Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

Science.gov (United States)

Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

2017-01-01

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

Design of Genomic Signatures of Pathogen Identification & Characterization

Energy Technology Data Exchange (ETDEWEB)

Slezak, T; Gardner, S; Allen, J; Vitalis, E; Jaing, C

2010-02-09

This chapter will address some of the many issues associated with the identification of signatures based on genomic DNA/RNA, which can be used to identify and characterize pathogens for biodefense and microbial forensic goals. For the purposes of this chapter, we define a signature as one or more strings of contiguous genomic DNA or RNA bases that are sufficient to identify a pathogenic target of interest at the desired resolution and which could be instantiated with particular detection chemistry on a particular platform. The target may be a whole organism, an individual functional mechanism (e.g., a toxin gene), or simply a nucleic acid indicative of the organism. The desired resolution will vary with each program's goals but could easily range from family to genus to species to strain to isolate. The resolution may not be taxonomically based but rather pan-mechanistic in nature: detecting virulence or antibiotic-resistance genes shared by multiple microbes. Entire industries exist around different detection chemistries and instrument platforms for identification of pathogens, and we will only briefly mention a few of the techniques that we have used at Lawrence Livermore National Laboratory (LLNL) to support our biosecurity-related work since 2000. Most nucleic acid based detection chemistries involve the ability to isolate and amplify the signature target region(s), combined with a technique to detect the amplification. Genomic signature based identification techniques have the advantage of being precise, highly sensitive and relatively fast in comparison to biochemical typing methods and protein signatures. Classical biochemical typing methods were developed long before knowledge of DNA and resulted in dozens of tests (Gram's stain, differential growth characteristics media, etc.) that could be used to roughly characterize the major known pathogens (of course some are uncultivable). These tests could take many days to complete and precise resolution

GIGGLE: a search engine for large-scale integrated genome analysis

Science.gov (United States)

Layer, Ryan M; Pedersen, Brent S; DiSera, Tonya; Marth, Gabor T; Gertz, Jason; Quinlan, Aaron R

2018-01-01

GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (https://github.com/ryanlayer/giggle) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation. PMID:29309061

Toward the automated generation of genome-scale metabolic networks in the SEED.

Science.gov (United States)

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the

Toward the automated generation of genome-scale metabolic networks in the SEED

Directory of Open Access Journals (Sweden)

Gould John

2007-04-01

Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative

IONS: Identification of Orthologs by Neighborhood and Similarity-an Automated Method to Identify Orthologs in Chromosomal Regions of Common Evolutionary Ancestry and its Application to Hemiascomycetous Yeasts.

Science.gov (United States)

Seret, Marie-Line; Baret, Philippe V

2011-01-01

Comparative sequence analysis is widely used to infer gene function and study genome evolution and requires proper ortholog identification across different genomes. We have developed a program for the Identification of Orthologs in one-to-one relationship by Neighborhood and Similarity (IONS) between closely related species. The algorithm combines two levels of evidence to determine co-ancestrality at the genome scale: sequence similarity and shared neighborhood. The method was initially designed to provide anchor points for syntenic blocks within the Génolevures project concerning nine hemiascomycetous yeasts (about 50,000 genes) and is applicable to different input databases. Comparison based on use of a Rand index shows that the results are highly consistent with the pillars of the Yeast Gene Order Browser, a manually curated database. Compared with SYNERGY, another algorithm reporting homology relationships, our method's main advantages are its automation and the absence of dataset-dependent parameters, facilitating consistent integration of newly released genomes.

Ensembl Genomes 2013: scaling up access to genome-wide data.

Science.gov (United States)

Kersey, Paul Julian; Allen, James E; Christensen, Mikkel; Davis, Paul; Falin, Lee J; Grabmueller, Christoph; Hughes, Daniel Seth Toney; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Langridge, Nicholas; McDowall, Mark D; Maheswari, Uma; Maslen, Gareth; Nuhn, Michael; Ong, Chuang Kee; Paulini, Michael; Pedro, Helder; Toneva, Iliana; Tuli, Mary Ann; Walts, Brandon; Williams, Gareth; Wilson, Derek; Youens-Clark, Ken; Monaco, Marcela K; Stein, Joshua; Wei, Xuehong; Ware, Doreen; Bolser, Daniel M; Howe, Kevin Lee; Kulesha, Eugene; Lawson, Daniel; Staines, Daniel Michael

2014-01-01

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. This article provides an update to the previous publications about the resource, with a focus on recent developments. These include the addition of important new genomes (and related data sets) including crop plants, vectors of human disease and eukaryotic pathogens. In addition, the resource has scaled up its representation of bacterial genomes, and now includes the genomes of over 9000 bacteria. Specific extensions to the web and programmatic interfaces have been developed to support users in navigating these large data sets. Looking forward, analytic tools to allow targeted selection of data for visualization and download are likely to become increasingly important in future as the number of available genomes increases within all domains of life, and some of the challenges faced in representing bacterial data are likely to become commonplace for eukaryotes in future.

Efficient identification of Y chromosome sequences in the human and Drosophila genomes

Science.gov (United States)

Carvalho, Antonio Bernardo; Clark, Andrew G.

2013-01-01

Notwithstanding their biological importance, Y chromosomes remain poorly known in most species. A major obstacle to their study is the identification of Y chromosome sequences; due to its high content of repetitive DNA, in most genome projects, the Y chromosome sequence is fragmented into a large number of small, unmapped scaffolds. Identification of Y-linked genes among these fragments has yielded important insights about the origin and evolution of Y chromosomes, but the process is labor intensive, restricting studies to a small number of species. Apart from these fragmentary assemblies, in a few mammalian species, the euchromatic sequence of the Y is essentially complete, owing to painstaking BAC mapping and sequencing. Here we use female short-read sequencing and k-mer comparison to identify Y-linked sequences in two very different genomes, Drosophila virilis and human. Using this method, essentially all D. virilis scaffolds were unambiguously classified as Y-linked or not Y-linked. We found 800 new scaffolds (totaling 8.5 Mbp), and four new genes in the Y chromosome of D. virilis, including JYalpha, a gene involved in hybrid male sterility. Our results also strongly support the preponderance of gene gains over gene losses in the evolution of the Drosophila Y. In the intensively studied human genome, used here as a positive control, we recovered all previously known genes or gene families, plus a small amount (283 kb) of new, unfinished sequence. Hence, this method works in large and complex genomes and can be applied to any species with sex chromosomes. PMID:23921660

Efficient identification of Y chromosome sequences in the human and Drosophila genomes.

Science.gov (United States)

Carvalho, Antonio Bernardo; Clark, Andrew G

2013-11-01

Notwithstanding their biological importance, Y chromosomes remain poorly known in most species. A major obstacle to their study is the identification of Y chromosome sequences; due to its high content of repetitive DNA, in most genome projects, the Y chromosome sequence is fragmented into a large number of small, unmapped scaffolds. Identification of Y-linked genes among these fragments has yielded important insights about the origin and evolution of Y chromosomes, but the process is labor intensive, restricting studies to a small number of species. Apart from these fragmentary assemblies, in a few mammalian species, the euchromatic sequence of the Y is essentially complete, owing to painstaking BAC mapping and sequencing. Here we use female short-read sequencing and k-mer comparison to identify Y-linked sequences in two very different genomes, Drosophila virilis and human. Using this method, essentially all D. virilis scaffolds were unambiguously classified as Y-linked or not Y-linked. We found 800 new scaffolds (totaling 8.5 Mbp), and four new genes in the Y chromosome of D. virilis, including JYalpha, a gene involved in hybrid male sterility. Our results also strongly support the preponderance of gene gains over gene losses in the evolution of the Drosophila Y. In the intensively studied human genome, used here as a positive control, we recovered all previously known genes or gene families, plus a small amount (283 kb) of new, unfinished sequence. Hence, this method works in large and complex genomes and can be applied to any species with sex chromosomes.

An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.

Science.gov (United States)

Liu, Bingqiang; Zhang, Hanyuan; Zhou, Chuan; Li, Guojun; Fennell, Anne; Wang, Guanghui; Kang, Yu; Liu, Qi; Ma, Qin

2016-08-09

Phylogenetic footprinting is an important computational technique for identifying cis-regulatory motifs in orthologous regulatory regions from multiple genomes, as motifs tend to evolve slower than their surrounding non-functional sequences. Its application, however, has several difficulties for optimizing the selection of orthologous data and reducing the false positives in motif prediction. Here we present an integrative phylogenetic footprinting framework for accurate motif predictions in prokaryotic genomes (MP(3)). The framework includes a new orthologous data preparation procedure, an additional promoter scoring and pruning method and an integration of six existing motif finding algorithms as basic motif search engines. Specifically, we collected orthologous genes from available prokaryotic genomes and built the orthologous regulatory regions based on sequence similarity of promoter regions. This procedure made full use of the large-scale genomic data and taxonomy information and filtered out the promoters with limited contribution to produce a high quality orthologous promoter set. The promoter scoring and pruning is implemented through motif voting by a set of complementary predicting tools that mine as many motif candidates as possible and simultaneously eliminate the effect of random noise. We have applied the framework to Escherichia coli k12 genome and evaluated the prediction performance through comparison with seven existing programs. This evaluation was systematically carried out at the nucleotide and binding site level, and the results showed that MP(3) consistently outperformed other popular motif finding tools. We have integrated MP(3) into our motif identification and analysis server DMINDA, allowing users to efficiently identify and analyze motifs in 2,072 completely sequenced prokaryotic genomes. The performance evaluation indicated that MP(3) is effective for predicting regulatory motifs in prokaryotic genomes. Its application may enhance

Genome-Enhanced Detection and Identification (GEDI of plant pathogens

Directory of Open Access Journals (Sweden)

Nicolas Feau

2018-02-01

Full Text Available Plant diseases caused by fungi and Oomycetes represent worldwide threats to crops and forest ecosystems. Effective prevention and appropriate management of emerging diseases rely on rapid detection and identification of the causal pathogens. The increase in genomic resources makes it possible to generate novel genome-enhanced DNA detection assays that can exploit whole genomes to discover candidate genes for pathogen detection. A pipeline was developed to identify genome regions that discriminate taxa or groups of taxa and can be converted into PCR assays. The modular pipeline is comprised of four components: (1 selection and genome sequencing of phylogenetically related taxa, (2 identification of clusters of orthologous genes, (3 elimination of false positives by filtering, and (4 assay design. This pipeline was applied to some of the most important plant pathogens across three broad taxonomic groups: Phytophthoras (Stramenopiles, Oomycota, Dothideomycetes (Fungi, Ascomycota and Pucciniales (Fungi, Basidiomycota. Comparison of 73 fungal and Oomycete genomes led the discovery of 5,939 gene clusters that were unique to the targeted taxa and an additional 535 that were common at higher taxonomic levels. Approximately 28% of the 299 tested were converted into qPCR assays that met our set of specificity criteria. This work demonstrates that a genome-wide approach can efficiently identify multiple taxon-specific genome regions that can be converted into highly specific PCR assays. The possibility to easily obtain multiple alternative regions to design highly specific qPCR assays should be of great help in tackling challenging cases for which higher taxon-resolution is needed.

Large-scale chromosome folding versus genomic DNA sequences: A discrete double Fourier transform technique.

Science.gov (United States)

Chechetkin, V R; Lobzin, V V

2017-08-07

Using state-of-the-art techniques combining imaging methods and high-throughput genomic mapping tools leaded to the significant progress in detailing chromosome architecture of various organisms. However, a gap still remains between the rapidly growing structural data on the chromosome folding and the large-scale genome organization. Could a part of information on the chromosome folding be obtained directly from underlying genomic DNA sequences abundantly stored in the databanks? To answer this question, we developed an original discrete double Fourier transform (DDFT). DDFT serves for the detection of large-scale genome regularities associated with domains/units at the different levels of hierarchical chromosome folding. The method is versatile and can be applied to both genomic DNA sequences and corresponding physico-chemical parameters such as base-pairing free energy. The latter characteristic is closely related to the replication and transcription and can also be used for the assessment of temperature or supercoiling effects on the chromosome folding. We tested the method on the genome of E. coli K-12 and found good correspondence with the annotated domains/units established experimentally. As a brief illustration of further abilities of DDFT, the study of large-scale genome organization for bacteriophage PHIX174 and bacterium Caulobacter crescentus was also added. The combined experimental, modeling, and bioinformatic DDFT analysis should yield more complete knowledge on the chromosome architecture and genome organization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identification methods for structural health monitoring

CERN Document Server

Papadimitriou, Costas

2016-01-01

The papers in this volume provide an introduction to well known and established system identification methods for structural health monitoring and to more advanced, state-of-the-art tools, able to tackle the challenges associated with actual implementation. Starting with an overview on fundamental methods, introductory concepts are provided on the general framework of time and frequency domain, parametric and non-parametric methods, input-output or output only techniques. Cutting edge tools are introduced including, nonlinear system identification methods; Bayesian tools; and advanced modal identification techniques (such as the Kalman and particle filters, the fast Bayesian FFT method). Advanced computational tools for uncertainty quantification are discussed to provide a link between monitoring and structural integrity assessment. In addition, full scale applications and field deployments that illustrate the workings and effectiveness of the introduced monitoring schemes are demonstrated.

