including both sense and antisense noncoding transcripts (relative to nearby genes). We find that many noncoding transcripts are conserved in both location and expression level between species, suggesting a possible functional role. Lastly, we examined the overlap between differential gene expression and signatures of positive selection within putative promoter regions, a sign that these differences represent adaptations during human evolution. Comparative approaches may provide important insights into genes responsible for differences in cognitive functions between humans and nonhuman primates, as well as highlighting new candidate genes for studies investigating neurological disorders.

  16. MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

    Directory of Open Access Journals (Sweden)

    Kohlbacher Oliver

    2009-09-01

    Full Text Available Abstract Background Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2.

  17. PanCoreGen - Profiling, detecting, annotating protein-coding genes in microbial genomes.

    Science.gov (United States)

    Paul, Sandip; Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V; Chattopadhyay, Sujay

    2015-12-01

    A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing the pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen - a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for a species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars - Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Single nucleotide polymorphisms (SNPs in coding regions of canine dopamine- and serotonin-related genes

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    Lingaas Frode

    2008-01-01

    Full Text Available Abstract Background Polymorphism in genes of regulating enzymes, transporters and receptors of the neurotransmitters of the central nervous system have been associated with altered behaviour, and single nucleotide polymorphisms (SNPs represent the most frequent type of genetic variation. The serotonin and dopamine signalling systems have a central influence on different behavioural phenotypes, both of invertebrates and vertebrates, and this study was undertaken in order to explore genetic variation that may be associated with variation in behaviour. Results Single nucleotide polymorphisms in canine genes related to behaviour were identified by individually sequencing eight dogs (Canis familiaris of different breeds. Eighteen genes from the dopamine and the serotonin systems were screened, revealing 34 SNPs distributed in 14 of the 18 selected genes. A total of 24,895 bp coding sequence was sequenced yielding an average frequency of one SNP per 732 bp (1/732. A total of 11 non-synonymous SNPs (nsSNPs, which may be involved in alteration of protein function, were detected. Of these 11 nsSNPs, six resulted in a substitution of amino acid residue with concomitant change in structural parameters. Conclusion We have identified a number of coding SNPs in behaviour-related genes, several of which change the amino acids of the proteins. Some of the canine SNPs exist in codons that are evolutionary conserved between five compared species, and predictions indicate that they may have a functional effect on the protein. The reported coding SNP frequency of the studied genes falls within the range of SNP frequencies reported earlier in the dog and other mammalian species. Novel SNPs are presented and the results show a significant genetic variation in expressed sequences in this group of genes. The results can contribute to an improved understanding of the genetics of behaviour.

  19. Genome-Wide Identification and Analysis of Genes Encoding PHD-Finger Protein in Tomato

    International Nuclear Information System (INIS)

    Hayat, S.; Cheng, Z.; Chen, X.

    2016-01-01

    The PHD-finger proteins are conserved in eukaryotic organisms and are involved in a variety of important functions in different biological processes in plants. However, the function of PHD fingers are poorly known in tomato (Solanum lycopersicum L.). In current study, we identified 45 putative genes coding Phd finger protein in tomato distributed on 11 chromosomes except for chromosome 8. Some of the genes encode other conserved key domains besides Phd-finger. Phylogenetic analysis of these 45 proteins resulted in seven clusters. Most Phd finger proteins were predicted to PML body location. These PHD-finger genes displayed differential expression either in various organs, at different development stages and under stresses in tomato. Our study provides the first systematic analysis of PHD-finger genes and proteins in tomato. This preliminary study provides a very useful reference information for Phd-finger proteins in tomato. They will be helpful for cloning and functional study of tomato PHD-finger genes. (author)

  20. Improvement of genome assembly completeness and identification of novel full-length protein-coding genes by RNA-seq in the giant panda genome.

    Science.gov (United States)

    Chen, Meili; Hu, Yibo; Liu, Jingxing; Wu, Qi; Zhang, Chenglin; Yu, Jun; Xiao, Jingfa; Wei, Fuwen; Wu, Jiayan

    2015-12-11

    High-quality and complete gene models are the basis of whole genome analyses. The giant panda (Ailuropoda melanoleuca) genome was the first genome sequenced on the basis of solely short reads, but the genome annotation had lacked the support of transcriptomic evidence. In this study, we applied RNA-seq to globally improve the genome assembly completeness and to detect novel expressed transcripts in 12 tissues from giant pandas, by using a transcriptome reconstruction strategy that combined reference-based and de novo methods. Several aspects of genome assembly completeness in the transcribed regions were effectively improved by the de novo assembled transcripts, including genome scaffolding, the detection of small-size assembly errors, the extension of scaffold/contig boundaries, and gap closure. Through expression and homology validation, we detected three groups of novel full-length protein-coding genes. A total of 12.62% of the novel protein-coding genes were validated by proteomic data. GO annotation analysis showed that some of the novel protein-coding genes were involved in pigmentation, anatomical structure formation and reproduction, which might be related to the development and evolution of the black-white pelage, pseudo-thumb and delayed embryonic implantation of giant pandas. The updated genome annotation will help further giant panda studies from both structural and functional perspectives.

  1. Prioritizing orphan proteins for further study using phylogenomics and gene expression profiles in Streptomyces coelicolor

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    Takano Eriko

    2011-09-01

    Full Text Available Abstract Background Streptomyces coelicolor, a model organism of antibiotic producing bacteria, has one of the largest genomes of the bacterial kingdom, including 7825 predicted protein coding genes. A large number of these genes, nearly 34%, are functionally orphan (hypothetical proteins with unknown function. However, in gene expression time course data, many of these functionally orphan genes show interesting expression patterns. Results In this paper, we analyzed all functionally orphan genes of Streptomyces coelicolor and identified a list of "high priority" orphans by combining gene expression analysis and additional phylogenetic information (i.e. the level of evolutionary conservation of each protein. Conclusions The prioritized orphan genes are promising candidates to be examined experimentally in the lab for further characterization of their function.

  2. Td4IN2: A drought-responsive durum wheat (Triticum durum Desf.) gene coding for a resistance like protein with serine/threonine protein kinase, nucleotide binding site and leucine rich domains.

    Science.gov (United States)

    Rampino, Patrizia; De Pascali, Mariarosaria; De Caroli, Monica; Luvisi, Andrea; De Bellis, Luigi; Piro, Gabriella; Perrotta, Carla

    2017-11-01

    Wheat, the main food source for a third of world population, appears strongly under threat because of predicted increasing temperatures coupled to drought. Plant complex molecular response to drought stress relies on the gene network controlling cell reactions to abiotic stress. In the natural environment, plants are subjected to the combination of abiotic and biotic stresses. Also the response of plants to biotic stress, to cope with pathogens, involves the activation of a molecular network. Investigations on combination of abiotic and biotic stresses indicate the existence of cross-talk between the two networks and a kind of overlapping can be hypothesized. In this work we describe the isolation and characterization of a drought-related durum wheat (Triticum durum Desf.) gene, identified in a previous study, coding for a protein combining features of NBS-LRR type resistance protein with a S/TPK domain, involved in drought stress response. This is one of the few examples reported where all three domains are present in a single protein and, to our knowledge, it is the first report on a gene specifically induced by drought stress and drought-related conditions, with this particular structure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  3. Novel overlapping coding sequences in Chlamydia trachomatis

    DEFF Research Database (Denmark)

    Jensen, Klaus Thorleif; Petersen, Lise; Falk, Søren

    2006-01-01

    that are in agreement with the primary annotation. Forty two genes from the primary annotation are not predicted by EasyGene. The majority of these genes are listed as hypothetical in the primary annotation. The 15 novel predicted genes all overlap with genes on the complementary strand. We find homologues of several...... of the novel genes in C. trachomatis Serovar A and Chlamydia muridarum. Several of the genes have typical gene-like and protein-like features. Furthermore, we confirm transcriptional activity from 10 of the putative genes. The combined evidence suggests that at least seven of the 15 are protein coding genes...

  4. Prediction of highly expressed genes in microbes based on chromatin accessibility

    Directory of Open Access Journals (Sweden)

    Ussery David W

    2007-02-01

    Full Text Available Abstract Background It is well known that gene expression is dependent on chromatin structure in eukaryotes and it is likely that chromatin can play a role in bacterial gene expression as well. Here, we use a nucleosomal position preference measure of anisotropic DNA flexibility to predict highly expressed genes in microbial genomes. We compare these predictions with those based on codon adaptation index (CAI values, and also with experimental data for 6 different microbial genomes, with a particular interest in experimental data from Escherichia coli. Moreover, position preference is examined further in 328 sequenced microbial genomes. Results We find that absolute gene expression levels are correlated with the position preference in many microbial genomes. It is postulated that in these regions, the DNA may be more accessible to the transcriptional machinery. Moreover, ribosomal proteins and ribosomal RNA are encoded by DNA having significantly lower position preference values than other genes in fast-replicating microbes. Conclusion This insight into DNA structure-dependent gene expression in microbes may be exploited for predicting the expression of non-translated genes such as non-coding RNAs that may not be predicted by any of the conventional codon usage bias approaches.

  5. Conserved syntenic clusters of protein coding genes are missing in birds.

    Science.gov (United States)

    Lovell, Peter V; Wirthlin, Morgan; Wilhelm, Larry; Minx, Patrick; Lazar, Nathan H; Carbone, Lucia; Warren, Wesley C; Mello, Claudio V

    2014-01-01

    Birds are one of the most highly successful and diverse groups of vertebrates, having evolved a number of distinct characteristics, including feathers and wings, a sturdy lightweight skeleton and unique respiratory and urinary/excretion systems. However, the genetic basis of these traits is poorly understood. Using comparative genomics based on extensive searches of 60 avian genomes, we have found that birds lack approximately 274 protein coding genes that are present in the genomes of most vertebrate lineages and are for the most part organized in conserved syntenic clusters in non-avian sauropsids and in humans. These genes are located in regions associated with chromosomal rearrangements, and are largely present in crocodiles, suggesting that their loss occurred subsequent to the split of dinosaurs/birds from crocodilians. Many of these genes are associated with lethality in rodents, human genetic disorders, or biological functions targeting various tissues. Functional enrichment analysis combined with orthogroup analysis and paralog searches revealed enrichments that were shared by non-avian species, present only in birds, or shared between all species. Together these results provide a clearer definition of the genetic background of extant birds, extend the findings of previous studies on missing avian genes, and provide clues about molecular events that shaped avian evolution. They also have implications for fields that largely benefit from avian studies, including development, immune system, oncogenesis, and brain function and cognition. With regards to the missing genes, birds can be considered ‘natural knockouts’ that may become invaluable model organisms for several human diseases.

  6. MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

    Science.gov (United States)

    Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

    2018-03-10

    Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

  7. PanCoreGen – profiling, detecting, annotating protein-coding genes in microbial genomes

    Science.gov (United States)

    Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V.

    2015-01-01

    A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen – a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars – Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. PMID:26456591

  8. Selection on Coding and Regulatory Variation Maintains Individuality in Major Urinary Protein Scent Marks in Wild Mice.

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    Michael J Sheehan

    2016-03-01

    Full Text Available Recognition of individuals by scent is widespread across animal taxa. Though animals can often discriminate chemical blends based on many compounds, recent work shows that specific protein pheromones are necessary and sufficient for individual recognition via scent marks in mice. The genetic nature of individuality in scent marks (e.g. coding versus regulatory variation and the evolutionary processes that maintain diversity are poorly understood. The individual signatures in scent marks of house mice are the protein products of a group of highly similar paralogs in the major urinary protein (Mup gene family. Using the offspring of wild-caught mice, we examine individuality in the major urinary protein (MUP scent marks at the DNA, RNA and protein levels. We show that individuality arises through a combination of variation at amino acid coding sites and differential transcription of central Mup genes across individuals, and we identify eSNPs in promoters. There is no evidence of post-transcriptional processes influencing phenotypic diversity as transcripts accurately predict the relative abundance of proteins in urine samples. The match between transcripts and urine samples taken six months earlier also emphasizes that the proportional relationships across central MUP isoforms in urine is stable. Balancing selection maintains coding variants at moderate frequencies, though pheromone diversity appears limited by interactions with vomeronasal receptors. We find that differential transcription of the central Mup paralogs within and between individuals significantly increases the individuality of pheromone blends. Balancing selection on gene regulation allows for increased individuality via combinatorial diversity in a limited number of pheromones.

  9. Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

    Energy Technology Data Exchange (ETDEWEB)

    Ansong, Charles; Tolic, Nikola; Purvine, Samuel O.; Porwollik, Steffen; Jones, Marcus B.; Yoon, Hyunjin; Payne, Samuel H.; Martin, Jessica L.; Burnet, Meagan C.; Monroe, Matthew E.; Venepally, Pratap; Smith, Richard D.; Peterson, Scott; Heffron, Fred; Mcclelland, Michael; Adkins, Joshua N.

    2011-08-25

    Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify coding regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.

  10. Construction of ontology augmented networks for protein complex prediction.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  11. Expression of protein-coding genes embedded in ribosomal DNA

    DEFF Research Database (Denmark)

    Johansen, Steinar D; Haugen, Peik; Nielsen, Henrik

    2007-01-01

    Ribosomal DNA (rDNA) is a specialised chromosomal location that is dedicated to high-level transcription of ribosomal RNA genes. Interestingly, rDNAs are frequently interrupted by parasitic elements, some of which carry protein genes. These are non-LTR retrotransposons and group II introns that e...... in the nucleolus....

  12. Partitioning of genetic variation between regulatory and coding gene segments: the predominance of software variation in genes encoding introvert proteins.

    Science.gov (United States)

    Mitchison, A

    1997-01-01

    In considering genetic variation in eukaryotes, a fundamental distinction can be made between variation in regulatory (software) and coding (hardware) gene segments. For quantitative traits the bulk of variation, particularly that near the population mean, appears to reside in regulatory segments. The main exceptions to this rule concern proteins which handle extrinsic substances, here termed extrovert proteins. The immune system includes an unusually large proportion of this exceptional category, but even so its chief source of variation may well be polymorphism in regulatory gene segments. The main evidence for this view emerges from genome scanning for quantitative trait loci (QTL), which in the case of the immune system points to a major contribution of pro-inflammatory cytokine genes. Further support comes from sequencing of major histocompatibility complex (Mhc) class II promoters, where a high level of polymorphism has been detected. These Mhc promoters appear to act, in part at least, by gating the back-signal from T cells into antigen-presenting cells. Both these forms of polymorphism are likely to be sustained by the need for flexibility in the immune response. Future work on promoter polymorphism is likely to benefit from the input from genome informatics.

  13. Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

    Science.gov (United States)

    Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

    2013-05-01

    Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

  14. Selfish DNA in protein-coding genes of Rickettsia.

    Science.gov (United States)

    Ogata, H; Audic, S; Barbe, V; Artiguenave, F; Fournier, P E; Raoult, D; Claverie, J M

    2000-10-13

    Rickettsia conorii, the aetiological agent of Mediterranean spotted fever, is an intracellular bacterium transmitted by ticks. Preliminary analyses of the nearly complete genome sequence of R. conorii have revealed 44 occurrences of a previously undescribed palindromic repeat (150 base pairs long) throughout the genome. Unexpectedly, this repeat was found inserted in-frame within 19 different R. conorii open reading frames likely to encode functional proteins. We found the same repeat in proteins of other Rickettsia species. The finding of a mobile element inserted in many unrelated genes suggests the potential role of selfish DNA in the creation of new protein sequences.

  15. A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

    Science.gov (United States)

    Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

    2006-01-01

    The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

  16. An operon from Lactobacillus helveticus composed of a proline iminopeptidase gene (pepI) and two genes coding for putative members of the ABC transporter family of proteins.

    Science.gov (United States)

    Varmanen, P; Rantanen, T; Palva, A

    1996-12-01

    A proline iminopeptidase gene (pepI) of an industrial Lactobacillus helveticus strain was cloned and found to be organized in an operon-like structure of three open reading frames (ORF1, ORF2 and ORF3). ORF1 was preceded by a typical prokaryotic promoter region, and a putative transcription terminator was found downstream of ORF3, identified as the pepI gene. Using primer-extension analyses, only one transcription start site, upstream of ORF1, was identifiable in the predicted operon. Although the size of mRNA could not be judged by Northern analysis either with ORF1-, ORF2- or pepI-specific probes, reverse transcription-PCR analyses further supported the operon structure of the three genes. ORF1, ORF2 and ORF3 had coding capacities for 50.7, 24.5 and 33.8 kDa proteins, respectively. The ORF3-encoded PepI protein showed 65% identity with the PepI proteins from Lactobacillus delbrueckii subsp. bulgaricus and Lactobacillus delbrueckii subsp. lactis. The ORF1-encoded protein had significant homology with several members of the ABC transporter family but, with two distinct putative ATP-binding sites, it would represent an unusual type among the bacterial ABC transporters. ORF2 encoded a putative integral membrane protein also characteristic of the ABC transporter family. The pepI gene was overexpressed in Escherichia coli. Purified PepI hydrolysed only di and tripeptides with proline in the first position. Optimum PepI activity was observed at pH 7.5 and 40 degrees C. A gel filtration analysis indicated that PepI is a dimer of M(r) 53,000. PepI was shown to be a metal-independent serine peptidase having thiol groups at or near the active site. Kinetic studies with proline-p-nitroanilide as substrate revealed Km and Vmax values of 0.8 mM and 350 mmol min-1 mg-1, respectively, and a very high turnover number of 135,000 s-1.

  17. Expression of genes encoding multi-transmembrane proteins in specific primate taste cell populations.

    Directory of Open Access Journals (Sweden)

    Bryan D Moyer

    Full Text Available BACKGROUND: Using fungiform (FG and circumvallate (CV taste buds isolated by laser capture microdissection and analyzed using gene arrays, we previously constructed a comprehensive database of gene expression in primates, which revealed over 2,300 taste bud-associated genes. Bioinformatics analyses identified hundreds of genes predicted to encode multi-transmembrane domain proteins with no previous association with taste function. A first step in elucidating the roles these gene products play in gustation is to identify the specific taste cell types in which they are expressed. METHODOLOGY/PRINCIPAL FINDINGS: Using double label in situ hybridization analyses, we identified seven new genes expressed in specific taste cell types, including sweet, bitter, and umami cells (TRPM5-positive, sour cells (PKD2L1-positive, as well as other taste cell populations. Transmembrane protein 44 (TMEM44, a protein with seven predicted transmembrane domains with no homology to GPCRs, is expressed in a TRPM5-negative and PKD2L1-negative population that is enriched in the bottom portion of taste buds and may represent developmentally immature taste cells. Calcium homeostasis modulator 1 (CALHM1, a component of a novel calcium channel, along with family members CALHM2 and CALHM3; multiple C2 domains; transmembrane 1 (MCTP1, a calcium-binding transmembrane protein; and anoctamin 7 (ANO7, a member of the recently identified calcium-gated chloride channel family, are all expressed in TRPM5 cells. These proteins may modulate and effect calcium signalling stemming from sweet, bitter, and umami receptor activation. Synaptic vesicle glycoprotein 2B (SV2B, a regulator of synaptic vesicle exocytosis, is expressed in PKD2L1 cells, suggesting that this taste cell population transmits tastant information to gustatory afferent nerve fibers via exocytic neurotransmitter release. CONCLUSIONS/SIGNIFICANCE: Identification of genes encoding multi-transmembrane domain proteins

  18. A compendium of transcription factor and Transcriptionally active protein coding gene families in cowpea (Vigna unguiculata L.).

    Science.gov (United States)

    Misra, Vikram A; Wang, Yu; Timko, Michael P

    2017-11-22

    Cowpea (Vigna unguiculata (L.) Walp.) is the most important food and forage legume in the semi-arid tropics of sub-Saharan Africa where approximately 80% of worldwide production takes place primarily on low-input, subsistence farm sites. Among the major goals of cowpea breeding and improvement programs are the rapid manipulation of agronomic traits for seed size and quality and improved resistance to abiotic and biotic stresses to enhance productivity. Knowing the suite of transcription factors (TFs) and transcriptionally active proteins (TAPs) that control various critical plant cellular processes would contribute tremendously to these improvement aims. We used a computational approach that employed three different predictive pipelines to data mine the cowpea genome and identified over 4400 genes representing 136 different TF and TAP families. We compare the information content of cowpea to two evolutionarily close species common bean (Phaseolus vulgaris), and soybean (Glycine max) to gauge the relative informational content. Our data indicate that correcting for genome size cowpea has fewer TF and TAP genes than common bean (4408 / 5291) and soybean (4408/ 11,065). Members of the GROWTH-REGULATING FACTOR (GRF) and Auxin/indole-3-acetic acid (Aux/IAA) gene families appear to be over-represented in the genome relative to common bean and soybean, whereas members of the MADS (Minichromosome maintenance deficient 1 (MCM1), AGAMOUS, DEFICIENS, and serum response factor (SRF)) and C2C2-YABBY appear to be under-represented. Analysis of the AP2-EREBP APETALA2-Ethylene Responsive Element Binding Protein (AP2-EREBP), NAC (NAM (no apical meristem), ATAF1, 2 (Arabidopsis transcription activation factor), CUC (cup-shaped cotyledon)), and WRKY families, known to be important in defense signaling, revealed changes and phylogenetic rearrangements relative to common bean and soybean that suggest these groups may have evolved different functions. The availability of detailed

  19. lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

    Science.gov (United States)

    Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

    2015-01-01

    Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

  20. Prediction and characterization of human ageing-related proteins by using machine learning.

    Science.gov (United States)

    Kerepesi, Csaba; Daróczy, Bálint; Sturm, Ádám; Vellai, Tibor; Benczúr, András

    2018-03-06

    Ageing has a huge impact on human health and economy, but its molecular basis - regulation and mechanism - is still poorly understood. By today, more than three hundred genes (almost all of them function as protein-coding genes) have been related to human ageing. Although individual ageing-related genes or some small subsets of these genes have been intensively studied, their analysis as a whole has been highly limited. To fill this gap, for each human protein we extracted 21000 protein features from various databases, and using these data as an input to state-of-the-art machine learning methods, we classified human proteins as ageing-related or non-ageing-related. We found a simple classification model based on only 36 protein features, such as the "number of ageing-related interaction partners", "response to oxidative stress", "damaged DNA binding", "rhythmic process" and "extracellular region". Predicted values of the model quantify the relevance of a given protein in the regulation or mechanisms of the human ageing process. Furthermore, we identified new candidate proteins having strong computational evidence of their important role in ageing. Some of them, like Cytochrome b-245 light chain (CY24A) and Endoribonuclease ZC3H12A (ZC12A) have no previous ageing-associated annotations.

  1. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes

    Directory of Open Access Journals (Sweden)

    Yang Yi-Fan

    2007-03-01

    Full Text Available Abstract Background Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. Results This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs and Translation Initiation Sites (TISs. The former is based on a linguistic "Entropy Density Profile" (EDP model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. Conclusion Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.

  2. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants.

    Science.gov (United States)

    Fu, Wenqing; O'Connor, Timothy D; Jun, Goo; Kang, Hyun Min; Abecasis, Goncalo; Leal, Suzanne M; Gabriel, Stacey; Rieder, Mark J; Altshuler, David; Shendure, Jay; Nickerson, Deborah A; Bamshad, Michael J; Akey, Joshua M

    2013-01-10

    Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history and will help to facilitate the development of new approaches for disease-gene discovery. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth, notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000-10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

  3. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  4. Meta4: a web application for sharing and annotating metagenomic gene predictions using web services.

    Science.gov (United States)

    Richardson, Emily J; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert J; Watson, Mick

    2013-01-01

    Whole-genome shotgun metagenomics experiments produce DNA sequence data from entire ecosystems, and provide a huge amount of novel information. Gene discovery projects require up-to-date information about sequence homology and domain structure for millions of predicted proteins to be presented in a simple, easy-to-use system. There is a lack of simple, open, flexible tools that allow the rapid sharing of metagenomics datasets with collaborators in a format they can easily interrogate. We present Meta4, a flexible and extensible web application that can be used to share and annotate metagenomic gene predictions. Proteins and predicted domains are stored in a simple relational database, with a dynamic front-end which displays the results in an internet browser. Web services are used to provide up-to-date information about the proteins from homology searches against public databases. Information about Meta4 can be found on the project website, code is available on Github, a cloud image is available, and an example implementation can be seen at.

  5. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  6. Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yu Yeh

    2012-01-01

    Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.

  7. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  8. Protein docking prediction using predicted protein-protein interface.

    Science.gov (United States)

    Li, Bin; Kihara, Daisuke

    2012-01-10

    Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  9. What does a worm want with 20,000 genes?

    OpenAIRE

    Hodgkin, Jonathan

    2001-01-01

    The number of genes predicted for the Caenorhabditis elegans genome is remarkably high: approximately 20,000, if both protein-coding and RNA-coding genes are counted. This article discusses possible explanations for such a high value.

  10. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

    Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

  11. Transfection of Chinese hamster ovary DHFR/sup -/ cells with the gene coding for heat shock protein 70 from drosophila melanogaster

    International Nuclear Information System (INIS)

    Duffy, J.J.; Carper, S.W.; Gerner, E.W.

    1987-01-01

    Chinese hamster ovary DHFR/sup -/ cells (CHO-DHFR/sup -/) were transfected with the plasmid pSV2-dhfr expressing the mouse gene coding for dhfr or with the same plasmid containing the gene coding for the Drosophila melanogaster heat shock protein 70 (hsp70), pSVd-hsp70. Three subcloned cell lines selected for expression of the dhfr gene were shown to contain either the vector sequence (G cells) or varying copies of pSVd-hsp70 (H cells). One line of H cells was shown to contain > 30 copies of the D. melanogaster hsp70 gene and to express the hsp70 RNA at significant levels. No difference between G and H cells was observed in the rate of growth, in the development of thermotolerance, or in the sensitivity of actin microfilament bundles to heat shock. However, H cells containing the transfected hsp70 gene had an altered morphology when compared to the G cells and the parental CHO-DHFR/sup -/ cells being more fibroblastic. The adhesion properties of the H cells was also decreased when compared to the G cells. These results show that insertion of the D. melanogaster gene into CHO cells does not effect growth rates or heat shock responses but may alter cell morphology and adhesion

  12. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  13. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  14. A novel bidirectional expression system for simultaneous expression of both the protein-coding genes and short hairpin RNAs in mammalian cells

    International Nuclear Information System (INIS)

    Hung, C.-F.; Cheng, T.-L.; Wu, R.-H.; Teng, C.-F.; Chang, W.-T.

    2006-01-01

    RNA interference (RNAi) is an extremely powerful and widely used gene silencing approach for reverse functional genomics and molecular therapeutics. In mammals, the conserved poly(ADP-ribose) polymerase 2 (PARP-2)/RNase P bidirectional control promoter simultaneously expresses both the PARP-2 protein and RNase P RNA by RNA polymerase II- and III-dependent mechanisms, respectively. To explore this unique bidirectional control system in RNAi-mediated gene silencing strategy, we have constructed two novel bidirectional expression vectors, pbiHsH1 and pbiMmH1, which contained the PARP-2/RNase P bidirectional control promoters from human and mouse, for simultaneous expression of both the protein-coding genes and short hairpin RNAs. Analyses of the dual transcriptional activities indicated that these two bidirectional expression vectors could not only express enhanced green fluorescent protein as a functional reporter but also simultaneously transcribe shLuc for inhibiting the firefly luciferase expression. In addition, to extend its utility for the establishment of inherited stable clones, we have also reconstructed this bidirectional expression system with the blasticidin S deaminase gene, an effective dominant drug resistance selectable marker, and examined both the selection and inhibition efficiencies in drug resistance and gene expression. Moreover, we have further demonstrated that this bidirectional expression system could efficiently co-regulate the functionally important genes, such as overexpression of tumor suppressor protein p53 and inhibition of anti-apoptotic protein Bcl-2 at the same time. In summary, the bidirectional expression vectors, pbiHsH1 and pbiMmH1, should provide a simple, convenient, and efficient novel tool for manipulating the gene function in mammalian cells

  15. AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

    Science.gov (United States)

    Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

    2017-06-15

    Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  16. A global analysis of protein expression profiles in Sinorhizobium meliloti: discovery of new genes for nodule occupancy and stress adaptation.

    Science.gov (United States)

    Djordjevic, Michael A; Chen, Han Cai; Natera, Siria; Van Noorden, Giel; Menzel, Christian; Taylor, Scott; Renard, Clotilde; Geiger, Otto; Weiller, Georg F

    2003-06-01

    A proteomic examination of Sinorhizobium meliloti strain 1021 was undertaken using a combination of 2-D gel electrophoresis, peptide mass fingerprinting, and bioinformatics. Our goal was to identify (i) putative symbiosis- or nutrient-stress-specific proteins, (ii) the biochemical pathways active under different conditions, (iii) potential new genes, and (iv) the extent of posttranslational modifications of S. meliloti proteins. In total, we identified the protein products of 810 genes (13.1% of the genome's coding capacity). The 810 genes generated 1,180 gene products, with chromosomal genes accounting for 78% of the gene products identified (18.8% of the chromosome's coding capacity). The activity of 53 metabolic pathways was inferred from bioinformatic analysis of proteins with assigned Enzyme Commission numbers. Of the remaining proteins that did not encode enzymes, ABC-type transporters composed 12.7% and regulatory proteins 3.4% of the total. Proteins with up to seven transmembrane domains were identified in membrane preparations. A total of 27 putative nodule-specific proteins and 35 nutrient-stress-specific proteins were identified and used as a basis to define genes and describe processes occurring in S. meliloti cells in nodules and under stress. Several nodule proteins from the plant host were present in the nodule bacteria preparations. We also identified seven potentially novel proteins not predicted from the DNA sequence. Post-translational modifications such as N-terminal processing could be inferred from the data. The posttranslational addition of UMP to the key regulator of nitrogen metabolism, PII, was demonstrated. This work demonstrates the utility of combining mass spectrometry with protein arraying or separation techniques to identify candidate genes involved in important biological processes and niche occupations that may be intransigent to other methods of gene expression profiling.

  17. Enrichment of Circular Code Motifs in the Genes of the Yeast Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Christian J. Michel

    2017-12-01

    Full Text Available A set X of 20 trinucleotides has been found to have the highest average occurrence in the reading frame, compared to the two shifted frames, of genes of bacteria, archaea, eukaryotes, plasmids and viruses. This set X has an interesting mathematical property, since X is a maximal C 3 self-complementary trinucleotide circular code. Furthermore, any motif obtained from this circular code X has the capacity to retrieve, maintain and synchronize the original (reading frame. Since 1996, the theory of circular codes in genes has mainly been developed by analysing the properties of the 20 trinucleotides of X , using combinatorics and statistical approaches. For the first time, we test this theory by analysing the X motifs, i.e., motifs from the circular code X , in the complete genome of the yeast Saccharomyces cerevisiae. Several properties of X motifs are identified by basic statistics (at the frequency level, and evaluated by comparison to R motifs, i.e., random motifs generated from 30 different random codes R . We first show that the frequency of X motifs is significantly greater than that of R motifs in the genome of S. cerevisiae. We then verify that no significant difference is observed between the frequencies of X and R motifs in the non-coding regions of S. cerevisiae, but that the occurrence number of X motifs is significantly higher than R motifs in the genes (protein-coding regions. This property is true for all cardinalities of X motifs (from 4 to 20 and for all 16 chromosomes. We further investigate the distribution of X motifs in the three frames of S. cerevisiae genes and show that they occur more frequently in the reading frame, regardless of their cardinality or their length. Finally, the ratio of X genes, i.e., genes with at least one X motif, to non- X genes, in the set of verified genes is significantly different to that observed in the set of putative or dubious genes with no experimental evidence. These results, taken together

  18. Enrichment of Circular Code Motifs in the Genes of the Yeast Saccharomyces cerevisiae.

    Science.gov (United States)

    Michel, Christian J; Ngoune, Viviane Nguefack; Poch, Olivier; Ripp, Raymond; Thompson, Julie D

    2017-12-03

    A set X of 20 trinucleotides has been found to have the highest average occurrence in the reading frame, compared to the two shifted frames, of genes of bacteria, archaea, eukaryotes, plasmids and viruses. This set X has an interesting mathematical property, since X is a maximal C3 self-complementary trinucleotide circular code. Furthermore, any motif obtained from this circular code X has the capacity to retrieve, maintain and synchronize the original (reading) frame. Since 1996, the theory of circular codes in genes has mainly been developed by analysing the properties of the 20 trinucleotides of X, using combinatorics and statistical approaches. For the first time, we test this theory by analysing the X motifs, i.e., motifs from the circular code X, in the complete genome of the yeast Saccharomyces cerevisiae . Several properties of X motifs are identified by basic statistics (at the frequency level), and evaluated by comparison to R motifs, i.e., random motifs generated from 30 different random codes R. We first show that the frequency of X motifs is significantly greater than that of R motifs in the genome of S. cerevisiae . We then verify that no significant difference is observed between the frequencies of X and R motifs in the non-coding regions of S. cerevisiae , but that the occurrence number of X motifs is significantly higher than R motifs in the genes (protein-coding regions). This property is true for all cardinalities of X motifs (from 4 to 20) and for all 16 chromosomes. We further investigate the distribution of X motifs in the three frames of S. cerevisiae genes and show that they occur more frequently in the reading frame, regardless of their cardinality or their length. Finally, the ratio of X genes, i.e., genes with at least one X motif, to non-X genes, in the set of verified genes is significantly different to that observed in the set of putative or dubious genes with no experimental evidence. These results, taken together, represent the first

  19. PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

    Science.gov (United States)

    Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

    2009-02-15

    Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http

  20. Porcine lung surfactant protein B gene (SFTPB)

    DEFF Research Database (Denmark)

    Cirera Salicio, Susanna; Fredholm, Merete

    2008-01-01

    The porcine surfactant protein B (SFTPB) is a single copy gene on chromosome 3. Three different cDNAs for the SFTPB have been isolated and sequenced. Nucleotide sequence comparison revealed six nonsynonymous single nucleotide polymorphisms (SNPs), four synonymous SNPs and an in-frame deletion of 69...... bp in the region coding for the active protein. Northern analysis showed lung-specific expression of three different isoforms of the SFTPB transcript. The expression level for the SFTPB gene is low in 50 days-old fetus and it increases during lung development. Quantitative real-time polymerase chain...

  1. WebScipio: An online tool for the determination of gene structures using protein sequences

    Directory of Open Access Journals (Sweden)

    Waack Stephan

    2008-09-01

    Full Text Available Abstract Background Obtaining the gene structure for a given protein encoding gene is an important step in many analyses. A software suited for this task should be readily accessible, accurate, easy to handle and should provide the user with a coherent representation of the most probable gene structure. It should be rigorous enough to optimise features on the level of single bases and at the same time flexible enough to allow for cross-species searches. Results WebScipio, a web interface to the Scipio software, allows a user to obtain the corresponding coding sequence structure of a here given a query protein sequence that belongs to an already assembled eukaryotic genome. The resulting gene structure is presented in various human readable formats like a schematic representation, and a detailed alignment of the query and the target sequence highlighting any discrepancies. WebScipio can also be used to identify and characterise the gene structures of homologs in related organisms. In addition, it offers a web service for integration with other programs. Conclusion WebScipio is a tool that allows users to get a high-quality gene structure prediction from a protein query. It offers more than 250 eukaryotic genomes that can be searched and produces predictions that are close to what can be achieved by manual annotation, for in-species and cross-species searches alike. WebScipio is freely accessible at http://www.webscipio.org.

  2. Roles for text mining in protein function prediction.

    Science.gov (United States)

    Verspoor, Karin M

    2014-01-01

    The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

  3. Do prion protein gene polymorphisms induce apoptosis in non ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... Genetic variations such as single nucleotide polymorphisms (SNPs) in prion protein coding gene, Prnp, greatly affect susceptibility to prion diseases in mammals. Here, the coding region of Prnp was screened for polymorphisms in redeared turtle, Trachemys scripta. Four polymorphisms, L203V, N205I, ...

  4. Distinct gene number-genome size relationships for eukaryotes and non-eukaryotes: gene content estimation for dinoflagellate genomes.

    Directory of Open Access Journals (Sweden)

    Yubo Hou

    Full Text Available The ability to predict gene content is highly desirable for characterization of not-yet sequenced genomes like those of dinoflagellates. Using data from completely sequenced and annotated genomes from phylogenetically diverse lineages, we investigated the relationship between gene content and genome size using regression analyses. Distinct relationships between log(10-transformed protein-coding gene number (Y' versus log(10-transformed genome size (X', genome size in kbp were found for eukaryotes and non-eukaryotes. Eukaryotes best fit a logarithmic model, Y' = ln(-46.200+22.678X', whereas non-eukaryotes a linear model, Y' = 0.045+0.977X', both with high significance (p0.91. Total gene number shows similar trends in both groups to their respective protein coding regressions. The distinct correlations reflect lower and decreasing gene-coding percentages as genome size increases in eukaryotes (82%-1% compared to higher and relatively stable percentages in prokaryotes and viruses (97%-47%. The eukaryotic regression models project that the smallest dinoflagellate genome (3x10(6 kbp contains 38,188 protein-coding (40,086 total genes and the largest (245x10(6 kbp 87,688 protein-coding (92,013 total genes, corresponding to 1.8% and 0.05% gene-coding percentages. These estimates do not likely represent extraordinarily high functional diversity of the encoded proteome but rather highly redundant genomes as evidenced by high gene copy numbers documented for various dinoflagellate species.

  5. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

    Science.gov (United States)

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2014-01-01

    Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

  6. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Predictions of RNA-binding ability and aggregation propensity of proteins

    OpenAIRE

    Agostini, Federico, 1985-

    2014-01-01

    RNA-binding proteins (RBPs) control the fate of a multitude of coding and non-coding transcripts. Formation of ribonucleoprotein (RNP) complexes fine-tunes regulation of post-transcriptional events and influences gene expression. Recently, it has been observed that non-canonical proteins with RNA-binding ability are enriched in structurally disordered and low-complexity regions that are generally involved in functional and dysfunctional associations. Therefore, it is possible that interaction...

  8. The coevolution of genes and genetic codes: Crick's frozen accident revisited.

    Science.gov (United States)

    Sella, Guy; Ardell, David H

    2006-09-01

    The standard genetic code is the nearly universal system for the translation of genes into proteins. The code exhibits two salient structural characteristics: it possesses a distinct organization that makes it extremely robust to errors in replication and translation, and it is highly redundant. The origin of these properties has intrigued researchers since the code was first discovered. One suggestion, which is the subject of this review, is that the code's organization is the outcome of the coevolution of genes and genetic codes. In 1968, Francis Crick explored the possible implications of coevolution at different stages of code evolution. Although he argues that coevolution was likely to influence the evolution of the code, he concludes that it falls short of explaining the organization of the code we see today. The recent application of mathematical modeling to study the effects of errors on the course of coevolution, suggests a different conclusion. It shows that coevolution readily generates genetic codes that are highly redundant and similar in their error-correcting organization to the standard code. We review this recent work and suggest that further affirmation of the role of coevolution can be attained by investigating the extent to which the outcome of coevolution is robust to other influences that were present during the evolution of the code.

  9. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

  11. Locating protein-coding sequences under selection for additional, overlapping functions in 29 mammalian genomes

    DEFF Research Database (Denmark)

    Lin, Michael F; Kheradpour, Pouya; Washietl, Stefan

    2011-01-01

    conservation compared to typical protein-coding genes—especially at synonymous sites. In this study, we use genome alignments of 29 placental mammals to systematically locate short regions within human ORFs that show conspicuously low estimated rates of synonymous substitution across these species. The 29......-species alignment provides statistical power to locate more than 10,000 such regions with resolution down to nine-codon windows, which are found within more than a quarter of all human protein-coding genes and contain ~2% of their synonymous sites. We collect numerous lines of evidence that the observed...... synonymous constraint in these regions reflects selection on overlapping functional elements including splicing regulatory elements, dual-coding genes, RNA secondary structures, microRNA target sites, and developmental enhancers. Our results show that overlapping functional elements are common in mammalian...

  12. Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

    Science.gov (United States)

    Meitinger, T; Meindl, A; Bork, P; Rost, B; Sander, C; Haasemann, M; Murken, J

    1993-12-01

    The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

  13. Role of horizontal gene transfer as a control on the coevolution of ribosomal proteins and the genetic code

    Energy Technology Data Exchange (ETDEWEB)

    Woese, Carl R.; Goldenfeld, Nigel; Luthey-Schulten, Zaida

    2011-03-31

    Our main goal is to develop the conceptual and computational tools necessary to understand the evolution of the universal processes of translation and replication and to identify events of horizontal gene transfer that occurred within the components. We will attempt to uncover the major evolutionary transitions that accompanied the development of protein synthesis by the ribosome and associated components of the translation apparatus. Our project goes beyond standard genomic approaches to explore homologs that are represented at both the structure and sequence level. Accordingly, use of structural phylogenetic analysis allows us to probe further back into deep evolutionary time than competing approaches, permitting greater resolution of primitive folds and structures. Specifically, our work focuses on the elements of translation, ranging from the emergence of the canonical genetic code to the evolution of specific protein folds, mediated by the predominance of horizontal gene transfer in early life. A unique element of this study is the explicit accounting for the impact of phenotype selection on translation, through a coevolutionary control mechanism. Our work contributes to DOE mission objectives through: (1) sophisticated computer simulation of protein dynamics and evolution, and the further refinement of techniques for structural phylogeny, which complement sequence information, leading to improved annotation of genomic databases; (2) development of evolutionary approaches to exploring cellular function and machinery in an integrated way; and (3) documentation of the phenotype interaction with translation over evolutionary time, reflecting the system response to changing selection pressures through horizontal gene transfer.

  14. Computer analysis of protein functional sites projection on exon structure of genes in Metazoa.

    Science.gov (United States)

    Medvedeva, Irina V; Demenkov, Pavel S; Ivanisenko, Vladimir A

    2015-01-01

    Study of the relationship between the structural and functional organization of proteins and their coding genes is necessary for an understanding of the evolution of molecular systems and can provide new knowledge for many applications for designing proteins with improved medical and biological properties. It is well known that the functional properties of proteins are determined by their functional sites. Functional sites are usually represented by a small number of amino acid residues that are distantly located from each other in the amino acid sequence. They are highly conserved within their functional group and vary significantly in structure between such groups. According to this facts analysis of the general properties of the structural organization of the functional sites at the protein level and, at the level of exon-intron structure of the coding gene is still an actual problem. One approach to this analysis is the projection of amino acid residue positions of the functional sites along with the exon boundaries to the gene structure. In this paper, we examined the discontinuity of the functional sites in the exon-intron structure of genes and the distribution of lengths and phases of the functional site encoding exons in vertebrate genes. We have shown that the DNA fragments coding the functional sites were in the same exons, or in close exons. The observed tendency to cluster the exons that code functional sites which could be considered as the unit of protein evolution. We studied the characteristics of the structure of the exon boundaries that code, and do not code, functional sites in 11 Metazoa species. This is accompanied by a reduced frequency of intercodon gaps (phase 0) in exons encoding the amino acid residue functional site, which may be evidence of the existence of evolutionary limitations to the exon shuffling. These results characterize the features of the coding exon-intron structure that affect the functionality of the encoded protein and

  15. Bioinformatic Analysis of Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs in the Coding Regions of Human Prion Protein Gene (PRNP

    Directory of Open Access Journals (Sweden)

    Kourosh Bamdad

    2016-12-01

    Full Text Available Background & Objective: Single nucleotide polymorphisms are the cause of genetic variation to living organisms. Single nucleotide polymorphisms alter residues in the protein sequence. In this investigation, the relationship between prion protein gene polymorphisms and its relevance to pathogenicity was studied. Material & Method: Amino acid sequence of the main isoform from the human prion protein gene (PRNP was extracted from UniProt database and evaluated by FoldAmyloid and AmylPred servers. All non-synonymous single nucleotide polymorphisms (nsSNPs from SNP database (dbSNP were further analyzed by bioinformatics servers including SIFT, PolyPhen-2, I-Mutant-3.0, PANTHER, SNPs & GO, PHD-SNP, Meta-SNP, and MutPred to determine the most damaging nsSNPs. Results: The results of the first structure analyses by FoldAmyloid and AmylPerd servers implied that regions including 5-15, 174-178, 180-184, 211-217, and 240-252 were the most sensitive parts of the protein sequence to amyloidosis. Screening all nsSNPs of the main protein isoform using bioinformatic servers revealed that substitution of Aspartic acid with Valine at position 178 (ID code: rs11538766 was the most deleterious nsSNP in the protein structure. Conclusion:  Substitution of the Aspartic acid with Valine at position 178 (D178V was the most pathogenic mutation in the human prion protein gene. Analyses from the MutPred server also showed that beta-sheets’ increment in the secondary structure was the main reason behind the molecular mechanism of the prion protein aggregation.

  16. Prediction and analysis of three gene families related to leaf rust (Puccinia triticina) resistance in wheat (Triticum aestivum L.).