Finding function: evaluation methods for functional genomic data

Directory of Open Access Journals (Sweden)

Barrett Daniel R

2006-07-01

Full Text Available Abstract Background Accurate evaluation of the quality of genomic or proteomic data and computational methods is vital to our ability to use them for formulating novel biological hypotheses and directing further experiments. There is currently no standard approach to evaluation in functional genomics. Our analysis of existing approaches shows that they are inconsistent and contain substantial functional biases that render the resulting evaluations misleading both quantitatively and qualitatively. These problems make it essentially impossible to compare computational methods or large-scale experimental datasets and also result in conclusions that generalize poorly in most biological applications. Results We reveal issues with current evaluation methods here and suggest new approaches to evaluation that facilitate accurate and representative characterization of genomic methods and data. Specifically, we describe a functional genomics gold standard based on curation by expert biologists and demonstrate its use as an effective means of evaluation of genomic approaches. Our evaluation framework and gold standard are freely available to the community through our website. Conclusion Proper methods for evaluating genomic data and computational approaches will determine how much we, as a community, are able to learn from the wealth of available data. We propose one possible solution to this problem here but emphasize that this topic warrants broader community discussion.

Identification of coding and non-coding mutational hotspots in cancer genomes.

Science.gov (United States)

Piraino, Scott W; Furney, Simon J

2017-01-05

The identification of mutations that play a causal role in tumour development, so called "driver" mutations, is of critical importance for understanding how cancers form and how they might be treated. Several large cancer sequencing projects have identified genes that are recurrently mutated in cancer patients, suggesting a role in tumourigenesis. While the landscape of coding drivers has been extensively studied and many of the most prominent driver genes are well characterised, comparatively less is known about the role of mutations in the non-coding regions of the genome in cancer development. The continuing fall in genome sequencing costs has resulted in a concomitant increase in the number of cancer whole genome sequences being produced, facilitating systematic interrogation of both the coding and non-coding regions of cancer genomes. To examine the mutational landscapes of tumour genomes we have developed a novel method to identify mutational hotspots in tumour genomes using both mutational data and information on evolutionary conservation. We have applied our methodology to over 1300 whole cancer genomes and show that it identifies prominent coding and non-coding regions that are known or highly suspected to play a role in cancer. Importantly, we applied our method to the entire genome, rather than relying on predefined annotations (e.g. promoter regions) and we highlight recurrently mutated regions that may have resulted from increased exposure to mutational processes rather than selection, some of which have been identified previously as targets of selection. Finally, we implicate several pan-cancer and cancer-specific candidate non-coding regions, which could be involved in tumourigenesis. We have developed a framework to identify mutational hotspots in cancer genomes, which is applicable to the entire genome. This framework identifies known and novel coding and non-coding mutional hotspots and can be used to differentiate candidate driver regions from

Genome-scale biological models for industrial microbial systems.

Science.gov (United States)

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

IdentiCS – Identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

Directory of Open Access Journals (Sweden)

Zeng An-Ping

2004-08-01

Full Text Available Abstract Background A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence. Results In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download

The OME Framework for genome-scale systems biology

Energy Technology Data Exchange (ETDEWEB)

Palsson, Bernhard O. [Univ. of California, San Diego, CA (United States); Ebrahim, Ali [Univ. of California, San Diego, CA (United States); Federowicz, Steve [Univ. of California, San Diego, CA (United States)

2014-12-19

The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed [1-3]. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome- scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind [4]. It is also evident that, given the plethora of databases and software efforts [5-12], it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models [13-15]. We work at the forefront of model creation and systems scale data generation [16-18]. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities [13, 19-24] towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale

Extreme-Scale De Novo Genome Assembly

Energy Technology Data Exchange (ETDEWEB)

Georganas, Evangelos [Intel Corporation, Santa Clara, CA (United States); Hofmeyr, Steven [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Joint Genome Inst.; Egan, Rob [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division; Buluc, Aydin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Joint Genome Inst.; Oliker, Leonid [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Joint Genome Inst.; Rokhsar, Daniel [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division; Yelick, Katherine [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Joint Genome Inst.

2017-09-26

De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMER, a high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. Genome assembly software has many components, each of which stresses different components of a computer system. This chapter explains the computational challenges involved in each step of the HipMer pipeline, the key distributed data structures, and communication costs in detail. We present performance results of assembling the human genome and the large hexaploid wheat genome on large supercomputers up to tens of thousands of cores.

The Effects of Signal Erosion and Core Genome Reduction on the Identification of Diagnostic Markers

Directory of Open Access Journals (Sweden)

Jason W. Sahl

2016-09-01

Full Text Available Whole-genome sequence (WGS data are commonly used to design diagnostic targets for the identification of bacterial pathogens. To do this effectively, genomics databases must be comprehensive to identify the strict core genome that is specific to the target pathogen. As additional genomes are analyzed, the core genome size is reduced and there is erosion of the target-specific regions due to commonality with related species, potentially resulting in the identification of false positives and/or false negatives.

Next-generation genome-scale models for metabolic engineering

DEFF Research Database (Denmark)

King, Zachary A.; Lloyd, Colton J.; Feist, Adam M.

2015-01-01

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict...... examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering....

TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

Directory of Open Access Journals (Sweden)

Jensen Paul A

2011-09-01

Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

Directory of Open Access Journals (Sweden)

Xiaoming Xi

2013-07-01

Full Text Available Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT, which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

IONS: Identification of Orthologs by Neighborhood and Similarity—an Automated Method to Identify Orthologs in Chromosomal Regions of Common Evolutionary Ancestry and its Application to Hemiascomycetous Yeasts

Science.gov (United States)

Seret, Marie-Line; Baret, Philippe V.

2011-01-01

Comparative sequence analysis is widely used to infer gene function and study genome evolution and requires proper ortholog identification across different genomes. We have developed a program for the Identification of Orthologs in one-to-one relationship by Neighborhood and Similarity (IONS) between closely related species. The algorithm combines two levels of evidence to determine co-ancestrality at the genome scale: sequence similarity and shared neighborhood. The method was initially designed to provide anchor points for syntenic blocks within the Génolevures project concerning nine hemiascomycetous yeasts (about 50,000 genes) and is applicable to different input databases. Comparison based on use of a Rand index shows that the results are highly consistent with the pillars of the Yeast Gene Order Browser, a manually curated database. Compared with SYNERGY, another algorithm reporting homology relationships, our method’s main advantages are its automation and the absence of dataset-dependent parameters, facilitating consistent integration of newly released genomes. PMID:21918595

The Genome-Scale Integrated Networks in Microorganisms

Directory of Open Access Journals (Sweden)

Tong Hao

2018-02-01

Full Text Available The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types of the molecular networks, for example, genome-scale metabolic network (GMN, transcriptional regulatory network (TRN, and signal transduction network (STN. It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme.

Science.gov (United States)

Bi, Jianjun; Song, Rengang; Yang, Huilan; Li, Bingling; Fan, Jianyong; Liu, Zhongrong; Long, Chaoqin

2011-01-01

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

KAUST Repository

Fan, Ming

2012-06-28

With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

SECOM: A novel hash seed and community detection based-approach for genome-scale protein domain identification

KAUST Repository

Fan, Ming; Wong, Ka-Chun; Ryu, Tae Woo; Ravasi, Timothy; Gao, Xin

2012-01-01

With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2012 Fan et al.

Harnessing Whole Genome Sequencing in Medical Mycology.

Science.gov (United States)

Cuomo, Christina A

2017-01-01

Comparative genome sequencing studies of human fungal pathogens enable identification of genes and variants associated with virulence and drug resistance. This review describes current approaches, resources, and advances in applying whole genome sequencing to study clinically important fungal pathogens. Genomes for some important fungal pathogens were only recently assembled, revealing gene family expansions in many species and extreme gene loss in one obligate species. The scale and scope of species sequenced is rapidly expanding, leveraging technological advances to assemble and annotate genomes with higher precision. By using iteratively improved reference assemblies or those generated de novo for new species, recent studies have compared the sequence of isolates representing populations or clinical cohorts. Whole genome approaches provide the resolution necessary for comparison of closely related isolates, for example, in the analysis of outbreaks or sampled across time within a single host. Genomic analysis of fungal pathogens has enabled both basic research and diagnostic studies. The increased scale of sequencing can be applied across populations, and new metagenomic methods allow direct analysis of complex samples.

Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

DEFF Research Database (Denmark)

Olivares Hernandez, Roberto

Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been construc......Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been...

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

Science.gov (United States)

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

Genome-wide Studies of Mycolic Acid Bacteria: Computational Identification and Analysis of a Minimal Genome

KAUST Repository

Kamanu, Frederick Kinyua

2012-12-01

The mycolic acid bacteria are a distinct suprageneric group of asporogenous Grampositive, high GC-content bacteria, distinguished by the presence of mycolic acids in their cell envelope. They exhibit great diversity in their cell and morphology; although primarily non-pathogens, this group contains three major pathogens Mycobacterium leprae, Mycobacterium tuberculosis complex, and Corynebacterium diphtheria. Although the mycolic acid bacteria are a clearly defined group of bacteria, the taxonomic relationships between its constituent genera and species are less well defined. Two approaches were tested for their suitability in describing the taxonomy of the group. First, a Multilocus Sequence Typing (MLST) experiment was assessed and found to be superior to monophyletic (16S small ribosomal subunit) in delineating a total of 52 mycolic acid bacterial species. Phylogenetic inference was performed using the neighbor-joining method. To further refine phylogenetic analysis and to take advantage of the widespread availability of bacterial genome data, a computational framework that simulates DNA-DNA hybridisation was developed and validated using multiscale bootstrap resampling. The tool classifies microbial genomes based on whole genome DNA, and was deployed as a web-application using PHP and Javascript. It is accessible online at http://cbrc.kaust.edu.sa/dna_hybridization/ A third study was a computational and statistical methods in the identification and analysis of a putative minimal mycolic acid bacterial genome so as to better understand (1) the genomic requirements to encode a mycolic acid bacterial cell and (2) the role and type of genes and genetic elements that lead to the massive increase in genome size in environmental mycolic acid bacteria. Using a reciprocal comparison approach, a total of 690 orthologous gene clusters forming a putative minimal genome were identified across 24 mycolic acid bacterial species. In order to identify new potential drug

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.

Cross-species genome-wide identification of evolutionary conserved microproteins

DEFF Research Database (Denmark)

Straub, Daniel; Wenkel, Stephan

2017-01-01

Protein concept beyond transcription factors to other protein families. Here, we reveal potential microProtein candidates in several plant and animal reference genomes. A large number of these microProteins are species-specific while others evolved early and are evolutionary highly conserved. Most known micro...... act in plant transcriptional regulation, signal transduction and anatomical structure development. MiPFinder is freely available to find microProteins in any genome and will aid in the identification of novel microProteins in plants and animals....

Reverse sample genome probing, a new technique for identification of bacteria in environmental samples by DNA hybridization, and its application to the identification of sulfate-reducing bacteria in oil field samples

International Nuclear Information System (INIS)

Voordouw, G.; Voordouw, J.K.; Karkhoff-Schweizer, R.R.; Fedorak, P.M.; Westlake, D.W.S.

1991-01-01

A novel method for identification of bacteria in environmental samples by DNA hybridization is presented. It is based on the fact that, even within a genus, the genomes of different bacteria may have little overall sequence homology. This allows the use of the labeled genomic DNA of a given bacterium (referred to as a standard) to probe for its presence and that of bacteria with highly homologous genomes in total DNA obtained from an environmental sample. Alternatively, total DNA extracted from the sample can be labeled and used to probe filters on which denatured chromosomal DNA from relevant bacterial standards has been spotted. The latter technique is referred to as reverse sample genome probing, since it is the reverse of the usual practice of deriving probes from reference bacteria for analyzing a DNA sample. Reverse sample genome probing allows identification of bacteria in a sample in a single step once a master filter with suitable standards has been developed. Application of reverse sample genome probing to the identification of sulfate-reducing bacteria in 31 samples obtained primarily from oil fields in the province of Alberta has indicated that there are at least 20 genotypically different sulfate-reducing bacteria in these samples

Genome scale engineering techniques for metabolic engineering.

Science.gov (United States)

Liu, Rongming; Bassalo, Marcelo C; Zeitoun, Ramsey I; Gill, Ryan T

2015-11-01

Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data.