    Science.gov (United States)

    Peng, Fred Y; Yang, Rong-Cai

    2017-06-20

    The resistance to leaf rust (Lr) caused by Puccinia triticina in wheat (Triticum aestivum L.) has been well studied over the past decades with over 70 Lr genes being mapped on different chromosomes and numerous QTLs (quantitative trait loci) being detected or mapped using DNA markers. Such resistance is often divided into race-specific and race-nonspecific resistance. The race-nonspecific resistance can be further divided into resistance to most or all races of the same pathogen and resistance to multiple pathogens. At the molecular level, these three types of resistance may cover across the whole spectrum of pathogen specificities that are controlled by genes encoding different protein families in wheat. The objective of this study is to predict and analyze genes in three such families: NBS-LRR (nucleotide-binding sites and leucine-rich repeats or NLR), START (Steroidogenic Acute Regulatory protein [STaR] related lipid-transfer) and ABC (ATP-Binding Cassette) transporter. The focus of the analysis is on the patterns of relationships between these protein-coding genes within the gene families and QTLs detected for leaf rust resistance. We predicted 526 ABC, 1117 NLR and 144 START genes in the hexaploid wheat genome through a domain analysis of wheat proteome. Of the 1809 SNPs from leaf rust resistance QTLs in seedling and adult stages of wheat, 126 SNPs were found within coding regions of these genes or their neighborhood (5 Kb upstream from transcription start site [TSS] or downstream from transcription termination site [TTS] of the genes). Forty-three of these SNPs for adult resistance and 18 SNPs for seedling resistance reside within coding or neighboring regions of the ABC genes whereas 14 SNPs for adult resistance and 29 SNPs for seedling resistance reside within coding or neighboring regions of the NLR gene. Moreover, we found 17 nonsynonymous SNPs for adult resistance and five SNPs for seedling resistance in the ABC genes, and five nonsynonymous SNPs for

  17. Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering

    Directory of Open Access Journals (Sweden)

    Li Weizhong

    2008-04-01

    Full Text Available Abstract Background The identification and study of proteins from metagenomic datasets can shed light on the roles and interactions of the source organisms in their communities. However, metagenomic datasets are characterized by the presence of organisms with varying GC composition, codon usage biases etc., and consequently gene identification is challenging. The vast amount of sequence data also requires faster protein family classification tools. Results We present a computational improvement to a sequence clustering approach that we developed previously to identify and classify protein coding genes in large microbial metagenomic datasets. The clustering approach can be used to identify protein coding genes in prokaryotes, viruses, and intron-less eukaryotes. The computational improvement is based on an incremental clustering method that does not require the expensive all-against-all compute that was required by the original approach, while still preserving the remote homology detection capabilities. We present evaluations of the clustering approach in protein-coding gene identification and classification, and also present the results of updating the protein clusters from our previous work with recent genomic and metagenomic sequences. The clustering results are available via CAMERA, (http://camera.calit2.net. Conclusion The clustering paradigm is shown to be a very useful tool in the analysis of microbial metagenomic data. The incremental clustering method is shown to be much faster than the original approach in identifying genes, grouping sequences into existing protein families, and also identifying novel families that have multiple members in a metagenomic dataset. These clusters provide a basis for further studies of protein families.

  18. Incorporating functional inter-relationships into protein function prediction algorithms

    Directory of Open Access Journals (Sweden)

    Kumar Vipin

    2009-05-01

    Full Text Available Abstract Background Functional classification schemes (e.g. the Gene Ontology that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches. Results We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the k-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of

  19. CHIR99021 promotes self-renewal of mouse embryonic stem cells by modulation of protein-encoding gene and long intergenic non-coding RNA expression

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Yongyan [College of Veterinary Medicine, Northwest A and F University, Yangling 712100, Shaanxi (China); Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China); Ai, Zhiying [Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China); College of Life Sciences, Northwest A and F University, Yangling 712100, Shaanxi (China); Yao, Kezhen [College of Veterinary Medicine, Northwest A and F University, Yangling 712100, Shaanxi (China); Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China); Cao, Lixia; Du, Juan; Shi, Xiaoyan [Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China); College of Life Sciences, Northwest A and F University, Yangling 712100, Shaanxi (China); Guo, Zekun, E-mail: gzk@nwsuaf.edu.cn [College of Veterinary Medicine, Northwest A and F University, Yangling 712100, Shaanxi (China); Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China); Zhang, Yong, E-mail: zhylab@hotmail.com [College of Veterinary Medicine, Northwest A and F University, Yangling 712100, Shaanxi (China); Key Laboratory of Animal Biotechnology, Ministry of Agriculture, Northwest A and F University, Yangling 712100, Shaanxi (China)

    2013-10-15

    Embryonic stem cells (ESCs) can proliferate indefinitely in vitro and differentiate into cells of all three germ layers. These unique properties make them exceptionally valuable for drug discovery and regenerative medicine. However, the practical application of ESCs is limited because it is difficult to derive and culture ESCs. It has been demonstrated that CHIR99021 (CHIR) promotes self-renewal and enhances the derivation efficiency of mouse (m)ESCs. However, the downstream targets of CHIR are not fully understood. In this study, we identified CHIR-regulated genes in mESCs using microarray analysis. Our microarray data demonstrated that CHIR not only influenced the Wnt/β-catenin pathway by stabilizing β-catenin, but also modulated several other pluripotency-related signaling pathways such as TGF-β, Notch and MAPK signaling pathways. More detailed analysis demonstrated that CHIR inhibited Nodal signaling, while activating bone morphogenetic protein signaling in mESCs. In addition, we found that pluripotency-maintaining transcription factors were up-regulated by CHIR, while several developmental-related genes were down-regulated. Furthermore, we found that CHIR altered the expression of epigenetic regulatory genes and long intergenic non-coding RNAs. Quantitative real-time PCR results were consistent with microarray data, suggesting that CHIR alters the expression pattern of protein-encoding genes (especially transcription factors), epigenetic regulatory genes and non-coding RNAs to establish a relatively stable pluripotency-maintaining network. - Highlights: • Combined use of CHIR with LIF promotes self-renewal of J1 mESCs. • CHIR-regulated genes are involved in multiple pathways. • CHIR inhibits Nodal signaling and promotes Bmp4 expression to activate BMP signaling. • Expression of epigenetic regulatory genes and lincRNAs is altered by CHIR.

  20. CHIR99021 promotes self-renewal of mouse embryonic stem cells by modulation of protein-encoding gene and long intergenic non-coding RNA expression

    International Nuclear Information System (INIS)

    Wu, Yongyan; Ai, Zhiying; Yao, Kezhen; Cao, Lixia; Du, Juan; Shi, Xiaoyan; Guo, Zekun; Zhang, Yong

    2013-01-01

    Embryonic stem cells (ESCs) can proliferate indefinitely in vitro and differentiate into cells of all three germ layers. These unique properties make them exceptionally valuable for drug discovery and regenerative medicine. However, the practical application of ESCs is limited because it is difficult to derive and culture ESCs. It has been demonstrated that CHIR99021 (CHIR) promotes self-renewal and enhances the derivation efficiency of mouse (m)ESCs. However, the downstream targets of CHIR are not fully understood. In this study, we identified CHIR-regulated genes in mESCs using microarray analysis. Our microarray data demonstrated that CHIR not only influenced the Wnt/β-catenin pathway by stabilizing β-catenin, but also modulated several other pluripotency-related signaling pathways such as TGF-β, Notch and MAPK signaling pathways. More detailed analysis demonstrated that CHIR inhibited Nodal signaling, while activating bone morphogenetic protein signaling in mESCs. In addition, we found that pluripotency-maintaining transcription factors were up-regulated by CHIR, while several developmental-related genes were down-regulated. Furthermore, we found that CHIR altered the expression of epigenetic regulatory genes and long intergenic non-coding RNAs. Quantitative real-time PCR results were consistent with microarray data, suggesting that CHIR alters the expression pattern of protein-encoding genes (especially transcription factors), epigenetic regulatory genes and non-coding RNAs to establish a relatively stable pluripotency-maintaining network. - Highlights: • Combined use of CHIR with LIF promotes self-renewal of J1 mESCs. • CHIR-regulated genes are involved in multiple pathways. • CHIR inhibits Nodal signaling and promotes Bmp4 expression to activate BMP signaling. • Expression of epigenetic regulatory genes and lincRNAs is altered by CHIR

  1. Signalign: An Ontology of DNA as Signal for Comparative Gene Structure Prediction Using Information-Coding-and-Processing Techniques.

    Science.gov (United States)

    Yu, Ning; Guo, Xuan; Gu, Feng; Pan, Yi

    2016-03-01

    Conventional character-analysis-based techniques in genome analysis manifest three main shortcomings-inefficiency, inflexibility, and incompatibility. In our previous research, a general framework, called DNA As X was proposed for character-analysis-free techniques to overcome these shortcomings, where X is the intermediates, such as digit, code, signal, vector, tree, graph network, and so on. In this paper, we further implement an ontology of DNA As Signal, by designing a tool named Signalign for comparative gene structure analysis, in which DNA sequences are converted into signal series, processed by modified method of dynamic time warping and measured by signal-to-noise ratio (SNR). The ontology of DNA As Signal integrates the principles and concepts of other disciplines including information coding theory and signal processing into sequence analysis and processing. Comparing with conventional character-analysis-based methods, Signalign can not only have the equivalent or superior performance, but also enrich the tools and the knowledge library of computational biology by extending the domain from character/string to diverse areas. The evaluation results validate the success of the character-analysis-free technique for improved performances in comparative gene structure prediction.

  2. Purifying selection acts on coding and non-coding sequences of paralogous genes in Arabidopsis thaliana.

    Science.gov (United States)

    Hoffmann, Robert D; Palmgren, Michael

    2016-06-13

    Whole-genome duplications in the ancestors of many diverse species provided the genetic material for evolutionary novelty. Several models explain the retention of paralogous genes. However, how these models are reflected in the evolution of coding and non-coding sequences of paralogous genes is unknown. Here, we analyzed the coding and non-coding sequences of paralogous genes in Arabidopsis thaliana and compared these sequences with those of orthologous genes in Arabidopsis lyrata. Paralogs with lower expression than their duplicate had more nonsynonymous substitutions, were more likely to fractionate, and exhibited less similar expression patterns with their orthologs in the other species. Also, lower-expressed genes had greater tissue specificity. Orthologous conserved non-coding sequences in the promoters, introns, and 3' untranslated regions were less abundant at lower-expressed genes compared to their higher-expressed paralogs. A gene ontology (GO) term enrichment analysis showed that paralogs with similar expression levels were enriched in GO terms related to ribosomes, whereas paralogs with different expression levels were enriched in terms associated with stress responses. Loss of conserved non-coding sequences in one gene of a paralogous gene pair correlates with reduced expression levels that are more tissue specific. Together with increased mutation rates in the coding sequences, this suggests that similar forces of purifying selection act on coding and non-coding sequences. We propose that coding and non-coding sequences evolve concurrently following gene duplication.

  3. Natural selection on protein-coding genes in the human genome

    DEFF Research Database (Denmark)

    Bustamente, Carlos D.; Fledel-Alon, Adi; Williamson, Scott

    2005-01-01

    , showing an excess of deleterious variation within local populations 9, 10 . Here we contrast patterns of coding sequence polymorphism identified by direct sequencing of 39 humans for over 11,000 genes to divergence between humans and chimpanzees, and find strong evidence that natural selection has shaped......Comparisons of DNA polymorphism within species to divergence between species enables the discovery of molecular adaptation in evolutionarily constrained genes as well as the differentiation of weak from strong purifying selection 1, 2, 3, 4 . The extent to which weak negative and positive darwinian...... selection have driven the molecular evolution of different species varies greatly 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 , with some species, such as Drosophila melanogaster, showing strong evidence of pervasive positive selection 6, 7, 8, 9 , and others, such as the selfing weed Arabidopsis thaliana...

  4. Nucleotide sequence of the gene coding for human factor VII, a vitamin K-dependent protein participating in blood coagulation

    International Nuclear Information System (INIS)

    O'Hara, P.J.; Grant, F.J.; Haldeman, B.A.; Gray, C.L.; Insley, M.Y.; Hagen, F.S.; Murray, M.J.

    1987-01-01

    Activated factor VII (factor VIIa) is a vitamin K-dependent plasma serine protease that participates in a cascade of reactions leading to the coagulation of blood. Two overlapping genomic clones containing sequences encoding human factor VII were isolated and characterized. The complete sequence of the gene was determined and found to span about 12.8 kilobases. The mRNA for factor VII as demonstrated by cDNA cloning is polyadenylylated at multiple sites but contains only one AAUAAA poly(A) signal sequence. The mRNA can undergo alternative splicing, forming one transcript containing eight segments as exons and another with an additional exon that encodes a larger prepro leader sequence. The latter transcript has no known counterpart in the other vitamin K-dependent proteins. The positions of the introns with respect to the amino acid sequence encoded by the eight essential exons of factor VII are the same as those present in factor IX, factor X, protein C, and the first three exons of prothrombin. These exons code for domains generally conserved among members of this gene family. The comparable introns in these genes, however, are dissimilar with respect to size and sequence, with the exception of intron C in factor VII and protein C. The gene for factor VII also contains five regions made up of tandem repeats of oligonucleotide monomer elements. More than a quarter of the intron sequences and more than a third of the 3' untranslated portion of the mRNA transcript consist of these minisatellite tandem repeats

  5. MOCAT: a metagenomics assembly and gene prediction toolkit.

    Science.gov (United States)

    Kultima, Jens Roat; Sunagawa, Shinichi; Li, Junhua; Chen, Weineng; Chen, Hua; Mende, Daniel R; Arumugam, Manimozhiyan; Pan, Qi; Liu, Binghang; Qin, Junjie; Wang, Jun; Bork, Peer

    2012-01-01

    MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  6. MOCAT: a metagenomics assembly and gene prediction toolkit.

    Directory of Open Access Journals (Sweden)

    Jens Roat Kultima

    Full Text Available MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.

  7. Transduplication resulted in the incorporation of two protein-coding sequences into the Turmoil-1 transposable element of C. elegans

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    Pupko Tal

    2008-10-01

    Full Text Available Abstract Transposable elements may acquire unrelated gene fragments into their sequences in a process called transduplication. Transduplication of protein-coding genes is common in plants, but is unknown of in animals. Here, we report that the Turmoil-1 transposable element in C. elegans has incorporated two protein-coding sequences into its inverted terminal repeat (ITR sequences. The ITRs of Turmoil-1 contain a conserved RNA recognition motif (RRM that originated from the rsp-2 gene and a fragment from the protein-coding region of the cpg-3 gene. We further report that an open reading frame specific to C. elegans may have been created as a result of a Turmoil-1 insertion. Mutations at the 5' splice site of this open reading frame may have reactivated the transduplicated RRM motif. Reviewers This article was reviewed by Dan Graur and William Martin. For the full reviews, please go to the Reviewers' Reports section.

  8. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

    Science.gov (United States)

    Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

    2018-02-15

    A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  9. nocoRNAc: Characterization of non-coding RNAs in prokaryotes

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    Nieselt Kay

    2011-01-01

    Full Text Available Abstract Background The interest in non-coding RNAs (ncRNAs constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not. Results We present NOCORNAc, a program for the genome-wide prediction of ncRNA transcripts in bacteria. NOCORNAc incorporates various procedures for the detection of transcriptional features which are then integrated with functional ncRNA loci to determine the transcript coordinates. We applied RNAz and NOCORNAc to the genome of Streptomyces coelicolor and detected more than 800 putative ncRNA transcripts most of them located antisense to protein-coding regions. Using a custom design microarray we profiled the expression of about 400 of these elements and found more than 300 to be transcribed, 38 of them are predicted novel ncRNA genes in intergenic regions. The expression patterns of many ncRNAs are similarly complex as those of the protein-coding genes, in particular many antisense ncRNAs show a high expression correlation with their protein-coding partner. Conclusions We have developed NOCORNAc, a framework that facilitates the automated characterization of functional ncRNAs. NOCORNAc increases the confidence of predicted ncRNA loci, especially if they contain transcribed ncRNAs. NOCORNAc is not restricted to

  10. Expression of the Long Intergenic Non-Protein Coding RNA 665 (LINC00665) Gene and the Cell Cycle in Hepatocellular Carcinoma Using The Cancer Genome Atlas, the Gene Expression Omnibus, and Quantitative Real-Time Polymerase Chain Reaction.

    Science.gov (United States)

    Wen, Dong-Yue; Lin, Peng; Pang, Yu-Yan; Chen, Gang; He, Yun; Dang, Yi-Wu; Yang, Hong

    2018-05-05

    BACKGROUND Long non-coding RNAs (lncRNAs) have a role in physiological and pathological processes, including cancer. The aim of this study was to investigate the expression of the long intergenic non-protein coding RNA 665 (LINC00665) gene and the cell cycle in hepatocellular carcinoma (HCC) using database analysis including The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and quantitative real-time polymerase chain reaction (qPCR). MATERIAL AND METHODS Expression levels of LINC00665 were compared between human tissue samples of HCC and adjacent normal liver, clinicopathological correlations were made using TCGA and the GEO, and qPCR was performed to validate the findings. Other public databases were searched for other genes associated with LINC00665 expression, including The Atlas of Noncoding RNAs in Cancer (TANRIC), the Multi Experiment Matrix (MEM), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) networks. RESULTS Overexpression of LINC00665 in patients with HCC was significantly associated with gender, tumor grade, stage, and tumor cell type. Overexpression of LINC00665 in patients with HCC was significantly associated with overall survival (OS) (HR=1.47795%; CI: 1.046-2.086). Bioinformatics analysis identified 469 related genes and further analysis supported a hypothesis that LINC00665 regulates pathways in the cell cycle to facilitate the development and progression of HCC through ten identified core genes: CDK1, BUB1B, BUB1, PLK1, CCNB2, CCNB1, CDC20, ESPL1, MAD2L1, and CCNA2. CONCLUSIONS Overexpression of the lncRNA, LINC00665 may be involved in the regulation of cell cycle pathways in HCC through ten identified hub genes.

  11. Code-assisted discovery of TAL effector targets in bacterial leaf streak of rice reveals contrast with bacterial blight and a novel susceptibility gene.

    Directory of Open Access Journals (Sweden)

    Raul A Cernadas

    2014-02-01

    Full Text Available Bacterial leaf streak of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc is an increasingly important yield constraint in this staple crop. A mesophyll colonizer, Xoc differs from X. oryzae pv. oryzae (Xoo, which invades xylem to cause bacterial blight of rice. Both produce multiple distinct TAL effectors, type III-delivered proteins that transactivate effector-specific host genes. A TAL effector finds its target(s via a partially degenerate code whereby the modular effector amino acid sequence identifies nucleotide sequences to which the protein binds. Virulence contributions of some Xoo TAL effectors have been shown, and their relevant targets, susceptibility (S genes, identified, but the role of TAL effectors in leaf streak is uncharacterized. We used host transcript profiling to compare leaf streak to blight and to probe functions of Xoc TAL effectors. We found that Xoc and Xoo induce almost completely different host transcriptional changes. Roughly one in three genes upregulated by the pathogens is preceded by a candidate TAL effector binding element. Experimental analysis of the 44 such genes predicted to be Xoc TAL effector targets verified nearly half, and identified most others as false predictions. None of the Xoc targets is a known bacterial blight S gene. Mutational analysis revealed that Tal2g, which activates two genes, contributes to lesion expansion and bacterial exudation. Use of designer TAL effectors discriminated a sulfate transporter gene as the S gene. Across all targets, basal expression tended to be higher than genome-average, and induction moderate. Finally, machine learning applied to real vs. falsely predicted targets yielded a classifier that recalled 92% of the real targets with 88% precision, providing a tool for better target prediction in the future. Our study expands the number of known TAL effector targets, identifies a new class of S gene, and improves our ability to predict functional targeting.

  12. Fast rate of evolution in alternatively spliced coding regions of mammalian genes

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    Nurtdinov Ramil N

    2006-04-01

    Full Text Available Abstract Background At least half of mammalian genes are alternatively spliced. Alternative isoforms are often genome-specific and it has been suggested that alternative splicing is one of the major mechanisms for generating protein diversity in the course of evolution. Another way of looking at alternative splicing is to consider sequence evolution of constitutive and alternative regions of protein-coding genes. Indeed, it turns out that constitutive and alternative regions evolve in different ways. Results A set of 3029 orthologous pairs of human and mouse alternatively spliced genes was considered. The rate of nonsynonymous substitutions (dN, the rate of synonymous substitutions (dS, and their ratio (ω = dN/dS appear to be significantly higher in alternatively spliced coding regions compared to constitutive regions. When N-terminal, internal and C-terminal alternatives are analysed separately, C-terminal alternatives appear to make the main contribution to the observed difference. The effects become even more pronounced in a subset of fast evolving genes. Conclusion These results provide evidence of weaker purifying selection and/or stronger positive selection in alternative regions and thus one more confirmation of accelerated evolution in alternative regions. This study corroborates the theory that alternative splicing serves as a testing ground for molecular evolution.

  13. The water-borne protein signals (pheromones) of the Antarctic ciliated protozoan Euplotes nobilii: structure of the gene coding for the En-6 pheromone.

    Science.gov (United States)

    La Terza, Antonietta; Dobri, Nicoleta; Alimenti, Claudio; Vallesi, Adriana; Luporini, Pierangelo

    2009-01-01

    The marine Antarctic ciliate, Euplotes nobilii, secretes a family of water-borne signal proteins, denoted as pheromones, which control vegetative proliferation and mating in the cell. Based on the knowledge of the amino acid sequences of a set of these pheromones isolated from the culture supernatant of wild-type strains, we designed probes to identify their encoding genes in the cell somatic nucleus (macronucleus). The full-length gene of the pheromone En-6 was determined and found to contain an open-reading frame specific for the synthesis of the En-6 cytoplasmic precursor (pre-pro-En-6), which requires 2 proteolytic cleavages to remove the signal peptide (pre) and the prosegment before secretion of the mature protein. In contrast to the sequence variability that distinguishes the secreted pheromones, the pre- and pro-sequences appear to be tightly conserved and useful for the construction of probes to clone every other E. nobilii pheromone gene. Potential intron sequences in the coding region of the En-6 gene imply the synthesis of more En-6 isoforms.

  14. CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

    Science.gov (United States)

    Abbasi, Wajid Arshad; Asif, Amina; Andleeb, Saiqa; Minhas, Fayyaz Ul Amir Afsar

    2017-09-01

    Due to Ca 2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels. © 2017 Wiley Periodicals, Inc.

  15. Protein annotation from protein interaction networks and Gene Ontology.

    Science.gov (United States)

    Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

    2011-10-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

    Directory of Open Access Journals (Sweden)

    Neto Armando

    2012-11-01

    Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

  17. The predictive nature of transcript expression levels on protein expression in adult human brain.

    Science.gov (United States)

    Bauernfeind, Amy L; Babbitt, Courtney C

    2017-04-24

    Next generation sequencing methods are the gold standard for evaluating expression of the transcriptome. When determining the biological implications of such studies, the assumption is often made that transcript expression levels correspond to protein levels in a meaningful way. However, the strength of the overall correlation between transcript and protein expression is inconsistent, particularly in brain samples. Following high-throughput transcriptomic (RNA-Seq) and proteomic (liquid chromatography coupled with tandem mass spectrometry) analyses of adult human brain samples, we compared the correlation in the expression of transcripts and proteins that support various biological processes, molecular functions, and that are located in different areas of the cell. Although most categories of transcripts have extremely weak predictive value for the expression of their associated proteins (R 2 values of < 10%), transcripts coding for protein kinases and membrane-associated proteins, including those that are part of receptors or ion transporters, are among those that are most predictive of downstream protein expression levels. The predictive value of transcript expression for corresponding proteins is variable in human brain samples, reflecting the complex regulation of protein expression. However, we found that transcriptomic analyses are appropriate for assessing the expression levels of certain classes of proteins, including those that modify proteins, such as kinases and phosphatases, regulate metabolic and synaptic activity, or are associated with a cellular membrane. These findings can be used to guide the interpretation of gene expression results from primate brain samples.

  18. The artificial zinc finger coding gene 'Jazz' binds the utrophin promoter and activates transcription.

    Science.gov (United States)

    Corbi, N; Libri, V; Fanciulli, M; Tinsley, J M; Davies, K E; Passananti, C

    2000-06-01

    Up-regulation of utrophin gene expression is recognized as a plausible therapeutic approach in the treatment of Duchenne muscular dystrophy (DMD). We have designed and engineered new zinc finger-based transcription factors capable of binding and activating transcription from the promoter of the dystrophin-related gene, utrophin. Using the recognition 'code' that proposes specific rules between zinc finger primary structure and potential DNA binding sites, we engineered a new gene named 'Jazz' that encodes for a three-zinc finger peptide. Jazz belongs to the Cys2-His2 zinc finger type and was engineered to target the nine base pair DNA sequence: 5'-GCT-GCT-GCG-3', present in the promoter region of both the human and mouse utrophin gene. The entire zinc finger alpha-helix region, containing the amino acid positions that are crucial for DNA binding, was specifically chosen on the basis of the contacts more frequently represented in the available list of the 'code'. Here we demonstrate that Jazz protein binds specifically to the double-stranded DNA target, with a dissociation constant of about 32 nM. Band shift and super-shift experiments confirmed the high affinity and specificity of Jazz protein for its DNA target. Moreover, we show that chimeric proteins, named Gal4-Jazz and Sp1-Jazz, are able to drive the transcription of a test gene from the human utrophin promoter.

  19. Evaluation of 10 genes encoding cardiac proteins in Doberman Pinschers with dilated cardiomyopathy.

    Science.gov (United States)

    O'Sullivan, M Lynne; O'Grady, Michael R; Pyle, W Glen; Dawson, John F

    2011-07-01

    To identify a causative mutation for dilated cardiomyopathy (DCM) in Doberman Pinschers by sequencing the coding regions of 10 cardiac genes known to be associated with familial DCM in humans. 5 Doberman Pinschers with DCM and congestive heart failure and 5 control mixed-breed dogs that were euthanized or died. RNA was extracted from frozen ventricular myocardial samples from each dog, and first-strand cDNA was synthesized via reverse transcription, followed by PCR amplification with gene-specific primers. Ten cardiac genes were analyzed: cardiac actin, α-actinin, α-tropomyosin, β-myosin heavy chain, metavinculin, muscle LIM protein, myosinbinding protein C, tafazzin, titin-cap (telethonin), and troponin T. Sequences for DCM-affected and control dogs and the published canine genome were compared. None of the coding sequences yielded a common causative mutation among all Doberman Pinscher samples. However, 3 variants were identified in the α-actinin gene in the DCM-affected Doberman Pinschers. One of these variants, identified in 2 of the 5 Doberman Pinschers, resulted in an amino acid change in the rod-forming triple coiled-coil domain. Mutations in the coding regions of several genes associated with DCM in humans did not appear to consistently account for DCM in Doberman Pinschers. However, an α-actinin variant was detected in some Doberman Pinschers that may contribute to the development of DCM given its potential effect on the structure of this protein. Investigation of additional candidate gene coding and noncoding regions and further evaluation of the role of α-actinin in development of DCM in Doberman Pinschers are warranted.

  20. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  1. Bifurcations in the interplay of messenger RNA, protein and nonprotein coding RNA

    International Nuclear Information System (INIS)

    Zhdanov, Vladimir P

    2008-01-01

    The interplay of messenger RNA (mRNA), protein, produced via translation of this RNA, and nonprotein coding RNA (ncRNA) may include regulation of the ncRNA production by protein and (i) ncRNA-protein association resulting in suppression of the protein regulatory activity or (ii) ncRNA-mRNA association resulting in degradation of the miRNA-mRNA complex. The kinetic models describing these two scenarios are found to predict bistability provided that protein suppresses the ncRNA formation

  2. High abundance of Serine/Threonine-rich regions predicted to be hyper-O-glycosylated in the secretory proteins coded by eight fungal genomes

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    González Mario

    2012-09-01

    Full Text Available Abstract Background O-glycosylation of secretory proteins has been found to be an important factor in fungal biology and virulence. It consists in the addition of short glycosidic chains to Ser or Thr residues in the protein backbone via O-glycosidic bonds. Secretory proteins in fungi frequently display Ser/Thr rich regions that could be sites of extensive O-glycosylation. We have analyzed in silico the complete sets of putatively secretory proteins coded by eight fungal genomes (Botrytis cinerea, Magnaporthe grisea, Sclerotinia sclerotiorum, Ustilago maydis, Aspergillus nidulans, Neurospora crassa, Trichoderma reesei, and Saccharomyces cerevisiae in search of Ser/Thr-rich regions as well as regions predicted to be highly O-glycosylated by NetOGlyc (http://www.cbs.dtu.dk. Results By comparison with experimental data, NetOGlyc was found to overestimate the number of O-glycosylation sites in fungi by a factor of 1.5, but to be quite reliable in the prediction of highly O-glycosylated regions. About half of secretory proteins have at least one Ser/Thr-rich region, with a Ser/Thr content of at least 40% over an average length of 40 amino acids. Most secretory proteins in filamentous fungi were predicted to be O-glycosylated, sometimes in dozens or even hundreds of sites. Residues predicted to be O-glycosylated have a tendency to be grouped together forming hyper-O-glycosylated regions of varying length. Conclusions About one fourth of secretory fungal proteins were predicted to have at least one hyper-O-glycosylated region, which consists of 45 amino acids on average and displays at least one O-glycosylated Ser or Thr every four residues. These putative highly O-glycosylated regions can be found anywhere along the proteins but have a slight tendency to be at either one of the two ends.

  3. Evolution of hepatic glucose metabolism: liver-specific glucokinase deficiency explained by parallel loss of the gene for glucokinase regulatory protein (GCKR.

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    Zhao Yang Wang

    Full Text Available Glucokinase (GCK plays an important role in the regulation of carbohydrate metabolism. In the liver, phosphorylation of glucose to glucose-6-phosphate by GCK is the first step for both glycolysis and glycogen synthesis. However, some vertebrate species are deficient in GCK activity in the liver, despite containing GCK genes that appear to be compatible with function in their genomes. Glucokinase regulatory protein (GCKR is the most important post-transcriptional regulator of GCK in the liver; it participates in the modulation of GCK activity and location depending upon changes in glucose levels. In experimental models, loss of GCKR has been shown to associate with reduced hepatic GCK protein levels and activity.GCKR genes and GCKR-like sequences were identified in the genomes of all vertebrate species with available genome sequences. The coding sequences of GCKR and GCKR-like genes were identified and aligned; base changes likely to disrupt coding potential or splicing were also identified.GCKR genes could not be found in the genomes of 9 vertebrate species, including all birds. In addition, in multiple mammalian genomes, whereas GCKR-like gene sequences could be identified, these genes could not predict a functional protein. Vertebrate species that were previously reported to be deficient in hepatic GCK activity were found to have deleted (birds and lizard or mutated (mammals GCKR genes. Our results suggest that mutation of the GCKR gene leads to hepatic GCK deficiency due to the loss of the stabilizing effect of GCKR.

  4. The Arabidopsis TOR Kinase Specifically Regulates the Expression of Nuclear Genes Coding for Plastidic Ribosomal Proteins and the Phosphorylation of the Cytosolic Ribosomal Protein S6.

    Science.gov (United States)

    Dobrenel, Thomas; Mancera-Martínez, Eder; Forzani, Céline; Azzopardi, Marianne; Davanture, Marlène; Moreau, Manon; Schepetilnikov, Mikhail; Chicher, Johana; Langella, Olivier; Zivy, Michel; Robaglia, Christophe; Ryabova, Lyubov A; Hanson, Johannes; Meyer, Christian

    2016-01-01

    Protein translation is an energy consuming process that has to be fine-tuned at both the cell and organism levels to match the availability of resources. The target of rapamycin kinase (TOR) is a key regulator of a large range of biological processes in response to environmental cues. In this study, we have investigated the effects of TOR inactivation on the expression and regulation of Arabidopsis ribosomal proteins at different levels of analysis, namely from transcriptomic to phosphoproteomic. TOR inactivation resulted in a coordinated down-regulation of the transcription and translation of nuclear-encoded mRNAs coding for plastidic ribosomal proteins, which could explain the chlorotic phenotype of the TOR silenced plants. We have identified in the 5' untranslated regions (UTRs) of this set of genes a conserved sequence related to the 5' terminal oligopyrimidine motif, which is known to confer translational regulation by the TOR kinase in other eukaryotes. Furthermore, the phosphoproteomic analysis of the ribosomal fraction following TOR inactivation revealed a lower phosphorylation of the conserved Ser240 residue in the C-terminal region of the 40S ribosomal protein S6 (RPS6). These results were confirmed by Western blot analysis using an antibody that specifically recognizes phosphorylated Ser240 in RPS6. Finally, this antibody was used to follow TOR activity in plants. Our results thus uncover a multi-level regulation of plant ribosomal genes and proteins by the TOR kinase.

  5. Dopamine reward prediction error coding.

    Science.gov (United States)

    Schultz, Wolfram

    2016-03-01

    Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

  6. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene.

    Directory of Open Access Journals (Sweden)

    Liam R Brunham

    2005-12-01

    Full Text Available The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008. These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.

  7. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

  8. Cloning of human genes encoding novel G protein-coupled receptors

    Energy Technology Data Exchange (ETDEWEB)

    Marchese, A.; Docherty, J.M.; Heiber, M. [Univ. of Toronto, (Canada)] [and others

    1994-10-01

    We report the isolation and characterization of several novel human genes encoding G protein-coupled receptors. Each of the receptors contained the familiar seven transmembrane topography and most closely resembled peptide binding receptors. Gene GPR1 encoded a receptor protein that is intronless in the coding region and that shared identity (43% in the transmembrane regions) with the opioid receptors. Northern blot analysis revealed that GPR1 transcripts were expressed in the human hippocampus, and the gene was localized to chromosome 15q21.6. Gene GPR2 encoded a protein that most closely resembled an interleukin-8 receptor (51% in the transmembrane regions), and this gene, not expressed in the six brain regions examined, was localized to chromosome 17q2.1-q21.3. A third gene, GPR3, showed identity (56% in the transmembrane regions) with a previously characterized cDNA clone from rat and was localized to chromosome 1p35-p36.1. 31 refs., 5 figs., 1 tab.

  9. Cloning and identification of the gene coding for the 140-kd subunit of Drosophila RNA polymerase II

    OpenAIRE

    Faust, Daniela M.; Renkawitz-Pohl, Renate; Falkenburg, Dieter; Gasch, Alexander; Bialojan, Siegfried; Young, Richard A.; Bautz, Ekkehard K. F.

    1986-01-01

    Genomic clones of Drosophila melanogaster were isolated from a λ library by cross-hybridization with the yeast gene coding for the 150-kd subunit of RNA polymerase II. Clones containing a region of ∼2.0 kb with strong homology to the yeast gene were shown to code for a 3.9-kb poly(A)+-RNA. Part of the coding region was cloned into an expression vector. A fusion protein was obtained which reacted with an antibody directed against RNA polymerase II of Drosophila. Peptide mapping of the fusion p...

  10. A Drosophila gene encoding a protein resembling the human β-amyloid protein precursor

    International Nuclear Information System (INIS)

    Rosen, D.R.; Martin-Morris, L.; Luo, L.; White, K.

    1989-01-01

    The authors have isolated genomic and cDNA clones for a Drosophila gene resembling the human β-amyloid precursor protein (APP). This gene produces a nervous system-enriched 6.5-kilobase transcript. Sequencing of cDNAs derived from the 6.5-kilobase transcript predicts an 886-amino acid polypeptide. This polypeptide contains a putative transmembrane domain and exhibits strong sequence similarity to cytoplasmic and extracellular regions of the human β-amyloid precursor protein. There is a high probability that this Drosophila gene corresponds to the essential Drosophila locus vnd, a gene required for embryonic nervous system development

  11. A Bioinformatics Analysis Reveals a Group of MocR Bacterial Transcriptional Regulators Linked to a Family of Genes Coding for Membrane Proteins

    Directory of Open Access Journals (Sweden)

    Teresa Milano

    2016-01-01

    Full Text Available The MocR bacterial transcriptional regulators are characterized by an N-terminal domain, 60 residues long on average, possessing the winged-helix-turn-helix (wHTH architecture responsible for DNA recognition and binding, linked to a large C-terminal domain (350 residues on average that is homologous to fold type-I pyridoxal 5′-phosphate (PLP dependent enzymes like aspartate aminotransferase (AAT. These regulators are involved in the expression of genes taking part in several metabolic pathways directly or indirectly connected to PLP chemistry, many of which are still uncharacterized. A bioinformatics analysis is here reported that studied the features of a distinct group of MocR regulators predicted to be functionally linked to a family of homologous genes coding for integral membrane proteins of unknown function. This group occurs mainly in the Actinobacteria and Gammaproteobacteria phyla. An analysis of the multiple sequence alignments of their wHTH and AAT domains suggested the presence of specificity-determining positions (SDPs. Mapping of SDPs onto a homology model of the AAT domain hinted at possible structural/functional roles in effector recognition. Likewise, SDPs in wHTH domain suggested the basis of specificity of Transcription Factor Binding Site recognition. The results reported represent a framework for rational design of experiments and for bioinformatics analysis of other MocR subgroups.

  12. Novel methods for the molecular discrimination of Fasciola spp. on the basis of nuclear protein-coding genes.

    Science.gov (United States)

    Shoriki, Takuya; Ichikawa-Seki, Madoka; Suganuma, Keisuke; Naito, Ikunori; Hayashi, Kei; Nakao, Minoru; Aita, Junya; Mohanta, Uday Kumar; Inoue, Noboru; Murakami, Kenji; Itagaki, Tadashi

    2016-06-01

    Fasciolosis is an economically important disease of livestock caused by Fasciola hepatica, Fasciola gigantica, and aspermic Fasciola flukes. The aspermic Fasciola flukes have been discriminated morphologically from the two other species by the absence of sperm in their seminal vesicles. To date, the molecular discrimination of F. hepatica and F. gigantica has relied on the nucleotide sequences of the internal transcribed spacer 1 (ITS1) region. However, ITS1 genotypes of aspermic Fasciola flukes cannot be clearly differentiated from those of F. hepatica and F. gigantica. Therefore, more precise and robust methods are required to discriminate Fasciola spp. In this study, we developed PCR restriction fragment length polymorphism and multiplex PCR methods to discriminate F. hepatica, F. gigantica, and aspermic Fasciola flukes on the basis of the nuclear protein-coding genes, phosphoenolpyruvate carboxykinase and DNA polymerase delta, which are single locus genes in most eukaryotes. All aspermic Fasciola flukes used in this study had mixed fragment pattern of F. hepatica and F. gigantica for both of these genes, suggesting that the flukes are descended through hybridization between the two species. These molecular methods will facilitate the identification of F. hepatica, F. gigantica, and aspermic Fasciola flukes, and will also prove useful in etiological studies of fasciolosis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  13. Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica.

    Science.gov (United States)

    Fernandez-Valverde, Selene L; Calcino, Andrew D; Degnan, Bernard M

    2015-05-15

    The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence. Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3' and 5' untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3' UTRs. The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.

  14. DeepLoc: prediction of protein subcellular localization using deep learning

    DEFF Research Database (Denmark)

    Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2017-01-01

    The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

  15. Predicting co-complexed protein pairs using genomic and proteomic data integration

    Directory of Open Access Journals (Sweden)

    King Oliver D

    2004-04-01

    Full Text Available Abstract Background Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H and affinity purification coupled with mass spectrometry (APMS have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. Results Using a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue, a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database, and the remaining predictions may potentially represent unknown CCPs. Conclusions We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

  16. Study on Fusion Protein and Its gene in Baculovirus Specificity

    International Nuclear Information System (INIS)

    Nemr, W.A.H.

    2012-01-01

    Baculoviruses are subdivided into two groups depending on the type of budded virus envelop fusion protein; group I utilized gp64 which include the most of nucleopolyhedroviruses (NPVs), group II utilized F protein which include the remnants of NPVs and all Granuloviruses (GVs). Recent studies reported the viral F protein coding gene as a host cellular sourced gene and may evolutionary acquired from the host genome referring to phylogeny analysis of fusion proteins. Thus, it was deduced that F protein coding gene is species- specific nucleotide sequence related to the type of the specific host and if virus could infect an unexpected host, the resulted virus may encode a vary F gene. In this regard, the present study utilized the mentioned properties of F gene in an attempt to produce a model of specific and more economic wider range granulovirus bio- pesticide able to infect both Spodoptera littoralis and Phthorimaea operculella larvae. Multiple sequence alignment and phylogeny analysis were performed on six members of group II baculovirus, novel universal PCR primers were manually designed from the conserved regions in the alignment graph, targeted to amplify species- specific sequence entire F gene open reading frame (ORF) which is useful in molecular identification of baculovirus in unknown samples. So, the PCR product of SpliGV used to prepare a specific probe for the F gene of this type of virus. Results reflected that it is possible to infect S. littoralis larvae by PhopGV if injected into larval haemocoel, the resulted virus of this infection showed by using DNA hybridization technique to be encode to F gene homologous with the F gene of Spli GV, which is revealed that the resulted virus acquired this F gene sequence from the host genome after infection. Consequently, these results may infer that if genetic aberrations occur in the host genome, this may affect in baculoviral infectivity. So, this study aimed to investigate the effect of gamma radiation at

  17. Gene ontology based transfer learning for protein subcellular localization

    Directory of Open Access Journals (Sweden)

    Zhou Shuigeng

    2011-02-01

    Full Text Available Abstract Background Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology. Results In this paper, we propose a Gene Ontology Based Transfer Learning Model (GO-TLM for large-scale protein subcellular localization. The model transfers the signature-based homologous GO terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false GO terms that are resulted from evolutionary divergence. We derive three GO kernels from the three aspects of gene ontology to measure the GO similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for

  18. Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Jing; Ma, Zihao; Carr, Steven A.; Mertins, Philipp; Zhang, Hui; Zhang, Zhen; Chan, Daniel W.; Ellis, Matthew J. C.; Townsend, R. Reid; Smith, Richard D.; McDermott, Jason E.; Chen, Xian; Paulovich, Amanda G.; Boja, Emily S.; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Rodland, Karin D.; Liebler, Daniel C.; Zhang, Bing

    2016-11-11

    Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

  19. Death of a dogma: eukaryotic mRNAs can code for more than one protein.

    Science.gov (United States)

    Mouilleron, Hélène; Delcourt, Vivian; Roucou, Xavier

    2016-01-08

    mRNAs carry the genetic information that is translated by ribosomes. The traditional view of a mature eukaryotic mRNA is a molecule with three main regions, the 5' UTR, the protein coding open reading frame (ORF) or coding sequence (CDS), and the 3' UTR. This concept assumes that ribosomes translate one ORF only, generally the longest one, and produce one protein. As a result, in the early days of genomics and bioinformatics, one CDS was associated with each protein-coding gene. This fundamental concept of a single CDS is being challenged by increasing experimental evidence indicating that annotated proteins are not the only proteins translated from mRNAs. In particular, mass spectrometry (MS)-based proteomics and ribosome profiling have detected productive translation of alternative open reading frames. In several cases, the alternative and annotated proteins interact. Thus, the expression of two or more proteins translated from the same mRNA may offer a mechanism to ensure the co-expression of proteins which have functional interactions. Translational mechanisms already described in eukaryotic cells indicate that the cellular machinery is able to translate different CDSs from a single viral or cellular mRNA. In addition to summarizing data showing that the protein coding potential of eukaryotic mRNAs has been underestimated, this review aims to challenge the single translated CDS dogma. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Comparative analysis of codon usage patterns and identification of predicted highly expressed genes in five Salmonella genomes

    Directory of Open Access Journals (Sweden)

    Mondal U

    2008-01-01

    Full Text Available Purpose: To anlyse codon usage patterns of five complete genomes of Salmonella , predict highly expressed genes, examine horizontally transferred pathogenicity-related genes to detect their presence in the strains, and scrutinize the nature of highly expressed genes to infer upon their lifestyle. Methods: Protein coding genes, ribosomal protein genes, and pathogenicity-related genes were analysed with Codon W and CAI (codon adaptation index Calculator. Results: Translational efficiency plays a role in codon usage variation in Salmonella genes. Low bias was noticed in most of the genes. GC3 (guanine cytosine at third position composition does not influence codon usage variation in the genes of these Salmonella strains. Among the cluster of orthologous groups (COGs, translation, ribosomal structure biogenesis [J], and energy production and conversion [C] contained the highest number of potentially highly expressed (PHX genes. Correspondence analysis reveals the conserved nature of the genes. Highly expressed genes were detected. Conclusions: Selection for translational efficiency is the major source of variation of codon usage in the genes of Salmonella . Evolution of pathogenicity-related genes as a unit suggests their ability to infect and exist as a pathogen. Presence of a lot of PHX genes in the information and storage-processing category of COGs indicated their lifestyle and revealed that they were not subjected to genome reduction.

  1. The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

    Science.gov (United States)

    Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

    2014-06-01

    With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

  2. Operon Gene Order Is Optimized for Ordered Protein Complex Assembly

    Science.gov (United States)

    Wells, Jonathan N.; Bergendahl, L. Therese; Marsh, Joseph A.

    2016-01-01

    Summary The assembly of heteromeric protein complexes is an inherently stochastic process in which multiple genes are expressed separately into proteins, which must then somehow find each other within the cell. Here, we considered one of the ways by which prokaryotic organisms have attempted to maximize the efficiency of protein complex assembly: the organization of subunit-encoding genes into operons. Using structure-based assembly predictions, we show that operon gene order has been optimized to match the order in which protein subunits assemble. Exceptions to this are almost entirely highly expressed proteins for which assembly is less stochastic and for which precisely ordered translation offers less benefit. Overall, these results show that ordered protein complex assembly pathways are of significant biological importance and represent a major evolutionary constraint on operon gene organization. PMID:26804901

  3. Differential DNA methylation profiles of coding and non-coding genes define hippocampal sclerosis in human temporal lobe epilepsy

    Science.gov (United States)

    Miller-Delaney, Suzanne F.C.; Bryan, Kenneth; Das, Sudipto; McKiernan, Ross C.; Bray, Isabella M.; Reynolds, James P.; Gwinn, Ryder; Stallings, Raymond L.