Science.gov (United States)

Jorjani, Hadi; Zavolan, Mihaela

2014-04-01

Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually. In this work, we present a computational method called 'TSSer' that enables the automatic inference of TSSs from dRNA-seq data. The method rests on a probabilistic framework for identifying both genomic positions that are preferentially enriched in the dRNA-seq data as well as preferentially captured relative to neighboring genomic regions. Evaluating our approach for TSS calling on several publicly available datasets, we find that TSSer achieves high consistency with the curated lists of annotated TSSs, but identifies many additional TSSs. Therefore, TSSer can accelerate genome-wide identification of TSSs in bacterial genomes and can aid in further characterization of bacterial transcription regulatory networks. TSSer is freely available under GPL license at http://www.clipz.unibas.ch/TSSer/index.php

Enumeration of smallest intervention strategies in genome-scale metabolic networks.

Directory of Open Access Journals (Sweden)

Axel von Kamp

2014-01-01

Full Text Available One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions in genome-scale metabolic network models. For this we combine two approaches, namely (i the mapping of MCSs to EMs in a dual network, and (ii a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth than reported previously. The strength of the presented approach is that smallest intervention strategies can be

Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

Science.gov (United States)

2015-12-01

do it. Thus, instead of simply sequencing all the FFPE samples, we used 10 tumor samples (5 recurrent and 5 non recurrent ) to test sequencing and...Award Number: W81XWH-12-1-0521 TITLE: Identification of a Genomic Signature Predicting for Recurrence in Early-Stage Ovarian Cancer PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0521 Identification of a Genomic Signature Predicting for Recurrence in

Use of genome-scale microbial models for metabolic engineering

DEFF Research Database (Denmark)

Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

2004-01-01

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metaboli...... network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction....

Multi-scale structural community organisation of the human genome.

Science.gov (United States)

Boulos, Rasha E; Tremblay, Nicolas; Arneodo, Alain; Borgnat, Pierre; Audit, Benjamin

2017-04-11

Structural interaction frequency matrices between all genome loci are now experimentally achievable thanks to high-throughput chromosome conformation capture technologies. This ensues a new methodological challenge for computational biology which consists in objectively extracting from these data the structural motifs characteristic of genome organisation. We deployed the fast multi-scale community mining algorithm based on spectral graph wavelets to characterise the networks of intra-chromosomal interactions in human cell lines. We observed that there exist structural domains of all sizes up to chromosome length and demonstrated that the set of structural communities forms a hierarchy of chromosome segments. Hence, at all scales, chromosome folding predominantly involves interactions between neighbouring sites rather than the formation of links between distant loci. Multi-scale structural decomposition of human chromosomes provides an original framework to question structural organisation and its relationship to functional regulation across the scales. By construction the proposed methodology is independent of the precise assembly of the reference genome and is thus directly applicable to genomes whose assembly is not fully determined.

Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

Science.gov (United States)

Sadhukhan, Priyanka P; Raghunathan, Anu

2014-01-01

Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

Statistical Methods in Integrative Genomics

Science.gov (United States)

Richardson, Sylvia; Tseng, George C.; Sun, Wei

2016-01-01

Statistical methods in integrative genomics aim to answer important biology questions by jointly analyzing multiple types of genomic data (vertical integration) or aggregating the same type of data across multiple studies (horizontal integration). In this article, we introduce different types of genomic data and data resources, and then review statistical methods of integrative genomics, with emphasis on the motivation and rationale of these methods. We conclude with some summary points and future research directions. PMID:27482531

High-Throughput Block Optical DNA Sequence Identification.

Science.gov (United States)

Sagar, Dodderi Manjunatha; Korshoj, Lee Erik; Hanson, Katrina Bethany; Chowdhury, Partha Pratim; Otoupal, Peter Britton; Chatterjee, Anushree; Nagpal, Prashant

2018-01-01

Optical techniques for molecular diagnostics or DNA sequencing generally rely on small molecule fluorescent labels, which utilize light with a wavelength of several hundred nanometers for detection. Developing a label-free optical DNA sequencing technique will require nanoscale focusing of light, a high-throughput and multiplexed identification method, and a data compression technique to rapidly identify sequences and analyze genomic heterogeneity for big datasets. Such a method should identify characteristic molecular vibrations using optical spectroscopy, especially in the "fingerprinting region" from ≈400-1400 cm -1 . Here, surface-enhanced Raman spectroscopy is used to demonstrate label-free identification of DNA nucleobases with multiplexed 3D plasmonic nanofocusing. While nanometer-scale mode volumes prevent identification of single nucleobases within a DNA sequence, the block optical technique can identify A, T, G, and C content in DNA k-mers. The content of each nucleotide in a DNA block can be a unique and high-throughput method for identifying sequences, genes, and other biomarkers as an alternative to single-letter sequencing. Additionally, coupling two complementary vibrational spectroscopy techniques (infrared and Raman) can improve block characterization. These results pave the way for developing a novel, high-throughput block optical sequencing method with lossy genomic data compression using k-mer identification from multiplexed optical data acquisition. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SeMPI: a genome-based secondary metabolite prediction and identification web server.

Science.gov (United States)

Zierep, Paul F; Padilla, Natàlia; Yonchev, Dimitar G; Telukunta, Kiran K; Klementz, Dennis; Günther, Stefan

2017-07-03

The secondary metabolism of bacteria, fungi and plants yields a vast number of bioactive substances. The constantly increasing amount of published genomic data provides the opportunity for an efficient identification of gene clusters by genome mining. Conversely, for many natural products with resolved structures, the encoding gene clusters have not been identified yet. Even though genome mining tools have become significantly more efficient in the identification of biosynthetic gene clusters, structural elucidation of the actual secondary metabolite is still challenging, especially due to as yet unpredictable post-modifications. Here, we introduce SeMPI, a web server providing a prediction and identification pipeline for natural products synthesized by polyketide synthases of type I modular. In order to limit the possible structures of PKS products and to include putative tailoring reactions, a structural comparison with annotated natural products was introduced. Furthermore, a benchmark was designed based on 40 gene clusters with annotated PKS products. The web server of the pipeline (SeMPI) is freely available at: http://www.pharmaceutical-bioinformatics.de/sempi. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Identification of acquired antimicrobial resistance genes

DEFF Research Database (Denmark)

Zankari, Ea; Hasman, Henrik; Cosentino, Salvatore

2012-01-01

ObjectivesIdentification of antimicrobial resistance genes is important for understanding the underlying mechanisms and the epidemiology of antimicrobial resistance. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available in routine diagnostic laborato......ObjectivesIdentification of antimicrobial resistance genes is important for understanding the underlying mechanisms and the epidemiology of antimicrobial resistance. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available in routine diagnostic...... laboratories and is anticipated to substitute traditional methods for resistance gene identification. Thus, the current challenge is to extract the relevant information from the large amount of generated data.MethodsWe developed a web-based method, ResFinder that uses BLAST for identification of acquired...... antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete or partial genomes, and short sequence reads from four different sequencing platforms. The method was evaluated on 1862 GenBank files containing 1411 different resistance genes, as well as on 23 de...

SOLiD sequencing of four Vibrio vulnificus genomes enables comparative genomic analysis and identification of candidate clade-specific virulence genes

Directory of Open Access Journals (Sweden)

Telonis-Scott Marina

2010-09-01

Full Text Available Abstract Background Vibrio vulnificus is the leading cause of reported death from consumption of seafood in the United States. Despite several decades of research on molecular pathogenesis, much remains to be learned about the mechanisms of virulence of this opportunistic bacterial pathogen. The two complete and annotated genomic DNA sequences of V. vulnificus belong to strains of clade 2, which is the predominant clade among clinical strains. Clade 2 strains generally possess higher virulence potential in animal models of disease compared with clade 1, which predominates among environmental strains. SOLiD sequencing of four V. vulnificus strains representing different clades (1 and 2 and biotypes (1 and 2 was used for comparative genomic analysis. Results Greater than 4,100,000 bases were sequenced of each strain, yielding approximately 100-fold coverage for each of the four genomes. Although the read lengths of SOLiD genomic sequencing were only 35 nt, we were able to make significant conclusions about the unique and shared sequences among the genomes, including identification of single nucleotide polymorphisms. Comparative analysis of the newly sequenced genomes to the existing reference genomes enabled the identification of 3,459 core V. vulnificus genes shared among all six strains and 80 clade 2-specific genes. We identified 523,161 SNPs among the six genomes. Conclusions We were able to glean much information about the genomic content of each strain using next generation sequencing. Flp pili, GGDEF proteins, and genomic island XII were identified as possible virulence factors because of their presence in virulent sequenced strains. Genomic comparisons also point toward the involvement of sialic acid catabolism in pathogenesis.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

Science.gov (United States)

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

Directory of Open Access Journals (Sweden)

Hansaim Lim

2016-10-01

Full Text Available Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

Science.gov (United States)

Lim, Hansaim; Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; Xie, Lei

2016-10-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments.

Science.gov (United States)

Raviram, Ramya; Rocha, Pedro P; Müller, Christian L; Miraldi, Emily R; Badri, Sana; Fu, Yi; Swanzey, Emily; Proudhon, Charlotte; Snetkova, Valentina; Bonneau, Richard; Skok, Jane A

2016-03-01

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments.

Directory of Open Access Journals (Sweden)

Ramya Raviram

2016-03-01

Full Text Available 4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait" that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Mass spectrometry allows direct identification of proteins in large genomes

DEFF Research Database (Denmark)

Küster, B; Mortensen, Peter V.; Andersen, Jens S.

2001-01-01

Proteome projects seek to provide systematic functional analysis of the genes uncovered by genome sequencing initiatives. Mass spectrometric protein identification is a key requirement in these studies but to date, database searching tools rely on the availability of protein sequences derived fro...

Multiplexed genome engineering and genotyping methods applications for synthetic biology and metabolic engineering.

Science.gov (United States)

Wang, Harris H; Church, George M

2011-01-01

Engineering at the scale of whole genomes requires fundamentally new molecular biology tools. Recent advances in recombineering using synthetic oligonucleotides enable the rapid generation of mutants at high efficiency and specificity and can be implemented at the genome scale. With these techniques, libraries of mutants can be generated, from which individuals with functionally useful phenotypes can be isolated. Furthermore, populations of cells can be evolved in situ by directed evolution using complex pools of oligonucleotides. Here, we discuss ways to utilize these multiplexed genome engineering methods, with special emphasis on experimental design and implementation. Copyright © 2011 Elsevier Inc. All rights reserved.

Genome scale metabolic modeling of cancer

DEFF Research Database (Denmark)

Nilsson, Avlant; Nielsen, Jens

2017-01-01

of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...

Genome-wide SNP identification in multiple morphotypes of allohexaploid tall fescue (Festuca arundinacea Schreb

Directory of Open Access Journals (Sweden)

Hand Melanie L

2012-06-01

Full Text Available Abstract Background Single nucleotide polymorphisms (SNPs provide essential tools for the advancement of research in plant genomics, and the development of SNP resources for many species has been accelerated by the capabilities of second-generation sequencing technologies. The current study aimed to develop and use a novel bioinformatic pipeline to generate a comprehensive collection of SNP markers within the agriculturally important pasture grass tall fescue; an outbreeding allopolyploid species displaying three distinct morphotypes: Continental, Mediterranean and rhizomatous. Results A bioinformatic pipeline was developed that successfully identified SNPs within genotypes from distinct tall fescue morphotypes, following the sequencing of 414 polymerase chain reaction (PCR – generated amplicons using 454 GS FLX technology. Equivalent amplicon sets were derived from representative genotypes of each morphotype, including six Continental, five Mediterranean and one rhizomatous. A total of 8,584 and 2,292 SNPs were identified with high confidence within the Continental and Mediterranean morphotypes respectively. The success of the bioinformatic approach was demonstrated through validation (at a rate of 70% of a subset of 141 SNPs using both SNaPshot™ and GoldenGate™ assay chemistries. Furthermore, the quantitative genotyping capability of the GoldenGate™ assay revealed that approximately 30% of the putative SNPs were accessible to co-dominant scoring, despite the hexaploid genome structure. The sub-genome-specific origin of each SNP validated from Continental tall fescue was predicted using a phylogenetic approach based on comparison with orthologous sequences from predicted progenitor species. Conclusions Using the appropriate bioinformatic approach, amplicon resequencing based on 454 GS FLX technology is an effective method for the identification of polymorphic SNPs within the genomes of Continental and Mediterranean tall fescue. The

Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data

DEFF Research Database (Denmark)

Clausen, Philip T. L. C.; Zankari, Ea; Aarestrup, Frank Møller

2016-01-01

to two different methods in current use for identification of antibiotic resistance genes in bacterial WGS data. A novel method, KmerResistance, which examines the co-occurrence of k-mers between the WGS data and a database of resistance genes, was developed. The performance of this method was compared...... with two previously described methods; ResFinder and SRST2, which use an assembly/BLAST method and BWA, respectively, using two datasets with a total of 339 isolates, covering five species, originating from the Oxford University Hospitals NHS Trust and Danish pig farms. The predicted resistance...... was compared with the observed phenotypes for all isolates. To challenge further the sensitivity of the in silico methods, the datasets were also down-sampled to 1% of the reads and reanalysed. The best results were obtained by identification of resistance genes by mapping directly against the raw reads...

Identification of Ohnolog Genes Originating from Whole Genome Duplication in Early Vertebrates, Based on Synteny Comparison across Multiple Genomes.