    2015-01-01

    Temporal lobe epilepsy is associated with large-scale, wide-ranging changes in gene expression in the hippocampus. Epigenetic changes to DNA are attractive mechanisms to explain the sustained hyperexcitability of chronic epilepsy. Here, through methylation analysis of all annotated C-phosphate-G islands and promoter regions in the human genome, we report a pilot study of the methylation profiles of temporal lobe epilepsy with or without hippocampal sclerosis. Furthermore, by comparative analysis of expression and promoter methylation, we identify methylation sensitive non-coding RNA in human temporal lobe epilepsy. A total of 146 protein-coding genes exhibited altered DNA methylation in temporal lobe epilepsy hippocampus (n = 9) when compared to control (n = 5), with 81.5% of the promoters of these genes displaying hypermethylation. Unique methylation profiles were evident in temporal lobe epilepsy with or without hippocampal sclerosis, in addition to a common methylation profile regardless of pathology grade. Gene ontology terms associated with development, neuron remodelling and neuron maturation were over-represented in the methylation profile of Watson Grade 1 samples (mild hippocampal sclerosis). In addition to genes associated with neuronal, neurotransmitter/synaptic transmission and cell death functions, differential hypermethylation of genes associated with transcriptional regulation was evident in temporal lobe epilepsy, but overall few genes previously associated with epilepsy were among the differentially methylated. Finally, a panel of 13, methylation-sensitive microRNA were identified in temporal lobe epilepsy including MIR27A, miR-193a-5p (MIR193A) and miR-876-3p (MIR876), and the differential methylation of long non-coding RNA documented for the first time. The present study therefore reports select, genome-wide DNA methylation changes in human temporal lobe epilepsy that may contribute to the molecular architecture of the epileptic brain. PMID

  4. General theory for integrated analysis of growth, gene, and protein expression in biofilms.

    Science.gov (United States)

    Zhang, Tianyu; Pabst, Breana; Klapper, Isaac; Stewart, Philip S

    2013-01-01

    A theory for analysis and prediction of spatial and temporal patterns of gene and protein expression within microbial biofilms is derived. The theory integrates phenomena of solute reaction and diffusion, microbial growth, mRNA or protein synthesis, biomass advection, and gene transcript or protein turnover. Case studies illustrate the capacity of the theory to simulate heterogeneous spatial patterns and predict microbial activities in biofilms that are qualitatively different from those of planktonic cells. Specific scenarios analyzed include an inducible GFP or fluorescent protein reporter, a denitrification gene repressed by oxygen, an acid stress response gene, and a quorum sensing circuit. It is shown that the patterns of activity revealed by inducible stable fluorescent proteins or reporter unstable proteins overestimate the region of activity. This is due to advective spreading and finite protein turnover rates. In the cases of a gene induced by either limitation for a metabolic substrate or accumulation of a metabolic product, maximal expression is predicted in an internal stratum of the biofilm. A quorum sensing system that includes an oxygen-responsive negative regulator exhibits behavior that is distinct from any stage of a batch planktonic culture. Though here the analyses have been limited to simultaneous interactions of up to two substrates and two genes, the framework applies to arbitrarily large networks of genes and metabolites. Extension of reaction-diffusion modeling in biofilms to the analysis of individual genes and gene networks is an important advance that dovetails with the growing toolkit of molecular and genetic experimental techniques.

  5. Characterisation of silent and active genes for a variable large protein of Borrelia recurrentis

    Directory of Open Access Journals (Sweden)

    Scragg Ian G

    2002-10-01

    Full Text Available Abstract Background We report the characterisation of the variable large protein (vlp gene expressed by clinical isolate A1 of Borrelia recurrentis; the agent of the life-threatening disease louse-borne relapsing fever. Methods The major vlp protein of this isolate was characterised and a DNA probe created. Use of this together with standard molecular methods was used to determine the location of the vlp1B. recurrentis A1 gene in both this and other isolates. Results This isolate was found to carry silent and expressed copies of the vlp1B. recurrentis A1 gene on plasmids of 54 kbp and 24 kbp respectively, whereas a different isolate, A17, had only the silent vlp1B. recurrentis A17 on a 54 kbp plasmid. Silent and expressed vlp1 have identical mature protein coding regions but have different 5' regions, both containing different potential lipoprotein leader sequences. Only one form of vlp1 is transcribed in the A1 isolate of B. recurrentis, yet both 5' upstream sequences of this vlp1 gene possess features of bacterial promoters. Conclusion Taken together these results suggest that antigenic variation in B. recurrentis may result from recombination of variable large and small protein genes at the junction between lipoprotein leader sequence and mature protein coding region. However, this hypothetical model needs to be validated by further identification of expressed and silent variant protein genes in other B. recurrentis isolates.

  6. Adaptive Evolution Coupled with Retrotransposon Exaptation Allowed for the Generation of a Human-Protein-Specific Coding Gene That Promotes Cancer Cell Proliferation and Metastasis in Both Haematological Malignancies and Solid Tumours: The Extraordinary Case of MYEOV Gene

    Directory of Open Access Journals (Sweden)

    Spyros I. Papamichos

    2015-01-01

    Full Text Available The incidence of cancer in human is high as compared to chimpanzee. However previous analysis has documented that numerous human cancer-related genes are highly conserved in chimpanzee. Till date whether human genome includes species-specific cancer-related genes that could potentially contribute to a higher cancer susceptibility remains obscure. This study focuses on MYEOV, an oncogene encoding for two protein isoforms, reported as causally involved in promoting cancer cell proliferation and metastasis in both haematological malignancies and solid tumours. First we document, via stringent in silico analysis, that MYEOV arose de novo in Catarrhini. We show that MYEOV short-isoform start codon was evolutionarily acquired after Catarrhini/Platyrrhini divergence. Throughout the course of Catarrhini evolution MYEOV acquired a gradually elongated translatable open reading frame (ORF, a gradually shortened translation-regulatory upstream ORF, and alternatively spliced mRNA variants. A point mutation introduced in human allowed for the acquisition of MYEOV long-isoform start codon. Second, we demonstrate the precious impact of exonized transposable elements on the creation of MYEOV gene structure. Third, we highlight that the initial part of MYEOV long-isoform coding DNA sequence was under positive selection pressure during Catarrhini evolution. MYEOV represents a Primate Orphan Gene that acquired, via ORF expansion, a human-protein-specific coding potential.

  7. Influence of Coding Variability in APP-Aβ Metabolism Genes in Sporadic Alzheimer's Disease.

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    Celeste Sassi

    Full Text Available The cerebral deposition of Aβ42, a neurotoxic proteolytic derivate of amyloid precursor protein (APP, is a central event in Alzheimer's disease (AD(Amyloid hypothesis. Given the key role of APP-Aβ metabolism in AD pathogenesis, we selected 29 genes involved in APP processing, Aβ degradation and clearance. We then used exome and genome sequencing to investigate the single independent (single-variant association test and cumulative (gene-based association test effect of coding variants in these genes as potential susceptibility factors for AD, in a cohort composed of 332 sporadic and mainly late-onset AD cases and 676 elderly controls from North America and the UK. Our study shows that common coding variability in these genes does not play a major role for the disease development. In the single-variant association analysis, the main hits, none of which statistically significant after multiple testing correction (1.9e-4coding variants (0.009%genes mainly involved in Aβ extracellular degradation (TTR, ACE, clearance (LRP1 and APP trafficking and recycling (SORL1. These results were partially replicated in the gene-based analysis (c-alpha and SKAT tests, that reports ECE1, LYZ and TTR as nominally associated to AD (1.7e-3 coding variability in APP-Aβ genes is not a critical factor for AD development and 2 Aβ degradation and clearance, rather than Aβ production, may play a key role in the etiology of sporadic AD.

  8. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

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    Li Min

    2012-03-01

    Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

  9. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

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    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  10. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  11. Prediction of protein-protein interactions between viruses and human by an SVM model

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    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  12. Predictive coding in Agency Detection

    DEFF Research Database (Denmark)

    Andersen, Marc Malmdorf

    2017-01-01

    Agency detection is a central concept in the cognitive science of religion (CSR). Experimental studies, however, have so far failed to lend support to some of the most common predictions that follow from current theories on agency detection. In this article, I argue that predictive coding, a highly...... promising new framework for understanding perception and action, may solve pending theoretical inconsistencies in agency detection research, account for the puzzling experimental findings mentioned above, and provide hypotheses for future experimental testing. Predictive coding explains how the brain......, unbeknownst to consciousness, engages in sophisticated Bayesian statistics in an effort to constantly predict the hidden causes of sensory input. My fundamental argument is that most false positives in agency detection can be seen as the result of top-down interference in a Bayesian system generating high...

  13. Arabidopsis RNASE THREE LIKE2 Modulates the Expression of Protein-Coding Genes via 24-Nucleotide Small Interfering RNA-Directed DNA Methylation.

    Science.gov (United States)

    Elvira-Matelot, Emilie; Hachet, Mélanie; Shamandi, Nahid; Comella, Pascale; Sáez-Vásquez, Julio; Zytnicki, Matthias; Vaucheret, Hervé

    2016-02-01

    RNaseIII enzymes catalyze the cleavage of double-stranded RNA (dsRNA) and have diverse functions in RNA maturation. Arabidopsis thaliana RNASE THREE LIKE2 (RTL2), which carries one RNaseIII and two dsRNA binding (DRB) domains, is a unique Arabidopsis RNaseIII enzyme resembling the budding yeast small interfering RNA (siRNA)-producing Dcr1 enzyme. Here, we show that RTL2 modulates the production of a subset of small RNAs and that this activity depends on both its RNaseIII and DRB domains. However, the mode of action of RTL2 differs from that of Dcr1. Whereas Dcr1 directly cleaves dsRNAs into 23-nucleotide siRNAs, RTL2 likely cleaves dsRNAs into longer molecules, which are subsequently processed into small RNAs by the DICER-LIKE enzymes. Depending on the dsRNA considered, RTL2-mediated maturation either improves (RTL2-dependent loci) or reduces (RTL2-sensitive loci) the production of small RNAs. Because the vast majority of RTL2-regulated loci correspond to transposons and intergenic regions producing 24-nucleotide siRNAs that guide DNA methylation, RTL2 depletion modifies DNA methylation in these regions. Nevertheless, 13% of RTL2-regulated loci correspond to protein-coding genes. We show that changes in 24-nucleotide siRNA levels also affect DNA methylation levels at such loci and inversely correlate with mRNA steady state levels, thus implicating RTL2 in the regulation of protein-coding gene expression. © 2016 American Society of Plant Biologists. All rights reserved.

  14. The complete mitochondrial genome of the land snail Cornu aspersum (Helicidae: Mollusca: intra-specific divergence of protein-coding genes and phylogenetic considerations within Euthyneura.

    Directory of Open Access Journals (Sweden)

    Juan Diego Gaitán-Espitia

    Full Text Available The complete sequences of three mitochondrial genomes from the land snail Cornu aspersum were determined. The mitogenome has a length of 14050 bp, and it encodes 13 protein-coding genes, 22 transfer RNA genes and two ribosomal RNA genes. It also includes nine small intergene spacers, and a large AT-rich intergenic spacer. The intra-specific divergence analysis revealed that COX1 has the lower genetic differentiation, while the most divergent genes were NADH1, NADH3 and NADH4. With the exception of Euhadra herklotsi, the structural comparisons showed the same gene order within the family Helicidae, and nearly identical gene organization to that found in order Pulmonata. Phylogenetic reconstruction recovered Basommatophora as polyphyletic group, whereas Eupulmonata and Pulmonata as paraphyletic groups. Bayesian and Maximum Likelihood analyses showed that C. aspersum is a close relative of Cepaea nemoralis, and with the other Helicidae species form a sister group of Albinaria caerulea, supporting the monophyly of the Stylommatophora clade.

  15. A new method for species identification via protein-coding and non-coding DNA barcodes by combining machine learning with bioinformatic methods.

    Science.gov (United States)

    Zhang, Ai-bing; Feng, Jie; Ward, Robert D; Wan, Ping; Gao, Qiang; Wu, Jun; Zhao, Wei-zhong

    2012-01-01

    Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75-100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62-98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60-99.37%) for 1094 brown algae queries, both using ITS barcodes.

  16. A new method for species identification via protein-coding and non-coding DNA barcodes by combining machine learning with bioinformatic methods.

    Directory of Open Access Journals (Sweden)

    Ai-bing Zhang

    Full Text Available Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish and two representing non-coding ITS barcodes (rust fungi and brown algae. Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ and Maximum likelihood (ML methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI of 99.75-100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62-98.40% for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60-99.37% for 1094 brown algae queries, both using ITS barcodes.

  17. The Ever-Evolving Concept of the Gene: The Use of RNA/Protein Experimental Techniques to Understand Genome Functions

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    Andrea Cipriano

    2018-03-01

    Full Text Available The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as “junk” DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs, which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years.

  18. Computational prediction of miRNA genes from small RNA sequencing data

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    Wenjing eKang

    2015-01-01

    Full Text Available Next-generation sequencing now for the first time allows researchers to gauge the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. miRNAs are 22 nucleotide small RNAs (sRNAs that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq, which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.

  19. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

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    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  20. Nucleotide sequence of the Escherichia coli pyrE gene and of the DNA in front of the protein-coding region

    DEFF Research Database (Denmark)

    Poulsen, Peter; Jensen, Kaj Frank; Valentin-Hansen, Poul

    1983-01-01

    leader segment in front of the protein-coding region. This leader contains a structure with features characteristic for a (translated?) rho-independent transcriptional terminator, which is preceded by a cluster of uridylate residues. This indicates that the frequency of pyrE transcription is regulated......Orotate phosphoribosyltransferase (EC 2.4.2.10) was purified to electrophoretic homogeneity from a strain of Escherichia coli containing the pyrE gene cloned on a multicopy plasmid. The relative molecular masses (Mr) of the native enzyme and its subunit were estimated by means of gel filtration...

  1. Tetrahymena thermophila acidic ribosomal protein L37 contains an archaebacterial type of C-terminus

    DEFF Research Database (Denmark)

    Hansen, T S; Andreasen, P H; Dreisig, H

    1991-01-01

    We have cloned and characterized a Tetrahymena thermophila macronuclear gene (L37) encoding the acidic ribosomal protein (A-protein) L37. The gene contains a single intron located in the 3'-part of the coding region. Two major and three minor transcription start points (tsp) were mapped 39 to 63 ...... by protein sequencing. The T. thermophila L37 clearly belongs to the P1-type family of eukaryotic A-proteins, but the C-terminal region has the hallmarks of archaebacterial A-proteins.......We have cloned and characterized a Tetrahymena thermophila macronuclear gene (L37) encoding the acidic ribosomal protein (A-protein) L37. The gene contains a single intron located in the 3'-part of the coding region. Two major and three minor transcription start points (tsp) were mapped 39 to 63...... nucleotides upstream from the translational start codon. The uppermost tsp mapped to the first T in a putative T. thermophila RNA polymerase II initiator element, TATAA. The coding region of L37 predicts a protein of 109 amino acid (aa) residues. A substantial part of the deduced aa sequence was verified...

  2. Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

    Science.gov (United States)

    Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

    2015-01-01

    In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

  3. RNA editing differently affects protein-coding genes in D. melanogaster and H. sapiens.

    Science.gov (United States)

    Grassi, Luigi; Leoni, Guido; Tramontano, Anna

    2015-07-14

    When an RNA editing event occurs within a coding sequence it can lead to a different encoded amino acid. The biological significance of these events remains an open question: they can modulate protein functionality, increase the complexity of transcriptomes or arise from a loose specificity of the involved enzymes. We analysed the editing events in coding regions that produce or not a change in the encoded amino acid (nonsynonymous and synonymous events, respectively) in D. melanogaster and in H. sapiens and compared them with the appropriate random models. Interestingly, our results show that the phenomenon has rather different characteristics in the two organisms. For example, we confirm the observation that editing events occur more frequently in non-coding than in coding regions, and report that this effect is much more evident in H. sapiens. Additionally, in this latter organism, editing events tend to affect less conserved residues. The less frequently occurring editing events in Drosophila tend to avoid drastic amino acid changes. Interestingly, we find that, in Drosophila, changes from less frequently used codons to more frequently used ones are favoured, while this is not the case in H. sapiens.

  4. Neural Elements for Predictive Coding

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    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  5. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  6. ProteinSplit: splitting of multi-domain proteins using prediction of ordered and disordered regions in protein sequences for virtual structural genomics

    International Nuclear Information System (INIS)

    Wyrwicz, Lucjan S; Koczyk, Grzegorz; Rychlewski, Leszek; Plewczynski, Dariusz

    2007-01-01

    The annotation of protein folds within newly sequenced genomes is the main target for semi-automated protein structure prediction (virtual structural genomics). A large number of automated methods have been developed recently with very good results in the case of single-domain proteins. Unfortunately, most of these automated methods often fail to properly predict the distant homology between a given multi-domain protein query and structural templates. Therefore a multi-domain protein should be split into domains in order to overcome this limitation. ProteinSplit is designed to identify protein domain boundaries using a novel algorithm that predicts disordered regions in protein sequences. The software utilizes various sequence characteristics to assess the local propensity of a protein to be disordered or ordered in terms of local structure stability. These disordered parts of a protein are likely to create interdomain spacers. Because of its speed and portability, the method was successfully applied to several genome-wide fold annotation experiments. The user can run an automated analysis of sets of proteins or perform semi-automated multiple user projects (saving the results on the server). Additionally the sequences of predicted domains can be sent to the Bioinfo.PL Protein Structure Prediction Meta-Server for further protein three-dimensional structure and function prediction. The program is freely accessible as a web service at http://lucjan.bioinfo.pl/proteinsplit together with detailed benchmark results on the critical assessment of a fully automated structure prediction (CAFASP) set of sequences. The source code of the local version of protein domain boundary prediction is available upon request from the authors

  7. Molecular cloning of the gene for the human placental GTP-binding protein Gp (G25K): Identification of this GTP-binding protein as the human homolog of the yeast cell-division-cycle protein CDC42

    International Nuclear Information System (INIS)

    Shinjo, K.; Koland, J.G.; Hart, M.J.; Narasimhan, V.; Cerione, R.A.; Johnson, D.I.; Evans, T.

    1990-01-01

    The authors have isolated cDNA clones from a human placental library that code for a low molecular weight GTP-binding protein originally designated G p (also called G25K). This identification is based on comparisons with the available peptide sequences for the purified human G p protein and the use of two highly specific anti-peptide antibodies. The predicted amino acid sequence of the protein is very similar to those of various members of the ras superfamily of low molecular weight GTP-binding proteins, including the N-, Ki-, and Ha-ras proteins (30-35% identical), the rho proteins and the rac proteins. The highest degree of sequence identity (80%) is found with the Saccharomyces cerevisiae cell division-cycle protein CDC42. The human placental gene, which they designate CDC42Hs, complements the cdc42-1 mutation in S. cerevisiae, which suggests that this GTP-binding protein is the human homolog of the yeast protein

  8. Human myosin VIIA responsible for the Usher 1B syndrome: a predicted membrane-associated motor protein expressed in developing sensory epithelia.

    Science.gov (United States)

    Weil, D; Levy, G; Sahly, I; Levi-Acobas, F; Blanchard, S; El-Amraoui, A; Crozet, F; Philippe, H; Abitbol, M; Petit, C

    1996-04-16

    The gene encoding human myosin VIIA is responsible for Usher syndrome type III (USH1B), a disease which associates profound congenital sensorineural deafness, vestibular dysfunction, and retinitis pigmentosa. The reconstituted cDNA sequence presented here predicts a 2215 amino acid protein with a typical unconventional myosin structure. This protein is expected to dimerize into a two-headed molecule. The C terminus of its tail shares homology with the membrane-binding domain of the band 4.1 protein superfamily. The gene consists of 48 coding exons. It encodes several alternatively spliced forms. In situ hybridization analysis in human embryos demonstrates that the myosin VIIA gene is expressed in the pigment epithelium and the photoreceptor cells of the retina, thus indicating that both cell types may be involved in the USH1B retinal degenerative process. In addition, the gene is expressed in the human embryonic cochlear and vestibular neuroepithelia. We suggest that deafness and vestibular dysfunction in USH1B patients result from a defect in the morphogenesis of the inner ear sensory cell stereocilia.

  9. Bioinformatic prediction and functional characterization of human KIAA0100 gene

    Directory of Open Access Journals (Sweden)

    He Cui

    2017-02-01

    Full Text Available Our previous study demonstrated that human KIAA0100 gene was a novel acute monocytic leukemia-associated antigen (MLAA gene. But the functional characterization of human KIAA0100 gene has remained unknown to date. Here, firstly, bioinformatic prediction of human KIAA0100 gene was carried out using online softwares; Secondly, Human KIAA0100 gene expression was downregulated by the clustered regularly interspaced short palindromic repeats (CRISPR/CRISPR-associated (Cas 9 system in U937 cells. Cell proliferation and apoptosis were next evaluated in KIAA0100-knockdown U937 cells. The bioinformatic prediction showed that human KIAA0100 gene was located on 17q11.2, and human KIAA0100 protein was located in the secretory pathway. Besides, human KIAA0100 protein contained a signalpeptide, a transmembrane region, three types of secondary structures (alpha helix, extended strand, and random coil , and four domains from mitochondrial protein 27 (FMP27. The observation on functional characterization of human KIAA0100 gene revealed that its downregulation inhibited cell proliferation, and promoted cell apoptosis in U937 cells. To summarize, these results suggest human KIAA0100 gene possibly comes within mitochondrial genome; moreover, it is a novel anti-apoptotic factor related to carcinogenesis or progression in acute monocytic leukemia, and may be a potential target for immunotherapy against acute monocytic leukemia.

  10. Cloning and expression of gene encoding P23 protein from Cryptosporidium parvum

    Directory of Open Access Journals (Sweden)

    Dinh Thi Bich Lan

    2014-12-01

    Full Text Available We cloned the cp23 gene coding P23 (glycoprotein from Cryptosporidium parvum isolated from Thua Thien Hue province, Vietnam. The coding region of cp23 gene from C. parvum is 99% similar with cp23 gene deposited in NCBI (accession number: U34390. SDS-PAGE and Western blot analysis showed that the cp23 gene in E. coli BL21 StarTM (DE3 produced polypeptides with molecular weights of approximately 37, 40 and 49 kDa. These molecules may be non-glycosylated or glycosylated P23 fusion polypeptides. Recombinant P23 protein purified by GST (glutathione S-transferase affinity chromatography can be used as an antigen for C. parvum antibody production as well as to develop diagnostic kit for C. parvum.

  11. A network approach to predict pathogenic genes for Fusarium graminearum.

    Science.gov (United States)

    Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

    2010-10-04

    Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

  12. A network approach to predict pathogenic genes for Fusarium graminearum.

    Directory of Open Access Journals (Sweden)

    Xiaoping Liu

    Full Text Available Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB, which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other

  13. Molecular evolution of the Paramyxoviridae and Rhabdoviridae multiple-protein-encoding P gene.

    Science.gov (United States)

    Jordan, I K; Sutter, B A; McClure, M A

    2000-01-01

    Presented here is an analysis of the molecular evolutionary dynamics of the P gene among 76 representative sequences of the Paramyxoviridae and Rhabdoviridae RNA virus families. In a number of Paramyxoviridae taxa, as well as in vesicular stomatitis viruses of the Rhabdoviridae, the P gene encodes multiple proteins from a single genomic RNA sequence. These products include the phosphoprotein (P), as well as the C and V proteins. The complexity of the P gene makes it an intriguing locus to study from an evolutionary perspective. Amino acid sequence alignments of the proteins encoded at the P and N loci were used in independent phylogenetic reconstructions of the Paramyxoviridae and Rhabdoviridae families. P-gene-coding capacities were mapped onto the Paramyxoviridae phylogeny, and the most parsimonious path of multiple-coding-capacity evolution was determined. Levels of amino acid variation for Paramyxoviridae and Rhabdoviridae P-gene-encoded products were also analyzed. Proteins encoded in overlapping reading frames from the same nucleotides have different levels of amino acid variation. The nucleotide architecture that underlies the amino acid variation was determined in order to evaluate the role of selection in the evolution of the P gene overlapping reading frames. In every case, the evolution of one of the proteins encoded in the overlapping reading frames has been constrained by negative selection while the other has evolved more rapidly. The integrity of the overlapping reading frame that represents a derived state is generally maintained at the expense of the ancestral reading frame encoded by the same nucleotides. The evolution of such multicoding sequences is likely a response by RNA viruses to selective pressure to maximize genomic information content while maintaining small genome size. The ability to evolve such a complex genomic strategy is intimately related to the dynamics of the viral quasispecies, which allow enhanced exploration of the adaptive

  14. Effects of using coding potential, sequence conservation and mRNA structure conservation for predicting pyrroly-sine containing genes

    DEFF Research Database (Denmark)

    Have, Christian Theil; Zambach, Sine; Christiansen, Henning

    2013-01-01

    for prediction of pyrrolysine incorporating genes in genomes of bacteria and archaea leading to insights about the factors driving pyrrolysine translation and identification of new gene candidates. The method predicts known conserved genes with high recall and predicts several other promising candidates...... for experimental verification. The method is implemented as a computational pipeline which is available on request....

  15. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  16. Use of fluorescent proteins and color-coded imaging to visualize cancer cells with different genetic properties.

    Science.gov (United States)

    Hoffman, Robert M

    2016-03-01

    Fluorescent proteins are very bright and available in spectrally-distinct colors, enable the imaging of color-coded cancer cells growing in vivo and therefore the distinction of cancer cells with different genetic properties. Non-invasive and intravital imaging of cancer cells with fluorescent proteins allows the visualization of distinct genetic variants of cancer cells down to the cellular level in vivo. Cancer cells with increased or decreased ability to metastasize can be distinguished in vivo. Gene exchange in vivo which enables low metastatic cancer cells to convert to high metastatic can be color-coded imaged in vivo. Cancer stem-like and non-stem cells can be distinguished in vivo by color-coded imaging. These properties also demonstrate the vast superiority of imaging cancer cells in vivo with fluorescent proteins over photon counting of luciferase-labeled cancer cells.

  17. Deep Sequencing Reveals Uncharted Isoform Heterogeneity of the Protein-Coding Transcriptome in Cerebral Ischemia.

    Science.gov (United States)

    Bhattarai, Sunil; Aly, Ahmed; Garcia, Kristy; Ruiz, Diandra; Pontarelli, Fabrizio; Dharap, Ashutosh

    2018-06-03

    Gene expression in cerebral ischemia has been a subject of intense investigations for several years. Studies utilizing probe-based high-throughput methodologies such as microarrays have contributed significantly to our existing knowledge but lacked the capacity to dissect the transcriptome in detail. Genome-wide RNA-sequencing (RNA-seq) enables comprehensive examinations of transcriptomes for attributes such as strandedness, alternative splicing, alternative transcription start/stop sites, and sequence composition, thus providing a very detailed account of gene expression. Leveraging this capability, we conducted an in-depth, genome-wide evaluation of the protein-coding transcriptome of the adult mouse cortex after transient focal ischemia at 6, 12, or 24 h of reperfusion using RNA-seq. We identified a total of 1007 transcripts at 6 h, 1878 transcripts at 12 h, and 1618 transcripts at 24 h of reperfusion that were significantly altered as compared to sham controls. With isoform-level resolution, we identified 23 splice variants arising from 23 genes that were novel mRNA isoforms. For a subset of genes, we detected reperfusion time-point-dependent splice isoform switching, indicating an expression and/or functional switch for these genes. Finally, for 286 genes across all three reperfusion time-points, we discovered multiple, distinct, simultaneously expressed and differentially altered isoforms per gene that were generated via alternative transcription start/stop sites. Of these, 165 isoforms derived from 109 genes were novel mRNAs. Together, our data unravel the protein-coding transcriptome of the cerebral cortex at an unprecedented depth to provide several new insights into the flexibility and complexity of stroke-related gene transcription and transcript organization.

  18. Natural variation of rice blast resistance gene Pi-d2

    Science.gov (United States)

    Studying natural variation of rice resistance (R) genes in cultivated and wild rice relatives can predict resistance stability to rice blast fungus. In the present study, the protein coding regions of rice R gene Pi-d2 in 35 rice accessions of subgroups, aus (AUS), indica (IND), temperate japonica (...

  19. One, Two, Three: Polycomb Proteins Hit All Dimensions of Gene Regulation

    Directory of Open Access Journals (Sweden)

    Stefania del Prete

    2015-07-01

    Full Text Available Polycomb group (PcG proteins contribute to the formation and maintenance of a specific repressive chromatin state that prevents the expression of genes in a particular space and time. Polycomb repressive complexes (PRCs consist of several PcG proteins with specific regulatory or catalytic properties. PRCs are recruited to thousands of target genes, and various recruitment factors, including DNA-binding proteins and non-coding RNAs, are involved in the targeting. PcG proteins contribute to a multitude of biological processes by altering chromatin features at different scales. PcG proteins mediate both biochemical modifications of histone tails and biophysical modifications (e.g., chromatin fiber compaction and three-dimensional (3D chromatin conformation. Here, we review the role of PcG proteins in nuclear architecture, describing their impact on the structure of the chromatin fiber, on chromatin interactions, and on the spatial organization of the genome in nuclei. Although little is known about the role of plant PcG proteins in nuclear organization, much is known in the animal field, and we highlight similarities and differences in the roles of PcG proteins in 3D gene regulation in plants and animals.

  20. A novel TaqMan® assay for Nosema ceranae quantification in honey bee, based on the protein coding gene Hsp70.

    Science.gov (United States)

    Cilia, Giovanni; Cabbri, Riccardo; Maiorana, Giacomo; Cardaio, Ilaria; Dall'Olio, Raffaele; Nanetti, Antonio

    2018-04-01

    Nosema ceranae is now a widespread honey bee pathogen with high incidence in apiculture. Rapid and reliable detection and quantification methods are a matter of concern for research community, nowadays mainly relying on the use of biomolecular techniques such as PCR, RT-PCR or HRMA. The aim of this technical paper is to provide a new qPCR assay, based on the highly-conserved protein coding gene Hsp70, to detect and quantify the microsporidian Nosema ceranae affecting the western honey bee Apis mellifera. The validation steps to assess efficiency, sensitivity, specificity and robustness of the assay are described also. Copyright © 2018 Elsevier GmbH. All rights reserved.

  1. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

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    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  2. Complex organisation and structure of the ghrelin antisense strand gene GHRLOS, a candidate non-coding RNA gene

    Directory of Open Access Journals (Sweden)

    Herington Adrian C

    2008-10-01

    Full Text Available Abstract Background The peptide hormone ghrelin has many important physiological and pathophysiological roles, including the stimulation of growth hormone (GH release, appetite regulation, gut motility and proliferation of cancer cells. We previously identified a gene on the opposite strand of the ghrelin gene, ghrelinOS (GHRLOS, which spans the promoter and untranslated regions of the ghrelin gene (GHRL. Here we further characterise GHRLOS. Results We have described GHRLOS mRNA isoforms that extend over 1.4 kb of the promoter region and 106 nucleotides of exon 4 of the ghrelin gene, GHRL. These GHRLOS transcripts initiate 4.8 kb downstream of the terminal exon 4 of GHRL and are present in the 3' untranslated exon of the adjacent gene TATDN2 (TatD DNase domain containing 2. Interestingly, we have also identified a putative non-coding TATDN2-GHRLOS chimaeric transcript, indicating that GHRLOS RNA biogenesis is extremely complex. Moreover, we have discovered that the 3' region of GHRLOS is also antisense, in a tail-to-tail fashion to a novel terminal exon of the neighbouring SEC13 gene, which is important in protein transport. Sequence analyses revealed that GHRLOS is riddled with stop codons, and that there is little nucleotide and amino-acid sequence conservation of the GHRLOS gene between vertebrates. The gene spans 44 kb on 3p25.3, is extensively spliced and harbours multiple variable exons. We have also investigated the expression of GHRLOS and found evidence of differential tissue expression. It is highly expressed in tissues which are emerging as major sites of non-coding RNA expression (the thymus, brain, and testis, as well as in the ovary and uterus. In contrast, very low levels were found in the stomach where sense, GHRL derived RNAs are highly expressed. Conclusion GHRLOS RNA transcripts display several distinctive features of non-coding (ncRNA genes, including 5' capping, polyadenylation, extensive splicing and short open reading

  3. CNNcon: improved protein contact maps prediction using cascaded neural networks.

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    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  4. Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

    Directory of Open Access Journals (Sweden)

    McCarthy Fiona M

    2007-11-01

    Full Text Available Abstract Background The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned. Results We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology, we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines. Conclusion We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and

  5. From structure prediction to genomic screens for novel non-coding RNAs

    DEFF Research Database (Denmark)

    Gorodkin, Jan; Hofacker, Ivo L.

    2011-01-01

    Abstract: Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction....... This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early...... upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other....

  6. Challenging the dogma: the hidden layer of non-protein-coding RNAs in complex organisms.

    Science.gov (United States)

    Mattick, John S

    2003-10-01

    The central dogma of biology holds that genetic information normally flows from DNA to RNA to protein. As a consequence it has been generally assumed that genes generally code for proteins, and that proteins fulfil not only most structural and catalytic but also most regulatory functions, in all cells, from microbes to mammals. However, the latter may not be the case in complex organisms. A number of startling observations about the extent of non-protein-coding RNA (ncRNA) transcription in the higher eukaryotes and the range of genetic and epigenetic phenomena that are RNA-directed suggests that the traditional view of the structure of genetic regulatory systems in animals and plants may be incorrect. ncRNA dominates the genomic output of the higher organisms and has been shown to control chromosome architecture, mRNA turnover and the developmental timing of protein expression, and may also regulate transcription and alternative splicing. This paper re-examines the available evidence and suggests a new framework for considering and understanding the genomic programming of biological complexity, autopoietic development and phenotypic variation. Copyright 2003 Wiley Periodicals, Inc.

  7. Novel polymorphisms in UTR and coding region of inducible heat shock protein 70.1 gene in tropically adapted Indian zebu cattle (Bos indicus) and riverine buffalo (Bubalus bubalis).

    Science.gov (United States)

    Sodhi, M; Mukesh, M; Kishore, A; Mishra, B P; Kataria, R S; Joshi, B K

    2013-09-25

    Due to evolutionary divergence, cattle (taurine, and indicine) and buffalo are speculated to have different responses to heat stress condition. Variation in candidate genes associated with a heat-shock response may provide an insight into the dissimilarity and suggest targets for intervention. The present work was undertaken to characterize one of the inducible heat shock protein genes promoter and coding regions in diverse breeds of Indian zebu cattle and buffaloes. The genomic DNA from a panel of 117 unrelated animals representing 14 diversified native cattle breeds and 6 buffalo breeds were utilized to determine the complete sequence and gene diversity of HSP70.1 gene. The coding region of HSP70.1 gene in Indian zebu cattle, Bos taurus and buffalo was similar in length (1,926 bp) encoding a HSP70 protein of 641 amino acids with a calculated molecular weight (Mw) of 70.26 kDa. However buffalo had a longer 5' and 3' untranslated region (UTR) of 204 and 293 nucleotides respectively, in comparison to Indian zebu cattle and Bos taurus wherein length of 5' and 3'-UTR was 172 and 286 nucleotides, respectively. The increased length of buffalo HSP70.1 gene compared to indicine and taurine gene was due to two insertions each in 5' and 3'-UTR. Comparative sequence analysis of cattle (taurine and indicine) and buffalo HSP70.1 gene revealed a total of 54 gene variations (50 SNPs and 4 INDELs) among the three species in the HSP70.1 gene. The minor allele frequencies of these nucleotide variations varied from 0.03 to 0.5 with an average of 0.26. Among the 14 B. indicus cattle breeds studied, a total of 19 polymorphic sites were identified: 4 in the 5'-UTR and 15 in the coding region (of these 2 were non-synonymous). Analysis among buffalo breeds revealed 15 SNPs throughout the gene: 6 at the 5' flanking region and 9 in the coding region. In bubaline 5'-UTR, 2 additional putative transcription factor binding sites (Elk-1 and C-Re1) were identified, other than three common sites

  8. Genetic coding and gene expression - new Quadruplet genetic coding model

    Science.gov (United States)

    Shankar Singh, Rama

    2012-07-01

    Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

  9. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

    Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

  10. SinEx DB: a database for single exon coding sequences in mammalian genomes.

    Science.gov (United States)

    Jorquera, Roddy; Ortiz, Rodrigo; Ossandon, F; Cárdenas, Juan Pablo; Sepúlveda, Rene; González, Carolina; Holmes, David S

    2016-01-01

    Eukaryotic genes are typically interrupted by intragenic, noncoding sequences termed introns. However, some genes lack introns in their coding sequence (CDS) and are generally known as 'single exon genes' (SEGs). In this work, a SEG is defined as a nuclear, protein-coding gene that lacks introns in its CDS. Whereas, many public databases of Eukaryotic multi-exon genes are available, there are only two specialized databases for SEGs. The present work addresses the need for a more extensive and diverse database by creating SinEx DB, a publicly available, searchable database of predicted SEGs from 10 completely sequenced mammalian genomes including human. SinEx DB houses the DNA and protein sequence information of these SEGs and includes their functional predictions (KOG) and the relative distribution of these functions within species. The information is stored in a relational database built with My SQL Server 5.1.33 and the complete dataset of SEG sequences and their functional predictions are available for downloading. SinEx DB can be interrogated by: (i) a browsable phylogenetic schema, (ii) carrying out BLAST searches to the in-house SinEx DB of SEGs and (iii) via an advanced search mode in which the database can be searched by key words and any combination of searches by species and predicted functions. SinEx DB provides a rich source of information for advancing our understanding of the evolution and function of SEGs.Database URL: www.sinex.cl. © The Author(s) 2016. Published by Oxford University Press.

  11. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  12. Dataset of the first transcriptome assembly of the tree crop “yerba mate” (Ilex paraguariensis and systematic characterization of protein coding genes

    Directory of Open Access Journals (Sweden)

    Patricia M. Aguilera

    2018-04-01

    Full Text Available This contribution contains data associated to the research article entitled “Exploring the genes of yerba mate (Ilex paraguariensis A. St.-Hil. by NGS and de novo transcriptome assembly” (Debat et al., 2014 [1]. By means of a bioinformatic approach involving extensive NGS data analyses, we provide a resource encompassing the full transcriptome assembly of yerba mate, the first available reference for the Ilex L. genus. This dataset (Supplementary files 1 and 2 consolidates the transcriptome-wide assembled sequences of I. paraguariensis with further comprehensive annotation of the protein coding genes of yerba mate via the integration of Arabidopsis thaliana databases. The generated data is pivotal for the characterization of agronomical relevant genes in the tree crop yerba mate -a non-model species- and related taxa in Ilex. The raw sequencing data dissected here is available at DDBJ/ENA/GenBank (NCBI Resource Coordinators, 2016 [2] Sequence Read Archive (SRA under the accession SRP043293 and the assembled sequences have been deposited at the Transcriptome Shotgun Assembly Sequence Database (TSA under the accession GFHV00000000.

  13. Non-Coding RNAs in Arabidopsis

    DEFF Research Database (Denmark)

    van Wonterghem, Miranda

    This work evolves around elucidating the mechanisms of micro RNAs (miRNAs) in Arabidopsis thaliana. I identified a new class of nuclear non-coding RNAs derived from protein coding genes. The genes are miRNA targets with extensive gene body methylation. The RNA species are nuclear localized and de...

  14. Functional Diets Modulate lncRNA-Coding RNAs and Gene Interactions in the Intestine of Rainbow Trout Oncorhynchus mykiss.

    Science.gov (United States)

    Núñez-Acuña, Gustavo; Détrée, Camille; Gallardo-Escárate, Cristian; Gonçalves, Ana Teresa

    2017-06-01

    The advent of functional genomics has sparked the interest in inferring the function of non-coding regions from the transcriptome in non-model species. However, numerous biological processes remain understudied from this perspective, including intestinal immunity in farmed fish. The aim of this study was to infer long non-coding RNA (lncRNAs) expression profiles in rainbow trout (Oncorhynchus mykiss) fed for 30 days with functional diets based on pre- and probiotics. For this, whole transcriptome sequencing was conducted through Illumina technology, and lncRNAs were mined to evaluate transcriptional activity in conjunction with known protein sequences. To detect differentially expressed transcripts, 880 novels and 9067 previously described O. mykiss lncRNAs were used. Expression levels and genome co-localization correlations with coding genes were also analyzed. Significant differences in gene expression were primarily found in the probiotic diet, which had a twofold downregulation of lncRNAs compared to other treatments. Notable differences by diet were also evidenced between the coding genes of distinct metabolic processes. In contrast, genome co-localization of lncRNAs with coding genes was similar for all diets. This study contributes novel knowledge regarding lncRNAs in fish, suggesting key roles in salmons fed with in-feed additives with the capacity to modulate the intestinal homeostasis and host health.

  15. Integrative annotation of 21,037 human genes validated by full-length cDNA clones.

    Directory of Open Access Journals (Sweden)

    Tadashi Imanishi

    2004-06-01

    Full Text Available The human genome sequence defines our inherent biological potential; the realization of the biology encoded therein requires knowledge of the function of each gene. Currently, our knowledge in this area is still limited. Several lines of investigation have been used to elucidate the structure and function of the genes in the human genome. Even so, gene prediction remains a difficult task, as the varieties of transcripts of a gene may vary to a great extent. We thus performed an exhaustive integrative characterization of 41,118 full-length cDNAs that capture the gene transcripts as complete functional cassettes, providing an unequivocal report of structural and functional diversity at the gene level. Our international collaboration has validated 21,037 human gene candidates by analysis of high-quality full-length cDNA clones through curation using unified criteria. This led to the identification of 5,155 new gene candidates. It also manifested the most reliable way to control the quality of the cDNA clones. We have developed a human gene database, called the H-Invitational Database (H-InvDB; http://www.h-invitational.jp/. It provides the following: integrative annotation of human genes, description of gene structures, details of novel alternative splicing isoforms, non-protein-coding RNAs, functional domains, subcellular localizations, metabolic pathways, predictions of protein three-dimensional structure, mapping of known single nucleotide polymorphisms (SNPs, identification of polymorphic microsatellite repeats within human genes, and comparative results with mouse full-length cDNAs. The H-InvDB analysis has shown that up to 4% of the human genome sequence (National Center for Biotechnology Information build 34 assembly may contain misassembled or missing regions. We found that 6.5% of the human gene candidates (1,377 loci did not have a good protein-coding open reading frame, of which 296 loci are strong candidates for non-protein-coding RNA

  16. Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    Full Text Available Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.

  17. Organization of the gene coding for human protein C inhibitor (plasminogen activator inhibitor-3). Assignment of the gene to chromosome 14

    NARCIS (Netherlands)

    Meijers, J. C.; Chung, D. W.

    1991-01-01

    Protein C inhibitor (plasminogen activator inhibitor-3) is a plasma glycoprotein and a member of the serine proteinase inhibitor superfamily. In the present study, the human gene for protein C inhibitor was isolated and characterized from three independent phage that contained overlapping inserts

  18. Computational Approaches Reveal New Insights into Regulation and Function of Non; coding RNAs and their Targets

    KAUST Repository

    Alam, Tanvir

    2016-01-01

    Regulation and function of protein-coding genes are increasingly well-understood, but no comparable evidence exists for non-coding RNA (ncRNA) genes, which appear to be more numerous than protein-coding genes. We developed a novel machine

  19. Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

    Science.gov (United States)

    Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

    2016-03-15

    Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  20. Amino acid code of protein secondary structure.

    Science.gov (United States)

    Shestopalov, B V

    2003-01-01

    The calculation of protein three-dimensional structure from the amino acid sequence is a fundamental problem to be solved. This paper presents principles of the code theory of protein secondary structure, and their consequence--the amino acid code of protein secondary structure. The doublet code model of protein secondary structure, developed earlier by the author (Shestopalov, 1990), is part of this theory. The theory basis are: 1) the name secondary structure is assigned to the conformation, stabilized only by the nearest (intraresidual) and middle-range (at a distance no more than that between residues i and i + 5) interactions; 2) the secondary structure consists of regular (alpha-helical and beta-structural) and irregular (coil) segments; 3) the alpha-helices, beta-strands and coil segments are encoded, respectively, by residue pairs (i, i + 4), (i, i + 2), (i, i = 1), according to the numbers of residues per period, 3.6, 2, 1; 4) all such pairs in the amino acid sequence are codons for elementary structural elements, or structurons; 5) the codons are divided into 21 types depending on their strength, i.e. their encoding capability; 6) overlappings of structurons of one and the same structure generate the longer segments of this structure; 7) overlapping of structurons of different structures is forbidden, and therefore selection of codons is required, the codon selection is hierarchic; 8) the code theory of protein secondary structure generates six variants of the amino acid code of protein secondary structure. There are two possible kinds of model construction based on the theory: the physical one using physical properties of amino acid residues, and the statistical one using results of statistical analysis of a great body of structural data. Some evident consequences of the theory are: a) the theory can be used for calculating the secondary structure from the amino acid sequence as a partial solution of the problem of calculation of protein three

  1. A Pectate Lyase-Coding Gene Abundantly Expressed during Early Stages of Infection Is Required for Full Virulence in Alternaria brassicicola.

    Directory of Open Access Journals (Sweden)

    Yangrae Cho

    Full Text Available Alternaria brassicicola causes black spot disease of Brassica species. The functional importance of pectin digestion enzymes and unidentified phytotoxins in fungal pathogenesis has been suspected but not verified in A. brassicicola. The fungal transcription factor AbPf2 is essential for pathogenicity and induces 106 genes during early pathogenesis, including the pectate lyase-coding gene, PL1332. The aim of this study was to test the importance and roles of PL1332 in pathogenesis. We generated deletion strains of the PL1332 gene, produced heterologous PL1332 proteins, and evaluated their association with virulence. Deletion strains of the PL1332 gene were approximately 30% less virulent than wild-type A. brassicicola, without showing differences in colony expansion on solid media and mycelial growth in nutrient-rich liquid media or minimal media with pectins as a major carbon source. Heterologous PL1332 expressed as fusion proteins digested polygalacturons in vitro. When the fusion proteins were injected into the apoplast between leaf veins of host plants the tissues turned dark brown and soft, resembling necrotic leaf tissue. The PL1332 gene was the first example identified as a general toxin-coding gene and virulence factor among the 106 genes regulated by the transcription factor, AbPf2. It was also the first gene to have its functions investigated among the 19 pectate lyase genes and several hundred putative cell-wall degrading enzymes in A. brassicicola. These results further support the importance of the AbPf2 gene as a key pathogenesis regulator and possible target for agrochemical development.

  2. Genomic assessment of the evolution of the prion protein gene family in vertebrates.

    Science.gov (United States)

    Harrison, Paul M; Khachane, Amit; Kumar, Manish

    2010-05-01

    Prion diseases are devastating neurological disorders caused by the propagation of particles containing an alternative beta-sheet-rich form of the prion protein (PrP). Genes paralogous to PrP, called Doppel and Shadoo, have been identified, that also have neuropathological relevance. To aid in the further functional characterization of PrP and its relatives, we annotated completely the PrP gene family (PrP-GF), in the genomes of 42 vertebrates, through combined strategic application of gene prediction programs and advanced remote homology detection techniques (such as HMMs, PSI-TBLASTN and pGenThreader). We have uncovered several previously undescribed paralogous genes and pseudogenes. We find that current high-quality genomic evidence indicates that the PrP relative Doppel, was likely present in the last common ancestor of present-day Tetrapoda, but was lost in the bird lineage, since its divergence from reptiles. Using the new gene annotations, we have defined the consensus of structural features that are characteristic of the PrP and Doppel structures, across diverse Tetrapoda clades. Furthermore, we describe in detail a transcribed pseudogene derived from Shadoo that is conserved across primates, and that overlaps the meiosis gene, SYCE1, thus possibly regulating its expression. In addition, we analysed the locus of PRNP/PRND for significant conservation across the genomic DNA of eleven mammals, and determined the phylogenetic penetration of non-coding exons. The genomic evidence indicates that the second PRNP non-coding exon found in even-toed ungulates and rodents, is conserved in all high-coverage genome assemblies of primates (human, chimp, orang utan and macaque), and is, at least, likely to have fallen out of use during primate speciation. Furthermore, we have demonstrated that the PRNT gene (at the PRNP human locus) is conserved across at least sixteen mammals, and evolves like a long non-coding RNA, fashioned from fragments of ancient, long

  3. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

    Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

  4. Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas.

    Science.gov (United States)

    Mathelier, Anthony; Lefebvre, Calvin; Zhang, Allen W; Arenillas, David J; Ding, Jiarui; Wasserman, Wyeth W; Shah, Sohrab P

    2015-04-23

    With the rapid increase of whole-genome sequencing of human cancers, an important opportunity to analyze and characterize somatic mutations lying within cis-regulatory regions has emerged. A focus on protein-coding regions to identify nonsense or missense mutations disruptive to protein structure and/or function has led to important insights; however, the impact on gene expression of mutations lying within cis-regulatory regions remains under-explored. We analyzed somatic mutations from 84 matched tumor-normal whole genomes from B-cell lymphomas with accompanying gene expression measurements to elucidate the extent to which these cancers are disrupted by cis-regulatory mutations. We characterize mutations overlapping a high quality set of well-annotated transcription factor binding sites (TFBSs), covering a similar portion of the genome as protein-coding exons. Our results indicate that cis-regulatory mutations overlapping predicted TFBSs are enriched in promoter regions of genes involved in apoptosis or growth/proliferation. By integrating gene expression data with mutation data, our computational approach culminates with identification of cis-regulatory mutations most likely to participate in dysregulation of the gene expression program. The impact can be measured along with protein-coding mutations to highlight key mutations disrupting gene expression and pathways in cancer. Our study yields specific genes with disrupted expression triggered by genomic mutations in either the coding or the regulatory space. It implies that mutated regulatory components of the genome contribute substantially to cancer pathways. Our analyses demonstrate that identifying genomically altered cis-regulatory elements coupled with analysis of gene expression data will augment biological interpretation of mutational landscapes of cancers.

  5. Discovery of Proteomic Code with mRNA Assisted Protein Folding

    Directory of Open Access Journals (Sweden)

    Jan C. Biro

    2008-12-01

    Full Text Available The 3x redundancy of the Genetic Code is usually explained as a necessity to increase the mutation-resistance of the genetic information. However recent bioinformatical observations indicate that the redundant Genetic Code contains more biological information than previously known and which is additional to the 64/20 definition of amino acids. It might define the physico-chemical and structural properties of amino acids, the codon boundaries, the amino acid co-locations (interactions in the coded proteins and the free folding energy of mRNAs. This additional information, which seems to be necessary to determine the 3D structure of coding nucleic acids as well as the coded proteins, is known as the Proteomic Code and mRNA Assisted Protein Folding.

  6. Novel mutation predicted to disrupt SGOL1 protein function | Gupta ...

    African Journals Online (AJOL)

    L54Q, a mutation predicted as deleterious in this study was found to be located in N-terminal coiled coil domain which is effectively involved in the proper localization of PP2A to centromere. We further examined the effect of this mutation over the translational efficiency of the SGOL1 coding gene. Our analysis revealed ...

  7. Nucleotide sequence of the melA gene, coding for alpha-galactosidase in Escherichia coli K-12.

    OpenAIRE

    Liljeström, P L; Liljeström, P

    1987-01-01

    Melibiose uptake and hydrolysis in E.coli is performed by the MelB and MelA proteins, respectively. We report the cloning and sequencing of the melA gene. The nucleotide sequence data showed that melA codes for a 450 amino acid long protein with a molecular weight of 50.6 kd. The sequence data also supported the assumption that the mel locus forms an operon with melA in proximal position. A comparison of MelA with alpha-galactosidase proteins from yeast and human origin showed that these prot...

  8. Combinatorial Control of mRNA Fates by RNA-Binding Proteins and Non-Coding RNAs

    Directory of Open Access Journals (Sweden)

    Valentina Iadevaia

    2015-09-01

    Full Text Available Post-transcriptional control of gene expression is mediated by RNA-binding proteins (RBPs and small non-coding RNAs (e.g., microRNAs that bind to distinct elements in their mRNA targets. Here, we review recent examples describing the synergistic and/or antagonistic effects mediated by RBPs and miRNAs to determine the localisation, stability and translation of mRNAs in mammalian cells. From these studies, it is becoming increasingly apparent that dynamic rearrangements of RNA-protein complexes could have profound implications in human cancer, in synaptic plasticity, and in cellular differentiation.

  9. Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

    Science.gov (United States)

    Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

    2013-10-01

    Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

  10. Combined protein construct and synthetic gene engineering for heterologous protein expression and crystallization using Gene Composer

    Directory of Open Access Journals (Sweden)

    Walchli John

    2009-04-01

    Full Text Available Abstract Background With the goal of improving yield and success rates of heterologous protein production for structural studies we have developed the database and algorithm software package Gene Composer. This freely available electronic tool facilitates the information-rich design of protein constructs and their engineered synthetic gene sequences, as detailed in the accompanying manuscript. Results In this report, we compare heterologous protein expression levels from native sequences to that of codon engineered synthetic gene constructs designed by Gene Composer. A test set of proteins including a human kinase (P38α, viral polymerase (HCV NS5B, and bacterial structural protein (FtsZ were expressed in both E. coli and a cell-free wheat germ translation system. We also compare the protein expression levels in E. coli for a set of 11 different proteins with greatly varied G:C content and codon bias. Conclusion The results consistently demonstrate that protein yields from codon engineered Gene Composer designs are as good as or better than those achieved from the synonymous native genes. Moreover, structure guided N- and C-terminal deletion constructs designed with the aid of Gene Composer can lead to greater success in gene to structure work as exemplified by the X-ray crystallographic structure determination of FtsZ from Bacillus subtilis. These results validate the Gene Composer algorithms, and suggest that using a combination of synthetic gene and protein construct engineering tools can improve the economics of gene to structure research.

  11. The CUP2 gene product regulates the expression of the CUP1 gene, coding for yeast metallothionein.

    OpenAIRE

    Welch, J; Fogel, S; Buchman, C; Karin, M

    1989-01-01

    The yeast CUP1 gene codes for a copper-binding protein similar to metallothionein. Copper sensitive cup1s strains contain a single copy of the CUP1 locus. Resistant strains (CUP1r) carry 12 or more multiple tandem copies. We isolated 12 ethyl methane sulfonate-induced copper sensitive mutants in a wild-type CUP1r parental strain, X2180-1A. Most mutants reduce the copper resistance phenotype only slightly. However, the mutant cup2 lowers resistance by nearly two orders of magnitude. We cloned ...

  12. Gene-Auto: Automatic Software Code Generation for Real-Time Embedded Systems

    Science.gov (United States)

    Rugina, A.-E.; Thomas, D.; Olive, X.; Veran, G.

    2008-08-01

    This paper gives an overview of the Gene-Auto ITEA European project, which aims at building a qualified C code generator from mathematical models under Matlab-Simulink and Scilab-Scicos. The project is driven by major European industry partners, active in the real-time embedded systems domains. The Gene- Auto code generator will significantly improve the current development processes in such domains by shortening the time to market and by guaranteeing the quality of the generated code through the use of formal methods. The first version of the Gene-Auto code generator has already been released and has gone thought a validation phase on real-life case studies defined by each project partner. The validation results are taken into account in the implementation of the second version of the code generator. The partners aim at introducing the Gene-Auto results into industrial development by 2010.

  13. Phylogenetic relationships within Echinococcus and Taenia tapeworms (Cestoda: Taeniidae): an inference from nuclear protein-coding genes.

    Science.gov (United States)

    Knapp, Jenny; Nakao, Minoru; Yanagida, Tetsuya; Okamoto, Munehiro; Saarma, Urmas; Lavikainen, Antti; Ito, Akira

    2011-12-01

    The family Taeniidae of tapeworms is composed of two genera, Echinococcus and Taenia, which obligately parasitize mammals including humans. Inferring phylogeny via molecular markers is the only way to trace back their evolutionary histories. However, molecular dating approaches are lacking so far. Here we established new markers from nuclear protein-coding genes for RNA polymerase II second largest subunit (rpb2), phosphoenolpyruvate carboxykinase (pepck) and DNA polymerase delta (pold). Bayesian inference and maximum likelihood analyses of the concatenated gene sequences allowed us to reconstruct phylogenetic trees for taeniid parasites. The tree topologies clearly demonstrated that Taenia is paraphyletic and that the clade of Echinococcus oligarthrus and Echinococcusvogeli is sister to all other members of Echinococcus. Both species are endemic in Central and South America, and their definitive hosts originated from carnivores that immigrated from North America after the formation of the Panamanian land bridge about 3 million years ago (Ma). A time-calibrated phylogeny was estimated by a Bayesian relaxed-clock method based on the assumption that the most recent common ancestor of E. oligarthrus and E. vogeli existed during the late Pliocene (3.0 Ma). The results suggest that a clade of Taenia including human-pathogenic species diversified primarily in the late Miocene (11.2 Ma), whereas Echinococcus started to diversify later, in the end of the Miocene (5.8 Ma). Close genetic relationships among the members of Echinococcus imply that the genus is a young group in which speciation and global radiation occurred rapidly. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. Systematic screening for mutations in the promoter and the coding region of the 5-HT{sub 1A} gene

    Energy Technology Data Exchange (ETDEWEB)

    Erdmann, J.; Shimron-Abarbanell, D.; Cichon, S. [Univ. of Bonn (Germany)] [and others

    1995-10-09

    In the present study we sought to identify genetic variation in the 5-HT{sub 1A} receptor gene which through alteration of protein function or level of expression might contribute to the genetic predisposition to neuropsychiatric diseases. Genomic DNA samples from 159 unrelated subjects (including 45 schizophrenic, 46 bipolar affective, and 43 patients with Tourette`s syndrome, as well as 25 healthy controls) were investigated by single-strand conformation analysis. Overlapping PCR (polymerase chain reaction) fragments covered the whole coding sequence as well as the 5{prime} untranslated region of the 5-HT{sub 1A} gene. The region upstream to the coding sequence we investigated contains a functional promoter. We found two rare nucleotide sequence variants. Both mutations are located in the coding region of the gene: a coding mutation (A{yields}G) in nucleotide position 82 which leads to an amino acid exchange (Ile{yields}Val) in position 28 of the receptor protein and a silent mutation (C{yields}T) in nucleotide position 549. The occurrence of the Ile-28-Val substitution was studied in an extended sample of patients (n = 352) and controls (n = 210) but was found in similar frequencies in all groups. Thus, this mutation is unlikely to play a significant role in the genetic predisposition to the diseases investigated. In conclusion, our study does not provide evidence that the 5-HT{sub 1A} gene plays either a major or a minor role in the genetic predisposition to schizophrenia, bipolar affective disorder, or Tourette`s syndrome. 29 refs., 4 figs., 1 tab.

  15. osFP: a web server for predicting the oligomeric states of fluorescent proteins.

    Science.gov (United States)

    Simeon, Saw; Shoombuatong, Watshara; Anuwongcharoen, Nuttapat; Preeyanon, Likit; Prachayasittikul, Virapong; Wikberg, Jarl E S; Nantasenamat, Chanin

    2016-01-01

    Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

  16. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    Smaili, Fatima Z.

    2016-05-25

    The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.

  17. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

    Science.gov (United States)

    Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

    2014-02-01

    The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

  18. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  19. Integrative approaches to the prediction of protein functions based on the feature selection

    Directory of Open Access Journals (Sweden)

    Lee Hyunju

    2009-12-01

    Full Text Available Abstract Background Protein function prediction has been one of the most important issues in functional genomics. With the current availability of various genomic data sets, many researchers have attempted to develop integration models that combine all available genomic data for protein function prediction. These efforts have resulted in the improvement of prediction quality and the extension of prediction coverage. However, it has also been observed that integrating more data sources does not always increase the prediction quality. Therefore, selecting data sources that highly contribute to the protein function prediction has become an important issue. Results We present systematic feature selection methods that assess the contribution of genome-wide data sets to predict protein functions and then investigate the relationship between genomic data sources and protein functions. In this study, we use ten different genomic data sources in Mus musculus, including: protein-domains, protein-protein interactions, gene expressions, phenotype ontology, phylogenetic profiles and disease data sources to predict protein functions that are labelled with Gene Ontology (GO terms. We then apply two approaches to feature selection: exhaustive search feature selection using a kernel based logistic regression (KLR, and a kernel based L1-norm regularized logistic regression (KL1LR. In the first approach, we exhaustively measure the contribution of each data set for each function based on its prediction quality. In the second approach, we use the estimated coefficients of features as measures of contribution of data sources. Our results show that the proposed methods improve the prediction quality compared to the full integration of all data sources and other filter-based feature selection methods. We also show that contributing data sources can differ depending on the protein function. Furthermore, we observe that highly contributing data sets can be similar among

  20. Tetrahymena thermophila acidic ribosomal protein L37 contains an archaebacterial type of C-terminus.

    Science.gov (United States)

    Hansen, T S; Andreasen, P H; Dreisig, H; Højrup, P; Nielsen, H; Engberg, J; Kristiansen, K

    1991-09-15

    We have cloned and characterized a Tetrahymena thermophila macronuclear gene (L37) encoding the acidic ribosomal protein (A-protein) L37. The gene contains a single intron located in the 3'-part of the coding region. Two major and three minor transcription start points (tsp) were mapped 39 to 63 nucleotides upstream from the translational start codon. The uppermost tsp mapped to the first T in a putative T. thermophila RNA polymerase II initiator element, TATAA. The coding region of L37 predicts a protein of 109 amino acid (aa) residues. A substantial part of the deduced aa sequence was verified by protein sequencing. The T. thermophila L37 clearly belongs to the P1-type family of eukaryotic A-proteins, but the C-terminal region has the hallmarks of archaebacterial A-proteins.

  1. Detecting microRNA activity from gene expression data

    LENUS (Irish Health Repository)

    Madden, Stephen F

    2010-05-18

    Abstract Background MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. Results Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. Conclusions We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

  2. Detecting microRNA activity from gene expression data.

    LENUS (Irish Health Repository)

    Madden, Stephen F

    2010-01-01

    BACKGROUND: MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. RESULTS: Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. CONCLUSIONS: We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

  3. Long non-coding RNAs and mRNAs profiling during spleen development in pig.

    Science.gov (United States)

    Che, Tiandong; Li, Diyan; Jin, Long; Fu, Yuhua; Liu, Yingkai; Liu, Pengliang; Wang, Yixin; Tang, Qianzi; Ma, Jideng; Wang, Xun; Jiang, Anan; Li, Xuewei; Li, Mingzhou

    2018-01-01

    Genome-wide transcriptomic studies in humans and mice have become extensive and mature. However, a comprehensive and systematic understanding of protein-coding genes and long non-coding RNAs (lncRNAs) expressed during pig spleen development has not been achieved. LncRNAs are known to participate in regulatory networks for an array of biological processes. Here, we constructed 18 RNA libraries from developing fetal pig spleen (55 days before birth), postnatal pig spleens (0, 30, 180 days and 2 years after birth), and the samples from the 2-year-old Wild Boar. A total of 15,040 lncRNA transcripts were identified among these samples. We found that the temporal expression pattern of lncRNAs was more restricted than observed for protein-coding genes. Time-series analysis showed two large modules for protein-coding genes and lncRNAs. The up-regulated module was enriched for genes related to immune and inflammatory function, while the down-regulated module was enriched for cell proliferation processes such as cell division and DNA replication. Co-expression networks indicated the functional relatedness between protein-coding genes and lncRNAs, which were enriched for similar functions over the series of time points examined. We identified numerous differentially expressed protein-coding genes and lncRNAs in all five developmental stages. Notably, ceruloplasmin precursor (CP), a protein-coding gene participating in antioxidant and iron transport processes, was differentially expressed in all stages. This study provides the first catalog of the developing pig spleen, and contributes to a fuller understanding of the molecular mechanisms underpinning mammalian spleen development.

  4. Nanoparticles-cell association predicted by protein corona fingerprints

    Science.gov (United States)

    Palchetti, S.; Digiacomo, L.; Pozzi, D.; Peruzzi, G.; Micarelli, E.; Mahmoudi, M.; Caracciolo, G.

    2016-06-01

    In a physiological environment (e.g., blood and interstitial fluids) nanoparticles (NPs) will bind proteins shaping a ``protein corona'' layer. The long-lived protein layer tightly bound to the NP surface is referred to as the hard corona (HC) and encodes information that controls NP bioactivity (e.g. cellular association, cellular signaling pathways, biodistribution, and toxicity). Decrypting this complex code has become a priority to predict the NP biological outcomes. Here, we use a library of 16 lipid NPs of varying size (Ø ~ 100-250 nm) and surface chemistry (unmodified and PEGylated) to investigate the relationships between NP physicochemical properties (nanoparticle size, aggregation state and surface charge), protein corona fingerprints (PCFs), and NP-cell association. We found out that none of the NPs' physicochemical properties alone was exclusively able to account for association with human cervical cancer cell line (HeLa). For the entire library of NPs, a total of 436 distinct serum proteins were detected. We developed a predictive-validation modeling that provides a means of assessing the relative significance of the identified corona proteins. Interestingly, a minor fraction of the HC, which consists of only 8 PCFs were identified as main promoters of NP association with HeLa cells. Remarkably, identified PCFs have several receptors with high level of expression on the plasma membrane of HeLa cells.In a physiological environment (e.g., blood and interstitial fluids) nanoparticles (NPs) will bind proteins shaping a ``protein corona'' layer. The long-lived protein layer tightly bound to the NP surface is referred to as the hard corona (HC) and encodes information that controls NP bioactivity (e.g. cellular association, cellular signaling pathways, biodistribution, and toxicity). Decrypting this complex code has become a priority to predict the NP biological outcomes. Here, we use a library of 16 lipid NPs of varying size (Ø ~ 100-250 nm) and surface

  5. Identifying novel genes in C. elegans using SAGE tags

    Directory of Open Access Journals (Sweden)

    Chen Nansheng

    2010-12-01

    Full Text Available Abstract Background Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required. Results In this project, we have developed a method of reconstructing full-length cDNA sequences based on short expressed sequence tags which is called sequence tag-based amplification of cDNA ends (STACE. Expressed tags are used as anchors for retrieving full-length transcripts in two rounds of PCR amplification. We have demonstrated the application of STACE in reconstructing full-length cDNA sequences using expressed tags mined in an array of serial analysis of gene expression (SAGE of C. elegans cDNA libraries. We have successfully applied STACE to recover sequence information for 12 genes, for two of which we found isoforms. STACE was used to successfully recover full-length cDNA sequences for seven of these genes. Conclusions The STACE method can be used to effectively reconstruct full-length cDNA sequences of genes that are under-represented in cDNA sequencing projects and have been missed by existing gene prediction methods, but their existence has been suggested by short sequence tags such as SAGE tags.

  6. XGC developments for a more efficient XGC-GENE code coupling

    Science.gov (United States)

    Dominski, Julien; Hager, Robert; Ku, Seung-Hoe; Chang, Cs

    2017-10-01

    In the Exascale Computing Program, the High-Fidelity Whole Device Modeling project initially aims at delivering a tightly-coupled simulation of plasma neoclassical and turbulence dynamics from the core to the edge of the tokamak. To permit such simulations, the gyrokinetic codes GENE and XGC will be coupled together. Numerical efforts are made to improve the numerical schemes agreement in the coupling region. One of the difficulties of coupling those codes together is the incompatibility of their grids. GENE is a continuum grid-based code and XGC is a Particle-In-Cell code using unstructured triangular mesh. A field-aligned filter is thus implemented in XGC. Even if XGC originally had an approximately field-following mesh, this field-aligned filter permits to have a perturbation discretization closer to the one solved in the field-aligned code GENE. Additionally, new XGC gyro-averaging matrices are implemented on a velocity grid adapted to the plasma properties, thus ensuring same accuracy from the core to the edge regions.

  7. Amino acid codes in mitochondria as possible clues to primitive codes

    Science.gov (United States)

    Jukes, T. H.

    1981-01-01

    Differences between mitochondrial codes and the universal code indicate that an evolutionary simplification has taken place, rather than a return to a more primitive code. However, these differences make it evident that the universal code is not the only code possible, and therefore earlier codes may have differed markedly from the previous code. The present universal code is probably a 'frozen accident.' The change in CUN codons from leucine to threonine (Neurospora vs. yeast mitochondria) indicates that neutral or near-neutral changes occurred in the corresponding proteins when this code change took place, caused presumably by a mutation in a tRNA gene.

  8. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  9. Predicting metabolic pathways by sub-network extraction.

    Science.gov (United States)

    Faust, Karoline; van Helden, Jacques

    2012-01-01

    Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans.

  10. Cloning and expression of the coding regions of the heat shock proteins HSP10 and HSP16 from Piscirickettsia salmonis

    Directory of Open Access Journals (Sweden)

    VIVIAN WILHELM

    2003-01-01

    Full Text Available The genes encoding the heat shock proteins HSP10 and HSP16 of the salmon pathogen Piscirickettsia salmonis have been isolated and sequenced. The HSP10 coding sequence is located in an open reading frame of 291 base pairs encoding 96 aminoacids. The HSP16 coding region was isolated as a 471 base pair fragment encoding a protein of 156 aminoacids. The deduced aminoacid sequences of both proteins show a significant homology to the respective protein from other prokaryotic organisms. Both proteins were expressed in E. coli as fusion proteins with thioredoxin and purified by chromatography on Ni-column. A rabbit serum against P. salmonis total proteins reacts with the recombinant HSP10 and HSP16 proteins. Similar reactivity was determined by ELISA using serum from salmon infected with P. salmonis. The possibility of formulating a vaccine containing these two proteins is discussed

  11. Improvement of heterologous protein production in Aspergillus oryzae by RNA interference with alpha-amylase genes.

    Science.gov (United States)

    Nemoto, Takashi; Maruyama, Jun-ichi; Kitamoto, Katsuhiko

    2009-11-01

    Aspergillus oryzae RIB40 has three alpha-amylase genes (amyA, amyB, and amyC), and secretes alpha-amylase abundantly. However, large amounts of endogenous secretory proteins such as alpha-amylase can compete with heterologous protein in the secretory pathway and decrease its production yields. In this study, we examined the effects of suppression of alpha-amylase on heterologous protein production in A. oryzae, using the bovine chymosin (CHY) as a reporter heterologous protein. The three alpha-amylase genes in A. oryzae have nearly identical DNA sequences from those promoters to the coding regions. Hence we performed silencing of alpha-amylase genes by RNA interference (RNAi) in the A. oryzae CHY producing strain. The silenced strains exhibited a reduction in alpha-amylase activity and an increase in CHY production in the culture medium. This result suggests that suppression of alpha-amylase is effective in heterologous protein production in A. oryzae.

  12. Prediction of extracellular proteases of the human pathogen Helicobacter pylori reveals proteolytic activity of the Hp1018/19 protein HtrA.

    Directory of Open Access Journals (Sweden)

    Martin Löwer

    Full Text Available Exported proteases of Helicobacter pylori (H. pylori are potentially involved in pathogen-associated disorders leading to gastric inflammation and neoplasia. By comprehensive sequence screening of the H. pylori proteome for predicted secreted proteases, we retrieved several candidate genes. We detected caseinolytic activities of several such proteases, which are released independently from the H. pylori type IV secretion system encoded by the cag pathogenicity island (cagPAI. Among these, we found the predicted serine protease HtrA (Hp1019, which was previously identified in the bacterial secretome of H. pylori. Importantly, we further found that the H. pylori genes hp1018 and hp1019 represent a single gene likely coding for an exported protein. Here, we directly verified proteolytic activity of HtrA in vitro and identified the HtrA protease in zymograms by mass spectrometry. Overexpressed and purified HtrA exhibited pronounced proteolytic activity, which is inactivated after mutation of Ser205 to alanine in the predicted active center of HtrA. These data demonstrate that H. pylori secretes HtrA as an active protease, which might represent a novel candidate target for therapeutic intervention strategies.

  13. Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

    Science.gov (United States)

    Zhou, Hang; Yang, Yang; Shen, Hong-Bin

    2017-03-15

    Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  14. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

    Directory of Open Access Journals (Sweden)

    Gomez Shawn M

    2010-04-01

    Full Text Available Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

  15. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  16. Protein Structure Prediction by Protein Threading

    Science.gov (United States)

    Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

    The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

  17. Comparative studies of vertebrate endothelin-converting enzyme-like 1 genes and proteins

    Directory of Open Access Journals (Sweden)

    Holmes RS

    2013-01-01

    Full Text Available Roger S Holmes,1,2 Laura A Cox11Department of Genetics and Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA; 2Eskitis Institute for Cell and Molecular Therapies and School of Biomolecular and Physical Sciences, Griffith University, Nathan, Queensland, AustraliaAbstract: Endothelin-converting enzyme-like 1 (ECEL1 is a member of the M13 family of neutral endopeptidases which play an essential role in the neural regulation of vertebrate respiration. Genetic deficiency of this protein results in respiratory failure soon after birth. Comparative ECEL1 amino acid sequences and structures and ECEL1 gene locations were examined using data from several vertebrate genome projects. Vertebrate ECEL1 sequences shared 66%–99% identity as compared with 30%–63% sequence identities with other M13-like family members, ECE1, ECE2, and NEP (neprilysin or MME. Three N-glycosylation sites were conserved among most vertebrate ECEL1 proteins examined. Sequence alignments, conserved key amino acid residues, and predicted secondary and tertiary structures were also studied, including cytoplasmic, transmembrane, and luminal sequences and active site residues. Vertebrate ECEL1 genes usually contained 18 exons and 17 coding exons on the negative strand. Exons 1 and 2 of the human ECEL1 gene contained 5'-untranslated (5'-UTR regions, a large CpG island (CpG256, and several transcription factor binding sites which may contribute to the high levels of gene expression previously reported in neural tissues. Phylogenetic analyses examined the relationships and potential evolutionary origins of the vertebrate ECEL1 gene with six other vertebrate neutral endopeptidase M13 family genes. These suggested that ECEL1 originated in an ancestral vertebrate genome from a duplication event in an ancestral neutral endopeptidase M13-like gene.Keywords: vertebrates, amino acid sequence, ECEL1, ECE1, ECE2, KELL, NEP, NEPL1, PHEX

  18. Isolation and expression of the genes coding for the membrane bound transglycosylase B (MltB and the transferrin binding protein B (TbpB of the salmon pathogen Piscirickettsia salmonis

    Directory of Open Access Journals (Sweden)

    VIVIAN WILHELM

    2004-01-01

    Full Text Available We have isolated and sequenced the genes encoding the membrane bound transglycosylase B (MltB and the transferring binding protein B (TbpB of the salmon pathogen Piscirickettsia salmonis. The results of the sequence revealed two open reading frames that encode proteins with calculated molecular weights of 38,830 and 85,140. The deduced aminoacid sequences of both proteins show a significant homology to the respective protein from phylogenetically related microorganisms. Partial sequences coding the amino and carboxyl regions of MltB and a sequence of 761 base pairs encoding the amino region of TbpB have been expressed in E. coli. The strong humoral response elicited by these proteins in mouse confirmed the immunogenic properties of the recombinant proteins. A similar response was elicited by both proteins when injected intraperitoneally in Atlantic salmon. The present data indicates that these proteins are good candidates to be used in formulations to study the protective immunity of salmon to infection by P. salmonis.

  19. Prediction of RNA-Binding Proteins by Voting Systems

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    C. R. Peng

    2011-01-01

    Full Text Available It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy value of 79.72% and MCC (Matthew’s correlation coefficient value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier’s vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

  20. PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.

    Science.gov (United States)

    Kahanda, Indika; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa

    2015-01-01

    The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

  1. BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

    Science.gov (United States)

    Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

    2012-07-01

    Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

  2. LeARN: a platform for detecting, clustering and annotating non-coding RNAs

    Directory of Open Access Journals (Sweden)

    Schiex Thomas

    2008-01-01

    Full Text Available Abstract Background In the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules. Results LeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation. Conclusion This software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN

  3. Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

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    Richard Danger

    2018-01-01

    Full Text Available Bronchiolitis obliterans syndrome (BOS, the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group, and 26 samples at or after BOS diagnosis (diagnosis group. An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group. We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1, T-cell leukemia/lymphoma protein 1A (TCL1A, and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01 and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS.

  4. The Drosophila gene CG9918 codes for a pyrokinin-1 receptor

    DEFF Research Database (Denmark)

    Cazzamali, Giuseppe; Torp, Malene; Hauser, Frank

    2005-01-01

    The database from the Drosophila Genome Project contains a gene, CG9918, annotated to code for a G protein-coupled receptor. We cloned the cDNA of this gene and functionally expressed it in Chinese hamster ovary cells. We tested a library of about 25 Drosophila and other insect neuropeptides......, and seven insect biogenic amines on the expressed receptor and found that it was activated by low concentrations of the Drosophila neuropeptide, pyrokinin-1 (TGPSASSGLWFGPRLamide; EC50, 5 x 10(-8) M). The receptor was also activated by other Drosophila neuropeptides, terminating with the sequence PRLamide...... (Hug-gamma, ecdysis-triggering-hormone-1, pyrokinin-2), but in these cases about six to eight times higher concentrations were needed. The receptor was not activated by Drosophila neuropeptides, containing a C-terminal PRIamide sequence (such as ecdysis-triggering-hormone-2), or PRVamide (such as capa...

  5. Functional intersection of ATM and DNA-dependent protein kinase catalytic subunit in coding end joining during V(D)J recombination

    DEFF Research Database (Denmark)

    Lee, Baeck-Seung; Gapud, Eric J; Zhang, Shichuan

    2013-01-01

    V(D)J recombination is initiated by the RAG endonuclease, which introduces DNA double-strand breaks (DSBs) at the border between two recombining gene segments, generating two hairpin-sealed coding ends and two blunt signal ends. ATM and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) ar......V(D)J recombination is initiated by the RAG endonuclease, which introduces DNA double-strand breaks (DSBs) at the border between two recombining gene segments, generating two hairpin-sealed coding ends and two blunt signal ends. ATM and DNA-dependent protein kinase catalytic subunit (DNA......-PKcs) are serine-threonine kinases that orchestrate the cellular responses to DNA DSBs. During V(D)J recombination, ATM and DNA-PKcs have unique functions in the repair of coding DNA ends. ATM deficiency leads to instability of postcleavage complexes and the loss of coding ends from these complexes. DNA...... when ATM is present and its kinase activity is intact. The ability of ATM to compensate for DNA-PKcs kinase activity depends on the integrity of three threonines in DNA-PKcs that are phosphorylation targets of ATM, suggesting that ATM can modulate DNA-PKcs activity through direct phosphorylation of DNA...

  6. Computational Approaches Reveal New Insights into Regulation and Function of Non; coding RNAs and their Targets

    KAUST Repository

    Alam, Tanvir

    2016-11-28

    Regulation and function of protein-coding genes are increasingly well-understood, but no comparable evidence exists for non-coding RNA (ncRNA) genes, which appear to be more numerous than protein-coding genes. We developed a novel machine-learning model to distinguish promoters of long ncRNA (lncRNA) genes from those of protein-coding genes. This represents the first attempt to make this distinction based on properties of the associated gene promoters. From our analyses, several transcription factors (TFs), which are known to be regulated by lncRNAs, also emerged as potential global regulators of lncRNAs, suggesting that lncRNAs and TFs may participate in bidirectional feedback regulatory network. Our results also raise the possibility that, due to the historical dependence on protein-coding gene in defining the chromatin states of active promoters, an adjustment of these chromatin signature profiles to incorporate lncRNAs is warranted in the future. Secondly, we developed a novel method to infer functions for lncRNA and microRNA (miRNA) transcripts based on their transcriptional regulatory networks in 119 tissues and 177 primary cells of human. This method for the first time combines information of cell/tissueVspecific expression of a transcript and the TFs and transcription coVfactors (TcoFs) that control activation of that transcript. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues and associated knowledgebase (FARNA) is developed. FARNA, having the most comprehensive function annotation of considered ncRNAs across the widest spectrum of cells/tissues, has a potential to contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. Thirdly, we developed a novel machine-learning model to identify LD motif (a protein interaction motif) of paxillin, a ncRNA target that is involved in cell motility and cancer metastasis. Our recognition model identified new proteins not

  7. In-depth comparative analysis of malaria parasite genomes reveals protein-coding genes linked to human disease in Plasmodium falciparum genome.

    Science.gov (United States)

    Liu, Xuewu; Wang, Yuanyuan; Liang, Jiao; Wang, Luojun; Qin, Na; Zhao, Ya; Zhao, Gang

    2018-05-02

    Plasmodium falciparum is the most virulent malaria parasite capable of parasitizing human erythrocytes. The identification of genes related to this capability can enhance our understanding of the molecular mechanisms underlying human malaria and lead to the development of new therapeutic strategies for malaria control. With the availability of several malaria parasite genome sequences, performing computational analysis is now a practical strategy to identify genes contributing to this disease. Here, we developed and used a virtual genome method to assign 33,314 genes from three human malaria parasites, namely, P. falciparum, P. knowlesi and P. vivax, and three rodent malaria parasites, namely, P. berghei, P. chabaudi and P. yoelii, to 4605 clusters. Each cluster consisted of genes whose protein sequences were significantly similar and was considered as a virtual gene. Comparing the enriched values of all clusters in human malaria parasites with those in rodent malaria parasites revealed 115 P. falciparum genes putatively responsible for parasitizing human erythrocytes. These genes are mainly located in the chromosome internal regions and participate in many biological processes, including membrane protein trafficking and thiamine biosynthesis. Meanwhile, 289 P. berghei genes were included in the rodent parasite-enriched clusters. Most are located in subtelomeric regions and encode erythrocyte surface proteins. Comparing cluster values in P. falciparum with those in P. vivax and P. knowlesi revealed 493 candidate genes linked to virulence. Some of them encode proteins present on the erythrocyte surface and participate in cytoadhesion, virulence factor trafficking, or erythrocyte invasion, but many genes with unknown function were also identified. Cerebral malaria is characterized by accumulation of infected erythrocytes at trophozoite stage in brain microvascular. To discover cerebral malaria-related genes, fast Fourier transformation (FFT) was introduced to extract

  8. Origins of gene, genetic code, protein and life

    Indian Academy of Sciences (India)

    We have further presented the [GADV]-protein world hypothesis of the origin of life as well as a hypothesis of protein production, suggesting that proteins were originally produced by random peptide formation of amino acids restricted in specific amino acid compositions termed as GNC-, SNS- and GC-NSF(a)-0th order ...

  9. The relationship among gene expression, the evolution of gene dosage, and the rate of protein evolution.

    Directory of Open Access Journals (Sweden)

    Jean-François Gout

    2010-05-01

    Full Text Available The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.

  10. A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records

    DEFF Research Database (Denmark)

    Jiang, Li; Edwards, Stefan M.; Thomsen, Bo

    2014-01-01

    from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text...

  11. Overexpression Analysis of emv2 gene coding for Late Embryogenesis Abundant Protein from Vigna radiata (Wilczek

    Directory of Open Access Journals (Sweden)

    Rajesh S.

    2008-10-01

    Full Text Available Late embryogenesis abundant (LEA proteins are speculated to protect against water stress deficit in plants. An over expression system for mungbean late embryogenesis abundant protein, emv2 was constructed in a pET29a vector, designated pET-emv2 which is responsible for higher expression under the transcriptional/translational control of T7/lac promoter incorporated in the Escherichia coli BL21 (DE3.Induction protocol was optimized for pET recombinants harboring the target gene. Overexpressed EMV2 protein was purified to homogeneity and the protein profile monitored by SDS-PAGE.

  12. Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling

    Directory of Open Access Journals (Sweden)

    Mo Min

    2008-05-01

    extensive protein network was revealed for plant polyadenylation machinery, in which all predicted proteins were found to be connecting to the complex. The gene expression profiles are indicative that specialized sub-complexes may be formed to carry out targeted processing of mRNA in different developmental stages and tissue types. These results offer a roadmap for further functional characterizations of the protein factors, and for building models when testing the genetic contributions of these genes in plant growth and development.

  13. Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.

    Science.gov (United States)

    Yim, Soorin; Yu, Hasun; Jang, Dongjin; Lee, Doheon

    2018-04-11

    Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins. We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research. We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.

  14. Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

    Science.gov (United States)

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Analysis and prediction of gene splice sites in four Aspergillus genomes

    DEFF Research Database (Denmark)

    Wang, Kai; Ussery, David; Brunak, Søren

    2009-01-01

    Several Aspergillus fungal genomic sequences have been published, with many more in progress. Obviously, it is essential to have high-quality, consistently annotated sets of proteins from each of the genomes, in order to make meaningful comparisons. We have developed a dedicated, publicly available......, splice site prediction program called NetAspGene, for the genus Aspergillus. Gene sequences from Aspergillus fumigatus, the most common mould pathogen, were used to build and test our model. Compared to many animals and plants, Aspergillus contains smaller introns; thus we have applied a larger window...... better splice site prediction than other available tools. NetAspGene will be very helpful for the study in Aspergillus splice sites and especially in alternative splicing. A webpage for NetAspGene is publicly available at http://www.cbs.dtu.dk/services/NetAspGene....

  16. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    , but co-occurrence data still proves beneficial. Conclusion Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.

  17. Computational prediction and experimental validation of Ciona intestinalis microRNA genes

    Directory of Open Access Journals (Sweden)

    Pasquinelli Amy E

    2007-11-01

    Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

  18. Conservation of σ28-Dependent Non-Coding RNA Paralogs and Predicted σ54-Dependent Targets in Thermophilic Campylobacter Species.

    Directory of Open Access Journals (Sweden)

    My Thanh Le

    Full Text Available Assembly of flagella requires strict hierarchical and temporal control via flagellar sigma and anti-sigma factors, regulatory proteins and the assembly complex itself, but to date non-coding RNAs (ncRNAs have not been described to regulate genes directly involved in flagellar assembly. In this study we have investigated the possible role of two ncRNA paralogs (CjNC1, CjNC4 in flagellar assembly and gene regulation of the diarrhoeal pathogen Campylobacter jejuni. CjNC1 and CjNC4 are 37/44 nt identical and predicted to target the 5' untranslated region (5' UTR of genes transcribed from the flagellar sigma factor σ54. Orthologs of the σ54-dependent 5' UTRs and ncRNAs are present in the genomes of other thermophilic Campylobacter species, and transcription of CjNC1 and CNC4 is dependent on the flagellar sigma factor σ28. Surprisingly, inactivation and overexpression of CjNC1 and CjNC4 did not affect growth, motility or flagella-associated phenotypes such as autoagglutination. However, CjNC1 and CjNC4 were able to mediate sequence-dependent, but Hfq-independent, partial repression of fluorescence of predicted target 5' UTRs in an Escherichia coli-based GFP reporter gene system. This hints towards a subtle role for the CjNC1 and CjNC4 ncRNAs in post-transcriptional gene regulation in thermophilic Campylobacter species, and suggests that the currently used phenotypic methodologies are insufficiently sensitive to detect such subtle phenotypes. The lack of a role of Hfq in the E. coli GFP-based system indicates that the CjNC1 and CjNC4 ncRNAs may mediate post-transcriptional gene regulation in ways that do not conform to the paradigms obtained from the Enterobacteriaceae.

  19. Conservation of σ28-Dependent Non-Coding RNA Paralogs and Predicted σ54-Dependent Targets in Thermophilic Campylobacter Species

    Science.gov (United States)

    Le, My Thanh; van Veldhuizen, Mart; Porcelli, Ida; Bongaerts, Roy J.; Gaskin, Duncan J. H.; Pearson, Bruce M.; van Vliet, Arnoud H. M.

    2015-01-01

    Assembly of flagella requires strict hierarchical and temporal control via flagellar sigma and anti-sigma factors, regulatory proteins and the assembly complex itself, but to date non-coding RNAs (ncRNAs) have not been described to regulate genes directly involved in flagellar assembly. In this study we have investigated the possible role of two ncRNA paralogs (CjNC1, CjNC4) in flagellar assembly and gene regulation of the diarrhoeal pathogen Campylobacter jejuni. CjNC1 and CjNC4 are 37/44 nt identical and predicted to target the 5' untranslated region (5' UTR) of genes transcribed from the flagellar sigma factor σ54. Orthologs of the σ54-dependent 5' UTRs and ncRNAs are present in the genomes of other thermophilic Campylobacter species, and transcription of CjNC1 and CNC4 is dependent on the flagellar sigma factor σ28. Surprisingly, inactivation and overexpression of CjNC1 and CjNC4 did not affect growth, motility or flagella-associated phenotypes such as autoagglutination. However, CjNC1 and CjNC4 were able to mediate sequence-dependent, but Hfq-independent, partial repression of fluorescence of predicted target 5' UTRs in an Escherichia coli-based GFP reporter gene system. This hints towards a subtle role for the CjNC1 and CjNC4 ncRNAs in post-transcriptional gene regulation in thermophilic Campylobacter species, and suggests that the currently used phenotypic methodologies are insufficiently sensitive to detect such subtle phenotypes. The lack of a role of Hfq in the E. coli GFP-based system indicates that the CjNC1 and CjNC4 ncRNAs may mediate post-transcriptional gene regulation in ways that do not conform to the paradigms obtained from the Enterobacteriaceae. PMID:26512728

  20. Bistability in self-activating genes regulated by non-coding RNAs

    International Nuclear Information System (INIS)

    Miro-Bueno, Jesus

    2015-01-01

    Non-coding RNA molecules are able to regulate gene expression and play an essential role in cells. On the other hand, bistability is an important behaviour of genetic networks. Here, we propose and study an ODE model in order to show how non-coding RNA can produce bistability in a simple way. The model comprises a single gene with positive feedback that is repressed by non-coding RNA molecules. We show how the values of all the reaction rates involved in the model are able to control the transitions between the high and low states. This new model can be interesting to clarify the role of non-coding RNA molecules in genetic networks. As well, these results can be interesting in synthetic biology for developing new genetic memories and biomolecular devices based on non-coding RNAs

  1. Intron-exon organization of the active human protein S gene PS. alpha. and its pseudogene PS. beta. : Duplication and silencing during primate evolution

    Energy Technology Data Exchange (ETDEWEB)

    Ploos van Amstel, H.; Reitsma, P.H.; van der Logt, C.P.; Bertina, R.M. (University Hospital, Leiden (Netherlands))

    1990-08-28

    The human protein S locus on chromosome 3 consists of two protein S genes, PS{alpha} and PS{beta}. Here the authors report the cloning and characterization of both genes. Fifteen exons of the PS{alpha} gene were identified that together code for protein S mRNA as derived from the reported protein S cDNAs. Analysis by primer extension of liver protein S mRNA, however, reveals the presence of two mRNA forms that differ in the length of their 5{prime}-noncoding region. Both transcripts contain a 5{prime}-noncoding region longer than found in the protein S cDNAs. The two products may arise from alternative splicing of an additional intron in this region or from the usage of two start sites for transcription. The intron-exon organization of the PS{alpha} gene fully supports the hypothesis that the protein S gene is the product of an evolutional assembling process in which gene modules coding for structural/functional protein units also found in other coagulation proteins have been put upstream of the ancestral gene of a steroid hormone binding protein. The PS{beta} gene is identified as a pseudogene. It contains a large variety of detrimental aberrations, viz., the absence of exon I, a splice site mutation, three stop codons, and a frame shift mutation. Overall the two genes PS{alpha} and PS{beta} show between their exonic sequences 96.5% homology. Southern analysis of primate DNA showed that the duplication of the ancestral protein S gene has occurred after the branching of the orangutan from the African apes. A nonsense mutation that is present in the pseudogene of man also could be identified in one of the two protein S genes of both chimpanzee and gorilla. This implicates that silencing of one of the two protein S genes must have taken place before the divergence of the three African apes.

  2. Effect of the deletion of qmoABC and the promoter distal gene encoding a hypothetical protein on sulfate-reduction in Desulfovibrio vulgaris Hildenborough

    Energy Technology Data Exchange (ETDEWEB)

    Zane, Grant M.; Yen, Huei-chi Bill; Wall, Judy D.

    2010-03-18

    The pathway of electrons required for the reduction of sulfate in sulfate-reducing bacteria (SRB) is not yet fully characterized. In order to determine the role of a transmembrane protein complex suggested to be involved in this process, a deletion of Desulfovibrio vulgaris Hildenborough was created by marker exchange mutagenesis that eliminated four genes putatively encoding the QmoABC complex and a hypothetical protein (DVU0851). The Qmo complex (quinone-interacting membrane-bound oxidoreductase) is proposed to be responsible for transporting electrons to the dissimilatory adenosine-5?phosphosulfate (APS) reductase in SRB. In support of the predicted role of this complex, the deletion mutant was unable to grow using sulfate as its sole electron acceptor with a range of electron donors. To explore a possible role for the hypothetical protein in sulfate reduction, a second mutant was constructed that had lost only the gene that codes for DVU0851. The second constructed mutant grew with sulfate as the sole electron acceptor; however, there was a lag that was not present with the wild-type or complemented strain. Neither deletion strain was significantly impaired for growth with sulfite or thiosulfate as terminal electron acceptor. Complementation of the D(qmoABC-DVU0851) mutant with all four genes or only the qmoABC genes restored its ability to grow by sulfate respiration. These results confirmed the prediction that the Qmo complex is in the electron pathway for sulfate-reduction and revealed that no other transmembrane complex could compensate when Qmo was lacking.

  3. PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

    Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

  4. Sub-grouping of Plasmodium falciparum 3D7 var genes based on sequence analysis of coding and non-coding regions

    DEFF Research Database (Denmark)

    Lavstsen, Thomas; Salanti, Ali; Jensen, Anja T R

    2003-01-01

    and organization of the 3D7 PfEMP1 repertoire was investigated on the basis of the complete genome sequence. METHODS: Using two tree-building methods we analysed the coding and non-coding sequences of 3D7 var and rif genes as well as var genes of other parasite strains. RESULTS: var genes can be sub...