Science.gov (United States)

Singh, Param Priya; Arora, Jatin; Isambert, Hervé

2015-07-01

Whole genome duplications (WGD) have now been firmly established in all major eukaryotic kingdoms. In particular, all vertebrates descend from two rounds of WGDs, that occurred in their jawless ancestor some 500 MY ago. Paralogs retained from WGD, also coined 'ohnologs' after Susumu Ohno, have been shown to be typically associated with development, signaling and gene regulation. Ohnologs, which amount to about 20 to 35% of genes in the human genome, have also been shown to be prone to dominant deleterious mutations and frequently implicated in cancer and genetic diseases. Hence, identifying ohnologs is central to better understand the evolution of vertebrates and their susceptibility to genetic diseases. Early computational analyses to identify vertebrate ohnologs relied on content-based synteny comparisons between the human genome and a single invertebrate outgroup genome or within the human genome itself. These approaches are thus limited by lineage specific rearrangements in individual genomes. We report, in this study, the identification of vertebrate ohnologs based on the quantitative assessment and integration of synteny conservation between six amniote vertebrates and six invertebrate outgroups. Such a synteny comparison across multiple genomes is shown to enhance the statistical power of ohnolog identification in vertebrates compared to earlier approaches, by overcoming lineage specific genome rearrangements. Ohnolog gene families can be browsed and downloaded for three statistical confidence levels or recompiled for specific, user-defined, significance criteria at http://ohnologs.curie.fr/. In the light of the importance of WGD on the genetic makeup of vertebrates, our analysis provides a useful resource for researchers interested in gaining further insights on vertebrate evolution and genetic diseases.

Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

Science.gov (United States)

Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

2016-06-01

We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. Copyright © 2016 Elsevier B.V. All rights reserved.

Genome-wide DNA polymorphism analyses using VariScan

Directory of Open Access Journals (Sweden)

Vilella Albert J

2006-09-01

Full Text Available Abstract Background DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis. Results We have developed methods for the analysis of DNA sequence polymorphisms at the genome-wide scale. These methods, which have been tested on a coalescent-simulated and actual data files from mouse and human, have been implemented in the VariScan software package version 2.0. Additionally, we have also incorporated a graphical-user interface. The main features of this software are: i exhaustive population-genetic analyses including those based on the coalescent theory; ii analysis adapted to the shallow data generated by the high-throughput genome projects; iii use of genome annotations to conduct a comprehensive analyses separately for different functional regions; iv identification of relevant genomic regions by the sliding-window and wavelet-multiresolution approaches; v visualization of the results integrated with current genome annotations in commonly available genome browsers. Conclusion VariScan is a powerful and flexible suite of software for the analysis of DNA polymorphisms. The current version implements new algorithms, methods, and capabilities, providing an important tool for an exhaustive exploratory analysis of genome-wide DNA polymorphism data.

Low-pass shotgun sequencing of the barley genome facilitates rapid identification of genes, conserved non-coding sequences and novel repeats

Directory of Open Access Journals (Sweden)

Graner Andreas

2008-10-01

Full Text Available Abstract Background Barley has one of the largest and most complex genomes of all economically important food crops. The rise of new short read sequencing technologies such as Illumina/Solexa permits such large genomes to be effectively sampled at relatively low cost. Based on the corresponding sequence reads a Mathematically Defined Repeat (MDR index can be generated to map repetitive regions in genomic sequences. Results We have generated 574 Mbp of Illumina/Solexa sequences from barley total genomic DNA, representing about 10% of a genome equivalent. From these sequences we generated an MDR index which was then used to identify and mark repetitive regions in the barley genome. Comparison of the MDR plots with expert repeat annotation drawing on the information already available for known repetitive elements revealed a significant correspondence between the two methods. MDR-based annotation allowed for the identification of dozens of novel repeat sequences, though, which were not recognised by hand-annotation. The MDR data was also used to identify gene-containing regions by masking of repetitive sequences in eight de-novo sequenced bacterial artificial chromosome (BAC clones. For half of the identified candidate gene islands indeed gene sequences could be identified. MDR data were only of limited use, when mapped on genomic sequences from the closely related species Triticum monococcum as only a fraction of the repetitive sequences was recognised. Conclusion An MDR index for barley, which was obtained by whole-genome Illumina/Solexa sequencing, proved as efficient in repeat identification as manual expert annotation. Circumventing the labour-intensive step of producing a specific repeat library for expert annotation, an MDR index provides an elegant and efficient resource for the identification of repetitive and low-copy (i.e. potentially gene-containing sequences regions in uncharacterised genomic sequences. The restriction that a particular

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

Energy Technology Data Exchange (ETDEWEB)

Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

2015-03-10

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

The genome of Chenopodium quinoa

KAUST Repository

Jarvis, David Erwin; Ho, Yung Shwen; Lightfoot, Damien; Schmö ckel, Sandra M.; Li, Bo; Borm, Theo J. A.; Ohyanagi, Hajime; Mineta, Katsuhiko; Michell, Craig; Saber, Noha; Kharbatia, Najeh M.; Rupper, Ryan R.; Sharp, Aaron R.; Dally, Nadine; Boughton, Berin A.; Woo, Yong; Gao, Ge; Schijlen, Elio G. W. M.; Guo, Xiujie; Momin, Afaque Ahmad Imtiyaz; Negrã o, Só nia; Al-Babili, Salim; Gehring, Christoph A; Roessner, Ute; Jung, Christian; Murphy, Kevin; Arold, Stefan T.; Gojobori, Takashi; Linden, C. Gerard van der; Loo, Eibertus N. van; Jellen, Eric N.; Maughan, Peter J.; Tester, Mark A.

2017-01-01

Chenopodium quinoa (quinoa) is a highly nutritious grain identified as an important crop to improve world food security. Unfortunately, few resources are available to facilitate its genetic improvement. Here we report the assembly of a high-quality, chromosome-scale reference genome sequence for quinoa, which was produced using single-molecule real-time sequencing in combination with optical, chromosome-contact and genetic maps. We also report the sequencing of two diploids from the ancestral gene pools of quinoa, which enables the identification of sub-genomes in quinoa, and reduced-coverage genome sequences for 22 other samples of the allotetraploid goosefoot complex. The genome sequence facilitated the identification of the transcription factor likely to control the production of anti-nutritional triterpenoid saponins found in quinoa seeds, including a mutation that appears to cause alternative splicing and a premature stop codon in sweet quinoa strains. These genomic resources are an important first step towards the genetic improvement of quinoa.

The genome of Chenopodium quinoa

KAUST Repository

Jarvis, David Erwin

2017-02-08

Chenopodium quinoa (quinoa) is a highly nutritious grain identified as an important crop to improve world food security. Unfortunately, few resources are available to facilitate its genetic improvement. Here we report the assembly of a high-quality, chromosome-scale reference genome sequence for quinoa, which was produced using single-molecule real-time sequencing in combination with optical, chromosome-contact and genetic maps. We also report the sequencing of two diploids from the ancestral gene pools of quinoa, which enables the identification of sub-genomes in quinoa, and reduced-coverage genome sequences for 22 other samples of the allotetraploid goosefoot complex. The genome sequence facilitated the identification of the transcription factor likely to control the production of anti-nutritional triterpenoid saponins found in quinoa seeds, including a mutation that appears to cause alternative splicing and a premature stop codon in sweet quinoa strains. These genomic resources are an important first step towards the genetic improvement of quinoa.

The genome of Chenopodium quinoa.

Science.gov (United States)

Jarvis, David E; Ho, Yung Shwen; Lightfoot, Damien J; Schmöckel, Sandra M; Li, Bo; Borm, Theo J A; Ohyanagi, Hajime; Mineta, Katsuhiko; Michell, Craig T; Saber, Noha; Kharbatia, Najeh M; Rupper, Ryan R; Sharp, Aaron R; Dally, Nadine; Boughton, Berin A; Woo, Yong H; Gao, Ge; Schijlen, Elio G W M; Guo, Xiujie; Momin, Afaque A; Negrão, Sónia; Al-Babili, Salim; Gehring, Christoph; Roessner, Ute; Jung, Christian; Murphy, Kevin; Arold, Stefan T; Gojobori, Takashi; Linden, C Gerard van der; van Loo, Eibertus N; Jellen, Eric N; Maughan, Peter J; Tester, Mark

2017-02-16

Chenopodium quinoa (quinoa) is a highly nutritious grain identified as an important crop to improve world food security. Unfortunately, few resources are available to facilitate its genetic improvement. Here we report the assembly of a high-quality, chromosome-scale reference genome sequence for quinoa, which was produced using single-molecule real-time sequencing in combination with optical, chromosome-contact and genetic maps. We also report the sequencing of two diploids from the ancestral gene pools of quinoa, which enables the identification of sub-genomes in quinoa, and reduced-coverage genome sequences for 22 other samples of the allotetraploid goosefoot complex. The genome sequence facilitated the identification of the transcription factor likely to control the production of anti-nutritional triterpenoid saponins found in quinoa seeds, including a mutation that appears to cause alternative splicing and a premature stop codon in sweet quinoa strains. These genomic resources are an important first step towards the genetic improvement of quinoa.

Large-Scale Sequencing: The Future of Genomic Sciences Colloquium

Energy Technology Data Exchange (ETDEWEB)

Margaret Riley; Merry Buckley

2009-01-01

Genetic sequencing and the various molecular techniques it has enabled have revolutionized the field of microbiology. Examining and comparing the genetic sequences borne by microbes - including bacteria, archaea, viruses, and microbial eukaryotes - provides researchers insights into the processes microbes carry out, their pathogenic traits, and new ways to use microorganisms in medicine and manufacturing. Until recently, sequencing entire microbial genomes has been laborious and expensive, and the decision to sequence the genome of an organism was made on a case-by-case basis by individual researchers and funding agencies. Now, thanks to new technologies, the cost and effort of sequencing is within reach for even the smallest facilities, and the ability to sequence the genomes of a significant fraction of microbial life may be possible. The availability of numerous microbial genomes will enable unprecedented insights into microbial evolution, function, and physiology. However, the current ad hoc approach to gathering sequence data has resulted in an unbalanced and highly biased sampling of microbial diversity. A well-coordinated, large-scale effort to target the breadth and depth of microbial diversity would result in the greatest impact. The American Academy of Microbiology convened a colloquium to discuss the scientific benefits of engaging in a large-scale, taxonomically-based sequencing project. A group of individuals with expertise in microbiology, genomics, informatics, ecology, and evolution deliberated on the issues inherent in such an effort and generated a set of specific recommendations for how best to proceed. The vast majority of microbes are presently uncultured and, thus, pose significant challenges to such a taxonomically-based approach to sampling genome diversity. However, we have yet to even scratch the surface of the genomic diversity among cultured microbes. A coordinated sequencing effort of cultured organisms is an appropriate place to begin

A simple and inexpensive method for genomic restriction mapping analysis

International Nuclear Information System (INIS)

Huang, C.H.; Lam, V.M.S.; Tam, J.W.O.

1988-01-01

The Southern blotting procedure for the transfer of DNA fragments from agarose gels to nitrocellulose membranes has revolutionized nucleic acid detection methods, and it forms the cornerstone of research in molecular biology. Basically, the method involves the denaturation of DNA fragments that have been separated on an agarose gel, the immobilization of the fragments by transfer to a nitrocellulose membrane, and the identification of the fragments of interest through hybridization to /sup 32/P-labeled probes and autoradiography. While the method is sensitive and applicable to both genomic and cloned DNA, it suffers from the disadvantages of being time consuming and expensive, and fragments of greater than 15 kb are difficult to transfer. Moreover, although theoretically the nitrocellulose membrane can be washed and hybridized repeatedly using different probes, in practice, the membrane becomes brittle and difficult to handle after a few cycles. A direct hybridization method for pure DNA clones was developed in 1975 but has not been widely exploited. The authors report here a modification of their procedure as applied to genomic DNA. The method is simple, rapid, and inexpensive, and it does not involve transfer to nitrocellulose membranes

Analysing human genomes at different scales

DEFF Research Database (Denmark)

Liu, Siyang

The thriving of the Next-Generation sequencing (NGS) technologies in the past decade has dramatically revolutionized the field of human genetics. We are experiencing a wave of several large-scale whole genome sequencing studies of humans in the world. Those studies vary greatly regarding cohort...... will be reflected by the analysis of real data. This thesis covers studies in two human genome sequencing projects that distinctly differ in terms of studied population, sample size and sequencing depth. In the first project, we sequenced 150 Danish individuals from 50 trio families to 78x coverage....... The sophisticated experimental design enables high-quality de novo assembly of the genomes and provides a good opportunity for mapping the structural variations in the human population. We developed the AsmVar approach to discover, genotype and characterize the structural variations from the assemblies. Our...

New bioinformatic tool for quick identification of functionally relevant endogenous retroviral inserts in human genome.