  5. Genome wide gene expression regulation by HIP1 Protein Interactor, HIPPI: Prediction and validation

    Directory of Open Access Journals (Sweden)

    Lahiri Ansuman

    2011-09-01

    Full Text Available Abstract Background HIP1 Protein Interactor (HIPPI is a pro-apoptotic protein that induces Caspase8 mediated apoptosis in cell. We have shown earlier that HIPPI could interact with a specific 9 bp sequence motif, defined as the HIPPI binding site (HBS, present in the upstream promoter of Caspase1 gene and regulate its expression. We also have shown that HIPPI, without any known nuclear localization signal, could be transported to the nucleus by HIP1, a NLS containing nucleo-cytoplasmic shuttling protein. Thus our present work aims at the investigation of the role of HIPPI as a global transcription regulator. Results We carried out genome wide search for the presence of HBS in the upstream sequences of genes. Our result suggests that HBS was predominantly located within 2 Kb upstream from transcription start site. Transcription factors like CREBP1, TBP, OCT1, EVI1 and P53 half site were significantly enriched in the 100 bp vicinity of HBS indicating that they might co-operate with HIPPI for transcription regulation. To illustrate the role of HIPPI on transcriptome, we performed gene expression profiling by microarray. Exogenous expression of HIPPI in HeLa cells resulted in up-regulation of 580 genes (p HIP1 was knocked down. HIPPI-P53 interaction was necessary for HIPPI mediated up-regulation of Caspase1 gene. Finally, we analyzed published microarray data obtained with post mortem brains of Huntington's disease (HD patients to investigate the possible involvement of HIPPI in HD pathogenesis. We observed that along with the transcription factors like CREB, P300, SREBP1, Sp1 etc. which are already known to be involved in HD, HIPPI binding site was also significantly over-represented in the upstream sequences of genes altered in HD. Conclusions Taken together, the results suggest that HIPPI could act as an important transcription regulator in cell regulating a vast array of genes, particularly transcription factors and at least, in part, play a

  6. PRmePRed: A protein arginine methylation prediction tool.

    Directory of Open Access Journals (Sweden)

    Pawan Kumar

    Full Text Available Protein methylation is an important Post-Translational Modification (PTMs of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew's correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/.

  7. A comparison of oxide thickness predictability from the perspective of codes

    Energy Technology Data Exchange (ETDEWEB)

    Park, Joo-Young; Shin, Hye-In; Kim, Kyung-Tae; Han, Hee-Tak; Kim, Hong-Jin; Kim, Yong-Hwan [KEPCO Nuclear Fuel Co. Ltd., Daejeon (Korea, Republic of)

    2016-10-15

    In Korea, OPR1000 and Westinghouse type nuclear power plant reactor fuel rods oxide thickness has been evaluated by imported code A. Because of this, there have been multiple constraints in operation and maintenance of fuel rod design system. For this reason, there has been a growing demand to establish an independent fuel rod design system. To meet this goal, KNF has recently developed its own code B for fuel rod design. The objective of this study is to compare oxide thickness prediction performance between code A and code B and to check the validity of predicting corrosion behaviors of newly developed code B. This study is based on Pool Side Examination (PSE) data for the performance confirmation. For the examination procedures, the oxide thickness measurement methods and equipment of PSE are described in detail. In this study, code B is confirmed conservatism and validity on evaluating cladding oxide thickness through the comparison with code A. Code prediction values show higher value than measured data from PSE. Throughout this study, the values by code B are evaluated and proved to be valid in a view point of the oxide thickness evaluation. However, the code B input for prediction has been made by designer's judgment with complex handwork that might be lead to excessive conservative result and ineffective design process with some possibility of errors.

  8. Decision-making in schizophrenia: A predictive-coding perspective.

    Science.gov (United States)

    Sterzer, Philipp; Voss, Martin; Schlagenhauf, Florian; Heinz, Andreas

    2018-05-31

    Dysfunctional decision-making has been implicated in the positive and negative symptoms of schizophrenia. Decision-making can be conceptualized within the framework of hierarchical predictive coding as the result of a Bayesian inference process that uses prior beliefs to infer states of the world. According to this idea, prior beliefs encoded at higher levels in the brain are fed back as predictive signals to lower levels. Whenever these predictions are violated by the incoming sensory data, a prediction error is generated and fed forward to update beliefs encoded at higher levels. Well-documented impairments in cognitive decision-making support the view that these neural inference mechanisms are altered in schizophrenia. There is also extensive evidence relating the symptoms of schizophrenia to aberrant signaling of prediction errors, especially in the domain of reward and value-based decision-making. Moreover, the idea of altered predictive coding is supported by evidence for impaired low-level sensory mechanisms and motor processes. We review behavioral and neural findings from these research areas and provide an integrated view suggesting that schizophrenia may be related to a pervasive alteration in predictive coding at multiple hierarchical levels, including cognitive and value-based decision-making processes as well as sensory and motor systems. We relate these findings to decision-making processes and propose that varying degrees of impairment in the implicated brain areas contribute to the variety of psychotic experiences. Copyright © 2018 Elsevier Inc. All rights reserved.

  9. Efficient CRISPR/Cas9-Mediated Versatile, Predictable, and Donor-Free Gene Knockout in Human Pluripotent Stem Cells.

    Science.gov (United States)

    Liu, Zhongliang; Hui, Yi; Shi, Lei; Chen, Zhenyu; Xu, Xiangjie; Chi, Liankai; Fan, Beibei; Fang, Yujiang; Liu, Yang; Ma, Lin; Wang, Yiran; Xiao, Lei; Zhang, Quanbin; Jin, Guohua; Liu, Ling; Zhang, Xiaoqing

    2016-09-13

    Loss-of-function studies in human pluripotent stem cells (hPSCs) require efficient methodologies for lesion of genes of interest. Here, we introduce a donor-free paired gRNA-guided CRISPR/Cas9 knockout strategy (paired-KO) for efficient and rapid gene ablation in hPSCs. Through paired-KO, we succeeded in targeting all genes of interest with high biallelic targeting efficiencies. More importantly, during paired-KO, the cleaved DNA was repaired mostly through direct end joining without insertions/deletions (precise ligation), and thus makes the lesion product predictable. The paired-KO remained highly efficient for one-step targeting of multiple genes and was also efficient for targeting of microRNA, while for long non-coding RNA over 8 kb, cleavage of a short fragment of the core promoter region was sufficient to eradicate downstream gene transcription. This work suggests that the paired-KO strategy is a simple and robust system for loss-of-function studies for both coding and non-coding genes in hPSCs. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  10. Retrotransposons and non-protein coding RNAs

    DEFF Research Database (Denmark)

    Mourier, Tobias; Willerslev, Eske

    2009-01-01

    does not merely represent spurious transcription. We review examples of functional RNAs transcribed from retrotransposons, and address the collection of non-protein coding RNAs derived from transposable element sequences, including numerous human microRNAs and the neuronal BC RNAs. Finally, we review...

  11. Functional redundancy and/or ongoing pseudogenization among F-box protein genes expressed in Arabidopsis male gametophyte.

    Science.gov (United States)

    Ikram, Sobia; Durandet, Monique; Vesa, Simona; Pereira, Serge; Guerche, Philippe; Bonhomme, Sandrine

    2014-06-01

    F-box protein genes family is one of the largest gene families in plants, with almost 700 predicted genes in the model plant Arabidopsis. F-box proteins are key components of the ubiquitin proteasome system that allows targeted protein degradation. Transcriptome analyses indicate that half of these F-box protein genes are found expressed in microspore and/or pollen, i.e., during male gametogenesis. To assess the role of F-box protein genes during this crucial developmental step, we selected 34 F-box protein genes recorded as highly and specifically expressed in pollen and isolated corresponding insertion mutants. We checked the expression level of each selected gene by RT-PCR and confirmed pollen expression for 25 genes, but specific expression for only 10 of the 34 F-box protein genes. In addition, we tested the expression level of selected F-box protein genes in 24 mutant lines and showed that 11 of them were null mutants. Transmission analysis of the mutations to the progeny showed that none of the single mutations was gametophytic lethal. These unaffected transmission efficiencies suggested leaky mutations or functional redundancy among F-box protein genes. Cytological observation of the gametophytes in the mutants confirmed these results. Combinations of mutations in F-box protein genes from the same subfamily did not lead to transmission defect either, further highlighting functional redundancy and/or a high proportion of pseudogenes among these F-box protein genes.

  12. In silico Coding Sequence Analysis of Walnut GAI and PIP2 Genes and Comparison with Different Plant Species

    Directory of Open Access Journals (Sweden)

    Mahdi Mohseniazar

    2017-02-01

    done with MEGA from aligned sequences. The motifs of protein sequences were found using the program of T-COFEE at website (http://www.ebi.ac.uk/Tools/msa/tcoffee/. The Neighbor-Joining (NJ method was used to designing the phylogenetic tree. The predicted exons and introns in mRNA sequences were done by http://genes.mit.edu/GENSCAN.html website. The secondary structure of proteins was predicted by PSIORED online on http://bioinf.cs.ucl.ac.uk/psipred/. Prediction of 3D model of protein was performed using the 3D alignment of protein structure by BLASTp and PDB database as source. Also, targeting prediction of proteins was done online by TargetP at (http://www.cbs.dtu.dk/services/TargetP/ website. Results and discussion: In phylogenetic investigation among 17 different species, Walnut species evolutionary stand in dicotyledonous and woody plants by both of GAI and PIP2 genes and protein sequence clustering. By multiple alignments and investigation in conserved sequence of these genes in plant revealed that despite differences in cDNA length, there were very similarities in conserved region, secondary and tertiary structure. Protein analysis in the GAI gene family showed that the following domains including DELLA, TVHYNP, VHIID, RKVATYFGEALARR, AVNSVFELH, RVER, and SAW were conserved in this proteins. In secondary structure of protein, β-sheets and α-helixes specified by PSIPRED software for both of GAI and PIP2 proteins. GAI protein had 9 β-sheets and 15 α-helixes in its structure, also PIP2 protein had2 β-sheet (at 180-188 and 248-253 and 8 α-helixes. In comparison of 3D structure, walnut PIP2 protein was very similar to chain A of PIP2 protein of spinach (Spinacia oleracea and GAI protein of walnut was similar to B-subunit of Arabidopsis GAI protein with 48% similarity. The length of GAI protein was varied from 636 aa in Malus baccata var. xiaojinensis to 336 aa in Physcomitrella patens among species. In walnut, the length of GAI and PIP2 protein was 613 aa and

  13. In silico Coding Sequence Analysis of Walnut GAI and PIP2 Genes and Comparison with Different Plant Species

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    Mahdi Mohseniazar

    2017-09-01

    done with MEGA from aligned sequences. The motifs of protein sequences were found using the program of T-COFEE at website (http://www.ebi.ac.uk/Tools/msa/tcoffee/. The Neighbor-Joining (NJ method was used to designing the phylogenetic tree. The predicted exons and introns in mRNA sequences were done by http://genes.mit.edu/GENSCAN.html website. The secondary structure of proteins was predicted by PSIORED online on http://bioinf.cs.ucl.ac.uk/psipred/. Prediction of 3D model of protein was performed using the 3D alignment of protein structure by BLASTp and PDB database as source. Also, targeting prediction of proteins was done online by TargetP at (http://www.cbs.dtu.dk/services/TargetP/ website. Results and discussion: In phylogenetic investigation among 17 different species, Walnut species evolutionary stand in dicotyledonous and woody plants by both of GAI and PIP2 genes and protein sequence clustering. By multiple alignments and investigation in conserved sequence of these genes in plant revealed that despite differences in cDNA length, there were very similarities in conserved region, secondary and tertiary structure. Protein analysis in the GAI gene family showed that the following domains including DELLA, TVHYNP, VHIID, RKVATYFGEALARR, AVNSVFELH, RVER, and SAW were conserved in this proteins. In secondary structure of protein, β-sheets and α-helixes specified by PSIPRED software for both of GAI and PIP2 proteins. GAI protein had 9 β-sheets and 15 α-helixes in its structure, also PIP2 protein had2 β-sheet (at 180-188 and 248-253 and 8 α-helixes. In comparison of 3D structure, walnut PIP2 protein was very similar to chain A of PIP2 protein of spinach (Spinacia oleracea and GAI protein of walnut was similar to B-subunit of Arabidopsis GAI protein with 48% similarity. The length of GAI protein was varied from 636 aa in Malus baccata var. xiaojinensis to 336 aa in Physcomitrella patens among species. In walnut, the length of GAI and PIP2 protein was 613 aa and

  14. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

    2014-04-08

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

  15. Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

    Energy Technology Data Exchange (ETDEWEB)

    Haas, B J; Salzberg, S L; Zhu, W; Pertea, M; Allen, J E; Orvis, J; White, O; Buell, C R; Wortman, J R

    2007-12-10

    EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.

  16. Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

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    Eils Roland

    2006-06-01

    Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

  17. Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.

    Science.gov (United States)

    Zhu, Jie; Qin, Yufang; Liu, Taigang; Wang, Jun; Zheng, Xiaoqi

    2013-01-01

    Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. Programs and data are available upon request.

  18. The Expression of Genes Encoding Secreted Proteins in Medicago truncatula A17 Inoculated Roots

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    LUCIA KUSUMAWATI

    2013-09-01

    Full Text Available Subtilisin-like serine protease (MtSBT, serine carboxypeptidase (MtSCP, MtN5, non-specific lipid transfer protein (MtnsLTP, early nodulin2-like protein (MtENOD2-like, FAD-binding domain containing protein (MtFAD-BP1, and rhicadhesin receptor protein (MtRHRE1 were among 34 proteins found in the supernatant of M. truncatula 2HA and sickle cell suspension cultures. This study investigated the expression of genes encoding those proteins in roots and developing nodules. Two methods were used: quantitative real time RT-PCR and gene expression analysis (with promoter:GUS fusion in roots. Those proteins are predicted as secreted proteins which is indirectly supported by the findings that promoter:GUS fusions of six of the seven genes encoding secreted proteins were strongly expressed in the vascular bundle of transgenic hairy roots. All six genes have expressed in 14-day old nodule. The expression levels of the selected seven genes were quantified in Sinorhizobium-inoculated and control plants using quantitative real time RT-PCR. In conclusion, among seven genes encoding secreted proteins analyzed, the expression level of only one gene, MtN5, was up-regulated significantly in inoculated root segments compared to controls. The expression of MtSBT1, MtSCP1, MtnsLTP, MtFAD-BP1, MtRHRE1 and MtN5 were higher in root tip than in other tissues examined.

  19. Hidden Structural Codes in Protein Intrinsic Disorder.

    Science.gov (United States)

    Borkosky, Silvia S; Camporeale, Gabriela; Chemes, Lucía B; Risso, Marikena; Noval, María Gabriela; Sánchez, Ignacio E; Alonso, Leonardo G; de Prat Gay, Gonzalo

    2017-10-17

    Intrinsic disorder is a major structural category in biology, accounting for more than 30% of coding regions across the domains of life, yet consists of conformational ensembles in equilibrium, a major challenge in protein chemistry. Anciently evolved papillomavirus genomes constitute an unparalleled case for sequence to structure-function correlation in cases in which there are no folded structures. E7, the major transforming oncoprotein of human papillomaviruses, is a paradigmatic example among the intrinsically disordered proteins. Analysis of a large number of sequences of the same viral protein allowed for the identification of a handful of residues with absolute conservation, scattered along the sequence of its N-terminal intrinsically disordered domain, which intriguingly are mostly leucine residues. Mutation of these led to a pronounced increase in both α-helix and β-sheet structural content, reflected by drastic effects on equilibrium propensities and oligomerization kinetics, and uncovers the existence of local structural elements that oppose canonical folding. These folding relays suggest the existence of yet undefined hidden structural codes behind intrinsic disorder in this model protein. Thus, evolution pinpoints conformational hot spots that could have not been identified by direct experimental methods for analyzing or perturbing the equilibrium of an intrinsically disordered protein ensemble.

  20. Impacts of Nonsynonymous Single Nucleotide Polymorphisms of Adiponectin Receptor 1 Gene on Corresponding Protein Stability: A Computational Approach

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    Md. Abu Saleh

    2016-01-01

    Full Text Available Despite the reported association of adiponectin receptor 1 (ADIPOR1 gene mutations with vulnerability to several human metabolic diseases, there is lack of computational analysis on the functional and structural impacts of single nucleotide polymorphisms (SNPs of the human ADIPOR1 at protein level. Therefore, sequence- and structure-based computational tools were employed in this study to functionally and structurally characterize the coding nsSNPs of ADIPOR1 gene listed in the dbSNP database. Our in silico analysis by SIFT, nsSNPAnalyzer, PolyPhen-2, Fathmm, I-Mutant 2.0, SNPs&GO, PhD-SNP, PANTHER, and SNPeffect tools identified the nsSNPs with distorting functional impacts, namely, rs765425383 (A348G, rs752071352 (H341Y, rs759555652 (R324L, rs200326086 (L224F, and rs766267373 (L143P from 74 nsSNPs of ADIPOR1 gene. Finally the aforementioned five deleterious nsSNPs were introduced using Swiss-PDB Viewer package within the X-ray crystal structure of ADIPOR1 protein, and changes in free energy for these mutations were computed. Although increased free energy was observed for all the mutants, the nsSNP H341Y caused the highest energy increase amongst all. RMSD and TM scores predicted that mutants were structurally similar to wild type protein. Our analyses suggested that the aforementioned variants especially H341Y could directly or indirectly destabilize the amino acid interactions and hydrogen bonding networks of ADIPOR1.

  1. SINEUPs are modular antisense long-non coding RNAs that increase synthesis of target proteins in cells

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    Silvia eZucchelli

    2015-05-01

    Full Text Available Despite recent efforts in discovering novel long non-coding RNAs (lncRNAs and unveiling their functions in a wide range of biological processes their applications as biotechnological or therapeutic tools are still at their infancy. We have recently shown that AS Uchl1, a natural lncRNA antisense to the Parkinson’s disease-associated gene Ubiquitin carboxyl-terminal esterase L1 (Uchl1, is able to increase UchL1 protein synthesis at post-transcriptional level. Its activity requires two RNA elements: an embedded inverted SINEB2 sequence to increase translation and the overlapping region to target its sense mRNA. This functional organization is shared with several mouse lncRNAs antisense to protein coding genes. The potential use of AS Uchl1-derived lncRNAs as enhancers of target mRNA translation remains unexplored. Here we define AS Uchl1 as the representative member of a new functional class of natural and synthetic antisense lncRNAs that activate translation. We named this class of RNAs SINEUPs for their requirement of the inverted SINEB2 sequence to UP-regulate translation in a gene-specific manner. The overlapping region is indicated as the Binding Doman (BD while the embedded inverted SINEB2 element is the Effector Domain (ED. By swapping BD, synthetic SINEUPs are designed targeting mRNAs of interest. SINEUPs function in an array of cell lines and can be efficiently directed towards N-terminally tagged proteins. Their biological activity is retained in a miniaturized version within the range of small RNAs length. Its modular structure was exploited to successfully design synthetic SINEUPs targeting endogenous Parkinson’s disease-associated DJ-1 and proved to be active in different neuronal cell lines.In summary, SINEUPs represent the first scalable tool to increase synthesis of proteins of interest. We propose SINEUPs as reagents for molecular biology experiments, in protein manufacturing as well as in therapy of haploinsufficiencies.

  2. Function and Application Areas in Medicine of Non-Coding RNA

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    Figen Guzelgul

    2009-06-01

    Full Text Available RNA is the genetic material converting the genetic code that it gets from DNA into protein. While less than 2 % of RNA is converted into protein , more than 98 % of it can not be converted into protein and named as non-coding RNAs. 70 % of noncoding RNAs consists of introns , however, the rest part of them consists of exons. Non-coding RNAs are examined in two classes according to their size and functions. Whereas they are classified as long non-coding and small non-coding RNAs according to their size , they are grouped as housekeeping non-coding RNAs and regulating non-coding RNAs according to their function. For long years ,these non-coding RNAs have been considered as non-functional. However, today, it has been proved that these non-coding RNAs play role in regulating genes and in structural, functional and catalitic roles of RNAs converted into protein. Due to its taking a role in gene silencing mechanism, particularly in medical world , non-coding RNAs have led to significant developments. RNAi technolgy , which is used in designing drugs to be used in treatment of various diseases , is a ray of hope for medical world. [Archives Medical Review Journal 2009; 18(3.000: 141-155

  3. Automatic annotation of protein motif function with Gene Ontology terms

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    Gopalakrishnan Vanathi

    2004-09-01

    Full Text Available Abstract Background Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. Results This paperpresents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifsis viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association isfound to be a very useful feature. We take advantageof the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correctassociation. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. Conclusions In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about thefunctions of newly discovered candidate protein motifs.

  4. Selective Constraints on Coding Sequences of Nervous System Genes Are a Major Determinant of Duplicate Gene Retention in Vertebrates.

    Science.gov (United States)

    Roux, Julien; Liu, Jialin; Robinson-Rechavi, Marc

    2017-11-01

    The evolutionary history of vertebrates is marked by three ancient whole-genome duplications: two successive rounds in the ancestor of vertebrates, and a third one specific to teleost fishes. Biased loss of most duplicates enriched the genome for specific genes, such as slow evolving genes, but this selective retention process is not well understood. To understand what drives the long-term preservation of duplicate genes, we characterized duplicated genes in terms of their expression patterns. We used a new method of expression enrichment analysis, TopAnat, applied to in situ hybridization data from thousands of genes from zebrafish and mouse. We showed that the presence of expression in the nervous system is a good predictor of a higher rate of retention of duplicate genes after whole-genome duplication. Further analyses suggest that purifying selection against the toxic effects of misfolded or misinteracting proteins, which is particularly strong in nonrenewing neural tissues, likely constrains the evolution of coding sequences of nervous system genes, leading indirectly to the preservation of duplicate genes after whole-genome duplication. Whole-genome duplications thus greatly contributed to the expansion of the toolkit of genes available for the evolution of profound novelties of the nervous system at the base of the vertebrate radiation. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  5. Assessment of subchannel code ASSERT-PV for flow-distribution predictions

    International Nuclear Information System (INIS)

    Nava-Dominguez, A.; Rao, Y.F.; Waddington, G.M.

    2014-01-01

    Highlights: • Assessment of the subchannel code ASSERT-PV 3.2 for the prediction of flow distribution. • Open literature and in-house experimental data to quantify ASSERT-PV predictions. • Model changes assessed against vertical and horizontal flow experiments. • Improvement of flow-distribution predictions under CANDU-relevant conditions. - Abstract: This paper reports an assessment of the recently released subchannel code ASSERT-PV 3.2 for the prediction of flow-distribution in fuel bundles, including subchannel void fraction, quality and mass fluxes. Experimental data from open literature and from in-house tests are used to assess the flow-distribution models in ASSERT-PV 3.2. The prediction statistics using the recommended model set of ASSERT-PV 3.2 are compared to those from previous code versions. Separate-effects sensitivity studies are performed to quantify the contribution of each flow-distribution model change or enhancement to the improvement in flow-distribution prediction. The assessment demonstrates significant improvement in the prediction of flow-distribution in horizontal fuel channels containing CANDU bundles

  6. Assessment of subchannel code ASSERT-PV for flow-distribution predictions

    Energy Technology Data Exchange (ETDEWEB)

    Nava-Dominguez, A., E-mail: navadoma@aecl.ca; Rao, Y.F., E-mail: raoy@aecl.ca; Waddington, G.M., E-mail: waddingg@aecl.ca

    2014-08-15

    Highlights: • Assessment of the subchannel code ASSERT-PV 3.2 for the prediction of flow distribution. • Open literature and in-house experimental data to quantify ASSERT-PV predictions. • Model changes assessed against vertical and horizontal flow experiments. • Improvement of flow-distribution predictions under CANDU-relevant conditions. - Abstract: This paper reports an assessment of the recently released subchannel code ASSERT-PV 3.2 for the prediction of flow-distribution in fuel bundles, including subchannel void fraction, quality and mass fluxes. Experimental data from open literature and from in-house tests are used to assess the flow-distribution models in ASSERT-PV 3.2. The prediction statistics using the recommended model set of ASSERT-PV 3.2 are compared to those from previous code versions. Separate-effects sensitivity studies are performed to quantify the contribution of each flow-distribution model change or enhancement to the improvement in flow-distribution prediction. The assessment demonstrates significant improvement in the prediction of flow-distribution in horizontal fuel channels containing CANDU bundles.

  7. A central role for ubiquitination within a circadian clock protein modification code

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    Katarina eStojkovic

    2014-08-01

    Full Text Available Circadian rhythms, endogenous cycles of about 24 h in physiology, are generated by a master clock located in the suprachiasmatic nucleus of the hypothalamus and other clocks located in the brain and peripheral tissues. Circadian disruption is known to increase the incidence of various illnesses, such as mental disorders, metabolic syndrome and cancer. At the molecular level, periodicity is established by a set of clock genes via autoregulatory translation-transcription feedback loops. This clock mechanism is regulated by post-translational modifications such as phosphorylation and ubiquitination, which set the pace of the clock. Ubiquitination in particular has been found to regulate the stability of core clock components, but also other clock protein functions. Mutation of genes encoding ubiquitin ligases can cause either elongation or shortening of the endogenous circadian period. Recent research has also started to uncover roles for deubiquitination in the molecular clockwork. Here we review the role of the ubiquitin pathway in regulating the circadian clock and we propose that ubiquitination is a key element in a clock protein modification code that orchestrates clock mechanisms and circadian behavior over the daily cycle.

  8. Molecular cloning of a Candida albicans gene (SSB1) coding for a protein related to the Hsp70 family.

    Science.gov (United States)

    Maneu, V; Cervera, A M; Martinez, J P; Gozalbo, D

    1997-06-15

    We have cloned and sequenced a Candida albicans gene (SSB1) encoding a potential member of the heat-shock protein seventy (hsp70) family. The protein encoded by this gene contains 613 amino acids and shows a high degree (85%) of sequence identity to the ssb subfamily (ssb1 and ssb2) of the Saccharomyces cerevisiae hsp70 family. The transcribed mRNA (2.1 kb) is present in similar amounts both in yeast and germ tube cells of C. albicans.

  9. Endostatin gene variation and protein levels in breast cancer susceptibility and severity

    International Nuclear Information System (INIS)

    Balasubramanian, Sabapathy P; Cross, Simon S; Globe, Jenny; Cox, Angela; Brown, Nicola J; Reed, Malcolm W

    2007-01-01

    Endostatin is a potent endogenous anti-angiogenic agent which inhibits tumour growth. A non-synonymous coding polymorphism in the Endostatin gene is thought to affect Endostatin activity. We aimed to determine the role of this Endostatin polymorphism in breast cancer pathogenesis and any influence on serum Endostatin levels in healthy volunteers. Endostatin protein expression on a breast cancer micro array was also studied to determine any relationship to genotype and to breast cancer prognosis. The 4349G > A (coding non-synonymous) polymorphism in exon 42 of the Endostatin gene was genotyped in approximately 846 breast cancer cases and 707 appropriate controls. In a separate healthy cohort of 57 individuals, in addition to genotyping, serum Endostatin levels were measured using enzyme linked immunosorbant assay (ELISA). A semi-quantitative assessment of Endostatin protein expression on immunostained tissue micro arrays (TMA) constructed from breast cancer samples of patients with genotype data was performed. The rare allele (A) was significantly associated with invasive breast cancers compared to non-invasive tumours (p = 0.03), but there was no association with tumour grade, nodal status, vascular invasion or overall survival. There was no association with breast cancer susceptibility. Serum Endostatin levels and Endostatin protein expression on the tissue micro array were not associated with genotype. The Endostatin 4349A allele is associated with invasive breast cancer. The Endostatin 4349G > A polymorphism however does not appear to be associated with breast cancer susceptibility or severity in invasive disease. By studying circulating levels and tumour Endostatin protein expression, we have shown that any influence of this polymorphism is unlikely to be through an effect on the levels of protein produced

  10. Benchmarking of gene prediction programs for metagenomic data.

    Science.gov (United States)

    Yok, Non; Rosen, Gail

    2010-01-01

    This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.

  11. Investigation of genes encoding calcineurin B-like protein family in legumes and their expression analyses in chickpea (Cicer arietinum L.).

    Science.gov (United States)

    Meena, Mukesh Kumar; Ghawana, Sanjay; Sardar, Atish; Dwivedi, Vikas; Khandal, Hitaishi; Roy, Riti; Chattopadhyay, Debasis

    2015-01-01

    Calcium ion (Ca2+) is a ubiquitous second messenger that transmits various internal and external signals including stresses and, therefore, is important for plants' response process. Calcineurin B-like proteins (CBLs) are one of the plant calcium sensors, which sense and convey the changes in cytosolic Ca2+-concentration for response process. A search in four leguminous plant (soybean, Medicago truncatula, common bean and chickpea) genomes identified 9 to 15 genes in each species that encode CBL proteins. Sequence analyses of CBL peptides and coding sequences (CDS) suggested that there are nine original CBL genes in these legumes and some of them were multiplied during whole genome or local gene duplication. Coding sequences of chickpea CBL genes (CaCBL) were cloned from their cDNAs and sequenced, and their annotations in the genome assemblies were corrected accordingly. Analyses of protein sequences and gene structures of CBL family in plant kingdom indicated its diverse origin but showed a remarkable conservation in overall protein structure with appearance of complex gene structure in the course of evolution. Expression of CaCBL genes in different tissues and in response to different stress and hormone treatment were studied. Most of the CaCBL genes exhibited high expression in flowers. Expression profile of CaCBL genes in response to different abiotic stresses and hormones related to development and stresses (ABA, auxin, cytokinin, SA and JA) at different time intervals suggests their diverse roles in development and plant defence in addition to abiotic stress tolerance. These data not only contribute to a better understanding of the complex regulation of chickpea CBL gene family, but also provide valuable information for further research in chickpea functional genomics.

  12. Molecular characterization of a phloem-specific gene encoding the filament protein, phloem protein 1 (PP1), from Cucurbita maxima.

    Science.gov (United States)

    Clark, A M; Jacobsen, K R; Bostwick, D E; Dannenhoffer, J M; Skaggs, M I; Thompson, G A

    1997-07-01

    Sieve elements in the phloem of most angiosperms contain proteinaceous filaments and aggregates called P-protein. In the genus Cucurbita, these filaments are composed of two major proteins: PP1, the phloem filament protein, and PP2, the phloem lactin. The gene encoding the phloem filament protein in pumpkin (Cucurbita maxima Duch.) has been isolated and characterized. Nucleotide sequence analysis of the reconstructed gene gPP1 revealed a continuous 2430 bp protein coding sequence, with no introns, encoding an 809 amino acid polypeptide. The deduced polypeptide had characteristics of PP1 and contained a 15 amino acid sequence determined by N-terminal peptide sequence analysis of PP1. The sequence of PP1 was highly repetitive with four 200 amino acid sequence domains containing structural motifs in common with cysteine proteinase inhibitors. Expression of the PP1 gene was detected in roots, hypocotyls, cotyledons, stems, and leaves of pumpkin plants. PP1 and its mRNA accumulated in pumpkin hypocotyls during the period of rapid hypocotyl elongation after which mRNA levels declined, while protein levels remained elevated. PP1 was immunolocalized in slime plugs and P-protein bodies in sieve elements of the phloem. Occasionally, PP1 was detected in companion cells. PP1 mRNA was localized by in situ hybridization in companion cells at early stages of vascular differentiation. The developmental accumulation and localization of PP1 and its mRNA paralleled the phloem lactin, further suggesting an interaction between these phloem-specific proteins.

  13. Unusual Presentation of Pelizaeus-Merzbacher Disease: Female Patient with Deletion of the Proteolipid Protein 1 Gene

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    Teva Brender

    2015-01-01

    Full Text Available Pelizaeus-Merzbacher disease (PMD is neurodegenerative leukodystrophy caused by dysfunction of the proteolipid protein 1 (PLP1 gene on Xq22, which codes for an essential myelin protein. As an X-linked condition, PMD primarily affects males; however there have been a small number of affected females reported in the medical literature with a variety of different mutations in this gene. No affected females to date have a deletion like our patient. In addition to this, our patient has skewed X chromosome inactivation which adds to her presentation as her unaffected mother also carries the mutation.

  14. CAR gene cluster and transcript levels of carotenogenic genes in Rhodotorula mucilaginosa.

    Science.gov (United States)

    Landolfo, Sara; Ianiri, Giuseppe; Camiolo, Salvatore; Porceddu, Andrea; Mulas, Giuliana; Chessa, Rossella; Zara, Giacomo; Mannazzu, Ilaria

    2018-01-01

    A molecular approach was applied to the study of the carotenoid biosynthetic pathway of Rhodotorula mucilaginosa. At first, functional annotation of the genome of R. mucilaginosa C2.5t1 was carried out and gene ontology categories were assigned to 4033 predicted proteins. Then, a set of genes involved in different steps of carotenogenesis was identified and those coding for phytoene desaturase, phytoene synthase/lycopene cyclase and carotenoid dioxygenase (CAR genes) proved to be clustered within a region of ~10 kb. Quantitative PCR of the genes involved in carotenoid biosynthesis showed that genes coding for 3-hydroxy-3-methylglutharyl-CoA reductase and mevalonate kinase are induced during exponential phase while no clear trend of induction was observed for phytoene synthase/lycopene cyclase and phytoene dehydrogenase encoding genes. Thus, in R. mucilaginosa the induction of genes involved in the early steps of carotenoid biosynthesis is transient and accompanies the onset of carotenoid production, while that of CAR genes does not correlate with the amount of carotenoids produced. The transcript levels of genes coding for carotenoid dioxygenase, superoxide dismutase and catalase A increased during the accumulation of carotenoids, thus suggesting the activation of a mechanism aimed at the protection of cell structures from oxidative stress during carotenoid biosynthesis. The data presented herein, besides being suitable for the elucidation of the mechanisms that underlie carotenoid biosynthesis, will contribute to boosting the biotechnological potential of this yeast by improving the outcome of further research efforts aimed at also exploring other features of interest.

  15. CLONING AND SEQUENCING OF PSEUDOMONAS GENES DETERMINING SODIUM DODECYL-SULFATE BIODEGRADATION

    NARCIS (Netherlands)

    DAVISON, J; BRUNEL, F; PHANOPOULOS, A; PROZZI, D; TERPSTRA, P

    1992-01-01

    The nucleotide sequences of two genes involved in sodium dodecyl sulfate (SDS) degradation, by Pseudomonas, have been determined. One of these, sdsA, codes for an alkyl sulfatase (58 957 Da) and has similarity (31.8% identity over a 201-amino acid stretch) to the N terminus of a predicted protein of

  16. Primate-specific spliced PMCHL RNAs are non-protein coding in human and macaque tissues

    Directory of Open Access Journals (Sweden)

    Delerue-Audegond Audrey

    2008-12-01

    Full Text Available Abstract Background Brain-expressed genes that were created in primate lineage represent obvious candidates to investigate molecular mechanisms that contributed to neural reorganization and emergence of new behavioural functions in Homo sapiens. PMCHL1 arose from retroposition of a pro-melanin-concentrating hormone (PMCH antisense mRNA on the ancestral human chromosome 5p14 when platyrrhines and catarrhines diverged. Mutations before divergence of hylobatidae led to creation of new exons and finally PMCHL1 duplicated in an ancestor of hominids to generate PMCHL2 at the human chromosome 5q13. A complex pattern of spliced and unspliced PMCHL RNAs were found in human brain and testis. Results Several novel spliced PMCHL transcripts have been characterized in human testis and fetal brain, identifying an additional exon and novel splice sites. Sequencing of PMCHL genes in several non-human primates allowed to carry out phylogenetic analyses revealing that the initial retroposition event took place within an intron of the brain cadherin (CDH12 gene, soon after platyrrhine/catarrhine divergence, i.e. 30–35 Mya, and was concomitant with the insertion of an AluSg element. Sequence analysis of the spliced PMCHL transcripts identified only short ORFs of less than 300 bp, with low (VMCH-p8 and protein variants or no evolutionary conservation. Western blot analyses of human and macaque tissues expressing PMCHL RNA failed to reveal any protein corresponding to VMCH-p8 and protein variants encoded by spliced transcripts. Conclusion Our present results improve our knowledge of the gene structure and the evolutionary history of the primate-specific chimeric PMCHL genes. These genes produce multiple spliced transcripts, bearing short, non-conserved and apparently non-translated ORFs that may function as mRNA-like non-coding RNAs.

  17. A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

    Science.gov (United States)

    Fan, Ming; Zheng, Bin; Li, Lihua

    2015-10-01

    Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

  18. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  19. Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Julian Zubek

    2015-07-01

    Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

  20. ncRNA-class Web Tool: Non-coding RNA feature extraction and pre-miRNA classification web tool

    KAUST Repository

    Kleftogiannis, Dimitrios A.; Theofilatos, Konstantinos A.; Papadimitriou, Stergios; Tsakalidis, Athanasios K.; Likothanassis, Spiridon D.; Mavroudi, Seferina P.

    2012-01-01

    Until recently, it was commonly accepted that most genetic information is transacted by proteins. Recent evidence suggests that the majority of the genomes of mammals and other complex organisms are in fact transcribed into non-coding RNAs (ncRNAs), many of which are alternatively spliced and/or processed into smaller products. Non coding RNA genes analysis requires the calculation of several sequential, thermodynamical and structural features. Many independent tools have already been developed for the efficient calculation of such features but to the best of our knowledge there does not exist any integrative approach for this task. The most significant amount of existing work is related to the miRNA class of non-coding RNAs. MicroRNAs (miRNAs) are small non-coding RNAs that play a significant role in gene regulation and their prediction is a challenging bioinformatics problem. Non-coding RNA feature extraction and pre-miRNA classification Web Tool (ncRNA-class Web Tool) is a publicly available web tool ( http://150.140.142.24:82/Default.aspx ) which provides a user friendly and efficient environment for the effective calculation of a set of 58 sequential, thermodynamical and structural features of non-coding RNAs, plus a tool for the accurate prediction of miRNAs. © 2012 IFIP International Federation for Information Processing.

  1. MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants

    Directory of Open Access Journals (Sweden)

    Ning Zhang

    2018-05-01

    Full Text Available Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.

  2. Molecular mechanics work station for protein conformational studies

    International Nuclear Information System (INIS)

    Fine, R.; Levinthal, C.; Schoenborn, B.; Dimmier, G.; Rankowitz, C.

    1984-01-01

    Interest in computational problems in Biology has intensified over the last few years, partly due to the development of techniques for the rapid cloning, sequencing, and mutagenesis of genes from organisims ranging from E. coli to Man. The central dogma of molecular biology; that DNA codes for mRNA which codes for protein, has been understood in a linear programming sense since the genetic code was cracked. But what is not understood at present is how a protein, once assembled as a long sequence of amino acids, folds back on itself to produce a three-dimensional structure which is unique to that protein and which dictates its chemical and biological activity. This folding process is purely physics, and involves the time evolution of a system of several thousand atoms which interact with each other and with atoms from the surrounding solvent. Molecular dynamics simulations on smaller molecules suggest that approaches which treat the protein as a classical ensemble of atoms interacting with each other via an empirical Hamiltonian can yield the kind of predictive results one would like when applied to proteins

  3. Evolving a Dynamic Predictive Coding Mechanism for Novelty Detection

    OpenAIRE

    Haggett, Simon J.; Chu, Dominique; Marshall, Ian W.

    2007-01-01

    Novelty detection is a machine learning technique which identifies new or unknown information in data sets. We present our current work on the construction of a new novelty detector based on a dynamical version of predictive coding. We compare three evolutionary algorithms, a simple genetic algorithm, NEAT and FS-NEAT, for the task of optimising the structure of an illustrative dynamic predictive coding neural network to improve its performance over stimuli from a number of artificially gener...

  4. GWA Analysis for Milk Production Traits in Dairy Sheep and Genetic Support for a QTN Influencing Milk Protein Percentage in the LALBA Gene

    DEFF Research Database (Denmark)

    García-Gámez, Elsa; Gutiérrez-Gil, Beatriz; Sahana, Goutam

    2012-01-01

    In this study, we used the Illumina OvineSNP50 BeadChip to conduct a genome-wide association (GWA) analysis for milk production traits in dairy sheep by analyzing a commercial population of Spanish Churra sheep. The studied population consisted of a total of 1,681 Churra ewes belonging to 16 half...... identified was located within the coding gene sequence (LALBA_g.242T.C) and was predicted to cause an amino acid change in the protein (Val27Ala). Different approaches, including GWA analysis, a combined linkage and linkage disequilibrium study and a concordance test with the QTL segregating status...

  5. The Coding of Biological Information: From Nucleotide Sequence to Protein Recognition

    Science.gov (United States)

    Štambuk, Nikola

    The paper reviews the classic results of Swanson, Dayhoff, Grantham, Blalock and Root-Bernstein, which link genetic code nucleotide patterns to the protein structure, evolution and molecular recognition. Symbolic representation of the binary addresses defining particular nucleotide and amino acid properties is discussed, with consideration of: structure and metric of the code, direct correspondence between amino acid and nucleotide information, and molecular recognition of the interacting protein motifs coded by the complementary DNA and RNA strands.

  6. The Heat Shock Protein 26 Gene is Required for Ethanol Tolerance in Drosophila

    Directory of Open Access Journals (Sweden)

    Awoyemi A. Awofala

    2011-01-01

    Full Text Available Stress plays an important role in drug- and addiction-related behaviours. However, the mechanisms underlying these behavioural responses are still poorly understood. In the light of recent reports that show consistent regulation of many genes encoding stress proteins including heat shock proteins following ethanol exposure in Drosophila , it was hypothesised that transition to alcohol dependence may involve the dysregulation of the circuits that mediate behavioural responses to stressors. Thus, behavioural genetic methodologies were used to investigate the role of the Drosophila hsp26 gene, a small heat shock protein coding gene which is induced in response to various stresses, in the development of rapid tolerance to ethanol sedation. Rapid tolerance was quantified as the percentage difference in the mean sedation times between the second and first ethanol exposure. Two independently isolated P-element mutations near the hsp26 gene eliminated the capacity for tolerance. In addition, RNAi-mediated functional knockdown of hsp26 expression in the glial cells and the whole nervous system also caused a defect in tolerance development. The rapid tolerance phenotype of the hsp26 mutants was rescued by the expression of the wild-type hsp26 gene in the nervous system. None of these manipulations of the hsp26 gene caused changes in the rate of ethanol absorption. Hsp26 genes are evolutionary conserved, thus the role of hsp26 in ethanol tolerance may present a new direction for research into alcohol dependency.

  7. Comparison of GLIMPS and HFAST Stirling engine code predictions with experimental data

    Science.gov (United States)

    Geng, Steven M.; Tew, Roy C.

    1992-01-01

    Predictions from GLIMPS and HFAST design codes are compared with experimental data for the RE-1000 and SPRE free piston Stirling engines. Engine performance and available power loss predictions are compared. Differences exist between GLIMPS and HFAST loss predictions. Both codes require engine specific calibration to bring predictions and experimental data into agreement.

  8. Comparison of GLIMPS and HFAST Stirling engine code predictions with experimental data

    International Nuclear Information System (INIS)

    Geng, S.M.; Tew, R.C.

    1994-01-01

    Predictions from GLIMPS and HFAST design codes are compared with experimental data for the RE-1000 and SPRE free-piston Stirling engines. Engine performance and available power loss predictions are compared. Differences exist between GLIMPS and HFAST loss predictions. Both codes require engine-specific calibration to bring predictions and experimental data into agreement

  9. Chicken genome analysis reveals novel genes encoding biotin-binding proteins related to avidin family

    Directory of Open Access Journals (Sweden)

    Nordlund Henri R

    2005-03-01

    Full Text Available Abstract Background A chicken egg contains several biotin-binding proteins (BBPs, whose complete DNA and amino acid sequences are not known. In order to identify and characterise these genes and proteins we studied chicken cDNAs and genes available in the NCBI database and chicken genome database using the reported N-terminal amino acid sequences of chicken egg-yolk BBPs as search strings. Results Two separate hits showing significant homology for these N-terminal sequences were discovered. For one of these hits, the chromosomal location in the immediate proximity of the avidin gene family was found. Both of these hits encode proteins having high sequence similarity with avidin suggesting that chicken BBPs are paralogous to avidin family. In particular, almost all residues corresponding to biotin binding in avidin are conserved in these putative BBP proteins. One of the found DNA sequences, however, seems to encode a carboxy-terminal extension not present in avidin. Conclusion We describe here the predicted properties of the putative BBP genes and proteins. Our present observations link BBP genes together with avidin gene family and shed more light on the genetic arrangement and variability of this family. In addition, comparative modelling revealed the potential structural elements important for the functional and structural properties of the putative BBP proteins.

  10. Systematic validation of predicted microRNAs for cyclin D1

    International Nuclear Information System (INIS)

    Jiang, Qiong; Feng, Ming-Guang; Mo, Yin-Yuan

    2009-01-01

    MicroRNAs are the endogenous small non-coding RNA molecules capable of silencing protein coding genes at the posttranscriptional level. Based on computer-aided predictions, a single microRNA could have over a hundred of targets. On the other hand, a single protein-coding gene could be targeted by many potential microRNAs. However, only a relatively small number of these predicted microRNA/mRNA interactions are experimentally validated, and no systematic validation has been carried out using a reporter system. In this study, we used luciferease reporter assays to validate microRNAs that can silence cyclin D1 (CCND1) because CCND1 is a well known proto-oncogene implicated in a variety of types of cancers. We chose miRanda (http://www.microRNA.org) as a primary prediction method. We then cloned 51 of 58 predicted microRNA precursors into pCDH-CMV-MCS-EF1-copGFP and tested for their effect on the luciferase reporter carrying the 3'-untranslated region (UTR) of CCND1 gene. Real-time PCR revealed the 45 of 51 cloned microRNA precursors expressed a relatively high level of the exogenous microRNAs which were used in our validation experiments. By an arbitrary cutoff of 35% reduction, we identified 7 microRNAs that were able to suppress Luc-CCND1-UTR activity. Among them, 4 of them were previously validated targets and the rest 3 microRNAs were validated to be positive in this study. Of interest, we found that miR-503 not only suppressed the luciferase activity, but also suppressed the endogenous CCND1 both at protein and mRNA levels. Furthermore, we showed that miR-503 was able to reduce S phase cell populations and caused cell growth inhibition, suggesting that miR-503 may be a putative tumor suppressor. This study provides a more comprehensive picture of microRNA/CCND1 interactions and it further demonstrates the importance of experimental target validation

  11. A Gene Family Coding for Salivary Proteins (SHOT) of the Polyphagous Spider Mite Tetranychus urticae Exhibits Fast Host-Dependent Transcriptional Plasticity.