Science.gov (United States)

Garazha, Andrew; Ivanova, Alena; Suntsova, Maria; Malakhova, Galina; Roumiantsev, Sergey; Zhavoronkov, Alex; Buzdin, Anton

2015-01-01

Endogenous retroviruses (ERVs) and LTR retrotransposons (LRs) occupy ∼8% of human genome. Deep sequencing technologies provide clues to understanding of functional relevance of individual ERVs/LRs by enabling direct identification of transcription factor binding sites (TFBS) and other landmarks of functional genomic elements. Here, we performed the genome-wide identification of human ERVs/LRs containing TFBS according to the ENCODE project. We created the first interactive ERV/LRs database that groups the individual inserts according to their familial nomenclature, number of mapped TFBS and divergence from their consensus sequence. Information on any particular element can be easily extracted by the user. We also created a genome browser tool, which enables quick mapping of any ERV/LR insert according to genomic coordinates, known human genes and TFBS. These tools can be used to easily explore functionally relevant individual ERV/LRs, and for studying their impact on the regulation of human genes. Overall, we identified ∼110,000 ERV/LR genomic elements having TFBS. We propose a hypothesis of "domestication" of ERV/LR TFBS by the genome milieu including subsequent stages of initial epigenetic repression, partial functional release, and further mutation-driven reshaping of TFBS in tight coevolution with the enclosing genomic loci.

Ensembl Genomes: an integrative resource for genome-scale data from non-vertebrate species.

Science.gov (United States)

Kersey, Paul J; Staines, Daniel M; Lawson, Daniel; Kulesha, Eugene; Derwent, Paul; Humphrey, Jay C; Hughes, Daniel S T; Keenan, Stephan; Kerhornou, Arnaud; Koscielny, Gautier; Langridge, Nicholas; McDowall, Mark D; Megy, Karine; Maheswari, Uma; Nuhn, Michael; Paulini, Michael; Pedro, Helder; Toneva, Iliana; Wilson, Derek; Yates, Andrew; Birney, Ewan

2012-01-01

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrative resource for genome-scale data from non-vertebrate species. The project exploits and extends technology (for genome annotation, analysis and dissemination) developed in the context of the (vertebrate-focused) Ensembl project and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. Since its launch in 2009, Ensembl Genomes has undergone rapid expansion, with the goal of providing coverage of all major experimental organisms, and additionally including taxonomic reference points to provide the evolutionary context in which genes can be understood. Against the backdrop of a continuing increase in genome sequencing activities in all parts of the tree of life, we seek to work, wherever possible, with the communities actively generating and using data, and are participants in a growing range of collaborations involved in the annotation and analysis of genomes.

Rapid methods for the extraction and archiving of molecular grade fungal genomic DNA.

Science.gov (United States)

Borman, Andrew M; Palmer, Michael; Johnson, Elizabeth M

2013-01-01

The rapid and inexpensive extraction of fungal genomic DNA that is of sufficient quality for molecular approaches is central to the molecular identification, epidemiological analysis, taxonomy, and strain typing of pathogenic fungi. Although many commercially available and in-house extraction procedures do eliminate the majority of contaminants that commonly inhibit molecular approaches, the inherent difficulties in breaking fungal cell walls lead to protocols that are labor intensive and that routinely take several hours to complete. Here we describe several methods that we have developed in our laboratory that allow the extremely rapid and inexpensive preparation of fungal genomic DNA.

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

Science.gov (United States)

Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

2017-10-03

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

Comprehensive evaluation of SNP identification with the Restriction Enzyme-based Reduced Representation Library (RRL method

Directory of Open Access Journals (Sweden)

Du Ye

2012-02-01

Full Text Available Abstract Background Restriction Enzyme-based Reduced Representation Library (RRL method represents a relatively feasible and flexible strategy used for Single Nucleotide Polymorphism (SNP identification in different species. It has remarkable advantage of reducing the complexity of the genome by orders of magnitude. However, comprehensive evaluation for actual efficacy of SNP identification by this method is still unavailable. Results In order to evaluate the efficacy of Restriction Enzyme-based RRL method, we selected Tsp 45I enzyme which covers 266 Mb flanking region of the enzyme recognition site according to in silico simulation on human reference genome, then we sequenced YH RRL after Tsp 45I treatment and obtained reads of which 80.8% were mapped to target region with an 20-fold average coverage, about 96.8% of target region was covered by at least one read and 257 K SNPs were identified in the region using SOAPsnp software. Compared with whole genome resequencing data, we observed false discovery rate (FDR of 13.95% and false negative rate (FNR of 25.90%. The concordance rate of homozygote loci was over 99.8%, but that of heterozygote were only 92.56%. Repeat sequences and bases quality were proved to have a great effect on the accuracy of SNP calling, SNPs in recognition sites contributed evidently to the high FNR and the low concordance rate of heterozygote. Our results indicated that repeat masking and high stringent filter criteria could significantly decrease both FDR and FNR. Conclusions This study demonstrates that Restriction Enzyme-based RRL method was effective for SNP identification. The results highlight the important role of bias and the method-derived defects represented in this method and emphasize the special attentions noteworthy.

Multidimensional scaling for large genomic data sets

Directory of Open Access Journals (Sweden)

Lu Henry

2008-04-01

Full Text Available Abstract Background Multi-dimensional scaling (MDS is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2, so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data. Results We developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data. Conclusion Our new method reduces the computational complexity from O(N3 to O(N when the dimension of the feature space is far less than the number of genes N, and it successfully

Identification of conserved regulatory elements by comparative genome analysis

Directory of Open Access Journals (Sweden)

Jareborg Niclas

2003-05-01

Full Text Available Abstract Background For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments. Results We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at http://www.phylofoot.org/. Conclusions Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting.

A novel method for identification and quantification of consistently differentially methylated regions.

Directory of Open Access Journals (Sweden)

Ching-Lin Hsiao

Full Text Available Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called "supervised" methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative "unsupervised" approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Directory of Open Access Journals (Sweden)

Daniel M de Brito

Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.

Single-molecule optical genome mapping of a human HapMap and a colorectal cancer cell line.

Science.gov (United States)

Teo, Audrey S M; Verzotto, Davide; Yao, Fei; Nagarajan, Niranjan; Hillmer, Axel M

2015-01-01

Next-generation sequencing (NGS) technologies have changed our understanding of the variability of the human genome. However, the identification of genome structural variations based on NGS approaches with read lengths of 35-300 bases remains a challenge. Single-molecule optical mapping technologies allow the analysis of DNA molecules of up to 2 Mb and as such are suitable for the identification of large-scale genome structural variations, and for de novo genome assemblies when combined with short-read NGS data. Here we present optical mapping data for two human genomes: the HapMap cell line GM12878 and the colorectal cancer cell line HCT116. High molecular weight DNA was obtained by embedding GM12878 and HCT116 cells, respectively, in agarose plugs, followed by DNA extraction under mild conditions. Genomic DNA was digested with KpnI and 310,000 and 296,000 DNA molecules (≥ 150 kb and 10 restriction fragments), respectively, were analyzed per cell line using the Argus optical mapping system. Maps were aligned to the human reference by OPTIMA, a new glocal alignment method. Genome coverage of 6.8× and 5.7× was obtained, respectively; 2.9× and 1.7× more than the coverage obtained with previously available software. Optical mapping allows the resolution of large-scale structural variations of the genome, and the scaffold extension of NGS-based de novo assemblies. OPTIMA is an efficient new alignment method; our optical mapping data provide a resource for genome structure analyses of the human HapMap reference cell line GM12878, and the colorectal cancer cell line HCT116.

BFAST: an alignment tool for large scale genome resequencing.

Directory of Open Access Journals (Sweden)

Nils Homer

2009-11-01

Full Text Available The new generation of massively parallel DNA sequencers, combined with the challenge of whole human genome resequencing, result in the need for rapid and accurate alignment of billions of short DNA sequence reads to a large reference genome. Speed is obviously of great importance, but equally important is maintaining alignment accuracy of short reads, in the 25-100 base range, in the presence of errors and true biological variation.We introduce a new algorithm specifically optimized for this task, as well as a freely available implementation, BFAST, which can align data produced by any of current sequencing platforms, allows for user-customizable levels of speed and accuracy, supports paired end data, and provides for efficient parallel and multi-threaded computation on a computer cluster. The new method is based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. The final local alignment uses a Smith-Waterman method, with gaps to support the detection of small indels.We compare BFAST to a selection of large-scale alignment tools -- BLAT, MAQ, SHRiMP, and SOAP -- in terms of both speed and accuracy, using simulated and real-world datasets. We show BFAST can achieve substantially greater sensitivity of alignment in the context of errors and true variants, especially insertions and deletions, and minimize false mappings, while maintaining adequate speed compared to other current methods. We show BFAST can align the amount of data needed to fully resequence a human genome, one billion reads, with high sensitivity and accuracy, on a modest computer cluster in less than 24 hours. BFAST is available at (http://bfast.sourceforge.net.

A framework for annotating human genome in disease context.

Science.gov (United States)

Xu, Wei; Wang, Huisong; Cheng, Wenqing; Fu, Dong; Xia, Tian; Kibbe, Warren A; Lin, Simon M

2012-01-01

Identification of gene-disease association is crucial to understanding disease mechanism. A rapid increase in biomedical literatures, led by advances of genome-scale technologies, poses challenge for manually-curated-based annotation databases to characterize gene-disease associations effectively and timely. We propose an automatic method-The Disease Ontology Annotation Framework (DOAF) to provide a comprehensive annotation of the human genome using the computable Disease Ontology (DO), the NCBO Annotator service and NCBI Gene Reference Into Function (GeneRIF). DOAF can keep the resulting knowledgebase current by periodically executing automatic pipeline to re-annotate the human genome using the latest DO and GeneRIF releases at any frequency such as daily or monthly. Further, DOAF provides a computable and programmable environment which enables large-scale and integrative analysis by working with external analytic software or online service platforms. A user-friendly web interface (doa.nubic.northwestern.edu) is implemented to allow users to efficiently query, download, and view disease annotations and the underlying evidences.

A simple and efficient total genomic DNA extraction method for individual zooplankton.

Science.gov (United States)

Fazhan, Hanafiah; Waiho, Khor; Shahreza, Md Sheriff

2016-01-01

Molecular approaches are widely applied in species identification and taxonomic studies of minute zooplankton. One of the most focused zooplankton nowadays is from Subclass Copepoda. Accurate species identification of all life stages of the generally small sized copepods through molecular analysis is important, especially in taxonomic and systematic assessment of harpacticoid copepod populations and to understand their dynamics within the marine community. However, total genomic DNA (TGDNA) extraction from individual harpacticoid copepods can be problematic due to their small size and epibenthic behavior. In this research, six TGDNA extraction methods done on individual harpacticoid copepods were compared. The first new simple, feasible, efficient and consistent TGDNA extraction method was designed and compared with the commercial kit and modified available TGDNA extraction methods. The newly described TGDNA extraction method, "Incubation in PCR buffer" method, yielded good and consistent results based on the high success rate of PCR amplification (82%) compared to other methods. Coupled with its relatively consistent and economical method the "Incubation in PCR buffer" method is highly recommended in the TGDNA extraction of other minute zooplankton species.

Genome-wide identification of direct HBx genomic targets

KAUST Repository

Guerrieri, Francesca

2017-02-17

Background The Hepatitis B Virus (HBV) HBx regulatory protein is required for HBV replication and involved in HBV-related carcinogenesis. HBx interacts with chromatin modifying enzymes and transcription factors to modulate histone post-translational modifications and to regulate viral cccDNA transcription and cellular gene expression. Aiming to identify genes and non-coding RNAs (ncRNAs) directly targeted by HBx, we performed a chromatin immunoprecipitation sequencing (ChIP-Seq) to analyse HBV recruitment on host cell chromatin in cells replicating HBV. Results ChIP-Seq high throughput sequencing of HBx-bound fragments was used to obtain a high-resolution, unbiased, mapping of HBx binding sites across the genome in HBV replicating cells. Protein-coding genes and ncRNAs involved in cell metabolism, chromatin dynamics and cancer were enriched among HBx targets together with genes/ncRNAs known to modulate HBV replication. The direct transcriptional activation of genes/miRNAs that potentiate endocytosis (Ras-related in brain (RAB) GTPase family) and autophagy (autophagy related (ATG) genes, beclin-1, miR-33a) and the transcriptional repression of microRNAs (miR-138, miR-224, miR-576, miR-596) that directly target the HBV pgRNA and would inhibit HBV replication, contribute to HBx-mediated increase of HBV replication. Conclusions Our ChIP-Seq analysis of HBx genome wide chromatin recruitment defined the repertoire of genes and ncRNAs directly targeted by HBx and led to the identification of new mechanisms by which HBx positively regulates cccDNA transcription and HBV replication.

Genome-scale metabolic representation of Amycolatopsis balhimycina

DEFF Research Database (Denmark)

Vongsangnak, Wanwipa; Figueiredo, L. F.; Förster, Jochen

2012-01-01

Infection caused by methicillin‐resistant Staphylococcus aureus (MRSA) is an increasing societal problem. Typically, glycopeptide antibiotics are used in the treatment of these infections. The most comprehensively studied glycopeptide antibiotic biosynthetic pathway is that of balhimycin...... to reconstruct a genome‐scale metabolic model for the organism. Here we generated an almost complete A. balhimycina genome sequence comprising 10,562,587 base pairs assembled into 2,153 contigs. The high GC‐genome (∼69%) includes 8,585 open reading frames (ORFs). We used our integrative toolbox called SEQTOR...

Genome-wide identification of SAUR genes in watermelon (Citrullus lanatus).

Science.gov (United States)

Zhang, Na; Huang, Xing; Bao, Yaning; Wang, Bo; Zeng, Hongxia; Cheng, Weishun; Tang, Mi; Li, Yuhua; Ren, Jian; Sun, Yuhong

2017-07-01

The early auxin responsive SAUR family is an important gene family in auxin signal transduction. We here present the first report of a genome-wide identification of SAUR genes in watermelon genome. We successfully identified 65 ClaSAURs and provide a genomic framework for future study on these genes. Phylogenetic result revealed a Cucurbitaceae-specific SAUR subfamily and contribute to understanding of the evolutionary pattern of SAUR genes in plants. Quantitative RT-PCR analysis demonstrates the existed expression of 11 randomly selected SAUR genes in watermelon tissues. ClaSAUR36 was highly expressed in fruit, for which further study might bring a new prospective for watermelon fruit development. Moreover, correlation analysis revealed the similar expression profiles of SAUR genes between watermelon and Arabidopsis during shoot organogenesis. This work gives us a new support for the conserved auxin machinery in plants.

Application of Story-wise Shear Building Identification Method to Actual Ambient Vibration

Directory of Open Access Journals (Sweden)

Kohei eFujita

2015-02-01

Full Text Available A sophisticated and smart story stiffness System Identification (SI method for a shear building model is applied to a full-scale building frame subjected to micro-tremors. The advantageous and novel feature is that not only the modal parameters, such as natural frequencies and damping ratios, but also the physical model parameters, such as story stiffnesses and damping coefficients, can be identified using micro-tremors. While the building responses to earthquake ground motions are necessary in the previous SI method, it is shown in this paper that the micro-tremor measurements in a full-scale 5 story building frame can be used for identification within the same framework. The SI using micro-tremor measurements leads to the enhanced usability of the previously proposed story-wise shear building identification method. The degree of ARX models and cut-off frequencies of band-pass filter are determined to derive reliable results.

Survey of protein–DNA interactions in Aspergillus oryzae on a genomic scale

Science.gov (United States)

Wang, Chao; Lv, Yangyong; Wang, Bin; Yin, Chao; Lin, Ying; Pan, Li

2015-01-01

The genome-scale delineation of in vivo protein–DNA interactions is key to understanding genome function. Only ∼5% of transcription factors (TFs) in the Aspergillus genus have been identified using traditional methods. Although the Aspergillus oryzae genome contains >600 TFs, knowledge of the in vivo genome-wide TF-binding sites (TFBSs) in aspergilli remains limited because of the lack of high-quality antibodies. We investigated the landscape of in vivo protein–DNA interactions across the A. oryzae genome through coupling the DNase I digestion of intact nuclei with massively parallel sequencing and the analysis of cleavage patterns in protein–DNA interactions at single-nucleotide resolution. The resulting map identified overrepresented de novo TF-binding motifs from genomic footprints, and provided the detailed chromatin remodeling patterns and the distribution of digital footprints near transcription start sites. The TFBSs of 19 known Aspergillus TFs were also identified based on DNase I digestion data surrounding potential binding sites in conjunction with TF binding specificity information. We observed that the cleavage patterns of TFBSs were dependent on the orientation of TF motifs and independent of strand orientation, consistent with the DNA shape features of binding motifs with flanking sequences. PMID:25883143

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Assembly of 500,000 inter-specific catfish expressed sequence tags and large scale gene-associated marker development for whole genome association studies

Energy Technology Data Exchange (ETDEWEB)

Catfish Genome Consortium; Wang, Shaolin; Peatman, Eric; Abernathy, Jason; Waldbieser, Geoff; Lindquist, Erika; Richardson, Paul; Lucas, Susan; Wang, Mei; Li, Ping; Thimmapuram, Jyothi; Liu, Lei; Vullaganti, Deepika; Kucuktas, Huseyin; Murdock, Christopher; Small, Brian C; Wilson, Melanie; Liu, Hong; Jiang, Yanliang; Lee, Yoona; Chen, Fei; Lu, Jianguo; Wang, Wenqi; Xu, Peng; Somridhivej, Benjaporn; Baoprasertkul, Puttharat; Quilang, Jonas; Sha, Zhenxia; Bao, Baolong; Wang, Yaping; Wang, Qun; Takano, Tomokazu; Nandi, Samiran; Liu, Shikai; Wong, Lilian; Kaltenboeck, Ludmilla; Quiniou, Sylvie; Bengten, Eva; Miller, Norman; Trant, John; Rokhsar, Daniel; Liu, Zhanjiang

2010-03-23

Background-Through the Community Sequencing Program, a catfish EST sequencing project was carried out through a collaboration between the catfish research community and the Department of Energy's Joint Genome Institute. Prior to this project, only a limited EST resource from catfish was available for the purpose of SNP identification. Results-A total of 438,321 quality ESTs were generated from 8 channel catfish (Ictalurus punctatus) and 4 blue catfish (Ictalurus furcatus) libraries, bringing the number of catfish ESTs to nearly 500,000. Assembly of all catfish ESTs resulted in 45,306 contigs and 66,272 singletons. Over 35percent of the unique sequences had significant similarities to known genes, allowing the identification of 14,776 unique genes in catfish. Over 300,000 putative SNPs have been identified, of which approximately 48,000 are high-quality SNPs identified from contigs with at least four sequences and the minor allele presence of at least two sequences in the contig. The EST resource should be valuable for identification of microsatellites, genome annotation, large-scale expression analysis, and comparative genome analysis. Conclusions-This project generated a large EST resource for catfish that captured the majority of the catfish transcriptome. The parallel analysis of ESTs from two closely related Ictalurid catfishes should also provide powerful means for the evaluation of ancient and recent gene duplications, and for the development of high-density microarrays in catfish. The inter- and intra-specific SNPs identified from all catfish EST dataset assembly will greatly benefit the catfish introgression breeding program and whole genome association studies.

Genome-wide identification of significant aberrations in cancer genome.

Science.gov (United States)

Yuan, Xiguo; Yu, Guoqiang; Hou, Xuchu; Shih, Ie-Ming; Clarke, Robert; Zhang, Junying; Hoffman, Eric P; Wang, Roger R; Zhang, Zhen; Wang, Yue

2012-07-27

Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is

Resources for Functional Genomics Studies in Drosophila melanogaster

Science.gov (United States)

Mohr, Stephanie E.; Hu, Yanhui; Kim, Kevin; Housden, Benjamin E.; Perrimon, Norbert

2014-01-01

Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, “meta” information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally. PMID:24653003

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico.

Science.gov (United States)

McAnulty, Michael J; Yen, Jiun Y; Freedman, Benjamin G; Senger, Ryan S

2012-05-14

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

Science.gov (United States)

2012-01-01

Background Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes. Results Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype). Conclusion This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data. PMID:22631437

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

Science.gov (United States)

Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

2018-05-31

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

Genome-scale metabolic analysis of Clostridium thermocellum for bioethanol production

Directory of Open Access Journals (Sweden)

Brooks J Paul

2010-03-01

Full Text Available Abstract Background Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405 is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous. Results Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production. Conclusions By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum

Comparative Genomics in Homo sapiens.

Science.gov (United States)

Oti, Martin; Sammeth, Michael

2018-01-01

Genomes can be compared at different levels of divergence, either between species or within species. Within species genomes can be compared between different subpopulations, such as human subpopulations from different continents. Investigating the genomic differences between different human subpopulations is important when studying complex diseases that are affected by many genetic variants, as the variants involved can differ between populations. The 1000 Genomes Project collected genome-scale variation data for 2504 human individuals from 26 different populations, enabling a systematic comparison of variation between human subpopulations. In this chapter, we present step-by-step a basic protocol for the identification of population-specific variants employing the 1000 Genomes data. These variants are subsequently further investigated for those that affect the proteome or RNA splice sites, to investigate potentially biologically relevant differences between the populations.

Identification of Phosphorylated Proteins on a Global Scale.

Science.gov (United States)

Iliuk, Anton

2018-05-31

Liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) has enabled researchers to analyze complex biological samples with unprecedented depth. It facilitates the identification and quantification of modifications within thousands of proteins in a single large-scale proteomic experiment. Analysis of phosphorylation, one of the most common and important post-translational modifications, has particularly benefited from such progress in the field. Here, detailed protocols are provided for a few well-regarded, common sample preparation methods for an effective phosphoproteomic experiment. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

Large-scale analysis of antisense transcription in wheat using the Affymetrix GeneChip Wheat Genome Array

Directory of Open Access Journals (Sweden)

Settles Matthew L

2009-05-01

Full Text Available Abstract Background Natural antisense transcripts (NATs are transcripts of the opposite DNA strand to the sense-strand either at the same locus (cis-encoded or a different locus (trans-encoded. They can affect gene expression at multiple stages including transcription, RNA processing and transport, and translation. NATs give rise to sense-antisense transcript pairs and the number of these identified has escalated greatly with the availability of DNA sequencing resources and public databases. Traditionally, NATs were identified by the alignment of full-length cDNAs or expressed sequence tags to genome sequences, but an alternative method for large-scale detection of sense-antisense transcript pairs involves the use of microarrays. In this study we developed a novel protocol to assay sense- and antisense-strand transcription on the 55 K Affymetrix GeneChip Wheat Genome Array, which is a 3' in vitro transcription (3'IVT expression array. We selected five different tissue types for assay to enable maximum discovery, and used the 'Chinese Spring' wheat genotype because most of the wheat GeneChip probe sequences were based on its genomic sequence. This study is the first report of using a 3'IVT expression array to discover the expression of natural sense-antisense transcript pairs, and may be considered as proof-of-concept. Results By using alternative target preparation schemes, both the sense- and antisense-strand derived transcripts were labeled and hybridized to the Wheat GeneChip. Quality assurance verified that successful hybridization did occur in the antisense-strand assay. A stringent threshold for positive hybridization was applied, which resulted in the identification of 110 sense-antisense transcript pairs, as well as 80 potentially antisense-specific transcripts. Strand-specific RT-PCR validated the microarray observations, and showed that antisense transcription is likely to be tissue specific. For the annotated sense

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Genome-wide identification of significant aberrations in cancer genome

Directory of Open Access Journals (Sweden)

Yuan Xiguo

2012-07-01

Full Text Available Abstract Background Somatic Copy Number Alterations (CNAs in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC, a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1 exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2 performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3 iteratively detecting Significant Copy Number Aberrations (SCAs and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. Results We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma. When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC or tumor suppressor genes (e.g., CDKN2A/B. Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Conclusions Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes

Chemical Genomics and Emerging DNA Technologies in the Identification of Drug Mechanisms and Drug Targets

DEFF Research Database (Denmark)

Olsen, Louise Cathrine Braun; Færgeman, Nils J.

2012-01-01

and validate therapeutic targets and to discover drug candidates for rapidly and effectively generating new interventions for human diseases. The recent emergence of genomic technologies and their application on genetically tractable model organisms like Drosophila melanogaster,Caenorhabditis elegans...... critical roles in the genomic age of biological research and drug discovery. In the present review we discuss how simple biological model organisms can be used as screening platforms in combination with emerging genomic technologies to advance the identification of potential drugs and their molecular...

GEnomes Management Application (GEM.app): a new software tool for large-scale collaborative genome analysis.

Science.gov (United States)

Gonzalez, Michael A; Lebrigio, Rafael F Acosta; Van Booven, Derek; Ulloa, Rick H; Powell, Eric; Speziani, Fiorella; Tekin, Mustafa; Schüle, Rebecca; Züchner, Stephan

2013-06-01

Novel genes are now identified at a rapid pace for many Mendelian disorders, and increasingly, for genetically complex phenotypes. However, new challenges have also become evident: (1) effectively managing larger exome and/or genome datasets, especially for smaller labs; (2) direct hands-on analysis and contextual interpretation of variant data in large genomic datasets; and (3) many small and medium-sized clinical and research-based investigative teams around the world are generating data that, if combined and shared, will significantly increase the opportunities for the entire community to identify new genes. To address these challenges, we have developed GEnomes Management Application (GEM.app), a software tool to annotate, manage, visualize, and analyze large genomic datasets (https://genomics.med.miami.edu/). GEM.app currently contains ∼1,600 whole exomes from 50 different phenotypes studied by 40 principal investigators from 15 different countries. The focus of GEM.app is on user-friendly analysis for nonbioinformaticians to make next-generation sequencing data directly accessible. Yet, GEM.app provides powerful and flexible filter options, including single family filtering, across family/phenotype queries, nested filtering, and evaluation of segregation in families. In addition, the system is fast, obtaining results within 4 sec across ∼1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease. © 2013 Wiley Periodicals, Inc.