    Science.gov (United States)

    Jonckheere, Wim; Dermauw, Wannes; Khalighi, Mousaalreza; Pavlidi, Nena; Reubens, Wim; Baggerman, Geert; Tirry, Luc; Menschaert, Gerben; Kant, Merijn R; Vanholme, Bartel; Van Leeuwen, Thomas

    2018-01-01

    The salivary protein repertoire released by the herbivorous pest Tetranychus urticae is assumed to hold keys to its success on diverse crops. We report on a spider mite-specific protein family that is expanded in T. urticae. The encoding genes have an expression pattern restricted to the anterior podocephalic glands, while peptide fragments were found in the T. urticae secretome, supporting the salivary nature of these proteins. As peptide fragments were identified in a host-dependent manner, we designated this family as the SHOT (secreted host-responsive protein of Tetranychidae) family. The proteins were divided in three groups based on sequence similarity. Unlike TuSHOT3 genes, TuSHOT1 and TuSHOT2 genes were highly expressed when feeding on a subset of family Fabaceae, while expression was depleted on other hosts. TuSHOT1 and TuSHOT2 expression was induced within 24 h after certain host transfers, pointing toward transcriptional plasticity rather than selection as the cause. Transfer from an 'inducer' to a 'noninducer' plant was associated with slow yet strong downregulation of TuSHOT1 and TuSHOT2, occurring over generations rather than hours. This asymmetric on and off regulation points toward host-specific effects of SHOT proteins, which is further supported by the diversity of SHOT genes identified in Tetranychidae with a distinct host repertoire.

  12. Methylation of miRNA genes and oncogenesis.

    Science.gov (United States)

    Loginov, V I; Rykov, S V; Fridman, M V; Braga, E A

    2015-02-01

    Interaction between microRNA (miRNA) and messenger RNA of target genes at the posttranscriptional level provides fine-tuned dynamic regulation of cell signaling pathways. Each miRNA can be involved in regulating hundreds of protein-coding genes, and, conversely, a number of different miRNAs usually target a structural gene. Epigenetic gene inactivation associated with methylation of promoter CpG-islands is common to both protein-coding genes and miRNA genes. Here, data on functions of miRNAs in development of tumor-cell phenotype are reviewed. Genomic organization of promoter CpG-islands of the miRNA genes located in inter- and intragenic areas is discussed. The literature and our own results on frequency of CpG-island methylation in miRNA genes from tumors are summarized, and data regarding a link between such modification and changed activity of miRNA genes and, consequently, protein-coding target genes are presented. Moreover, the impact of miRNA gene methylation on key oncogenetic processes as well as affected signaling pathways is discussed.

  13. Properties of Sequence Conservation in Upstream Regulatory and Protein Coding Sequences among Paralogs in Arabidopsis thaliana

    Science.gov (United States)

    Richardson, Dale N.; Wiehe, Thomas

    Whole genome duplication (WGD) has catalyzed the formation of new species, genes with novel functions, altered expression patterns, complexified signaling pathways and has provided organisms a level of genetic robustness. We studied the long-term evolution and interrelationships of 5’ upstream regulatory sequences (URSs), protein coding sequences (CDSs) and expression correlations (EC) of duplicated gene pairs in Arabidopsis. Three distinct methods revealed significant evolutionary conservation between paralogous URSs and were highly correlated with microarray-based expression correlation of the respective gene pairs. Positional information on exact matches between sequences unveiled the contribution of micro-chromosomal rearrangements on expression divergence. A three-way rank analysis of URS similarity, CDS divergence and EC uncovered specific gene functional biases. Transcription factor activity was associated with gene pairs exhibiting conserved URSs and divergent CDSs, whereas a broad array of metabolic enzymes was found to be associated with gene pairs showing diverged URSs but conserved CDSs.

  14. Translational regulation of gene expression by an anaerobically induced small non-coding RNA in Escherichia coli

    DEFF Research Database (Denmark)

    Boysen, Anders; Møller-Jensen, Jakob; Kallipolitis, Birgitte H.

    2010-01-01

    Small non-coding RNAs (sRNA) have emerged as important elements of gene regulatory circuits. In enterobacteria such as Escherichia coli and Salmonella many of these sRNAs interact with the Hfq protein, an RNA chaperone similar to mammalian Sm-like proteins and act in the post...... that adaptation to anaerobic growth involves the action of a small regulatory RNA....... of at least one sRNA regulator. Here, we extend this view by the identification and characterization of a highly conserved, anaerobically induced small sRNA in E. coli, whose expression is strictly dependent on the anaerobic transcriptional fumarate and nitrate reductase regulator (FNR). The sRNA, named Fnr...

  15. Identification of evolutionarily conserved non-AUG-initiated N-terminal extensions in human coding sequences.

    LENUS (Irish Health Repository)

    Ivanov, Ivaylo P

    2011-05-01

    In eukaryotes, it is generally assumed that translation initiation occurs at the AUG codon closest to the messenger RNA 5\\' cap. However, in certain cases, initiation can occur at codons differing from AUG by a single nucleotide, especially the codons CUG, UUG, GUG, ACG, AUA and AUU. While non-AUG initiation has been experimentally verified for a handful of human genes, the full extent to which this phenomenon is utilized--both for increased coding capacity and potentially also for novel regulatory mechanisms--remains unclear. To address this issue, and hence to improve the quality of existing coding sequence annotations, we developed a methodology based on phylogenetic analysis of predicted 5\\' untranslated regions from orthologous genes. We use evolutionary signatures of protein-coding sequences as an indicator of translation initiation upstream of annotated coding sequences. Our search identified novel conserved potential non-AUG-initiated N-terminal extensions in 42 human genes including VANGL2, FGFR1, KCNN4, TRPV6, HDGF, CITED2, EIF4G3 and NTF3, and also affirmed the conservation of known non-AUG-initiated extensions in 17 other genes. In several instances, we have been able to obtain independent experimental evidence of the expression of non-AUG-initiated products from the previously published literature and ribosome profiling data.

  16. Network-based prediction and knowledge mining of disease genes.

    Science.gov (United States)

    Carson, Matthew B; Lu, Hui

    2015-01-01

    In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network

  17. The integration of weighted human gene association networks based on link prediction.

    Science.gov (United States)

    Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing

    2017-01-31

    Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.

  18. Prediction of Protein Configurational Entropy (Popcoen).

    Science.gov (United States)

    Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

    2018-03-13

    A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

  19. Investigation of genes encoding calcineurin B-like protein family in legumes and their expression analyses in chickpea (Cicer arietinum L..

    Directory of Open Access Journals (Sweden)

    Mukesh Kumar Meena

    Full Text Available Calcium ion (Ca2+ is a ubiquitous second messenger that transmits various internal and external signals including stresses and, therefore, is important for plants' response process. Calcineurin B-like proteins (CBLs are one of the plant calcium sensors, which sense and convey the changes in cytosolic Ca2+-concentration for response process. A search in four leguminous plant (soybean, Medicago truncatula, common bean and chickpea genomes identified 9 to 15 genes in each species that encode CBL proteins. Sequence analyses of CBL peptides and coding sequences (CDS suggested that there are nine original CBL genes in these legumes and some of them were multiplied during whole genome or local gene duplication. Coding sequences of chickpea CBL genes (CaCBL were cloned from their cDNAs and sequenced, and their annotations in the genome assemblies were corrected accordingly. Analyses of protein sequences and gene structures of CBL family in plant kingdom indicated its diverse origin but showed a remarkable conservation in overall protein structure with appearance of complex gene structure in the course of evolution. Expression of CaCBL genes in different tissues and in response to different stress and hormone treatment were studied. Most of the CaCBL genes exhibited high expression in flowers. Expression profile of CaCBL genes in response to different abiotic stresses and hormones related to development and stresses (ABA, auxin, cytokinin, SA and JA at different time intervals suggests their diverse roles in development and plant defence in addition to abiotic stress tolerance. These data not only contribute to a better understanding of the complex regulation of chickpea CBL gene family, but also provide valuable information for further research in chickpea functional genomics.

  20. Sequencing and Characterization of Novel PII Signaling Protein Gene in Microalga Haematococcus pluvialis

    Directory of Open Access Journals (Sweden)

    Ruijuan Ma

    2017-10-01

    Full Text Available The PII signaling protein is a key protein for controlling nitrogen assimilatory reactions in most organisms, but little information is reported on PII proteins of green microalga Haematococcus pluvialis. Since H. pluvialis cells can produce a large amount of astaxanthin upon nitrogen starvation, its PII protein may represent an important factor on elevated production of Haematococcus astaxanthin. This study identified and isolated the coding gene (HpGLB1 from this microalga. The full-length of HpGLB1 was 1222 bp, including 621 bp coding sequence (CDS, 103 bp 5′ untranslated region (5′ UTR, and 498 bp 3′ untranslated region (3′ UTR. The CDS could encode a protein with 206 amino acids (HpPII. Its calculated molecular weight (Mw was 22.4 kDa and the theoretical isoelectric point was 9.53. When H. pluvialis cells were exposed to nitrogen starvation, the HpGLB1 expression was increased 2.46 times in 48 h, concomitant with the raise of astaxanthin content. This study also used phylogenetic analysis to prove that HpPII was homogeneous to the PII proteins of other green microalgae. The results formed a fundamental basis for the future study on HpPII, for its potential physiological function in Haematococcus astaxanthin biosysthesis.

  1. The Purine Bias of Coding Sequences is Determined by Physicochemical Constraints on Proteins.

    Science.gov (United States)

    Ponce de Leon, Miguel; de Miranda, Antonio Basilio; Alvarez-Valin, Fernando; Carels, Nicolas

    2014-01-01

    For this report, we analyzed protein secondary structures in relation to the statistics of three nucleotide codon positions. The purpose of this investigation was to find which properties of the ribosome, tRNA or protein level, could explain the purine bias (Rrr) as it is observed in coding DNA. We found that the Rrr pattern is the consequence of a regularity (the codon structure) resulting from physicochemical constraints on proteins and thermodynamic constraints on ribosomal machinery. The physicochemical constraints on proteins mainly come from the hydropathy and molecular weight (MW) of secondary structures as well as the energy cost of amino acid synthesis. These constraints appear through a network of statistical correlations, such as (i) the cost of amino acid synthesis, which is in favor of a higher level of guanine in the first codon position, (ii) the constructive contribution of hydropathy alternation in proteins, (iii) the spatial organization of secondary structure in proteins according to solvent accessibility, (iv) the spatial organization of secondary structure according to amino acid hydropathy, (v) the statistical correlation of MW with protein secondary structures and their overall hydropathy, (vi) the statistical correlation of thymine in the second codon position with hydropathy and the energy cost of amino acid synthesis, and (vii) the statistical correlation of adenine in the second codon position with amino acid complexity and the MW of secondary protein structures. Amino acid physicochemical properties and functional constraints on proteins constitute a code that is translated into a purine bias within the coding DNA via tRNAs. In that sense, the Rrr pattern within coding DNA is the effect of information transfer on nucleotide composition from protein to DNA by selection according to the codon positions. Thus, coding DNA structure and ribosomal machinery co-evolved to minimize the energy cost of protein coding given the functional

  2. Cloning and expression of three thaumatin-like protein genes from Polyporus umbellatus

    Directory of Open Access Journals (Sweden)

    Mengmeng Liu

    2017-05-01

    Full Text Available Genes encoding thaumatin-like protein (TLPs are frequently found in fungal genomes. However, information on TLP genes in Polyporus umbellatus is still limited. In this study, three TLP genes were cloned from P. umbellatus. The full-length coding sequence of PuTLP1, PuTLP2 and PuTLP3 were 768, 759 and 561 bp long, respectively, encoding for 256, 253 and 187 amino acids. Phylogenetic trees showed that P. umbellatus PuTLP1, PuTLP2 and PuTLP3 were clustered with sequences from Gloeophyllum trabeum, Trametes versicolor and Stereum hirsutum, respectively. The expression patterns of the three TLP genes were higher in P. umbellatus with Armillaria mellea infection than in the sclerotia without A. mellea. Furthermore, over-expression of three PuTLPs were carried out in Escherichia coli BL21 (DE3 strain, and high quality proteins were obtained using Ni-NTA resin that can be used for preparation of specific antibodies. These results suggest that PuTLP1, PuTLP2 and PuTLP3 in P. umbellatus may be involved in the defense response to A. mellea infections.

  3. Gene composer: database software for protein construct design, codon engineering, and gene synthesis.

    Science.gov (United States)

    Lorimer, Don; Raymond, Amy; Walchli, John; Mixon, Mark; Barrow, Adrienne; Wallace, Ellen; Grice, Rena; Burgin, Alex; Stewart, Lance

    2009-04-21

    To improve efficiency in high throughput protein structure determination, we have developed a database software package, Gene Composer, which facilitates the information-rich design of protein constructs and their codon engineered synthetic gene sequences. With its modular workflow design and numerous graphical user interfaces, Gene Composer enables researchers to perform all common bio-informatics steps used in modern structure guided protein engineering and synthetic gene engineering. An interactive Alignment Viewer allows the researcher to simultaneously visualize sequence conservation in the context of known protein secondary structure, ligand contacts, water contacts, crystal contacts, B-factors, solvent accessible area, residue property type and several other useful property views. The Construct Design Module enables the facile design of novel protein constructs with altered N- and C-termini, internal insertions or deletions, point mutations, and desired affinity tags. The modifications can be combined and permuted into multiple protein constructs, and then virtually cloned in silico into defined expression vectors. The Gene Design Module uses a protein-to-gene algorithm that automates the back-translation of a protein amino acid sequence into a codon engineered nucleic acid gene sequence according to a selected codon usage table with minimal codon usage threshold, defined G:C% content, and desired sequence features achieved through synonymous codon selection that is optimized for the intended expression system. The gene-to-oligo algorithm of the Gene Design Module plans out all of the required overlapping oligonucleotides and mutagenic primers needed to synthesize the desired gene constructs by PCR, and for physically cloning them into selected vectors by the most popular subcloning strategies. We present a complete description of Gene Composer functionality, and an efficient PCR-based synthetic gene assembly procedure with mis-match specific endonuclease

  4. Gene Composer: database software for protein construct design, codon engineering, and gene synthesis

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    Mixon Mark

    2009-04-01

    Full Text Available Abstract Background To improve efficiency in high throughput protein structure determination, we have developed a database software package, Gene Composer, which facilitates the information-rich design of protein constructs and their codon engineered synthetic gene sequences. With its modular workflow design and numerous graphical user interfaces, Gene Composer enables researchers to perform all common bio-informatics steps used in modern structure guided protein engineering and synthetic gene engineering. Results An interactive Alignment Viewer allows the researcher to simultaneously visualize sequence conservation in the context of known protein secondary structure, ligand contacts, water contacts, crystal contacts, B-factors, solvent accessible area, residue property type and several other useful property views. The Construct Design Module enables the facile design of novel protein constructs with altered N- and C-termini, internal insertions or deletions, point mutations, and desired affinity tags. The modifications can be combined and permuted into multiple protein constructs, and then virtually cloned in silico into defined expression vectors. The Gene Design Module uses a protein-to-gene algorithm that automates the back-translation of a protein amino acid sequence into a codon engineered nucleic acid gene sequence according to a selected codon usage table with minimal codon usage threshold, defined G:C% content, and desired sequence features achieved through synonymous codon selection that is optimized for the intended expression system. The gene-to-oligo algorithm of the Gene Design Module plans out all of the required overlapping oligonucleotides and mutagenic primers needed to synthesize the desired gene constructs by PCR, and for physically cloning them into selected vectors by the most popular subcloning strategies. Conclusion We present a complete description of Gene Composer functionality, and an efficient PCR-based synthetic gene

  5. cDNA, genomic sequence cloning and overexpression of ribosomal protein S25 gene (RPS25) from the Giant Panda.

    Science.gov (United States)

    Hao, Yan-Zhe; Hou, Wan-Ru; Hou, Yi-Ling; Du, Yu-Jie; Zhang, Tian; Peng, Zheng-Song

    2009-11-01

    RPS25 is a component of the 40S small ribosomal subunit encoded by RPS25 gene, which is specific to eukaryotes. Studies in reference to RPS25 gene from animals were handful. The Giant Panda (Ailuropoda melanoleuca), known as a "living fossil", are increasingly concerned by the world community. Studies on RPS25 of the Giant Panda could provide scientific data for inquiring into the hereditary traits of the gene and formulating the protective strategy for the Giant Panda. The cDNA of the RPS25 cloned from Giant Panda is 436 bp in size, containing an open reading frame of 378 bp encoding 125 amino acids. The length of the genomic sequence is 1,992 bp, which was found to possess four exons and three introns. Alignment analysis indicated that the nucleotide sequence of the coding sequence shows a high homology to those of Homo sapiens, Bos taurus, Mus musculus and Rattus norvegicus as determined by Blast analysis, 92.6, 94.4, 89.2 and 91.5%, respectively. Primary structure analysis revealed that the molecular weight of the putative RPS25 protein is 13.7421 kDa with a theoretical pI 10.12. Topology prediction showed there is one N-glycosylation site, one cAMP and cGMP-dependent protein kinase phosphorylation site, two Protein kinase C phosphorylation sites and one Tyrosine kinase phosphorylation site in the RPS25 protein of the Giant Panda. The RPS25 gene was overexpressed in E. coli BL21 and Western Blotting of the RPS25 protein was also done. The results indicated that the RPS25 gene can be really expressed in E. coli and the RPS25 protein fusioned with the N-terminally his-tagged form gave rise to the accumulation of an expected 17.4 kDa polypeptide. The cDNA and the genomic sequence of RPS25 were cloned successfully for the first time from the Giant Panda using RT-PCR technology and Touchdown-PCR, respectively, which were both sequenced and analyzed preliminarily; then the cDNA of the RPS25 gene was overexpressed in E. coli BL21 and immunoblotted, which is the first

  6. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

    Science.gov (United States)

    Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

    2015-03-01

    Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

  7. Structure Prediction of Outer Membrane Protease Protein of Salmonella typhimurium Using Computational Techniques

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    Rozina Tabassum

    2016-03-01

    Full Text Available Salmonella typhimurium, a facultative gram-negative intracellular pathogen belonging to family Enterobacteriaceae, is the most frequent cause of human gastroenteritis worldwide. PgtE gene product, outer membrane protease emerges important in the intracellular phases of salmonellosis. The pgtE gene product of S. typhimurium was predicted to be capable of proteolyzing T7 RNA polymerase and localize in the outer membrane of these gram negative bacteria. PgtE product of S. enterica and OmpT of E. coli, having high sequence similarity have been revealed to degrade macrophages, causing salmonellosis and other diseases. The three-dimensional structure of the protein was not available through Protein Data Bank (PDB creating lack of structural information about E protein. In our study, by performing Comparative model building, the three dimensional structure of outer membrane protease protein was generated using the backbone of the crystal structure of Pla of Yersinia pestis, retrieved from PDB, with MODELLER (9v8. Quality of the model was assessed by validation tool PROCHECK, web servers like ERRAT and ProSA are used to certify the reliability of the predicted model. This information might offer clues for better understanding of E protein and consequently for developmet of better therapeutic treatment against pathogenic role of this protein in salmonellosis and other diseases.

  8. Biases in Drosophila melanogaster protein trap screens

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    Müller Ilka

    2009-05-01

    Full Text Available Abstract Background The ability to localise or follow endogenous proteins in real time in vivo is of tremendous utility for cell biology or systems biology studies. Protein trap screens utilise the random genomic insertion of a transposon-borne artificial reporter exon (e.g. encoding the green fluorescent protein, GFP into an intron of an endogenous gene to generate a fluorescent fusion protein. Despite recent efforts aimed at achieving comprehensive coverage of the genes encoded in the Drosophila genome, the repertoire of genes that yield protein traps is still small. Results We analysed the collection of available protein trap lines in Drosophila melanogaster and identified potential biases that are likely to restrict genome coverage in protein trap screens. The protein trap screens investigated here primarily used P-element vectors and thus exhibit some of the same positional biases associated with this transposon that are evident from the comprehensive Drosophila Gene Disruption Project. We further found that protein trap target genes usually exhibit broad and persistent expression during embryonic development, which is likely to facilitate better detection. In addition, we investigated the likely influence of the GFP exon on host protein structure and found that protein trap insertions have a significant bias for exon-exon boundaries that encode disordered protein regions. 38.8% of GFP insertions land in disordered protein regions compared with only 23.4% in the case of non-trapping P-element insertions landing in coding sequence introns (p -4. Interestingly, even in cases where protein domains are predicted, protein trap insertions frequently occur in regions encoding surface exposed areas that are likely to be functionally neutral. Considering the various biases observed, we predict that less than one third of intron-containing genes are likely to be amenable to trapping by the existing methods. Conclusion Our analyses suggest that the

  9. The fusion protein signal-peptide-coding region of canine distemper virus: a useful tool for phylogenetic reconstruction and lineage identification.

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    Nicolás Sarute

    Full Text Available Canine distemper virus (CDV; Paramyxoviridae, Morbillivirus is the etiologic agent of a multisystemic infectious disease affecting all terrestrial carnivore families with high incidence and mortality in domestic dogs. Sequence analysis of the hemagglutinin (H gene has been widely employed to characterize field strains, permitting the identification of nine CDV lineages worldwide. Recently, it has been established that the sequences of the fusion protein signal-peptide (Fsp coding region are extremely variable, suggesting that analysis of its sequence might be useful for strain characterization studies. However, the divergence of Fsp sequences among worldwide strains and its phylogenetic resolution has not yet been evaluated. We constructed datasets containing the Fsp-coding region and H gene sequences of the same strains belonging to eight CDV lineages. Both datasets were used to evaluate their phylogenetic resolution. The phylogenetic analysis revealed that both datasets clustered the same strains into eight different branches, corresponding to CDV lineages. The inter-lineage amino acid divergence was fourfold greater for the Fsp peptide than for the H protein. The likelihood mapping revealed that both datasets display strong phylogenetic signals in the region of well-resolved topologies. These features indicate that Fsp-coding region sequence analysis is suitable for evolutionary studies as it allows for straightforward identification of CDV lineages.

  10. The fusion protein signal-peptide-coding region of canine distemper virus: a useful tool for phylogenetic reconstruction and lineage identification.

    Science.gov (United States)

    Sarute, Nicolás; Calderón, Marina Gallo; Pérez, Ruben; La Torre, José; Hernández, Martín; Francia, Lourdes; Panzera, Yanina

    2013-01-01

    Canine distemper virus (CDV; Paramyxoviridae, Morbillivirus) is the etiologic agent of a multisystemic infectious disease affecting all terrestrial carnivore families with high incidence and mortality in domestic dogs. Sequence analysis of the hemagglutinin (H) gene has been widely employed to characterize field strains, permitting the identification of nine CDV lineages worldwide. Recently, it has been established that the sequences of the fusion protein signal-peptide (Fsp) coding region are extremely variable, suggesting that analysis of its sequence might be useful for strain characterization studies. However, the divergence of Fsp sequences among worldwide strains and its phylogenetic resolution has not yet been evaluated. We constructed datasets containing the Fsp-coding region and H gene sequences of the same strains belonging to eight CDV lineages. Both datasets were used to evaluate their phylogenetic resolution. The phylogenetic analysis revealed that both datasets clustered the same strains into eight different branches, corresponding to CDV lineages. The inter-lineage amino acid divergence was fourfold greater for the Fsp peptide than for the H protein. The likelihood mapping revealed that both datasets display strong phylogenetic signals in the region of well-resolved topologies. These features indicate that Fsp-coding region sequence analysis is suitable for evolutionary studies as it allows for straightforward identification of CDV lineages.

  11. Predictive Bias and Sensitivity in NRC Fuel Performance Codes

    Energy Technology Data Exchange (ETDEWEB)

    Geelhood, Kenneth J.; Luscher, Walter G.; Senor, David J.; Cunningham, Mitchel E.; Lanning, Donald D.; Adkins, Harold E.

    2009-10-01

    The latest versions of the fuel performance codes, FRAPCON-3 and FRAPTRAN were examined to determine if the codes are intrinsically conservative. Each individual model and type of code prediction was examined and compared to the data that was used to develop the model. In addition, a brief literature search was performed to determine if more recent data have become available since the original model development for model comparison.

  12. Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

    2013-01-01

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some

  13. The structure of the human interferon alpha/beta receptor gene.

    Science.gov (United States)

    Lutfalla, G; Gardiner, K; Proudhon, D; Vielh, E; Uzé, G

    1992-02-05

    Using the cDNA coding for the human interferon alpha/beta receptor (IFNAR), the IFNAR gene has been physically mapped relative to the other loci of the chromosome 21q22.1 region. 32,906 base pairs covering the IFNAR gene have been cloned and sequenced. Primer extension and solution hybridization-ribonuclease protection have been used to determine that the transcription of the gene is initiated in a broad region of 20 base pairs. Some aspects of the polymorphism of the gene, including noncoding sequences, have been analyzed; some are allelic differences in the coding sequence that induce amino acid variations in the resulting protein. The exon structure of the IFNAR gene and of that of the available genes for the receptors of the cytokine/growth hormone/prolactin/interferon receptor family have been compared with the predictions for the secondary structure of those receptors. From this analysis, we postulate a common origin and propose an hypothesis for the divergence from the immunoglobulin superfamily.

  14. Multispectral code excited linear prediction coding and its application in magnetic resonance images.

    Science.gov (United States)

    Hu, J H; Wang, Y; Cahill, P T

    1997-01-01

    This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

  15. Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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    Chen Xin

    2012-10-01

    Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

  16. IGF-1 modulates gene expression of proteins involved in inflammation, cytoskeleton, and liver architecture.

    Science.gov (United States)

    Lara-Diaz, V J; Castilla-Cortazar, I; Martín-Estal, I; García-Magariño, M; Aguirre, G A; Puche, J E; de la Garza, R G; Morales, L A; Muñoz, U

    2017-05-01

    Even though the liver synthesizes most of circulating IGF-1, it lacks its receptor under physiological conditions. However, according to previous studies, a damaged liver expresses the receptor. For this reason, herein, we examine hepatic histology and expression of genes encoding proteins of the cytoskeleton, extracellular matrix, and cell-cell molecules and inflammation-related proteins. A partial IGF-1 deficiency murine model was used to investigate IGF-1's effects on liver by comparing wild-type controls, heterozygous igf1 +/- , and heterozygous mice treated with IGF-1 for 10 days. Histology, microarray for mRNA gene expression, RT-qPCR, and lipid peroxidation were assessed. Microarray analyses revealed significant underexpression of igf1 in heterozygous mice compared to control mice, restoring normal liver expression after treatment, which then normalized its circulating levels. IGF-1 receptor mRNA was overexpressed in Hz mice liver, while treated mice displayed a similar expression to that of the controls. Heterozygous mice showed overexpression of several genes encoding proteins related to inflammatory and acute-phase proteins and underexpression or overexpression of genes which coded for extracellular matrix, cytoskeleton, and cell junction components. Histology revealed an altered hepatic architecture. In addition, liver oxidative damage was found increased in the heterozygous group. The mere IGF-1 partial deficiency is associated with relevant alterations of the hepatic architecture and expression of genes involved in cytoskeleton, hepatocyte polarity, cell junctions, and extracellular matrix proteins. Moreover, it induces hepatic expression of the IGF-1 receptor and elevated acute-phase and inflammation mediators, which all resulted in liver oxidative damage.

  17. Saccharomyces cerevisiae ribosomal protein L37 is encoded by duplicate genes that are differentially expressed.

    Science.gov (United States)

    Tornow, J; Santangelo, G M

    1994-06-01

    A duplicate copy of the RPL37A gene (encoding ribosomal protein L37) was cloned and sequenced. The coding region of RPL37B is very similar to that of RPL37A, with only one conservative amino-acid difference. However, the intron and flanking sequences of the two genes are extremely dissimilar. Disruption experiments indicate that the two loci are not functionally equivalent: disruption of RPL37B was insignificant, but disruption of RPL37A severely impaired the growth rate of the cell. When both RPL37 loci are disrupted, the cell is unable to grow at all, indicating that rpL37 is an essential protein. The functional disparity between the two RPL37 loci could be explained by differential gene expression. The results of two experiments support this idea: gene fusion of RPL37A to a reporter gene resulted in six-fold higher mRNA levels than was generated by the same reporter gene fused to RPL37B, and a modest increase in gene dosage of RPL37B overcame the lack of a functional RPL37A gene.

  18. Combining random gene fission and rational gene fusion to discover near-infrared fluorescent protein fragments that report on protein-protein interactions.

    Science.gov (United States)

    Pandey, Naresh; Nobles, Christopher L; Zechiedrich, Lynn; Maresso, Anthony W; Silberg, Jonathan J

    2015-05-15

    Gene fission can convert monomeric proteins into two-piece catalysts, reporters, and transcription factors for systems and synthetic biology. However, some proteins can be challenging to fragment without disrupting function, such as near-infrared fluorescent protein (IFP). We describe a directed evolution strategy that can overcome this challenge by randomly fragmenting proteins and concomitantly fusing the protein fragments to pairs of proteins or peptides that associate. We used this method to create libraries that express fragmented IFP as fusions to a pair of associating peptides (IAAL-E3 and IAAL-K3) and proteins (CheA and CheY) and screened for fragmented IFP with detectable near-infrared fluorescence. Thirteen novel fragmented IFPs were identified, all of which arose from backbone fission proximal to the interdomain linker. Either the IAAL-E3 and IAAL-K3 peptides or CheA and CheY proteins could assist with IFP fragment complementation, although the IAAL-E3 and IAAL-K3 peptides consistently yielded higher fluorescence. These results demonstrate how random gene fission can be coupled to rational gene fusion to create libraries enriched in fragmented proteins with AND gate logic that is dependent upon a protein-protein interaction, and they suggest that these near-infrared fluorescent protein fragments will be suitable as reporters for pairs of promoters and protein-protein interactions within whole animals.

  19. PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

    Science.gov (United States)

    Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

    2011-06-01

    networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor—DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys2His2 zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary

  20. FunGene: the functional gene pipeline and repository.

    Science.gov (United States)

    Fish, Jordan A; Chai, Benli; Wang, Qiong; Sun, Yanni; Brown, C Titus; Tiedje, James M; Cole, James R

    2013-01-01

    Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

  1. FunGene: the Functional Gene Pipeline and Repository

    Directory of Open Access Journals (Sweden)

    Jordan A. Fish

    2013-10-01

    Full Text Available Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer.While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/ offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

  2. Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.

    Directory of Open Access Journals (Sweden)

    Colin A Smith

    Full Text Available Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface, interactions between and within parts of the structure (e.g. domains can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.

  3. Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

    International Nuclear Information System (INIS)

    Yu, Jack X; Sieuwerts, Anieta M; Zhang, Yi; Martens, John WM; Smid, Marcel; Klijn, Jan GM; Wang, Yixin; Foekens, John A

    2007-01-01

    Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different

  4. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

    Science.gov (United States)

    Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  5. Characterization of the ovine ribosomal protein SA gene and its pseudogenes

    Directory of Open Access Journals (Sweden)

    Van Zeveren Alex

    2010-03-01

    Full Text Available Abstract Background The ribosomal protein SA (RPSA, previously named 37-kDa laminin receptor precursor/67-kDa laminin receptor (LRP/LR is a multifunctional protein that plays a role in a number of pathological processes, such as cancer and prion diseases. In all investigated species, RPSA is a member of a multicopy gene family consisting of one full length functional gene and several pseudogenes. Therefore, for studies on RPSA related pathways/pathologies, it is important to characterize the whole family and to address the possible function of the other RPSA family members. The present work aims at deciphering the RPSA family in sheep. Results In addition to the full length functional ovine RPSA gene, 11 other members of this multicopy gene family, all processed pseudogenes, were identified. Comparison between the RPSA transcript and these pseudogenes shows a large variety in sequence identities ranging from 99% to 74%. Only one of the 11 pseudogenes, i.e. RPSAP7, shares the same open reading frame (ORF of 295 amino acids with the RPSA gene, differing in only one amino acid. All members of the RPSA family were annotated by comparative mapping and fluorescence in situ hybridization (FISH localization. Transcription was investigated in the cerebrum, cerebellum, spleen, muscle, lymph node, duodenum and blood, and transcripts were detected for 6 of the 11 pseudogenes in some of these tissues. Conclusions In the present work we have characterized the ovine RPSA family. Our results have revealed the existence of 11 ovine RPSA pseudogenes and provide new data on their structure and sequence. Such information will facilitate molecular studies of the functional RPSA gene taking into account the existence of these pseudogenes in the design of experiments. It remains to be investigated if the transcribed members are functional as regulatory non-coding RNA or as functional proteins.

  6. A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records

    DEFF Research Database (Denmark)

    Jiang, Li; Edwards, Stefan M.; Thomsen, Bo

    2014-01-01

    from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining......Background: Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic...

  7. Recent studies on the ATM gene

    International Nuclear Information System (INIS)

    Lavin, M.F.; Khanna, K.K.; Waters, D.

    1996-01-01

    Full text: Radiosensitivity is a universal characteristic of ataxia-telangiectasia (A-T), observed after exposure of patients and of cells in culture to radiation. This sensitivity is manifested as higher levels of radiation-induced chromosomal aberrations and reduced survival compared to controls. The gene for A-T was mapped to chromosome 11q 22-23 seven years ago and more recently we have been involved in the cloning of a single gene, ATM (ataxia-telangiectasia mutated), mutated in this syndrome. ATM is a large gene, approximately 150 kb in size, composed of 66 exons and codes for a major mRNA of 13 kb with a predicted open reading frame of 9.135 kb. It is not yet known what activity the ATM gene product possesses, but the ralatedness of this gene sequence to the phosphatidylinositol 3-kinase gene family supports a role for ATM in intracellular signalling. Considerable information is already available on defective signalling through the p53 damage-inducible pathway in A-T. This includes failure to arrest at either the G1/S or G2/M checkpoints as well as radioresistant DNA synthesis. A reduced and/or delayed response in the induction of p53 after exposure of A-T cells to ionizing radiation can account for the defective G1/S checkpoint. More recently we have demonstrated that the ATM gene product is involved in the control of multiple cell cycle checkpoints. Antibodies prepared against ATM peptides demonstrate the presence of a protein 350 kDa in size, which is the predicted size for this protein based on open reading frame of 9 kb. This protein is present both in the nucleus and in the cytoplasm where it is present in vesicular structures. As expected from mutation data the ATM protein is absent in cells from some patients with A-T. The cloning of the ATM gene will allow for screening of radiosensitive patients for mutations in this gene and will provide a means of identifying interacting proteins and thus an understanding of how it functions

  8. Integration of relational and hierarchical network information for protein function prediction

    Directory of Open Access Journals (Sweden)

    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  9. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

    Directory of Open Access Journals (Sweden)

    Chi-Hua Tung

    2016-01-01

    Full Text Available Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

  10. Complete genome sequence analysis of the fish pathogen Flavobacterium columnare provides insights into antibiotic resistance and pathogenicity related genes.

    Science.gov (United States)

    Zhang, Yulei; Zhao, Lijuan; Chen, Wenjie; Huang, Yunmao; Yang, Ling; Sarathbabu, V; Wu, Zaohe; Li, Jun; Nie, Pin; Lin, Li

    2017-10-01

    We analyzed here the complete genome sequences of a highly virulent Flavobacterium columnare Pf1 strain isolated in our laboratory. The complete genome consists of a 3,171,081 bp circular DNA with 2784 predicted protein-coding genes. Among these, 286 genes were predicted as antibiotic resistance genes, including 32 RND-type efflux pump related genes which were associated with the export of aminoglycosides, indicating inducible aminoglycosides resistances in F. columnare. On the other hand, 328 genes were predicted as pathogenicity related genes which could be classified as virulence factors, gliding motility proteins, adhesins, and many putative secreted proteases. These genes were probably involved in the colonization, invasion and destruction of fish tissues during the infection of F. columnare. Apparently, our obtained complete genome sequences provide the basis for the explanation of the interactions between the F. columnare and the infected fish. The predicted antibiotic resistance and pathogenicity related genes will shed a new light on the development of more efficient preventional strategies against the infection of F. columnare, which is a major worldwide fish pathogen. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  12. Cloning and expression of a small heat shock protein gene ...

    African Journals Online (AJOL)

    The cDNA sequence of this gene is 920 bp in size (GenBank: HM132040) and contains an open reading frame (ORF) of 636 bp, which was predicted to encode a protein with 211 amino acid residues. The phylogenetic tree showed that CaHSP24 was quite similar to mitochondrial sHSPs from other plants but was distantly ...

  13. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat

    2017-09-27

    Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

  14. Expression profile of genes coding for carotenoid biosynthetic ...

    Indian Academy of Sciences (India)

    Expression profile of genes coding for carotenoid biosynthetic pathway during ripening and their association with accumulation of lycopene in tomato fruits. Shuchi Smita, Ravi Rajwanshi, Sangram Keshari Lenka, Amit Katiyar, Viswanathan Chinnusamy and. Kailash Chander Bansal. J. Genet. 92, 363–368. Table 1.

  15. Automatically identifying gene/protein terms in MEDLINE abstracts.

    Science.gov (United States)

    Yu, Hong; Hatzivassiloglou, Vasileios; Rzhetsky, Andrey; Wilbur, W John

    2002-01-01

    Natural language processing (NLP) techniques are used to extract information automatically from computer-readable literature. In biology, the identification of terms corresponding to biological substances (e.g., genes and proteins) is a necessary step that precedes the application of other NLP systems that extract biological information (e.g., protein-protein interactions, gene regulation events, and biochemical pathways). We have developed GPmarkup (for "gene/protein-full name mark up"), a software system that automatically identifies gene/protein terms (i.e., symbols or full names) in MEDLINE abstracts. As a part of marking up process, we also generated automatically a knowledge source of paired gene/protein symbols and full names (e.g., LARD for lymphocyte associated receptor of death) from MEDLINE. We found that many of the pairs in our knowledge source do not appear in the current GenBank database. Therefore our methods may also be used for automatic lexicon generation. GPmarkup has 73% recall and 93% precision in identifying and marking up gene/protein terms in MEDLINE abstracts. A random sample of gene/protein symbols and full names and a sample set of marked up abstracts can be viewed at http://www.cpmc.columbia.edu/homepages/yuh9001/GPmarkup/. Contact. hy52@columbia.edu. Voice: 212-939-7028; fax: 212-666-0140.

  16. Molecular cloning and chromosome mapping of the human gene encoding protein phosphotyrosyl phosphatase 1B

    International Nuclear Information System (INIS)

    Brown-Shimer, S.; Johnson, K.A.; Bruskin, A.; Green, N.R.; Hill, D.E.; Lawrence, J.B.; Johnson, C.

    1990-01-01

    The inactivation of growth suppressor genes appears to play a major role in the malignant process. To assess whether protein phosphotyrosyl phosphatases function as growth suppressors, the authors have isolated a cDNA clone encoding human protein phosphotyrosyl phosphatase 1B for structural and functional characterization. The translation product deduced from the 1,305-nucleotide open reading frame predicts a protein containing 435 amino acids and having a molecular mass of 49,966 Da. The amino-terminal 321 amino acids deduced from the cDNA sequence are identical to the empirically determined sequence of protein phosphotyrosyl phosphatase 1B. A genomic clone has been isolated and used in an in situ hybridization to banded metaphase chromosomes to determine that the gene encoding protein phosphotyrosyl phosphatase 1B maps as a single-copy gene to the long arm of chromosome 20 in the region q13.1-q13.2

  17. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

    Science.gov (United States)

    Heilbron, Micha; Chait, Maria

    2017-08-04

    Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  19. Real coded genetic algorithm for fuzzy time series prediction

    Science.gov (United States)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  20. Comprehensive analysis of coding-lncRNA gene co-expression network uncovers conserved functional lncRNAs in zebrafish.

    Science.gov (United States)

    Chen, Wen; Zhang, Xuan; Li, Jing; Huang, Shulan; Xiang, Shuanglin; Hu, Xiang; Liu, Changning

    2018-05-09

    Zebrafish is a full-developed model system for studying development processes and human disease. Recent studies of deep sequencing had discovered a large number of long non-coding RNAs (lncRNAs) in zebrafish. However, only few of them had been functionally characterized. Therefore, how to take advantage of the mature zebrafish system to deeply investigate the lncRNAs' function and conservation is really intriguing. We systematically collected and analyzed a series of zebrafish RNA-seq data, then combined them with resources from known database and literatures. As a result, we obtained by far the most complete dataset of zebrafish lncRNAs, containing 13,604 lncRNA genes (21,128 transcripts) in total. Based on that, a co-expression network upon zebrafish coding and lncRNA genes was constructed and analyzed, and used to predict the Gene Ontology (GO) and the KEGG annotation of lncRNA. Meanwhile, we made a conservation analysis on zebrafish lncRNA, identifying 1828 conserved zebrafish lncRNA genes (1890 transcripts) that have their putative mammalian orthologs. We also found that zebrafish lncRNAs play important roles in regulation of the development and function of nervous system; these conserved lncRNAs present a significant sequential and functional conservation, with their mammalian counterparts. By integrative data analysis and construction of coding-lncRNA gene co-expression network, we gained the most comprehensive dataset of zebrafish lncRNAs up to present, as well as their systematic annotations and comprehensive analyses on function and conservation. Our study provides a reliable zebrafish-based platform to deeply explore lncRNA function and mechanism, as well as the lncRNA commonality between zebrafish and human.

  1. The VirA protein of Agrobacterium tumefaciens is autophosphorylated and is essential for vir gene regulation

    International Nuclear Information System (INIS)

    Jin, S.; Roitsch, T.; Ankenbauer, R.G.; Gordon, M.P.; Nester, E.W.

    1990-01-01

    The virA and virG gene products are required for the regulation of the vir regulon on the tumor-inducing (Ti) plasmid of Agrobacterium tumefaciens. VirA is a membrane-associated protein which is homologous to the sensor molecules of other two-component regulatory systems. The authors overproduced truncated VirA proteins in Escherichia coli be deleting different lengths of the 5'-coding region of the virA gene and placing these genes under lacZ control. These proteins were purified from polyacrylamide gels and renatured. The renatured proteins became radiolabeled when they were incubated with [γ- 32 P]ATP but not with [γ- 32 P]GTP or [α- 32 P]ATP, which suggests an ATP γ-phosphate-specific autophosphorylation. The smallest VirA protein, which retained only the C-terminal half of the protein, gave the strongest autophosphorylation signal, which demonstrates that the C-terminal domain has the autophosphorylation site. The phosphorylated amino acid was identified as phosphohistidine, and a highly conserved histidine was found in all of the VirA homologs. When this histidine was changed to glutamine, which cannot be phosphorylated, the resulting VirA protein lost both its ability to autophosphorylate and its biological function as a vir gene regulator. Results of this study indicate that VirA autophosphorylation is required for the induction of the vir regulon and subsequent tumor induction on plants by A. tumefaciens

  2. Estradiol-Induced Transcriptional Regulation of Long Non-Coding RNA, HOTAIR.

    Science.gov (United States)

    Bhan, Arunoday; Mandal, Subhrangsu S

    2016-01-01

    HOTAIR (HOX antisense intergenic RNA) is a 2.2 kb long non-coding RNA (lncRNA), transcribed from the antisense strand of homeobox C (HOXC) gene locus in chromosome 12. HOTAIR acts as a scaffolding lncRNA. It interacts and guides various chromatin-modifying complexes such as PRC2 (polycomb-repressive complex 2) and LSD1 (lysine-specific demethylase 1) to the target gene promoters leading to their gene silencing. Various studies have demonstrated that HOTAIR overexpression is associated with breast cancer. Recent studies from our laboratory demonstrate that HOTAIR is required for viability of breast cancer cells and is transcriptionally regulated by estradiol (E2) in vitro and in vivo. This chapter describes protocols for analysis of the HOTAIR promoter, cloning, transfection and dual luciferase assays, knockdown of protein synthesis by antisense oligonucleotides, and chromatin immunoprecipitation (ChIP) assay. These protocols are useful for studying the estrogen-mediated transcriptional regulation of lncRNA HOTAIR, as well as other protein coding genes and non-coding RNAs.

  3. A predictive coding account of bistable perception - a model-based fMRI study.

    Science.gov (United States)

    Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

    2017-05-01

    In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

  4. A predictive coding account of bistable perception - a model-based fMRI study.

    Directory of Open Access Journals (Sweden)

    Veith Weilnhammer

    2017-05-01

    Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together

  5. An ensemble method for predicting subnuclear localizations from primary protein structures.

    Directory of Open Access Journals (Sweden)

    Guo Sheng Han

    Full Text Available BACKGROUND: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL FINDINGS: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. CONCLUSIONS: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method

  6. Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation.

    Science.gov (United States)

    Pujar, Shashikant; O'Leary, Nuala A; Farrell, Catherine M; Loveland, Jane E; Mudge, Jonathan M; Wallin, Craig; Girón, Carlos G; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; Martin, Fergal J; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Suner, Marie-Marthe; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bruford, Elspeth A; Bult, Carol J; Frankish, Adam; Murphy, Terence; Pruitt, Kim D

    2018-01-04

    The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. Published by Oxford University Press on behalf of Nucleic Acids Research 2017.

  7. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  8. Topology of transmembrane channel-like gene 1 protein.

    Science.gov (United States)

    Labay, Valentina; Weichert, Rachel M; Makishima, Tomoko; Griffith, Andrew J

    2010-10-05

    Mutations of transmembrane channel-like gene 1 (TMC1) cause hearing loss in humans and mice. TMC1 is the founding member of a family of genes encoding proteins of unknown function that are predicted to contain multiple transmembrane domains. The goal of our study was to define the topology of mouse TMC1 expressed heterologously in tissue culture cells. TMC1 was retained in the endoplasmic reticulum (ER) membrane of five tissue culture cell lines that we tested. We used anti-TMC1 and anti-HA antibodies to probe the topologic orientation of three native epitopes and seven HA epitope tags along full-length TMC1 after selective or complete permeabilization of transfected cells with digitonin or Triton X-100, respectively. TMC1 was present within the ER as an integral membrane protein containing six transmembrane domains and cytosolic N- and C-termini. There is a large cytoplasmic loop, between the fourth and fifth transmembrane domains, with two highly conserved hydrophobic regions that might associate with or penetrate, but do not span, the plasma membrane. Our study is the first to demonstrate that TMC1 is a transmembrane protein. The topologic organization revealed by this study shares some features with that of the shaker-TRP superfamily of ion channels.

  9. FOLATE CYCLE GENE POLYMORPHISM AND ENDOGENOUS PEPTIDES IN CHILDREN WITH COW’S MILK PROTEIN ALLERGY

    Directory of Open Access Journals (Sweden)

    T. A. Shumatova

    2016-01-01

    Full Text Available Folate cycle gene polymorphisms and the levels of endogenous antimicrobial peptides and proteins in the blood and coprofiltrates were studied in 45 children aged 3 to 12 months with cow’s milk protein allergy. The polymorphic variants of the MTHFR, MTRR, and MTR genes were shown to be considered as a risk factor for the development of allergy. There was a significant increase in the levels of zonulin, β-defensin 2, transthyretin, and eosinophil cationic protein in the coprofiltrates and in those of eotaxin, fatty acidbinding proteins, and membrane permeability-increasing protein in the serum (p<0.05. The finding can improve the diagnosis of the disease for a predictive purpose for the evaluation of the efficiency of performed therapy.

  10. Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

    Science.gov (United States)

    Mørk, Søren; Holmes, Ian

    2012-03-01

    Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

  11. On the role of the second coding exon of the HIV-1 Tat protein in virus replication and MHC class I downregulation

    NARCIS (Netherlands)

    Verhoef, K.; Bauer, M.; Meyerhans, A.; Berkhout, B.

    1998-01-01

    Tat is an essential protein of human immunodeficiency virus type 1 (HIV-1) and activates transcription from the viral long terminal repeat (LTR) promoter. The tat gene is composed of two coding exons of which the first, corresponding to the N-terminal 72 amino acid residues, has been reported to be

  12. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  13. Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods

    Science.gov (United States)

    Arora, Sanjeevani; Huwe, Peter J.; Sikder, Rahmat; Shah, Manali; Browne, Amanda J.; Lesh, Randy; Nicolas, Emmanuelle; Deshpande, Sanat; Hall, Michael J.; Dunbrack, Roland L.; Golemis, Erica A.

    2017-01-01

    ABSTRACT The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS. PMID:28494185

  14. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang

    2010-11-08

    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  15. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang; Yu, Jun

    2010-01-01

    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  16. On the total number of genes and their length distribution in complete microbial genomes

    DEFF Research Database (Denmark)

    Skovgaard, M; Jensen, L J; Brunak, S

    2001-01-01

    In sequenced microbial genomes, some of the annotated genes are actually not protein-coding genes, but rather open reading frames that occur by chance. Therefore, the number of annotated genes is higher than the actual number of genes for most of these microbes. Comparison of the length distribut......In sequenced microbial genomes, some of the annotated genes are actually not protein-coding genes, but rather open reading frames that occur by chance. Therefore, the number of annotated genes is higher than the actual number of genes for most of these microbes. Comparison of the length...... distribution of the annotated genes with the length distribution of those matching a known protein reveals that too many short genes are annotated in many genomes. Here we estimate the true number of protein-coding genes for sequenced genomes. Although it is often claimed that Escherichia coli has about 4300...... genes, we show that it probably has only approximately 3800 genes, and that a similar discrepancy exists for almost all published genomes....

  17. Gene prediction in metagenomic fragments: A large scale machine learning approach

    Directory of Open Access Journals (Sweden)

    Morgenstern Burkhard

    2008-04-01

    Full Text Available Abstract Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene

  18. Modification in the FUDA computer code to predict fuel performance at high burnup

    Energy Technology Data Exchange (ETDEWEB)

    Das, M; Arunakumar, B V; Prasad, P N [Nuclear Power Corp., Mumbai (India)

    1997-08-01

    The computer code FUDA (FUel Design Analysis) participated in the blind exercises organized by the IAEA CRP (Co-ordinated Research Programme) on FUMEX (Fuel Modelling at Extended Burnup). While the code prediction compared well with the experiments at Halden under various parametric and operating conditions, the fission gas release and fission gas pressure were found to be slightly over-predicted, particularly at high burnups. In view of the results of 6 FUMEX cases, the main models and submodels of the code were reviewed and necessary improvements were made. The new version of the code FUDA MOD 2 is now able to predict fuel performance parameter for burn-ups up to 50000 MWD/TeU. The validation field of the code has been extended to prediction of thorium oxide fuel performance. An analysis of local deformations at pellet interfaces and near the end caps is carried out considering the hourglassing of the pellet by finite element technique. (author). 15 refs, 1 fig.

  19. Modification in the FUDA computer code to predict fuel performance at high burnup

    International Nuclear Information System (INIS)

    Das, M.; Arunakumar, B.V.; Prasad, P.N.

    1997-01-01

    The computer code FUDA (FUel Design Analysis) participated in the blind exercises organized by the IAEA CRP (Co-ordinated Research Programme) on FUMEX (Fuel Modelling at Extended Burnup). While the code prediction compared well with the experiments at Halden under various parametric and operating conditions, the fission gas release and fission gas pressure were found to be slightly over-predicted, particularly at high burnups. In view of the results of 6 FUMEX cases, the main models and submodels of the code were reviewed and necessary improvements were made. The new version of the code FUDA MOD 2 is now able to predict fuel performance parameter for burn-ups up to 50000 MWD/TeU. The validation field of the code has been extended to prediction of thorium oxide fuel performance. An analysis of local deformations at pellet interfaces and near the end caps is carried out considering the hourglassing of the pellet by finite element technique. (author). 15 refs, 1 fig

  20. The equine herpesvirus-1 IR3 gene that lies antisense to the sole immediate-early (IE) gene is trans-activated by the IE protein, and is poorly expressed to a protein

    International Nuclear Information System (INIS)

    Ahn, Byung Chul; Breitenbach, Jonathan E.; Kim, Seong K.; O'Callaghan, Dennis J.

    2007-01-01

    The unique IR3 gene of equine herpesvirus 1 (EHV-1) is expressed as a late 1.0-kb transcript. Previous studies confirmed the IR3 transcription initiation site and tentatively identified other cis-acting elements specific to IR3 such as a TATA box, a 443 base pair 5'untranslated region (UTR), a 285 base pair open reading frame (ORF), and a poly adenylation (A) signal [Holden, V.R., Harty, R.N., Yalamanchili, R.R., O'Callaghan, D.J., 1992. The IR3 gene of equine herpesvirus type 1: a unique gene regulated by sequences within the intron of the immediate-early gene. DNA Seq. 3, 143-152]. Transient transfection assays revealed that the IR3 promoter is strongly trans-activated by the IE protein (IEP) and that coexpression of the IEP with the early EICP0 and IR4 regulatory proteins results in maximal trans-activation of the IR3 promoter. Gel shift assays revealed that the IEP directly binds to the IR3 promoter region. Western blot analysis showed that the IR3 protein produced in E. coli was detected by antibodies to IR3 synthetic peptides; however, the IR3 protein was not detected in EHV-1 infected cell extracts by these same anti-IR3 antibodies, even though the IR3 transcript was detected by northern blot. These findings suggest that the IR3 may not be expressed to a protein. Expression of an IR3/GFP fusion gene was not observed, but expression of a GFP/IR3 fusion gene was detected by fluorescent microscopy. In further attempts to detect the IR3/GFP fusion protein using anti-GFP antibody, western blot analysis showed that the IR3/GFP fusion protein was not detected in vivo. Interestingly, a truncated form of the GFP/IR3 protein was synthesized from the GFP/IR3 fusion gene. However, GFP/IR3 and IR3/GFP fusion proteins of the predicted sizes were synthesized by in vitro coupled transcription and translation of the fusion genes, suggesting poor expression of the IR3 protein in vivo. The possible role of the IR3 transcript in EHV-1 infection is discussed

  1. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  2. New Markov Model Approaches to Deciphering Microbial Genome Function and Evolution: Comparative Genomics of Laterally Transferred Genes

    Energy Technology Data Exchange (ETDEWEB)

    Borodovsky, M.

    2013-04-11

    Algorithmic methods for gene prediction have been developed and successfully applied to many different prokaryotic genome sequences. As the set of genes in a particular genome is not homogeneous with respect to DNA sequence composition features, the GeneMark.hmm program utilizes two Markov models representing distinct classes of protein coding genes denoted "typical" and "atypical". Atypical genes are those whose DNA features deviate significantly from those classified as typical and they represent approximately 10% of any given genome. In addition to the inherent interest of more accurately predicting genes, the atypical status of these genes may also reflect their separate evolutionary ancestry from other genes in that genome. We hypothesize that atypical genes are largely comprised of those genes that have been relatively recently acquired through lateral gene transfer (LGT). If so, what fraction of atypical genes are such bona fide LGTs? We have made atypical gene predictions for all fully completed prokaryotic genomes; we have been able to compare these results to other "surrogate" methods of LGT prediction.

  3. How to find soluble proteins: a comprehensive analysis of alpha/beta hydrolases for recombinant expression in E. coli

    Directory of Open Access Journals (Sweden)

    Barth Sandra

    2005-04-01

    Full Text Available Abstract Background In screening of libraries derived by expression cloning, expression of active proteins in E. coli can be limited by formation of inclusion bodies. In these cases it would be desirable to enrich gene libraries for coding sequences with soluble gene products in E. coli and thus to improve the efficiency of screening. Previously Wilkinson and Harrison showed that solubility can be predicted from amino acid composition (Biotechnology 1991, 9(5:443–448. We have applied this analysis to members of the alpha/beta hydrolase fold family to predict their solubility in E. coli. alpha/beta hydrolases are a highly diverse family with more than 1800 proteins which have been grouped into homologous families and superfamilies. Results The predicted solubility in E. coli depends on hydrolase size, phylogenetic origin of the host organism, the homologous family and the superfamily, to which the hydrolase belongs. In general small hydrolases are predicted to be more soluble than large hydrolases, and eukaryotic hydrolases are predicted to be less soluble in E. coli than prokaryotic ones. However, combining phylogenetic origin and size leads to more complex conclusions. Hydrolases from prokaryotic, fungal and metazoan origin are predicted to be most soluble if they are of small, medium and large size, respectively. We observed large variations of predicted solubility between hydrolases from different homologous families and from different taxa. Conclusion A comprehensive analysis of all alpha/beta hydrolase sequences allows more efficient screenings for new soluble alpha/beta hydrolases by the use of libraries which contain more soluble gene products. Screening of hydrolases from families whose members are hard to express as soluble proteins in E. coli should first be done in coding sequences of organisms from phylogenetic groups with the highest average of predicted solubility for proteins of this family. The tools developed here can be used

  4. Update of the human secretoglobin (SCGB gene superfamily and an example of 'evolutionary bloom' of androgen-binding protein genes within the mouse Scgb gene superfamily

    Directory of Open Access Journals (Sweden)

    Jackson Brian C

    2011-10-01

    Full Text Available Abstract The secretoglobins (SCGBs comprise a family of small, secreted proteins found in animals exclusively of mammalian lineage. There are 11 human SCGB genes and five pseudogenes. Interestingly, mice have 68 Scgb genes, four of which are highly orthologous to human SCGB genes; the remainder represent an 'evolutionary bloom' and make up a large gene family represented by only six counterparts in humans. SCGBs are found in high concentrations in many mammalian secretions, including fluids of the lung, lacrimal gland, salivary gland, prostate and uterus. Whereas the biological activities of most individual SCGBs have not been fully characterised, what already has been discovered suggests that this family has an important role in the modulation of inflammation, tissue repair and tumorigenesis. In mice, the large Scgb1b and Scgb2b gene families encode the androgen-binding proteins, which have been shown to play a role in mate selection. Although much has been learned about SCGBs in recent years, clearly more research remains to be done to allow a better understanding of the roles of these proteins in human health and disease. Such information is predicted to reveal valuable novel drug targets for the treatment of inflammation, as well as designing biomarkers that might identify tissue damage or cancer.

  5. Developmental programming of long non-coding RNAs during postnatal liver maturation in mice.

    Directory of Open Access Journals (Sweden)

    Lai Peng

    Full Text Available The liver is a vital organ with critical functions in metabolism, protein synthesis, and immune defense. Most of the liver functions are not mature at birth and many changes happen during postnatal liver development. However, it is unclear what changes occur in liver after birth, at what developmental stages they occur, and how the developmental processes are regulated. Long non-coding RNAs (lncRNAs are involved in organ development and cell differentiation. Here, we analyzed the transcriptome of lncRNAs in mouse liver from perinatal (day -2 to adult (day 60 by RNA-Sequencing, with an attempt to understand the role of lncRNAs in liver maturation. We found around 15,000 genes expressed, including about 2,000 lncRNAs. Most lncRNAs were expressed at a lower level than coding RNAs. Both coding RNAs and lncRNAs displayed three major ontogenic patterns: enriched at neonatal, adolescent, or adult stages. Neighboring coding and non-coding RNAs showed the trend to exhibit highly correlated ontogenic expression patterns. Gene ontology (GO analysis revealed that some lncRNAs enriched at neonatal ages have their neighbor protein coding genes also enriched at neonatal ages and associated with cell proliferation, immune activation related processes, tissue organization pathways, and hematopoiesis; other lncRNAs enriched at adolescent ages have their neighbor protein coding genes associated with different metabolic processes. These data reveal significant functional transition during postnatal liver development and imply the potential importance of lncRNAs in liver maturation.

  6. Coding potential of the products of alternative splicing in human.

    KAUST Repository

    Leoni, Guido

    2011-01-20

    BACKGROUND: Analysis of the human genome has revealed that as much as an order of magnitude more of the genomic sequence is transcribed than accounted for by the predicted and characterized genes. A number of these transcripts are alternatively spliced forms of known protein coding genes; however, it is becoming clear that many of them do not necessarily correspond to a functional protein. RESULTS: In this study we analyze alternative splicing isoforms of human gene products that are unambiguously identified by mass spectrometry and compare their properties with those of isoforms of the same genes for which no peptide was found in publicly available mass spectrometry datasets. We analyze them in detail for the presence of uninterrupted functional domains, active sites as well as the plausibility of their predicted structure. We report how well each of these strategies and their combination can correctly identify translated isoforms and derive a lower limit for their specificity, that is, their ability to correctly identify non-translated products. CONCLUSIONS: The most effective strategy for correctly identifying translated products relies on the conservation of active sites, but it can only be applied to a small fraction of isoforms, while a reasonably high coverage, sensitivity and specificity can be achieved by analyzing the presence of non-truncated functional domains. Combining the latter with an assessment of the plausibility of the modeled structure of the isoform increases both coverage and specificity with a moderate cost in terms of sensitivity.

  7. Coding potential of the products of alternative splicing in human.

    KAUST Repository

    Leoni, Guido; Le Pera, Loredana; Ferrè , Fabrizio; Raimondo, Domenico; Tramontano, Anna

    2011-01-01

    BACKGROUND: Analysis of the human genome has revealed that as much as an order of magnitude more of the genomic sequence is transcribed than accounted for by the predicted and characterized genes. A number of these transcripts are alternatively spliced forms of known protein coding genes; however, it is becoming clear that many of them do not necessarily correspond to a functional protein. RESULTS: In this study we analyze alternative splicing isoforms of human gene products that are unambiguously identified by mass spectrometry and compare their properties with those of isoforms of the same genes for which no peptide was found in publicly available mass spectrometry datasets. We analyze them in detail for the presence of uninterrupted functional domains, active sites as well as the plausibility of their predicted structure. We report how well each of these strategies and their combination can correctly identify translated isoforms and derive a lower limit for their specificity, that is, their ability to correctly identify non-translated products. CONCLUSIONS: The most effective strategy for correctly identifying translated products relies on the conservation of active sites, but it can only be applied to a small fraction of isoforms, while a reasonably high coverage, sensitivity and specificity can be achieved by analyzing the presence of non-truncated functional domains. Combining the latter with an assessment of the plausibility of the modeled structure of the isoform increases both coverage and specificity with a moderate cost in terms of sensitivity.

  8. Improving performance of single-path code through a time-predictable memory hierarchy

    DEFF Research Database (Denmark)

    Cilku, Bekim; Puffitsch, Wolfgang; Prokesch, Daniel

    2017-01-01

    -predictable memory hierarchy with a prefetcher that exploits the predictability of execution traces in single-path code to speed up code execution. The new memory hierarchy reduces both the cache-miss penalty time and the cache-miss rate on the instruction cache. The benefit of the approach is demonstrated through...

  9. Defining the predicted protein secretome of the fungal wheat leaf pathogen Mycosphaerella graminicola.

    Directory of Open Access Journals (Sweden)

    Alexandre Morais do Amaral

    Full Text Available The Dothideomycete fungus Mycosphaerella graminicola is the causal agent of Septoria tritici blotch, a devastating disease of wheat leaves that causes dramatic decreases in yield. Infection involves an initial extended period of symptomless intercellular colonisation prior to the development of visible necrotic disease lesions. Previous functional genomics and gene expression profiling studies have implicated the production of secreted virulence effector proteins as key facilitators of the initial symptomless growth phase. In order to identify additional candidate virulence effectors, we re-analysed and catalogued the predicted protein secretome of M. graminicola isolate IPO323, which is currently regarded as the reference strain for this species. We combined several bioinformatic approaches in order to increase the probability of identifying truly secreted proteins with either a predicted enzymatic function or an as yet unknown function. An initial secretome of 970 proteins was predicted, whilst further stringent selection criteria predicted 492 proteins. Of these, 321 possess some functional annotation, the composition of which may reflect the strictly intercellular growth habit of this pathogen, leaving 171 with no functional annotation. This analysis identified a protein family encoding secreted peroxidases/chloroperoxidases (PF01328 which is expanded within all members of the family Mycosphaerellaceae. Further analyses were done on the non-annotated proteins for size and cysteine content (effector protein hallmarks, and then by studying the distribution of homologues in 17 other sequenced Dothideomycete fungi within an overall total of 91 predicted proteomes from fungal, oomycete and nematode species. This detailed M. graminicola secretome analysis provides the basis for further functional and comparative genomics studies.

  10. Assignment of the murine protein kinase gene DLK to chromosome 15 in the vicinity of the bt/Koa locus by genetic linkage analysis

    Energy Technology Data Exchange (ETDEWEB)

    Watanabe, Toshio; Yanagisawa, Masahiro; Matsubara, Nobumichi [Tokyo Univ. (Japan)] [and others

    1997-03-01

    We have cloned protein kinase genes from murine primordial germ cell-derived EG cells by a PCR-based strategy using degenerate primers corresponding to the conserved sequences in the catalytic domain of protein kinases. One of these clones, designated Gek2 (germ cell kinase 2), was used as a probe for screening of a mouse brain cDNA library and obtained clones contained an entire coding sequence. Comparison of the sequence of Gek2 with those in databases revealed that it was identical to a previously reported protein kinase gene, DLK. 8 refs., 1 fig.

  11. Evolutionary mechanisms driving the evolution of a large polydnavirus gene family coding for protein tyrosine phosphatases

    Directory of Open Access Journals (Sweden)

    Serbielle Céline

    2012-12-01

    Full Text Available Abstract Background Gene duplications have been proposed to be the main mechanism involved in genome evolution and in acquisition of new functions. Polydnaviruses (PDVs, symbiotic viruses associated with parasitoid wasps, are ideal model systems to study mechanisms of gene duplications given that PDV genomes consist of virulence genes organized into multigene families. In these systems the viral genome is integrated in a wasp chromosome as a provirus and virus particles containing circular double-stranded DNA are injected into the parasitoids’ hosts and are essential for parasitism success. The viral virulence factors, organized in gene families, are required collectively to induce host immune suppression and developmental arrest. The gene family which encodes protein tyrosine phosphatases (PTPs has undergone spectacular expansion in several PDV genomes with up to 42 genes. Results Here, we present strong indications that PTP gene family expansion occurred via classical mechanisms: by duplication of large segments of the chromosomally integrated form of the virus sequences (segmental duplication, by tandem duplications within this form and by dispersed duplications. We also propose a novel duplication mechanism specific to PDVs that involves viral circle reintegration into the wasp genome. The PTP copies produced were shown to undergo conservative evolution along with episodes of adaptive evolution. In particular recently produced copies have undergone positive selection in sites most likely involved in defining substrate selectivity. Conclusion The results provide evidence about the dynamic nature of polydnavirus proviral genomes. Classical and PDV-specific duplication mechanisms have been involved in the production of new gene copies. Selection pressures associated with antagonistic interactions with parasitized hosts have shaped these genes used to manipulate lepidopteran physiology with evidence for positive selection involved in

  12. Predicting the minimal translation apparatus: lessons from the reductive evolution of mollicutes.

    Directory of Open Access Journals (Sweden)

    Henri Grosjean

    2014-05-01

    Full Text Available Mollicutes is a class of parasitic bacteria that have evolved from a common Firmicutes ancestor mostly by massive genome reduction. With genomes under 1 Mbp in size, most Mollicutes species retain the capacity to replicate and grow autonomously. The major goal of this work was to identify the minimal set of proteins that can sustain ribosome biogenesis and translation of the genetic code in these bacteria. Using the experimentally validated genes from the model bacteria Escherichia coli and Bacillus subtilis as input, genes encoding proteins of the core translation machinery were predicted in 39 distinct Mollicutes species, 33 of which are culturable. The set of 260 input genes encodes proteins involved in ribosome biogenesis, tRNA maturation and aminoacylation, as well as proteins cofactors required for mRNA translation and RNA decay. A core set of 104 of these proteins is found in all species analyzed. Genes encoding proteins involved in post-translational modifications of ribosomal proteins and translation cofactors, post-transcriptional modifications of t+rRNA, in ribosome assembly and RNA degradation are the most frequently lost. As expected, genes coding for aminoacyl-tRNA synthetases, ribosomal proteins and initiation, elongation and termination factors are the most persistent (i.e. conserved in a majority of genomes. Enzymes introducing nucleotides modifications in the anticodon loop of tRNA, in helix 44 of 16S rRNA and in helices 69 and 80 of 23S rRNA, all essential for decoding and facilitating peptidyl transfer, are maintained in all species. Reconstruction of genome evolution in Mollicutes revealed that, beside many gene losses, occasional gains by horizontal gene transfer also occurred. This analysis not only showed that slightly different solutions for preserving a functional, albeit minimal, protein synthetizing machinery have emerged in these successive rounds of reductive evolution but also has broad implications in guiding the

  13. Concordance of gene expression in human protein complexes reveals tissue specificity and pathology

    DEFF Research Database (Denmark)

    Börnigen, Daniela; Pers, Tune Hannes; Thorrez, Lieven

    2013-01-01

    Disease-causing variants in human genes usually lead to phenotypes specific to only a few tissues. Here, we present a method for predicting tissue specificity based on quantitative deregulation of protein complexes. The underlying assumption is that the degree of coordinated expression among prot...

  14. Comprehensive search for intra- and inter-specific sequence polymorphisms among coding envelope genes of retroviral origin found in the human genome: genes and pseudogenes

    Directory of Open Access Journals (Sweden)

    Vasilescu Alexandre

    2005-09-01

    Full Text Available Abstract Background The human genome carries a high load of proviral-like sequences, called Human Endogenous Retroviruses (HERVs, which are the genomic traces of ancient infections by active retroviruses. These elements are in most cases defective, but open reading frames can still be found for the retroviral envelope gene, with sixteen such genes identified so far. Several of them are conserved during primate evolution, having possibly been co-opted by their host for a physiological role. Results To characterize further their status, we presently sequenced 12 of these genes from a panel of 91 Caucasian individuals. Genomic analyses reveal strong sequence conservation (only two non synonymous Single Nucleotide Polymorphisms [SNPs] for the two HERV-W and HERV-FRD envelope genes, i.e. for the two genes specifically expressed in the placenta and possibly involved in syncytiotrophoblast formation. We further show – using an ex vivo fusion assay for each allelic form – that none of these SNPs impairs the fusogenic function. The other envelope proteins disclose variable polymorphisms, with the occurrence of a stop codon and/or frameshift for most – but not all – of them. Moreover, the sequence conservation analysis of the orthologous genes that can be found in primates shows that three env genes have been maintained in a fully coding state throughout evolution including envW and envFRD. Conclusion Altogether, the present study strongly suggests that some but not all envelope encoding sequences are bona fide genes. It also provides new tools to elucidate the possible role of endogenous envelope proteins as susceptibility factors in a number of pathologies where HERVs have been suspected to be involved.

  15. From structure prediction to genomic screens for novel non-coding RNAs.

    Science.gov (United States)

    Gorodkin, Jan; Hofacker, Ivo L

    2011-08-01

    Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

  16. Identification and correction of abnormal, incomplete and mispredicted proteins in public databases

    Directory of Open Access Journals (Sweden)

    Bányai László

    2008-08-01

    Full Text Available Abstract Background Despite significant improvements in computational annotation of genomes, sequences of abnormal, incomplete or incorrectly predicted genes and proteins remain abundant in public databases. Since the majority of incomplete, abnormal or mispredicted entries are not annotated as such, these errors seriously affect the reliability of these databases. Here we describe the MisPred approach that may provide an efficient means for the quality control of databases. The current version of the MisPred approach uses five distinct routines for identifying abnormal, incomplete or mispredicted entries based on the principle that a sequence is likely to be incorrect if some of its features conflict with our current knowledge about protein-coding genes and proteins: (i conflict between the predicted subcellular localization of proteins and the absence of the corresponding sequence signals; (ii presence of extracellular and cytoplasmic domains and the absence of transmembrane segments; (iii co-occurrence of extracellular and nuclear domains; (iv violation of domain integrity; (v chimeras encoded by two or more genes located on different chromosomes. Results Analyses of predicted EnsEMBL protein sequences of nine deuterostome (Homo sapiens, Mus musculus, Rattus norvegicus, Monodelphis domestica, Gallus gallus, Xenopus tropicalis, Fugu rubripes, Danio rerio and Ciona intestinalis and two protostome species (Caenorhabditis elegans and Drosophila melanogaster have revealed that the absence of expected signal peptides and violation of domain integrity account for the majority of mispredictions. Analyses of sequences predicted by NCBI's GNOMON annotation pipeline show that the rates of mispredictions are comparable to those of EnsEMBL. Interestingly, even the manually curated UniProtKB/Swiss-Prot dataset is contaminated with mispredicted or abnormal proteins, although to a much lesser extent than UniProtKB/TrEMBL or the EnsEMBL or GNOMON-predicted

  17. Genic regions of a large salamander genome contain long introns and novel genes

    Directory of Open Access Journals (Sweden)

    Bryant Susan V

    2009-01-01

    Full Text Available Abstract Background The basis of genome size variation remains an outstanding question because DNA sequence data are lacking for organisms with large genomes. Sixteen BAC clones from the Mexican axolotl (Ambystoma mexicanum: c-value = 32 × 109 bp were isolated and sequenced to characterize the structure of genic regions. Results Annotation of genes within BACs showed that axolotl introns are on average 10× longer than orthologous vertebrate introns and they are predicted to contain more functional elements, including miRNAs and snoRNAs. Loci were discovered within BACs for two novel EST transcripts that are differentially expressed during spinal cord regeneration and skin metamorphosis. Unexpectedly, a third novel gene was also discovered while manually annotating BACs. Analysis of human-axolotl protein-coding sequences suggests there are 2% more lineage specific genes in the axolotl genome than the human genome, but the great majority (86% of genes between axolotl and human are predicted to be 1:1 orthologs. Considering that axolotl genes are on average 5× larger than human genes, the genic component of the salamander genome is estimated to be incredibly large, approximately 2.8 gigabases! Conclusion This study shows that a large salamander genome has a correspondingly large genic component, primarily because genes have incredibly long introns. These intronic sequences may harbor novel coding and non-coding sequences that regulate biological processes that are unique to salamanders.

  18. Nucleotide sequence of the coat protein gene of Lettuce big-vein virus.

    Science.gov (United States)

    Sasaya, T; Ishikawa, K; Koganezawa, H

    2001-06-01

    A sequence of 1425 nt was established that included the complete coat protein (CP) gene of Lettuce big-vein virus (LBVV). The LBVV CP gene encodes a 397 amino acid protein with a predicted M(r) of 44486. Antisera raised against synthetic peptides corresponding to N-terminal or C-terminal parts of the LBVV CP reacted in Western blot analysis with a protein with an M(r) of about 48000. RNA extracted from purified particles of LBVV by using proteinase K, SDS and phenol migrated in gels as two single-stranded RNA species of approximately 7.3 kb (ss-1) and 6.6 kb (ss-2). After denaturation by heat and annealing at room temperature, the RNA migrated as four species, ss-1, ss-2 and two additional double-stranded RNAs (ds-1 and ds-2). The Northern blot hybridization analysis using riboprobes from a full-length clone of the LBVV CP gene indicated that ss-2 has a negative-sense nature and contains the LBVV CP gene. Moreover, ds-2 is a double-stranded form of ss-2. Database searches showed that the LBVV CP most resembled the nucleocapsid proteins of rhabdoviruses. These results indicate that it would be appropriate to classify LBVV as a negative-sense single-stranded RNA virus rather than as a double-stranded RNA virus.

  19. Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation.

    Science.gov (United States)

    Li, Min; Zhang, Jiayi; Liu, Qing; Wang, Jianxin; Wu, Fang-Xiang

    2014-01-01

    Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

  20. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  1. De novo ORFs in Drosophila are important to organismal fitness and evolved rapidly from previously non-coding sequences.

    Directory of Open Access Journals (Sweden)

    Josephine A Reinhardt

    Full Text Available How non-coding DNA gives rise to new protein-coding genes (de novo genes is not well understood. Recent work has revealed the origins and functions of a few de novo genes, but common principles governing the evolution or biological roles of these genes are unknown. To better define these principles, we performed a parallel analysis of the evolution and function of six putatively protein-coding de novo genes described in Drosophila melanogaster. Reconstruction of the transcriptional history of de novo genes shows that two de novo genes emerged from novel long non-coding RNAs that arose at least 5 MY prior to evolution of an open reading frame. In contrast, four other de novo genes evolved a translated open reading frame and transcription within the same evolutionary interval suggesting that nascent open reading frames (proto-ORFs, while not required, can contribute to the emergence of a new de novo gene. However, none of the genes arose from proto-ORFs that existed long before expression evolved. Sequence and structural evolution of de novo genes was rapid compared to nearby genes and the structural complexity of de novo genes steadily increases over evolutionary time. Despite the fact that these genes are transcribed at a higher level in males than females, and are most strongly expressed in testes, RNAi experiments show that most of these genes are essential in both sexes during metamorphosis. This lethality suggests that protein coding de novo genes in Drosophila quickly become functionally important.

  2. BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

    Directory of Open Access Journals (Sweden)

    MuthuKrishnan Selvaraj

    2016-01-01

    Full Text Available The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM models were developed for predicting HbL proteins based upon amino acid composition (AC, dipeptide composition (DC, hybrid method (AC + DC, and position specific scoring matrix (PSSM. In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM profiles. The average accuracy, standard deviation (SD, false positive rate (FPR, confusion matrix, and receiver operating characteristic (ROC were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

  3. Unusually effective microRNA targeting within repeat-rich coding regions of mammalian mRNAs

    Science.gov (United States)

    Schnall-Levin, Michael; Rissland, Olivia S.; Johnston, Wendy K.; Perrimon, Norbert; Bartel, David P.; Berger, Bonnie

    2011-01-01

    MicroRNAs (miRNAs) regulate numerous biological processes by base-pairing with target messenger RNAs (mRNAs), primarily through sites in 3′ untranslated regions (UTRs), to direct the repression of these targets. Although miRNAs have sometimes been observed to target genes through sites in open reading frames (ORFs), large-scale studies have shown such targeting to be generally less effective than 3′ UTR targeting. Here, we show that several miRNAs each target significant groups of genes through multiple sites within their coding regions. This ORF targeting, which mediates both predictable and effective repression, arises from highly repeated sequences containing miRNA target sites. We show that such sequence repeats largely arise through evolutionary duplications and occur particularly frequently within families of paralogous C2H2 zinc-finger genes, suggesting the potential for their coordinated regulation. Examples of ORFs targeted by miR-181 include both the well-known tumor suppressor RB1 and RBAK, encoding a C2H2 zinc-finger protein and transcriptional binding partner of RB1. Our results indicate a function for repeat-rich coding sequences in mediating post-transcriptional regulation and reveal circumstances in which miRNA-mediated repression through ORF sites can be reliably predicted. PMID:21685129

  4. Sparsity in Linear Predictive Coding of Speech

    DEFF Research Database (Denmark)

    Giacobello, Daniele

    of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm. We first propose a novel near-optimal method to look for a sparse approximate excitation using a compressed...... one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to de- velop the concept...... sensing formulation. Furthermore, we define a novel re-estimation procedure to adapt the predictor coefficients to the given sparse excitation, balancing the two representations in the context of speech coding. Finally, the advantages of the compact parametric representation of a segment of speech, given...

  5. Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

    Science.gov (United States)

    Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

    2012-05-10

    RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM

  6. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    Hao, Ke; Zhong, Hua; Greenawalt, Danielle; Ferguson, Mark D; Ng, Irene O; Sham, Pak C; Poon, Ronnie T; Molony, Cliona; Schadt, Eric E; Dai, Hongyue; Luk, John M; Lamb, John; Zhang, Chunsheng; Xie, Tao; Wang, Kai; Zhang, Bin; Chudin, Eugene; Lee, Nikki P; Mao, Mao

    2011-01-01

    The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome

  7. Predictive coding of music--brain responses to rhythmic incongruity.

    Science.gov (United States)

    Vuust, Peter; Ostergaard, Leif; Pallesen, Karen Johanne; Bailey, Christopher; Roepstorff, Andreas

    2009-01-01

    During the last decades, models of music processing in the brain have mainly discussed the specificity of brain modules involved in processing different musical components. We argue that predictive coding offers an explanatory framework for functional integration in musical processing. Further, we provide empirical evidence for such a network in the analysis of event-related MEG-components to rhythmic incongruence in the context of strong metric anticipation. This is seen in a mismatch negativity (MMNm) and a subsequent P3am component, which have the properties of an error term and a subsequent evaluation in a predictive coding framework. There were both quantitative and qualitative differences in the evoked responses in expert jazz musicians compared with rhythmically unskilled non-musicians. We propose that these differences trace a functional adaptation and/or a genetic pre-disposition in experts which allows for a more precise rhythmic prediction.

  8. Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network

    Directory of Open Access Journals (Sweden)

    Buzhong Zhang

    2018-05-01

    Full Text Available Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson’s correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

  9. Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

    Science.gov (United States)

    Zhang, Buzhong; Li, Linqing; Lü, Qiang

    2018-05-25

    Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

  10. Expression Pattern Similarities Support the Prediction of Orthologs Retaining Common Functions after Gene Duplication Events1[OPEN

    Science.gov (United States)

    Haberer, Georg; Panda, Arup; Das Laha, Shayani; Ghosh, Tapas Chandra; Schäffner, Anton R.

    2016-01-01

    The identification of functionally equivalent, orthologous genes (functional orthologs) across genomes is necessary for accurate transfer of experimental knowledge from well-characterized organisms to others. This frequently relies on automated, coding sequence-based approaches such as OrthoMCL, Inparanoid, and KOG, which usually work well for one-to-one homologous states. However, this strategy does not reliably work for plants due to the occurrence of extensive gene/genome duplication. Frequently, for one query gene, multiple orthologous genes are predicted in the other genome, and it is not clear a priori from sequence comparison and similarity which one preserves the ancestral function. We have studied 11 organ-dependent and stress-induced gene expression patterns of 286 Arabidopsis lyrata duplicated gene groups and compared them with the respective Arabidopsis (Arabidopsis thaliana) genes to predict putative expressologs and nonexpressologs based on gene expression similarity. Promoter sequence divergence as an additional tool to substantiate functional orthology only partially overlapped with expressolog classification. By cloning eight A. lyrata homologs and complementing them in the respective four Arabidopsis loss-of-function mutants, we experimentally proved that predicted expressologs are indeed functional orthologs, while nonexpressologs or nonfunctionalized orthologs are not. Our study demonstrates that even a small set of gene expression data in addition to sequence homologies are instrumental in the assignment of functional orthologs in the presence of multiple orthologs. PMID:27303025

  11. dPORE-miRNA: Polymorphic regulation of microRNA genes

    KAUST Repository

    Schmeier, Sebastian; Schaefer, Ulf; MacPherson, Cameron R.; Bajic, Vladimir B.

    2011-01-01

    Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users. © 2011 Schmeier et al.

  12. dPORE-miRNA: Polymorphic regulation of microRNA genes

    KAUST Repository

    Schmeier, Sebastian

    2011-02-04

    Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users. © 2011 Schmeier et al.

  13. Regulation of protein homeostasis in neurodegenerative diseases : the role of coding and non-coding genes

    NARCIS (Netherlands)

    Alvarenga Fernandes Sin, Olga; Nollen, Ellen A. A.

    Protein homeostasis is fundamental for cell function and survival, because proteins are involved in all aspects of cellular function, ranging from cell metabolism and cell division to the cell's response to environmental challenges. Protein homeostasis is tightly regulated by the synthesis, folding,

  14. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize.

    Directory of Open Access Journals (Sweden)

    Matt eGeisler

    2015-06-01

    Full Text Available Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6,004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

  15. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  16. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Predictive Coding Strategies for Developmental Neurorobotics

    Science.gov (United States)

    Park, Jun-Cheol; Lim, Jae Hyun; Choi, Hansol; Kim, Dae-Shik

    2012-01-01

    In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions. PMID:22586416

  18. Predictive Coding Strategies for Developmental Neurorobotics

    Directory of Open Access Journals (Sweden)

    Jun-Cheol ePark

    2012-05-01

    Full Text Available In recent years, predictive coding strategies have been proposed as a possible way of how the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal believes to the observed error between what it expected to happen, and what actually happens. In this paper we present a potpourri of developmental neurorobotics experiments in which minimalist prediction-error based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions.

  19. Comprehensive transcriptome analysis reveals novel genes involved in cardiac glycoside biosynthesis and mlncRNAs associated with secondary metabolism and stress response in Digitalis purpurea

    Directory of Open Access Journals (Sweden)

    Wu Bin

    2012-01-01

    Full Text Available Abstract Background Digitalis purpurea is an important ornamental and medicinal plant. There is considerable interest in exploring its transcriptome. Results Through high-throughput 454 sequencing and subsequent assembly, we obtained 23532 genes, of which 15626 encode conserved proteins. We determined 140 unigenes to be candidates involved in cardiac glycoside biosynthesis. It could be grouped into 30 families, of which 29 were identified for the first time in D. purpurea. We identified 2660 mRNA-like npcRNA (mlncRNA candidates, an emerging class of regulators, using a computational mlncRNA identification pipeline and 13 microRNA-producing unigenes based on sequence conservation and hairpin structure-forming capability. Twenty five protein-coding unigenes were predicted to be targets of these microRNAs. Among the mlncRNA candidates, only 320 could be grouped into 140 families with at least two members in a family. The majority of D. purpurea mlncRNAs were species-specific and many of them showed tissue-specific expression and responded to cold and dehydration stresses. We identified 417 protein-coding genes with regions significantly homologous or complementary to 375 mlncRNAs. It includes five genes involved in secondary metabolism. A positive correlation was found in gene expression between protein-coding genes and the homologous mlncRNAs in response to cold and dehydration stresses, while the correlation was negative when protein-coding genes and mlncRNAs were complementary to each other. Conclusions Through comprehensive transcriptome analysis, we not only identified 29 novel gene families potentially involved in the biosynthesis of cardiac glycosides but also characterized a large number of mlncRNAs. Our results suggest the importance of mlncRNAs in secondary metabolism and stress response in D. purpurea.

  20. Biophysics of protein evolution and evolutionary protein biophysics

    Science.gov (United States)

    Sikosek, Tobias; Chan, Hue Sun

    2014-01-01

    The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution. PMID:25165599

  1. The putative protein methyltransferase LAE1 controls cellulase gene expression in Trichoderma reesei

    Science.gov (United States)

    Seiboth, Bernhard; Karimi, Razieh Aghcheh; Phatale, Pallavi A; Linke, Rita; Hartl, Lukas; Sauer, Dominik G; Smith, Kristina M; Baker, Scott E; Freitag, Michael; Kubicek, Christian P

    2012-01-01

    Summary Trichoderma reesei is an industrial producer of enzymes that degrade lignocellulosic polysaccharides to soluble monomers, which can be fermented to biofuels. Here we show that the expression of genes for lignocellulose degradation are controlled by the orthologous T. reesei protein methyltransferase LAE1. In a lae1 deletion mutant we observed a complete loss of expression of all seven cellulases, auxiliary factors for cellulose degradation, β-glucosidases and xylanases were no longer expressed. Conversely, enhanced expression of lae1 resulted in significantly increased cellulase gene transcription. Lae1-modulated cellulase gene expression was dependent on the function of the general cellulase regulator XYR1, but also xyr1 expression was LAE1-dependent. LAE1 was also essential for conidiation of T. reesei. Chromatin immunoprecipitation followed by high-throughput sequencing (‘ChIP-seq’) showed that lae1 expression was not obviously correlated with H3K4 di- or trimethylation (indicative of active transcription) or H3K9 trimethylation (typical for heterochromatin regions) in CAZyme coding regions, suggesting that LAE1 does not affect CAZyme gene expression by directly modulating H3K4 or H3K9 methylation. Our data demonstrate that the putative protein methyltransferase LAE1 is essential for cellulase gene expression in T. reesei through mechanisms that remain to be identified. PMID:22554051

  2. Genome-wide identification of coding and non-coding conserved sequence tags in human and mouse genomes

    Directory of Open Access Journals (Sweden)

    Maggi Giorgio P

    2008-06-01

    Full Text Available Abstract Background The accurate detection of genes and the identification of functional regions is still an open issue in the annotation of genomic sequences. This problem affects new genomes but also those of very well studied organisms such as human and mouse where, despite the great efforts, the inventory of genes and regulatory regions is far from complete. Comparative genomics is an effective approach to address this problem. Unfortunately it is limited by the computational requirements needed to perform genome-wide comparisons and by the problem of discriminating between conserved coding and non-coding sequences. This discrimination is often based (thus dependent on the availability of annotated proteins. Results In this paper we present the results of a comprehensive comparison of human and mouse genomes performed with a new high throughput grid-based system which allows the rapid detection of conserved sequences and accurate assessment of their coding potential. By detecting clusters of coding conserved sequences the system is also suitable to accurately identify potential gene loci. Following this analysis we created a collection of human-mouse conserved sequence tags and carefully compared our results to reliable annotations in order to benchmark the reliability of our classifications. Strikingly we were able to detect several potential gene loci supported by EST sequences but not corresponding to as yet annotated genes. Conclusion Here we present a new system which allows comprehensive comparison of genomes to detect conserved coding and non-coding sequences and the identification of potential gene loci. Our system does not require the availability of any annotated sequence thus is suitable for the analysis of new or poorly annotated genomes.

  3. Clustered coding variants in the glutamate receptor complexes of individuals with schizophrenia and bipolar disorder.

    Directory of Open Access Journals (Sweden)

    René A W Frank

    2011-04-01

    Full Text Available Current models of schizophrenia and bipolar disorder implicate multiple genes, however their biological relationships remain elusive. To test the genetic role of glutamate receptors and their interacting scaffold proteins, the exons of ten glutamatergic 'hub' genes in 1304 individuals were re-sequenced in case and control samples. No significant difference in the overall number of non-synonymous single nucleotide polymorphisms (nsSNPs was observed between cases and controls. However, cluster analysis of nsSNPs identified two exons encoding the cysteine-rich domain and first transmembrane helix of GRM1 as a risk locus with five mutations highly enriched within these domains. A new splice variant lacking the transmembrane GPCR domain of GRM1 was discovered in the human brain and the GRM1 mutation cluster could perturb the regulation of this variant. The predicted effect on individuals harbouring multiple mutations distributed in their ten hub genes was also examined. Diseased individuals possessed an increased load of deleteriousness from multiple concurrent rare and common coding variants. Together, these data suggest a disease model in which the interplay of compound genetic coding variants, distributed among glutamate receptors and their interacting proteins, contribute to the pathogenesis of schizophrenia and bipolar disorders.

  4. Discovery and annotation of small proteins using genomics, proteomics and computational approaches

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaohan; Tschaplinski, Timothy J.; Hurst, Gregory B.; Jawdy, Sara; Abraham, Paul E.; Lankford, Patricia K.; Adams, Rachel M.; Shah, Manesh B.; Hettich, Robert L.; Lindquist, Erika; Kalluri, Udaya C.; Gunter, Lee E.; Pennacchio, Christa; Tuskan, Gerald A.

    2011-03-02

    Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

  5. Analysis of gene and protein name synonyms in Entrez Gene and UniProtKB resources

    KAUST Repository

    Arkasosy, Basil

    2013-01-01

    be ambiguous, referring in some cases to more than one gene or one protein, or in others, to both genes and proteins at the same time. Public biological databases give a very useful insight about genes and proteins information, including their names

  6. Identification of single nucleotide polymorphisms in the agouti signaling protein (ASIP) gene in some goat breeds in tropical and temperate climates.