A simple and effective method for construction of Escherichia coli strains proficient for genome engineering.

Directory of Open Access Journals (Sweden)

Young Shin Ryu

Full Text Available Multiplex genome engineering is a standalone recombineering tool for large-scale programming and accelerated evolution of cells. However, this advanced genome engineering technique has been limited to use in selected bacterial strains. We developed a simple and effective strain-independent method for effective genome engineering in Escherichia coli. The method involves introducing a suicide plasmid carrying the λ Red recombination system into the mutS gene. The suicide plasmid can be excised from the chromosome via selection in the absence of antibiotics, thus allowing transient inactivation of the mismatch repair system during genome engineering. In addition, we developed another suicide plasmid that enables integration of large DNA fragments into the lacZ genomic locus. These features enable this system to be applied in the exploitation of the benefits of genome engineering in synthetic biology, as well as the metabolic engineering of different strains of E. coli.

Murasaki: a fast, parallelizable algorithm to find anchors from multiple genomes.

Directory of Open Access Journals (Sweden)

Kris Popendorf

Full Text Available BACKGROUND: With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows. METHODOLOGY/PRINCIPAL FINDINGS: Our algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1 adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2 parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow in 21 hours CPU time (42 minutes wall time. This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy. CONCLUSIONS/SIGNIFICANCE: Murasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with

Identification of the Scale of Changes in Personnel Motivation Techniques at Mechanical-Engineering Enterprises

Directory of Open Access Journals (Sweden)

Melnyk Olga G.

2016-02-01

Full Text Available The method for identification of the scale of changes in personnel motivation techniques at mechanical-engineering enterprises based on structural and logical sequence of implementation of relevant stages (identification of the mission, strategy and objectives of the enterprise; forecasting the development of the enterprise business environment; SWOT-analysis of actual motivation techniques, deciding on the scale of changes in motivation techniques, choosing providers for changing personnel motivation techniques, choosing an alternative to changing motivation techniques, implementation of changes in motivation techniques; control over changes in motivation techniques. It has been substantiated that the improved method enables providing a systematic and analytical justification for management decisionmaking in this field and choosing the best for the mechanical-engineering enterprise scale and variant of changes in motivation techniques. The method for identification of the scale of changes in motivation techniques at mechanical-engineering enterprises takes into account the previous, current and prospective character. Firstly, the approach is based on considering the past state in the motivational sphere of the mechanical-engineering enterprise; secondly, the method involves identifying the current state of personnel motivation techniques; thirdly, within the method framework the prospective, which is manifested in strategic vision of the enterprise development as well as in forecasting the development of its business environment, is taken into account. The advantage of the proposed method is that the level of its specification may vary depending on the set goals, resource constraints and necessity. Among other things, this method allows integrating various formalized and non-formalized causal relationships in the sphere of personnel motivation at machine-building enterprises and management of relevant processes. This creates preconditions for a

Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

Energy Technology Data Exchange (ETDEWEB)

Navid, A; Almaas, E

2009-01-13

The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

Experimental annotation of the human genome using microarray technology.

Science.gov (United States)

Shoemaker, D D; Schadt, E E; Armour, C D; He, Y D; Garrett-Engele, P; McDonagh, P D; Loerch, P M; Leonardson, A; Lum, P Y; Cavet, G; Wu, L F; Altschuler, S J; Edwards, S; King, J; Tsang, J S; Schimmack, G; Schelter, J M; Koch, J; Ziman, M; Marton, M J; Li, B; Cundiff, P; Ward, T; Castle, J; Krolewski, M; Meyer, M R; Mao, M; Burchard, J; Kidd, M J; Dai, H; Phillips, J W; Linsley, P S; Stoughton, R; Scherer, S; Boguski, M S

2001-02-15

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using 'exon' and 'tiling' arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

Science.gov (United States)

Passalacqua, P.; Sangireddy, H.; Stark, C. P.

2015-12-01

With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

Rapid Prototyping of Microbial Cell Factories via Genome-scale Engineering

Science.gov (United States)

Si, Tong; Xiao, Han; Zhao, Huimin

2014-01-01

Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. PMID:25450192

ITPI: Initial Transcription Process-Based Identification Method of Bioactive Components in Traditional Chinese Medicine Formula

Directory of Open Access Journals (Sweden)

Baixia Zhang

2016-01-01

Full Text Available Identification of bioactive components is an important area of research in traditional Chinese medicine (TCM formula. The reported identification methods only consider the interaction between the components and the target proteins, which is not sufficient to explain the influence of TCM on the gene expression. Here, we propose the Initial Transcription Process-based Identification (ITPI method for the discovery of bioactive components that influence transcription factors (TFs. In this method, genome-wide chip detection technology was used to identify differentially expressed genes (DEGs. The TFs of DEGs were derived from GeneCards. The components influencing the TFs were derived from STITCH. The bioactive components in the formula were identified by evaluating the molecular similarity between the components in formula and the components that influence the TF of DEGs. Using the formula of Tian-Zhu-San (TZS as an example, the reliability and limitation of ITPI were examined and 16 bioactive components that influence TFs were identified.

An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

Directory of Open Access Journals (Sweden)

Liu Lili

2013-06-01

Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

Directory of Open Access Journals (Sweden)

Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Power Laws, Scale-Free Networks and Genome Biology

CERN Document Server

Koonin, Eugene V; Karev, Georgy P

2006-01-01

Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

Identification and Characterization of Microsatellite Markers Derived from the Whole Genome Analysis of Taenia solium.

Science.gov (United States)

Pajuelo, Mónica J; Eguiluz, María; Dahlstrom, Eric; Requena, David; Guzmán, Frank; Ramirez, Manuel; Sheen, Patricia; Frace, Michael; Sammons, Scott; Cama, Vitaliano; Anzick, Sarah; Bruno, Dan; Mahanty, Siddhartha; Wilkins, Patricia; Nash, Theodore; Gonzalez, Armando; García, Héctor H; Gilman, Robert H; Porcella, Steve; Zimic, Mirko

2015-12-01

Infections with Taenia solium are the most common cause of adult acquired seizures worldwide, and are the leading cause of epilepsy in developing countries. A better understanding of the genetic diversity of T. solium will improve parasite diagnostics and transmission pathways in endemic areas thereby facilitating the design of future control measures and interventions. Microsatellite markers are useful genome features, which enable strain typing and identification in complex pathogen genomes. Here we describe microsatellite identification and characterization in T. solium, providing information that will assist in global efforts to control this important pathogen. For genome sequencing, T. solium cysts and proglottids were collected from Huancayo and Puno in Peru, respectively. Using next generation sequencing (NGS) and de novo assembly, we assembled two draft genomes and one hybrid genome. Microsatellite sequences were identified and 36 of them were selected for further analysis. Twenty T. solium isolates were collected from Tumbes in the northern region, and twenty from Puno in the southern region of Peru. The size-polymorphism of the selected microsatellites was determined with multi-capillary electrophoresis. We analyzed the association between microsatellite polymorphism and the geographic origin of the samples. The predicted size of the hybrid (proglottid genome combined with cyst genome) T. solium genome was 111 MB with a GC content of 42.54%. A total of 7,979 contigs (>1,000 nt) were obtained. We identified 9,129 microsatellites in the Puno-proglottid genome and 9,936 in the Huancayo-cyst genome, with 5 or more repeats, ranging from mono- to hexa-nucleotide. Seven microsatellites were polymorphic and 29 were monomorphic within the analyzed isolates. T. solium tapeworms were classified into two genetic groups that correlated with the North/South geographic origin of the parasites. The availability of draft genomes for T. solium represents a significant step

Identification and Characterization of Microsatellite Markers Derived from the Whole Genome Analysis of Taenia solium.

Directory of Open Access Journals (Sweden)

Mónica J Pajuelo

2015-12-01

Full Text Available Infections with Taenia solium are the most common cause of adult acquired seizures worldwide, and are the leading cause of epilepsy in developing countries. A better understanding of the genetic diversity of T. solium will improve parasite diagnostics and transmission pathways in endemic areas thereby facilitating the design of future control measures and interventions. Microsatellite markers are useful genome features, which enable strain typing and identification in complex pathogen genomes. Here we describe microsatellite identification and characterization in T. solium, providing information that will assist in global efforts to control this important pathogen.For genome sequencing, T. solium cysts and proglottids were collected from Huancayo and Puno in Peru, respectively. Using next generation sequencing (NGS and de novo assembly, we assembled two draft genomes and one hybrid genome. Microsatellite sequences were identified and 36 of them were selected for further analysis. Twenty T. solium isolates were collected from Tumbes in the northern region, and twenty from Puno in the southern region of Peru. The size-polymorphism of the selected microsatellites was determined with multi-capillary electrophoresis. We analyzed the association between microsatellite polymorphism and the geographic origin of the samples.The predicted size of the hybrid (proglottid genome combined with cyst genome T. solium genome was 111 MB with a GC content of 42.54%. A total of 7,979 contigs (>1,000 nt were obtained. We identified 9,129 microsatellites in the Puno-proglottid genome and 9,936 in the Huancayo-cyst genome, with 5 or more repeats, ranging from mono- to hexa-nucleotide. Seven microsatellites were polymorphic and 29 were monomorphic within the analyzed isolates. T. solium tapeworms were classified into two genetic groups that correlated with the North/South geographic origin of the parasites.The availability of draft genomes for T. solium represents a

Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing

Directory of Open Access Journals (Sweden)

María P. Cortés

2017-12-01

Full Text Available Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen’s biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

Energy Technology Data Exchange (ETDEWEB)

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

2017-03-27

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

Rapid prototyping of microbial cell factories via genome-scale engineering.

Science.gov (United States)

Si, Tong; Xiao, Han; Zhao, Huimin

2015-11-15

Advances in reading, writing and editing genetic materials have greatly expanded our ability to reprogram biological systems at the resolution of a single nucleotide and on the scale of a whole genome. Such capacity has greatly accelerated the cycles of design, build and test to engineer microbes for efficient synthesis of fuels, chemicals and drugs. In this review, we summarize the emerging technologies that have been applied, or are potentially useful for genome-scale engineering in microbial systems. We will focus on the development of high-throughput methodologies, which may accelerate the prototyping of microbial cell factories. Copyright © 2014 Elsevier Inc. All rights reserved.

From genomes to in silico cells via metabolic networks

DEFF Research Database (Denmark)

Borodina, Irina; Nielsen, Jens

2005-01-01

Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis...... of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising...

Genome-scale metabolic models as platforms for strain design and biological discovery.

Science.gov (United States)

Mienda, Bashir Sajo

2017-07-01

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.

Multi-scale Material Parameter Identification Using LS-DYNA® and LS-OPT®

Energy Technology Data Exchange (ETDEWEB)

Stander, Nielen [Livermore Software Technology Corporation, CA (United States); Basudhar, Anirban [Livermore Software Technology Corporation, CA (United States); Basu, Ushnish [Livermore Software Technology Corporation, CA (United States); Gandikota, Imtiaz [Livermore Software Technology Corporation, CA (United States); Savic, Vesna [General Motors, Flint, MI (United States); Sun, Xin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hu, XiaoHua [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Pourboghrat, Farhang [The Ohio State Univ., Columbus, OH (United States); Park, Taejoon [The Ohio State Univ., Columbus, OH (United States); Mapar, Aboozar [Michigan State Univ., East Lansing, MI (United States); Kumar, Sharvan [Brown Univ., Providence, RI (United States); Ghassemi-Armaki, Hassan [Brown Univ., Providence, RI (United States); Abu-Farha, Fadi [Clemson Univ., SC (United States)

2015-06-15

Ever-tightening regulations on fuel economy and carbon emissions demand continual innovation in finding ways for reducing vehicle mass. Classical methods for computational mass reduction include sizing, shape and topology optimization. One of the few remaining options for weight reduction can be found in materials engineering and material design optimization. Apart from considering different types of materials by adding material diversity, an appealing option in automotive design is to engineer steel alloys for the purpose of reducing thickness while retaining sufficient strength and ductility required for durability and safety. Such a project was proposed and is currently being executed under the auspices of the United States Automotive Materials Partnership (USAMP) funded by the Department of Energy. Under this program, new steel alloys (Third Generation Advanced High Strength Steel or 3GAHSS) are being designed, tested and integrated with the remaining design variables of a benchmark vehicle Finite Element model. In this project the principal phases identified are (i) material identification, (ii) formability optimization and (iii) multi-disciplinary vehicle optimization. This paper serves as an introduction to the LS-OPT methodology and therefore mainly focuses on the first phase, namely an approach to integrate material identification using material models of different length scales. For this purpose, a multi-scale material identification strategy, consisting of a Crystal Plasticity (CP) material model and a Homogenized State Variable (SV) model, is discussed and demonstrated. The paper concludes with proposals for integrating the multi-scale methodology into the overall vehicle design.