    Science.gov (United States)

    Adefenwa, Mufliat A; Peters, Sunday O; Agaviezor, Brilliant O; Wheto, Matthew; Adekoya, Khalid O; Okpeku, Moses; Oboh, Bola; Williams, Gabriel O; Adebambo, Olufunmilayo A; Singh, Mahipal; Thomas, Bolaji; De Donato, Marcos; Imumorin, Ikhide G

    2013-07-01

    The agouti-signaling protein (ASIP) plays a major role in mammalian pigmentation as an antagonist to melanocortin-1 receptor gene to stimulate pheomelanin synthesis, a major pigment conferring mammalian coat color. We sequenced a 352 bp fragment of ASIP gene spanning part of exon 2 and part of intron 2 in 215 animals representing six goat breeds from Nigeria and the United States: West African Dwarf, predominantly black; Red Sokoto, mostly red; and Sahel, mostly white from Nigeria; black and white Alpine, brown and white Spanish and white Saanen from the US. Twenty haplotypes from nine mutations representing three intronic, one silent and five missense (p.S19R, p.N35K, p.L36V, p.M42L and p.L45W) mutations were identified in Nigerian goats. Approximately 89 % of Nigerian goats carry haplotype 1 (TGCCATCCG) which seems to be the wild type configuration of mutations in this region of the gene. Although we found no association between these polymorphisms in the ASIP gene and coat color in Nigerian goats, in-silico functional analysis predicts putative deleterious functional impact of the p.L45W mutation on the basic amino-terminal domain of ASIP. In the American goats, two intronic mutations, g.293G>A and g.327C>A, were identified in the Alpine breed, although the g.293G>A mutation is common to American and Nigerian goat populations. All Sannen and Sahel goats in this study belong to haplotypes 1 of both populations which seem to be the wild-type composite ASIP haplotype. Overall, there was no clear association of this portion of the ASIP gene interrogated in this study with coat color variation. Therefore, additional genomic analyses of promoter sequence, the entire coding and non-coding regions of the ASIP gene will be required to obtain a definite conclusion.

  7. HomPPI: a class of sequence homology based protein-protein interface prediction methods

    Directory of Open Access Journals (Sweden)

    Dobbs Drena

    2011-06-01

    Full Text Available Abstract Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i NPS-HomPPI (Non partner-specific HomPPI, which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii PS-HomPPI (Partner-specific HomPPI, which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of

  8. Predictive codes of familiarity and context during the perceptual learning of facial identities

    Science.gov (United States)

    Apps, Matthew A. J.; Tsakiris, Manos

    2013-11-01

    Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

  9. Dinucleotide controlled null models for comparative RNA gene prediction.

    Science.gov (United States)

    Gesell, Tanja; Washietl, Stefan

    2008-05-27

    Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

  10. Dinucleotide controlled null models for comparative RNA gene prediction

    Directory of Open Access Journals (Sweden)

    Gesell Tanja

    2008-05-01

    Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

  11. Diversity and evolution of ABC proteins in mycorrhiza-forming fungi.

    Science.gov (United States)

    Kovalchuk, Andriy; Kohler, Annegret; Martin, Francis; Asiegbu, Fred O

    2015-12-28

    Transporter proteins are predicted to have an important role in the mycorrhizal symbiosis, due to the fact that this type of an interaction between plants and fungi requires a continuous nutrient and signalling exchange. ABC transporters are one of the large groups of transporter proteins found both in plants and in fungi. The crucial role of plant ABC transporters in the formation of the mycorrhizal symbiosis has been demonstrated recently. Some of the fungal ABC transporter-encoding genes are also induced during the mycorrhiza formation. However, no experimental evidences of the direct involvement of fungal ABC transporters in this process are available so far. To facilitate the identification of fungal ABC proteins with a potential role in the establishment of the mycorrhizal symbiosis, we have performed an inventory of the ABC protein-encoding genes in the genomes of 25 species of mycorrhiza-forming fungi. We have identified, manually annotated and curated more than 1300 gene models of putative ABC protein-encoding genes. Out of those, more than 1000 models are predicted to encode functional proteins, whereas about 300 models represent gene fragments or putative pseudogenes. We have also performed the phylogenetic analysis of the identified sequences. The sets of ABC proteins in the mycorrhiza-forming species were compared to the related saprotrophic or plant-pathogenic fungal species. Our results demonstrate the high diversity of ABC genes in the genomes of mycorrhiza-forming fungi. Via comparison of transcriptomics data from different species, we have identified candidate groups of ABC transporters that might have a role in the process of the mycorrhiza formation. Results of our inventory will facilitate the identification of fungal transporters with a role in the mycorrhiza formation. We also provide the first data on ABC protein-coding genes for the phylum Glomeromycota and for orders Pezizales, Atheliales, Cantharellales and Sebacinales, contributing to

  12. Sequence of the intron/exon junctions of the coding region of the human androgen receptor gene and identification of a point mutation in a family with complete androgen insensitivity

    International Nuclear Information System (INIS)

    Lubahn, D.B.; Simental, J.A.; Higgs, H.N.; Wilson, E.M.; French, F.S.; Brown, T.R.; Migeon, C.J.

    1989-01-01

    Androgens act through a receptor protein (AR) to mediate sex differentiation and development of the male phenotype. The authors have isolated the eight exons in the amino acid coding region of the AR gene from a human X chromosome library. Nucleotide sequences of the AR gene intron/exon boundaries were determined for use in designing synthetic oligonucleotide primers to bracket coding exons for amplification by the polymerase chain reaction. Genomic DNA was amplified from 46, XY phenotypic female siblings with complete androgen insensitivity syndrome. AR binding affinity for dihydrotestosterone in the affected siblings was lower than in normal males, but the binding capacity was normal. Sequence analysis of amplified exons demonstrated within the AR steroid-binding domain (exon G) a single guanine to adenine mutation, resulting in replacement of valine with methionine at amino acid residue 866. As expected, the carrier mother had both normal and mutant AR genes. Thus, a single point mutation in the steroid-binding domain of the AR gene correlated with the expression of an AR protein ineffective in stimulating male sexual development

  13. Fragile X mental retardation protein participates in non-coding RNA pathways.

    Science.gov (United States)

    Li, En-Hui; Zhao, Xin; Zhang, Ce; Liu, Wei

    2018-02-20

    Fragile X syndrome is one of the most common forms of inherited intellectual disability. It is caused by mutations of the Fragile X mental retardation 1(FMR1) gene, resulting in either the loss or abnormal expression of the Fragile X mental retardation protein (FMRP). Recent research showed that FMRP participates in non-coding RNA pathways and plays various important roles in physiology, thereby extending our knowledge of the pathogenesis of the Fragile X syndrome. Initial studies showed that the Drosophila FMRP participates in siRNA and miRNA pathways by interacting with Dicer, Ago1 and Ago2, involved in neural activity and the fate determination of the germline stem cells. Subsequent studies showed that the Drosophila FMRP participates in piRNA pathway by interacting with Aub, Ago1 and Piwi in the maintenance of normal chromatin structures and genomic stability. More recent studies showed that FMRP is associated with lncRNA pathway, suggesting a potential role for the involvement in the clinical manifestations. In this review, we summarize the novel findings and explore the relationship between FMRP and non-coding RNA pathways, particularly the piRNA pathway, thereby providing critical insights on the molecular pathogenesis of Fragile X syndrome, and potential translational applications in clinical management of the disease.

  14. Structure of the human gene encoding the associated microfibrillar protein (MFAP1) and localization to chromosome 15q15-q21

    Energy Technology Data Exchange (ETDEWEB)

    Yeh, H.; Chow, M.; Abrams, W.R. [Univ. of Pennsylvania, Philadelphia, PA (United States)] [and others

    1994-09-15

    Microfibrils with a diameter of 10-12 nm, found either in assocation with elastin or independently, are an important component of the extracellular matrix of many tissues. To extend understanding of the proteins composing these microfibrils, the cDNA and gene encoding the human associated microfibril protein (MRAP1) have been cloned and characterized. The coding portion is contained in 9 exons, and the sequence is very homologous to the previously described chick cDNA, but does not appear to share homology or domain motifs with any other known protein. Interestingly, the gene has been localized to chromosome 15q15-q21 by somatic hybrid cell and chromosome in situ analyses. This is the same chromosomal region to which the fibrillin gene, FBN1, known to be defective in the Marfan syndrome, has been mapped. MFAP1 is a candidate gene for heritable diseases affecting microfibrils. 38 refs., 6 figs.

  15. Synergistic interactions of biotic and abiotic environmental stressors on gene expression.

    Science.gov (United States)

    Altshuler, Ianina; McLeod, Anne M; Colbourne, John K; Yan, Norman D; Cristescu, Melania E

    2015-03-01

    Understanding the response of organisms to multiple stressors is critical for predicting if populations can adapt to rapid environmental change. Natural and anthropogenic stressors often interact, complicating general predictions. In this study, we examined the interactive and cumulative effects of two common environmental stressors, lowered calcium concentration, an anthropogenic stressor, and predator presence, a natural stressor, on the water flea Daphnia pulex. We analyzed expression changes of five genes involved in calcium homeostasis - cuticle proteins (Cutie, Icp2), calbindin (Calb), and calcium pump and channel (Serca and Ip3R) - using real-time quantitative PCR (RT-qPCR) in a full factorial experiment. We observed strong synergistic interactions between low calcium concentration and predator presence. While the Ip3R gene was not affected by the stressors, the other four genes were affected in their transcriptional levels by the combination of the stressors. Transcriptional patterns of genes that code for cuticle proteins (Cutie and Icp2) and a sarcoplasmic calcium pump (Serca) only responded to the combination of stressors, changing their relative expression levels in a synergistic response, while a calcium-binding protein (Calb) responded to low calcium stress and the combination of both stressors. The expression pattern of these genes (Cutie, Icp2, and Serca) were nonlinear, yet they were dose dependent across the calcium gradient. Multiple stressors can have complex, often unexpected effects on ecosystems. This study demonstrates that the dominant interaction for the set of tested genes appears to be synergism. We argue that gene expression patterns can be used to understand and predict the type of interaction expected when organisms are exposed simultaneously to natural and anthropogenic stressors.

  16. [HMGA proteins and their genes as a potential neoplastic biomarkers].

    Science.gov (United States)

    Balcerczak, Ewa; Balcerczak, Mariusz; Mirowski, Marek

    2005-01-01

    HMGA proteins and their genes are described in this article. HMGA proteins reveal ability to bind DNA in AT-rich regions, which are characteristic for gene promoter sequences. This interaction lead to gene silencing or their overexpression. In normal tissue HMGA proteins level is low or even undetectable. During embriogenesis their level is increasing. High HMGA proteins level is characteristic for tumor phenotype of spontaneous and experimental malignant neoplasms. High HMGA proteins expression correlate with bad prognostic factors and with metastases formation. HMGA genes expression can be used as a marker of tumor progression. Present studies connected with tumor gene therapy based on HMGA proteins sythesis inhibition by the use of viral vectors containing gene encoding these proteins in antisence orientation, as well as a new potential anticancer drugs acting as crosslinkers between DNA and HMGA proteins suggest their usefulness as a targets in cancer therapy.

  17. An algorithm to discover gene signatures with predictive potential

    Directory of Open Access Journals (Sweden)

    Hallett Robin M

    2010-09-01

    Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

  18. Characterization and Prediction of Protein Phosphorylation Hotspots in Arabidopsis thaliana.

    Science.gov (United States)

    Christian, Jan-Ole; Braginets, Rostyslav; Schulze, Waltraud X; Walther, Dirk

    2012-01-01

    The regulation of protein function by modulating the surface charge status via sequence-locally enriched phosphorylation sites (P-sites) in so called phosphorylation "hotspots" has gained increased attention in recent years. We set out to identify P-hotspots in the model plant Arabidopsis thaliana. We analyzed the spacing of experimentally detected P-sites within peptide-covered regions along Arabidopsis protein sequences as available from the PhosPhAt database. Confirming earlier reports (Schweiger and Linial, 2010), we found that, indeed, P-sites tend to cluster and that distributions between serine and threonine P-sites to their respected closest next P-site differ significantly from those for tyrosine P-sites. The ability to predict P-hotspots by applying available computational P-site prediction programs that focus on identifying single P-sites was observed to be severely compromised by the inevitable interference of nearby P-sites. We devised a new approach, named HotSPotter, for the prediction of phosphorylation hotspots. HotSPotter is based primarily on local amino acid compositional preferences rather than sequence position-specific motifs and uses support vector machines as the underlying classification engine. HotSPotter correctly identified experimentally determined phosphorylation hotspots in A. thaliana with high accuracy. Applied to the Arabidopsis proteome, HotSPotter-predicted 13,677 candidate P-hotspots in 9,599 proteins corresponding to 7,847 unique genes. Hotspot containing proteins are involved predominantly in signaling processes confirming the surmised modulating role of hotspots in signaling and interaction events. Our study provides new bioinformatics means to identify phosphorylation hotspots and lays the basis for further investigating novel candidate P-hotspots. All phosphorylation hotspot annotations and predictions have been made available as part of the PhosPhAt database at http://phosphat.mpimp-golm.mpg.de.

  19. Molecular identification of a Drosophila G protein-coupled receptor specific for crustacean cardioactive peptide

    DEFF Research Database (Denmark)

    Cazzamali, Giuseppe; Hauser, Frank; Kobberup, Sune

    2003-01-01

    The Drosophila Genome Project website (www.flybase.org) contains the sequence of an annotated gene (CG6111) expected to code for a G protein-coupled receptor. We have cloned this receptor and found that its gene was not correctly predicted, because an annotated neighbouring gene (CG14547) was also...... part of the receptor gene. DNA corresponding to the corrected gene CG6111 was expressed in Chinese hamster ovary cells, where it was found to code for a receptor that could be activated by low concentrations of crustacean cardioactive peptide, which is a neuropeptide also known to occur in Drosophila...... and other insects (EC(50), 5.4 x 10(-10)M). Other known Drosophila neuropeptides, such as adipokinetic hormone, did not activate the receptor. The receptor is expressed in all developmental stages from Drosophila, but only very weakly in larvae. In adult flies, the receptor is mainly expressed in the head...

  20. Viral Genome DataBase: storing and analyzing genes and proteins from complete viral genomes.

    Science.gov (United States)

    Hiscock, D; Upton, C

    2000-05-01

    The Viral Genome DataBase (VGDB) contains detailed information of the genes and predicted protein sequences from 15 completely sequenced genomes of large (&100 kb) viruses (2847 genes). The data that is stored includes DNA sequence, protein sequence, GenBank and user-entered notes, molecular weight (MW), isoelectric point (pI), amino acid content, A + T%, nucleotide frequency, dinucleotide frequency and codon use. The VGDB is a mySQL database with a user-friendly JAVA GUI. Results of queries can be easily sorted by any of the individual parameters. The software and additional figures and information are available at http://athena.bioc.uvic.ca/genomes/index.html .

  1. Protein Sorting Prediction

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2017-01-01

    and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.......Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths...

  2. Functional requirements for bacteriophage growth: gene essentiality and expression in mycobacteriophage Giles.

    Science.gov (United States)

    Dedrick, Rebekah M; Marinelli, Laura J; Newton, Gerald L; Pogliano, Kit; Pogliano, Joseph; Hatfull, Graham F

    2013-05-01

    Bacteriophages represent a majority of all life forms, and the vast, dynamic population with early origins is reflected in their enormous genetic diversity. A large number of bacteriophage genomes have been sequenced. They are replete with novel genes without known relatives. We know little about their functions, which genes are required for lytic growth, and how they are expressed. Furthermore, the diversity is such that even genes with required functions - such as virion proteins and repressors - cannot always be recognized. Here we describe a functional genomic dissection of mycobacteriophage Giles, in which the virion proteins are identified, genes required for lytic growth are determined, the repressor is identified, and the transcription patterns determined. We find that although all of the predicted phage genes are expressed either in lysogeny or in lytic growth, 45% of the predicted genes are non-essential for lytic growth. We also describe genes required for DNA replication, show that recombination is required for lytic growth, and that Giles encodes a novel repressor. RNAseq analysis reveals abundant expression of a small non-coding RNA in a lysogen and in late lytic growth, although it is non-essential for lytic growth and does not alter lysogeny. © 2013 Blackwell Publishing Ltd.

  3. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

  4. Establishment the code for prediction of waste volume on NPP decommissioning

    International Nuclear Information System (INIS)

    Cho, W. H.; Park, S. K.; Choi, Y. D.; Kim, I. S.; Moon, J. K.

    2013-01-01

    In practice, decommissioning waste volume can be estimated appropriately by finding the differences between prediction and actual operation and considering the operational problem or supplementary matters. So in the nuclear developed countries such as U.S. or Japan, the decommissioning waste volume is predicted on the basis of the experience in their own decommissioning projects. Because of the contamination caused by radioactive material, decontamination activity and management of radio-active waste should be considered in decommissioning of nuclear facility unlike the usual plant or facility. As the decommissioning activity is performed repeatedly, data for similar activities are accumulated, and optimal strategy can be achieved by comparison with the predicted strategy. Therefore, a variety of decommissioning experiences are the most important. In Korea, there is no data on the decommissioning of commercial nuclear power plants yet. However, KAERI has accumulated the basis decommissioning data of nuclear facility through decommissioning of research reactor (KRR-2) and uranium conversion plant (UCP). And DECOMMIS(DECOMMissioning Information Management System) was developed to provide and manage the whole data of decommissioning project. Two codes, FAC code and WBS code, were established in this process. FAC code is the one which is classified by decommissioning target of nuclear facility, and WBS code is classified by each decommissioning activity. The reason why two codes where created is that the codes used in DEFACS (Decommissioning Facility Characterization management System) and DEWOCS (Decommissioning Work-unit productivity Calculation System) are different from each other, and they were classified each purpose. DEFACS which manages the facility needs the code that categorizes facility characteristics, and DEWOCS which calculates unit productivity needs the code that categorizes decommissioning waste volume. KAERI has accumulated decommissioning data of KRR

  5. Comparative genomic analysis reveals a novel mitochondrial isoform of human rTS protein and unusual phylogenetic distribution of the rTS gene

    Directory of Open Access Journals (Sweden)

    McGuire John J

    2005-09-01

    Full Text Available Abstract Background The rTS gene (ENOSF1, first identified in Homo sapiens as a gene complementary to the thymidylate synthase (TYMS mRNA, is known to encode two protein isoforms, rTSα and rTSβ. The rTSβ isoform appears to be an enzyme responsible for the synthesis of signaling molecules involved in the down-regulation of thymidylate synthase, but the exact cellular functions of rTS genes are largely unknown. Results Through comparative genomic sequence analysis, we predicted the existence of a novel protein isoform, rTS, which has a 27 residue longer N-terminus by virtue of utilizing an alternative start codon located upstream of the start codon in rTSβ. We observed that a similar extended N-terminus could be predicted in all rTS genes for which genomic sequences are available and the extended regions are conserved from bacteria to human. Therefore, we reasoned that the protein with the extended N-terminus might represent an ancestral form of the rTS protein. Sequence analysis strongly predicts a mitochondrial signal sequence in the extended N-terminal of human rTSγ, which is absent in rTSβ. We confirmed the existence of rTS in human mitochondria experimentally by demonstrating the presence of both rTSγ and rTSβ proteins in mitochondria isolated by subcellular fractionation. In addition, our comprehensive analysis of rTS orthologous sequences reveals an unusual phylogenetic distribution of this gene, which suggests the occurrence of one or more horizontal gene transfer events. Conclusion The presence of two rTS isoforms in mitochondria suggests that the rTS signaling pathway may be active within mitochondria. Our report also presents an example of identifying novel protein isoforms and for improving gene annotation through comparative genomic analysis.

  6. Comparative genomic analysis reveals a novel mitochondrial isoform of human rTS protein and unusual phylogenetic distribution of the rTS gene

    Science.gov (United States)

    Liang, Ping; Nair, Jayakumar R; Song, Lei; McGuire, John J; Dolnick, Bruce J

    2005-01-01

    Background The rTS gene (ENOSF1), first identified in Homo sapiens as a gene complementary to the thymidylate synthase (TYMS) mRNA, is known to encode two protein isoforms, rTSα and rTSβ. The rTSβ isoform appears to be an enzyme responsible for the synthesis of signaling molecules involved in the down-regulation of thymidylate synthase, but the exact cellular functions of rTS genes are largely unknown. Results Through comparative genomic sequence analysis, we predicted the existence of a novel protein isoform, rTS, which has a 27 residue longer N-terminus by virtue of utilizing an alternative start codon located upstream of the start codon in rTSβ. We observed that a similar extended N-terminus could be predicted in all rTS genes for which genomic sequences are available and the extended regions are conserved from bacteria to human. Therefore, we reasoned that the protein with the extended N-terminus might represent an ancestral form of the rTS protein. Sequence analysis strongly predicts a mitochondrial signal sequence in the extended N-terminal of human rTSγ, which is absent in rTSβ. We confirmed the existence of rTS in human mitochondria experimentally by demonstrating the presence of both rTSγ and rTSβ proteins in mitochondria isolated by subcellular fractionation. In addition, our comprehensive analysis of rTS orthologous sequences reveals an unusual phylogenetic distribution of this gene, which suggests the occurrence of one or more horizontal gene transfer events. Conclusion The presence of two rTS isoforms in mitochondria suggests that the rTS signaling pathway may be active within mitochondria. Our report also presents an example of identifying novel protein isoforms and for improving gene annotation through comparative genomic analysis. PMID:16162288

  7. Fatty acid-binding protein genes of the ancient, air-breathing, ray-finned fish, spotted gar (Lepisosteus oculatus).

    Science.gov (United States)

    Venkatachalam, Ananda B; Fontenot, Quenton; Farrara, Allyse; Wright, Jonathan M

    2018-03-01

    With the advent of high-throughput DNA sequencing technology, the genomic sequence of many disparate species has led to the relatively new discipline of genomics, the study of genome structure, function and evolution. Much work has been focused on the role of whole genome duplications (WGD) in the architecture of extant vertebrate genomes, particularly those of teleost fishes which underwent a WGD early in the teleost radiation >230 million years ago (mya). Our past work has focused on the fate of duplicated copies of a multigene family coding for the intracellular lipid-binding protein (iLBP) genes in the teleost fishes. To define the evolutionary processes that determined the fate of duplicated genes and generated the structure of extant fish genomes, however, requires comparative genomic analysis with a fish lineage that diverged before the teleost WGD, such as the spotted gar (Lepisosteus oculatus), an ancient, air-breathing, ray-finned fish. Here, we describe the genomic organization, chromosomal location and tissue-specific expression of a subfamily of the iLBP genes that code for fatty acid-binding proteins (Fabps) in spotted gar. Based on this work, we have defined the minimum suite of fabp genes prior to their duplication in the teleost lineages ~230-400 mya. Spotted gar, therefore, serves as an appropriate outgroup, or ancestral/ancient fish, that did not undergo the teleost-specific WGD. As such, analyses of the spatio-temporal regulation of spotted gar genes provides a foundation to determine whether the duplicated fabp genes have been retained in teleost genomes owing to either sub- or neofunctionalization. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. From structure prediction to genomic screens for novel non-coding RNAs.

    Directory of Open Access Journals (Sweden)

    Jan Gorodkin

    2011-08-01

    Full Text Available Non-coding RNAs (ncRNAs are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs. A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

  9. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

    Science.gov (United States)

    Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

    2012-01-01

    Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

  10. Cloning and sequencing of a gene encoding a 21-kilodalton outer membrane protein from Bordetella avium and expression of the gene in Salmonella typhimurium.

    Science.gov (United States)

    Gentry-Weeks, C R; Hultsch, A L; Kelly, S M; Keith, J M; Curtiss, R

    1992-01-01

    Three gene libraries of Bordetella avium 197 DNA were prepared in Escherichia coli LE392 by using the cosmid vectors pCP13 and pYA2329, a derivative of pCP13 specifying spectinomycin resistance. The cosmid libraries were screened with convalescent-phase anti-B. avium turkey sera and polyclonal rabbit antisera against B. avium 197 outer membrane proteins. One E. coli recombinant clone produced a 56-kDa protein which reacted with convalescent-phase serum from a turkey infected with B. avium 197. In addition, five E. coli recombinant clones were identified which produced B. avium outer membrane proteins with molecular masses of 21, 38, 40, 43, and 48 kDa. At least one of these E. coli clones, which encoded the 21-kDa protein, reacted with both convalescent-phase turkey sera and antibody against B. avium 197 outer membrane proteins. The gene for the 21-kDa outer membrane protein was localized by Tn5seq1 mutagenesis, and the nucleotide sequence was determined by dideoxy sequencing. DNA sequence analysis of the 21-kDa protein revealed an open reading frame of 582 bases that resulted in a predicted protein of 194 amino acids. Comparison of the predicted amino acid sequence of the gene encoding the 21-kDa outer membrane protein with protein sequences in the National Biomedical Research Foundation protein sequence data base indicated significant homology to the OmpA proteins of Shigella dysenteriae, Enterobacter aerogenes, E. coli, and Salmonella typhimurium and to Neisseria gonorrhoeae outer membrane protein III, Haemophilus influenzae protein P6, and Pseudomonas aeruginosa porin protein F. The gene (ompA) encoding the B. avium 21-kDa protein hybridized with 4.1-kb DNA fragments from EcoRI-digested, chromosomal DNA of Bordetella pertussis and Bordetella bronchiseptica and with 6.0- and 3.2-kb DNA fragments from EcoRI-digested, chromosomal DNA of B. avium and B. avium-like DNA, respectively. A 6.75-kb DNA fragment encoding the B. avium 21-kDa protein was subcloned into the

  11. Expression of estrogen-related gene markers in breast cancer tissue predicts aromatase inhibitor responsiveness.

    Directory of Open Access Journals (Sweden)

    Irene Moy

    Full Text Available Aromatase inhibitors (AIs are the most effective class of drugs in the endocrine treatment of breast cancer, with an approximate 50% treatment response rate. Our objective was to determine whether intratumoral expression levels of estrogen-related genes are predictive of AI responsiveness in postmenopausal women with breast cancer. Primary breast carcinomas were obtained from 112 women who received AI therapy after failing adjuvant tamoxifen therapy and developing recurrent breast cancer. Tumor ERα and PR protein expression were analyzed by immunohistochemistry (IHC. Messenger RNA (mRNA levels of 5 estrogen-related genes-AKR1C3, aromatase, ERα, and 2 estradiol/ERα target genes, BRCA1 and PR-were measured by real-time PCR. Tumor protein and mRNA levels were compared with breast cancer progression rates to determine predictive accuracy. Responsiveness to AI therapy-defined as the combined complete response, partial response, and stable disease rates for at least 6 months-was 51%; rates were 56% in ERα-IHC-positive and 14% in ERα-IHC-negative tumors. Levels of ERα, PR, or BRCA1 mRNA were independently predictive for responsiveness to AI. In cross-validated analyses, a combined measurement of tumor ERα and PR mRNA levels yielded a more superior specificity (36% and identical sensitivity (96% to the current clinical practice (ERα/PR-IHC. In patients with ERα/PR-IHC-negative tumors, analysis of mRNA expression revealed either non-significant trends or statistically significant positive predictive values for AI responsiveness. In conclusion, expression levels of estrogen-related mRNAs are predictive for AI responsiveness in postmenopausal women with breast cancer, and mRNA expression analysis may improve patient selection.

  12. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  13. Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project

    Directory of Open Access Journals (Sweden)

    McDonagh Paul D

    2003-06-01

    Full Text Available Abstract Background Seattle Biomedical Research Institute (SBRI as part of the Leishmania Genome Network (LGN is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. Results Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. Conclusion An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

  14. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

    International Nuclear Information System (INIS)

    Nigsch, Florian; Mitchell, John B.O.

    2008-01-01

    The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

  15. On splice site prediction using weight array models: a comparison of smoothing techniques

    International Nuclear Information System (INIS)

    Taher, Leila; Meinicke, Peter; Morgenstern, Burkhard

    2007-01-01

    In most eukaryotic genes, protein-coding exons are separated by non-coding introns which are removed from the primary transcript by a process called 'splicing'. The positions where introns are cut and exons are spliced together are called 'splice sites'. Thus, computational prediction of splice sites is crucial for gene finding in eukaryotes. Weight array models are a powerful probabilistic approach to splice site detection. Parameters for these models are usually derived from m-tuple frequencies in trusted training data and subsequently smoothed to avoid zero probabilities. In this study we compare three different ways of parameter estimation for m-tuple frequencies, namely (a) non-smoothed probability estimation, (b) standard pseudo counts and (c) a Gaussian smoothing procedure that we recently developed

  16. Least-Square Prediction for Backward Adaptive Video Coding

    Directory of Open Access Journals (Sweden)

    Li Xin

    2006-01-01

    Full Text Available Almost all existing approaches towards video coding exploit the temporal redundancy by block-matching-based motion estimation and compensation. Regardless of its popularity, block matching still reflects an ad hoc understanding of the relationship between motion and intensity uncertainty models. In this paper, we present a novel backward adaptive approach, named "least-square prediction" (LSP, and demonstrate its potential in video coding. Motivated by the duality between edge contour in images and motion trajectory in video, we propose to derive the best prediction of the current frame from its causal past using least-square method. It is demonstrated that LSP is particularly effective for modeling video material with slow motion and can be extended to handle fast motion by temporal warping and forward adaptation. For typical QCIF test sequences, LSP often achieves smaller MSE than , full-search, quarter-pel block matching algorithm (BMA without the need of transmitting any overhead.

  17. RNAi mediates post-transcriptional repression of gene expression in fission yeast Schizosaccharomyces pombe

    International Nuclear Information System (INIS)

    Smialowska, Agata; Djupedal, Ingela; Wang, Jingwen; Kylsten, Per; Swoboda, Peter; Ekwall, Karl

    2014-01-01

    Highlights: • Protein coding genes accumulate anti-sense sRNAs in fission yeast S. pombe. • RNAi represses protein-coding genes in S. pombe. • RNAi-mediated gene repression is post-transcriptional. - Abstract: RNA interference (RNAi) is a gene silencing mechanism conserved from fungi to mammals. Small interfering RNAs are products and mediators of the RNAi pathway and act as specificity factors in recruiting effector complexes. The Schizosaccharomyces pombe genome encodes one of each of the core RNAi proteins, Dicer, Argonaute and RNA-dependent RNA polymerase (dcr1, ago1, rdp1). Even though the function of RNAi in heterochromatin assembly in S. pombe is established, its role in controlling gene expression is elusive. Here, we report the identification of small RNAs mapped anti-sense to protein coding genes in fission yeast. We demonstrate that these genes are up-regulated at the protein level in RNAi mutants, while their mRNA levels are not significantly changed. We show that the repression by RNAi is not a result of heterochromatin formation. Thus, we conclude that RNAi is involved in post-transcriptional gene silencing in S. pombe

  18. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  19. Functional characterisation of an Arabidopsis gene strongly induced by ionising radiation: the gene coding the poly(ADP-ribose)polymerase-1 (AthPARP-1)

    International Nuclear Information System (INIS)

    Doucet-Chabeaud, G.

    2000-01-01

    Arabidopsis thaliana, the model-system in plant genetics, has been used to study the responses to DNA damage, experimentally introduced by γ-irradiation. We have characterised a radiation-induced gene coding a 111 kDa protein, AthPARP-1, homologous to the human poly(ADP-ribose)polymerase-1 (hPARP-1). As hPARP-1 is composed by three functional domain with characteristic motifs, AthPARP-1 binds to DNA bearing single-strand breaks and shows DNA damage-dependent poly(ADP-ribosyl)ation. The preferential expression of AthPARP-1 in mitotically active tissues is in agreement with a potential role in the maintenance of genome integrity during DNA replication, as proposed for its human counterpart. Transcriptional gene activation by ionising radiation of AthPARP-1 and AthPARP-2 genes is to date plant specific activation. Our expression analyses after exposure to various stress indicate that 1) AthPARP-1 and AthPARP-2 play an important role in the response to DNA lesions, particularly they are activated by genotoxic agents implicating the BER DNA repair pathway 2) AthPARP-2 gene seems to play an additional role in the signal transduction induced by oxidative stress 3) the observed expression profile of AthPARP-1 is in favour of the regulation of AthPARP-1 gene expression at the level of transcription and translation. This mode of regulation of AthPARP-1 protein biosynthesis, clearly distinct from that observed in animals, needs the implication of a so far unidentified transcription factor that is activated by the presence of DNA lesions. The major outcome of this work resides in the isolation and characterisation of such new transcription factor, which will provide new insight on the regulation of plant gene expression by genotoxic stress. (author) [fr

  20. [Cloning, mutagenesis and symbiotic phenotype of three lipid transfer protein encoding genes from Mesorhizobium huakuii 7653R].

    Science.gov (United States)

    Li, Yanan; Zeng, Xiaobo; Zhou, Xuejuan; Li, Youguo

    2016-12-04

    Lipid transfer protein superfamily is involved in lipid transport and metabolism. This study aimed to construct mutants of three lipid transfer protein encoding genes in Mesorhizobium huakuii 7653R, and to study the phenotypes and function of mutations during symbiosis with Astragalus sinicus. We used bioinformatics to predict structure characteristics and biological functions of lipid transfer proteins, and conducted semi-quantitative and fluorescent quantitative real-time PCR to analyze the expression levels of target genes in free-living and symbiotic conditions. Using pK19mob insertion mutagenesis to construct mutants, we carried out pot plant experiments to observe symbiotic phenotypes. MCHK-5577, MCHK-2172 and MCHK-2779 genes encoding proteins belonged to START/RHO alpha_C/PITP/Bet_v1/CoxG/CalC (SRPBCC) superfamily, involved in lipid transport or metabolism, and were identical to M. loti at 95% level. Gene relative transcription level of the three genes all increased compared to free-living condition. We obtained three mutants. Compared with wild-type 7653R, above-ground biomass of plants and nodulenitrogenase activity induced by the three mutants significantly decreased. Results indicated that lipid transfer protein encoding genes of Mesorhizobium huakuii 7653R may play important roles in symbiotic nitrogen fixation, and the mutations significantly affected the symbiotic phenotypes. The present work provided a basis to study further symbiotic function mechanism associated with lipid transfer proteins from rhizobia.

  1. C code generation applied to nonlinear model predictive control for an artificial pancreas

    DEFF Research Database (Denmark)

    Boiroux, Dimitri; Jørgensen, John Bagterp

    2017-01-01

    This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C...

  2. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

    Science.gov (United States)

    Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

    2018-05-08

    Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on

  3. Origins of gene, genetic code, protein and life: comprehensive view ...

    Indian Academy of Sciences (India)

    Unknown

    production, suggesting that proteins were originally produced by random peptide formation of amino acids restricted in specific amino acid compositions .... using random numbers by a computer, to confirm whether main chains of ...... world on the origin of life by the pseudo-replication of. [GADV]-proteins in the absence of ...

  4. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

    Science.gov (United States)

    Adhikari, Badri; Hou, Jie; Cheng, Jianlin

    2018-05-01

    Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction. In this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks-the first five predict contacts at 6, 7.5, 8, 8.5 and 10 Å distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11 and 12 experiments, DNCON2 achieves mean precisions of 35, 50 and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length. The web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11 and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/. chengji@missouri.edu. Supplementary data are available at Bioinformatics online.

  5. A versatile palindromic amphipathic repeat coding sequence horizontally distributed among diverse bacterial and eucaryotic microbes

    Directory of Open Access Journals (Sweden)

    Glass John I

    2010-07-01

    Full Text Available Abstract Background Intragenic tandem repeats occur throughout all domains of life and impart functional and structural variability to diverse translation products. Repeat proteins confer distinctive surface phenotypes to many unicellular organisms, including those with minimal genomes such as the wall-less bacterial monoderms, Mollicutes. One such repeat pattern in this clade is distributed in a manner suggesting its exchange by horizontal gene transfer (HGT. Expanding genome sequence databases reveal the pattern in a widening range of bacteria, and recently among eucaryotic microbes. We examined the genomic flux and consequences of the motif by determining its distribution, predicted structural features and association with membrane-targeted proteins. Results Using a refined hidden Markov model, we document a 25-residue protein sequence motif tandemly arrayed in variable-number repeats in ORFs lacking assigned functions. It appears sporadically in unicellular microbes from disparate bacterial and eucaryotic clades, representing diverse lifestyles and ecological niches that include host parasitic, marine and extreme environments. Tracts of the repeats predict a malleable configuration of recurring domains, with conserved hydrophobic residues forming an amphipathic secondary structure in which hydrophilic residues endow extensive sequence variation. Many ORFs with these domains also have membrane-targeting sequences that predict assorted topologies; others may comprise reservoirs of sequence variants. We demonstrate expressed variants among surface lipoproteins that distinguish closely related animal pathogens belonging to a subgroup of the Mollicutes. DNA sequences encoding the tandem domains display dyad symmetry. Moreover, in some taxa the domains occur in ORFs selectively associated with mobile elements. These features, a punctate phylogenetic distribution, and different patterns of dispersal in genomes of related taxa, suggest that the

  6. A Gene Encoding a DUF247 Domain Protein Cosegregates with the S Self-Incompatibility Locus in Perennial Ryegrass

    DEFF Research Database (Denmark)

    Manzanares, Chloe; Barth, Susanne; Thorogood, Daniel

    2016-01-01

    genes cosegregating with the S-locus, a highly polymorphic gene encoding for a protein containing a DUF247 was fully predictive of known S-locus genotypes at the amino acid level in the seven mapping populations. Strikingly, this gene showed a frameshift mutation in self-compatible darnel (Lolium...

  7. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.

    Science.gov (United States)

    Seok, Junhee; Davis, Ronald W; Xiao, Wenzhong

    2015-01-01

    Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.

  8. Computational prediction of protein hot spot residues.

    Science.gov (United States)

    Morrow, John Kenneth; Zhang, Shuxing

    2012-01-01

    Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

  9. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  10. Design parameters to control synthetic gene expression in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Mark Welch

    Full Text Available BACKGROUND: Production of proteins as therapeutic agents, research reagents and molecular tools frequently depends on expression in heterologous hosts. Synthetic genes are increasingly used for protein production because sequence information is easier to obtain than the corresponding physical DNA. Protein-coding sequences are commonly re-designed to enhance expression, but there are no experimentally supported design principles. PRINCIPAL FINDINGS: To identify sequence features that affect protein expression we synthesized and expressed in E. coli two sets of 40 genes encoding two commercially valuable proteins, a DNA polymerase and a single chain antibody. Genes differing only in synonymous codon usage expressed protein at levels ranging from undetectable to 30% of cellular protein. Using partial least squares regression we tested the correlation of protein production levels with parameters that have been reported to affect expression. We found that the amount of protein produced in E. coli was strongly dependent on the codons used to encode a subset of amino acids. Favorable codons were predominantly those read by tRNAs that are most highly charged during amino acid starvation, not codons that are most abundant in highly expressed E. coli proteins. Finally we confirmed the validity of our models by designing, synthesizing and testing new genes using codon biases predicted to perform well. CONCLUSION: The systematic analysis of gene design parameters shown in this study has allowed us to identify codon usage within a gene as a critical determinant of achievable protein expression levels in E. coli. We propose a biochemical basis for this, as well as design algorithms to ensure high protein production from synthetic genes. Replication of this methodology should allow similar design algorithms to be empirically derived for any expression system.

  11. Uncovering the functional constraints underlying the genomic organization of the odorant-binding protein genes.

    Science.gov (United States)

    Librado, Pablo; Rozas, Julio

    2013-01-01

    Animal olfactory systems have a critical role for the survival and reproduction of individuals. In insects, the odorant-binding proteins (OBPs) are encoded by a moderately sized gene family, and mediate the first steps of the olfactory processing. Most OBPs are organized in clusters of a few paralogs, which are conserved over time. Currently, the biological mechanism explaining the close physical proximity among OBPs is not yet established. Here, we conducted a comprehensive study aiming to gain insights into the mechanisms underlying the OBP genomic organization. We found that the OBP clusters are embedded within large conserved arrangements. These organizations also include other non-OBP genes, which often encode proteins integral to plasma membrane. Moreover, the conservation degree of such large clusters is related to the following: 1) the promoter architecture of the confined genes, 2) a characteristic transcriptional environment, and 3) the chromatin conformation of the chromosomal region. Our results suggest that chromatin domains may restrict the location of OBP genes to regions having the appropriate transcriptional environment, leading to the OBP cluster structure. However, the appropriate transcriptional environment for OBP and the other neighbor genes is not dominated by reduced levels of expression noise. Indeed, the stochastic fluctuations in the OBP transcript abundance may have a critical role in the combinatorial nature of the olfactory coding process.

  12. Finding trans-regulatory genes and protein complexes modulating meiotic recombination hotspots of human, mouse and yeast.

    Science.gov (United States)

    Wu, Min; Kwoh, Chee-Keong; Li, Xiaoli; Zheng, Jie

    2014-09-11

    The regulatory mechanism of recombination is one of the most fundamental problems in genomics, with wide applications in genome wide association studies (GWAS), birth-defect diseases, molecular evolution, cancer research, etc. Recombination events cluster into short genomic regions called "recombination hotspots". Recently, a zinc finger protein PRDM9 was reported to regulate recombination hotspots in human and mouse genomes. In addition, a 13-mer motif contained in the binding sites of PRDM9 is found to be enriched in human hotspots. However, this 13-mer motif only covers a fraction of hotspots, indicating that PRDM9 is not the only regulator of recombination hotspots. Therefore, the challenge of discovering other regulators of recombination hotspots becomes significant. Furthermore, recombination is a complex process. Hence, multiple proteins acting as machinery, rather than individual proteins, are more likely to carry out this process in a precise and stable manner. Therefore, the extension of the prediction of individual trans-regulators to protein complexes is also highly desired. In this paper, we introduce a pipeline to identify genes and protein complexes associated with recombination hotspots. First, we prioritize proteins associated with hotspots based on their preference of binding to hotspots and coldspots. Second, using the above identified genes as seeds, we apply the Random Walk with Restart algorithm (RWR) to propagate their influences to other proteins in protein-protein interaction (PPI) networks. Hence, many proteins without DNA-binding information will also be assigned a score to implicate their roles in recombination hotspots. Third, we construct sub-PPI networks induced by top genes ranked by RWR for various species (e.g., yeast, human and mouse) and detect protein complexes in those sub-PPI networks. The GO term analysis show that our prioritizing methods and the RWR algorithm are capable of identifying novel genes associated with

  13. A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

    Science.gov (United States)

    Jiang, Hui; Du, Jun; Gu, Jiming; Jin, Liugen; Pu, Yong; Fei, Bojian

    2018-04-01

    oligomeric matrix protein, wingless‑type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65‑gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.

  14. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  15. RNA- and protein-mediated control of Listeria monocytogenes virulence gene expression

    Science.gov (United States)

    Lebreton, Alice; Cossart, Pascale

    2017-01-01

    ABSTRACT The model opportunistic pathogen Listeria monocytogenes has been the object of extensive research, aiming at understanding its ability to colonize diverse environmental niches and animal hosts. Bacterial transcriptomes in various conditions reflect this efficient adaptability. We review here our current knowledge of the mechanisms allowing L. monocytogenes to respond to environmental changes and trigger pathogenicity, with a special focus on RNA-mediated control of gene expression. We highlight how these studies have brought novel concepts in prokaryotic gene regulation, such as the ‘excludon’ where the 5′-UTR of a messenger also acts as an antisense regulator of an operon transcribed in opposite orientation, or the notion that riboswitches can regulate non-coding RNAs to integrate complex metabolic stimuli into regulatory networks. Overall, the Listeria model exemplifies that fine RNA tuners act together with master regulatory proteins to orchestrate appropriate transcriptional programmes. PMID:27217337

  16. A discriminatory function for prediction of protein-DNA interactions based on alpha shape modeling.

    Science.gov (United States)

    Zhou, Weiqiang; Yan, Hong

    2010-10-15

    Protein-DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein-DNA complex are becoming available, the surface characteristics of the complex become an important research topic. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein-DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein-DNA interactions. The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm kenandzhou@hotmail.com.

  17. Female-biased expression of long non-coding RNAs in domains that escape X-inactivation in mouse

    Directory of Open Access Journals (Sweden)

    Lu Lu

    2010-11-01

    Full Text Available Abstract Background Sexual dimorphism in brain gene expression has been recognized in several animal species. However, the relevant regulatory mechanisms remain poorly understood. To investigate whether sex-biased gene expression in mammalian brain is globally regulated or locally regulated in diverse brain structures, and to study the genomic organisation of brain-expressed sex-biased genes, we performed a large scale gene expression analysis of distinct brain regions in adult male and female mice. Results This study revealed spatial specificity in sex-biased transcription in the mouse brain, and identified 173 sex-biased genes in the striatum; 19 in the neocortex; 12 in the hippocampus and 31 in the eye. Genes located on sex chromosomes were consistently over-represented in all brain regions. Analysis on a subset of genes with sex-bias in more than one tissue revealed Y-encoded male-biased transcripts and X-encoded female-biased transcripts known to escape X-inactivation. In addition, we identified novel coding and non-coding X-linked genes with female-biased expression in multiple tissues. Interestingly, the chromosomal positions of all of the female-biased non-coding genes are in close proximity to protein-coding genes that escape X-inactivation. This defines X-chromosome domains each of which contains a coding and a non-coding female-biased gene. Lack of repressive chromatin marks in non-coding transcribed loci supports the possibility that they escape X-inactivation. Moreover, RNA-DNA combined FISH experiments confirmed the biallelic expression of one such novel domain. Conclusion This study demonstrated that the amount of genes with sex-biased expression varies between individual brain regions in mouse. The sex-biased genes identified are localized on many chromosomes. At the same time, sexually dimorphic gene expression that is common to several parts of the brain is mostly restricted to the sex chromosomes. Moreover, the study uncovered

  18. A comparative analysis of soft computing techniques for gene prediction.

    Science.gov (United States)

    Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

    2013-07-01

    The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Characterization of foot-and-mouth disease virus gene products with antisera against bacterially synthesized fusion proteins

    International Nuclear Information System (INIS)

    Strebel, K.; Beck, E.; Strohmaier, K.; Schaller, H.

    1986-01-01

    Defined segments of the cloned foot-and-mouth disease virus genome corresponding to all parts of the coding region were expressed in Escherichia coli as fusions to the N-terminal part of the MS2-polymerase gene under the control of the inducible λPL promoter. All constructs yielded large amounts of proteins, which were purified and used to raise sequence-specific antisera in rabbits. These antisera were used to identify the corresponding viral gene products in 35 S-labeled extracts from foot-and-mouth disease virus-infected BHK cells. This allowed us to locate unequivocally all mature foot-and-mouth disease virus gene products in the nucleotide sequence, to identify precursor-product relationships, and to detect several foot-and mouth disease virus gene products not previously identified in vivo or in vitro

  20. Inference of gene-phenotype associations via protein-protein interaction and orthology.

    Directory of Open Access Journals (Sweden)

    Panwen Wang

    Full Text Available One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.