Insertion Sequence-Caused Large Scale-Rearrangements in the Genome of Escherichia coli

Science.gov (United States)

2016-07-18

affordable ap- proach to genome-wide characterization of genetic varia - tion in bacterial and eukaryotic genomes (1–3). In addition to small-scale...Paired-End Reads), that uses a graph-based al- gorithm (27) capable of detecting most large-scale varia - tion involving repetitive regions, including novel...Avila,P., Grinsted,J. and De La Cruz,F. (1988) Analysis of the variable endpoints generated by one-ended transposition of Tn21.. J. Bacteriol., 170

Genome-wide identification of estrogen receptor alpha-binding sites in mouse liver

DEFF Research Database (Denmark)

Gao, Hui; Fält, Susann; Sandelin, Albin

2007-01-01

We report the genome-wide identification of estrogen receptor alpha (ERalpha)-binding regions in mouse liver using a combination of chromatin immunoprecipitation and tiled microarrays that cover all nonrepetitive sequences in the mouse genome. This analysis identified 5568 ERalpha-binding regions...... genes. The majority of ERalpha-binding regions lie in regions that are evolutionarily conserved between human and mouse. Motif-finding algorithms identified the estrogen response element, and variants thereof, together with binding sites for activator protein 1, basic-helix-loop-helix proteins, ETS...... signaling in mouse liver, by characterizing the first step in this signaling cascade, the binding of ERalpha to DNA in intact chromatin....

Recent and ongoing selection in the human genome

DEFF Research Database (Denmark)

Nielsen, Rasmus; Hellmann, Ines; Hubisz, Melissa

2007-01-01

The recent availability of genome-scale genotyping data has led to the identification of regions of the human genome that seem to have been targeted by selection. These findings have increased our understanding of the evolutionary forces that affect the human genome, have augmented our knowledge...... of gene function and promise to increase our understanding of the genetic basis of disease. However, inferences of selection are challenged by several confounding factors, especially the complex demographic history of human populations, and concordance between studies is variable. Although such studies...

SIGI: score-based identification of genomic islands

Directory of Open Access Journals (Sweden)

Merkl Rainer

2004-03-01

Full Text Available Abstract Background Genomic islands can be observed in many microbial genomes. These stretches of DNA have a conspicuous composition with regard to sequence or encoded functions. Genomic islands are assumed to be frequently acquired via horizontal gene transfer. For the analysis of genome structure and the study of horizontal gene transfer, it is necessary to reliably identify and characterize these islands. Results A scoring scheme on codon frequencies Score_G1G2(cdn = log(f_G2(cdn / f_G1(cdn was utilized. To analyse genes of a species G1 and to test their relatedness to species G2, scores were determined by applying the formula to log-odds derived from mean codon frequencies of the two genomes. A non-redundant set of nearly 400 codon usage tables comprising microbial species was derived; its members were used alternatively at position G2. Genes having at least one score value above a species-specific and dynamically determined cut-off value were analysed further. By means of cluster analysis, genes were identified that comprise clusters of statistically significant size. These clusters were predicted as genomic islands. Finally and individually for each of these genes, the taxonomical relation among those species responsible for significant scores was interpreted. The validity of the approach and its limitations were made plausible by an extensive analysis of natural genes and synthetic ones aimed at modelling the process of gene amelioration. Conclusions The method reliably allows to identify genomic island and the likely origin of alien genes.

Genome analysis methods - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods Genome analysis... methods Data detail Data name Genome analysis methods DOI 10.18908/lsdba.nbdc01194-01-005 De...scription of data contents The current status and related information of the genomic analysis about each org...anism (March, 2014). In the case of organisms carried out genomic analysis, the d...e File name: pgdbj_dna_marker_linkage_map_genome_analysis_methods_en.zip File URL: ftp://ftp.biosciencedbc.j

Quantitative Assessment of Thermodynamic Constraints on the Solution Space of Genome-Scale Metabolic Models

Science.gov (United States)

Hamilton, Joshua J.; Dwivedi, Vivek; Reed, Jennifer L.

2013-01-01

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. PMID:23870272

An Identification Key for Selecting Methods for Sustainability Assessments

Directory of Open Access Journals (Sweden)

Michiel C. Zijp

2015-03-01

Full Text Available Sustainability assessments can play an important role in decision making. This role starts with selecting appropriate methods for a given situation. We observed that scientists, consultants, and decision-makers often do not systematically perform a problem analyses that guides the choice of the method, partly related to a lack of systematic, though sufficiently versatile approaches to do so. Therefore, we developed and propose a new step towards method selection on the basis of question articulation: the Sustainability Assessment Identification Key. The identification key was designed to lead its user through all important choices needed for comprehensive question articulation. Subsequently, methods that fit the resulting specific questions are suggested by the key. The key consists of five domains, of which three determine method selection and two the design or use of the method. Each domain consists of four or more criteria that need specification. For example in the domain “system boundaries”, amongst others, the spatial and temporal scales are specified. The key was tested (retrospectively on a set of thirty case studies. Using the key appeared to contribute to improved: (i transparency in the link between the question and method selection; (ii consistency between questions asked and answers provided; and (iii internal consistency in methodological design. There is latitude to develop the current initial key further, not only for selecting methods pertinent to a problem definition, but also as a principle for associated opportunities such as stakeholder identification.

[Genome editing of industrial microorganism].

Science.gov (United States)

Zhu, Linjiang; Li, Qi

2015-03-01

Genome editing is defined as highly-effective and precise modification of cellular genome in a large scale. In recent years, such genome-editing methods have been rapidly developed in the field of industrial strain improvement. The quickly-updating methods thoroughly change the old mode of inefficient genetic modification, which is "one modification, one selection marker, and one target site". Highly-effective modification mode in genome editing have been developed including simultaneous modification of multiplex genes, highly-effective insertion, replacement, and deletion of target genes in the genome scale, cut-paste of a large DNA fragment. These new tools for microbial genome editing will certainly be applied widely, and increase the efficiency of industrial strain improvement, and promote the revolution of traditional fermentation industry and rapid development of novel industrial biotechnology like production of biofuel and biomaterial. The technological principle of these genome-editing methods and their applications were summarized in this review, which can benefit engineering and construction of industrial microorganism.

Facile mutant identification via a single parental backcross method and application of whole genome sequencing based mapping pipelines

Directory of Open Access Journals (Sweden)

Robert Silas Allen

2013-09-01

Full Text Available Forward genetic screens have identified numerous genes involved in development and metabolism, and remain a cornerstone of biological research. However to locate a causal mutation, the practice of crossing to a polymorphic background to generate a mapping population can be problematic if the mutant phenotype is difficult to recognise in the hybrid F2 progeny, or dependent on parental specific traits. Here in a screen for leaf hyponasty mutants, we have performed a single backcross of an Ethane Methyl Sulphonate (EMS generated hyponastic mutant to its parent. Whole genome deep sequencing of a bulked homozygous F2 population and analysis via the Next Generation EMS mutation mapping pipeline (NGM unambiguously determined the causal mutation to be a single nucleotide polymorphisim (SNP residing in HASTY, a previously characterised gene involved in microRNA biogenesis. We have evaluated the feasibility of this backcross approach using three additional SNP mapping pipelines; SHOREmap, the GATK pipeline, and the samtools pipeline. Although there was variance in the identification of EMS SNPs, all returned the same outcome in clearly identifying the causal mutation in HASTY. The simplicity of performing a single parental backcross and genome sequencing a small pool of segregating mutants has great promise for identifying mutations that may be difficult to map using conventional approaches.

Whole-genome in-silico subtractive hybridization (WISH - using massive sequencing for the identification of unique and repetitive sex-specific sequences: the example of Schistosoma mansoni

Directory of Open Access Journals (Sweden)

Parrinello Hugues

2010-06-01

Full Text Available Abstract Background Emerging methods of massive sequencing that allow for rapid re-sequencing of entire genomes at comparably low cost are changing the way biological questions are addressed in many domains. Here we propose a novel method to compare two genomes (genome-to-genome comparison. We used this method to identify sex-specific sequences of the human blood fluke Schistosoma mansoni. Results Genomic DNA was extracted from male and female (heterogametic S. mansoni adults and sequenced with a Genome Analyzer (Illumina. Sequences are available at the NCBI sequence read archive http://www.ncbi.nlm.nih.gov/Traces/sra/ under study accession number SRA012151.6. Sequencing reads were aligned to the genome, and a pseudogenome composed of known repeats. Straightforward comparative bioinformatics analysis was performed to compare male and female schistosome genomes and identify female-specific sequences. We found that the S. mansoni female W chromosome contains only few specific unique sequences (950 Kb i.e. about 0.2% of the genome. The majority of W-specific sequences are repeats (10.5 Mb i.e. about 2.5% of the genome. Arbitrarily selected W-specific sequences were confirmed by PCR. Primers designed for unique and repetitive sequences allowed to reliably identify the sex of both larval and adult stages of the parasite. Conclusion Our genome-to-genome comparison method that we call "whole-genome in-silico subtractive hybridization" (WISH allows for rapid identification of sequences that are specific for a certain genotype (e.g. the heterogametic sex. It can in principle be used for the detection of any sequence differences between isolates (e.g. strains, pathovars or even closely related species.

Decoding Synteny Blocks and Large-Scale Duplications in Mammalian and Plant Genomes

Science.gov (United States)

Peng, Qian; Alekseyev, Max A.; Tesler, Glenn; Pevzner, Pavel A.

The existing synteny block reconstruction algorithms use anchors (e.g., orthologous genes) shared over all genomes to construct the synteny blocks for multiple genomes. This approach, while efficient for a few genomes, cannot be scaled to address the need to construct synteny blocks in many mammalian genomes that are currently being sequenced. The problem is that the number of anchors shared among all genomes quickly decreases with the increase in the number of genomes. Another problem is that many genomes (plant genomes in particular) had extensive duplications, which makes decoding of genomic architecture and rearrangement analysis in plants difficult. The existing synteny block generation algorithms in plants do not address the issue of generating non-overlapping synteny blocks suitable for analyzing rearrangements and evolution history of duplications. We present a new algorithm based on the A-Bruijn graph framework that overcomes these difficulties and provides a unified approach to synteny block reconstruction for multiple genomes, and for genomes with large duplications.

K-State Problem Identification Rating Scales for College Students

Science.gov (United States)

Robertson, John M.; Benton, Stephen L.; Newton, Fred B.; Downey, Ronald G.; Marsh, Patricia A.; Benton, Sheryl A.; Tseng, Wen-Chih; Shin, Kang-Hyun

2006-01-01

The K-State Problem Identification Rating Scales, a new screening instrument for college counseling centers, gathers information about clients' presenting symptoms, functioning levels, and readiness to change. Three studies revealed 7 scales: Mood Difficulties, Learning Problems, Food Concerns, Interpersonal Conflicts, Career Uncertainties,…

Genomic divergences among cattle, dog and human estimated from large-scale alignments of genomic sequences

Directory of Open Access Journals (Sweden)

Shade Larry L

2006-06-01

Full Text Available Abstract Background Approximately 11 Mb of finished high quality genomic sequences were sampled from cattle, dog and human to estimate genomic divergences and their regional variation among these lineages. Results Optimal three-way multi-species global sequence alignments for 84 cattle clones or loci (each >50 kb of genomic sequence were constructed using the human and dog genome assemblies as references. Genomic divergences and substitution rates were examined for each clone and for various sequence classes under different functional constraints. Analysis of these alignments revealed that the overall genomic divergences are relatively constant (0.32–0.37 change/site for pairwise comparisons among cattle, dog and human; however substitution rates vary across genomic regions and among different sequence classes. A neutral mutation rate (2.0–2.2 × 10(-9 change/site/year was derived from ancestral repetitive sequences, whereas the substitution rate in coding sequences (1.1 × 10(-9 change/site/year was approximately half of the overall rate (1.9–2.0 × 10(-9 change/site/year. Relative rate tests also indicated that cattle have a significantly faster rate of substitution as compared to dog and that this difference is about 6%. Conclusion This analysis provides a large-scale and unbiased assessment of genomic divergences and regional variation of substitution rates among cattle, dog and human. It is expected that these data will serve as a baseline for future mammalian molecular evolution studies.

Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

Science.gov (United States)

Ouyang, Zhangyi; Liu, Feng; Zhao, Chenghui; Ren, Chao; An, Gaole; Mei, Chuan; Bo, Xiaochen; Shu, Wenjie

2018-04-16

RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

NARCIS (Netherlands)

Bouwman, Aniek C.; Hayes, Ben J.; Calus, Mario P.L.

2017-01-01

Background: Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

Science.gov (United States)

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Considerations in the identification of functional RNA structural elements in genomic alignments

Directory of Open Access Journals (Sweden)

Blencowe Benjamin J

2007-01-01

Full Text Available Abstract Background Accurate identification of novel, functional noncoding (nc RNA features in genome sequence has proven more difficult than for exons. Current algorithms identify and score potential RNA secondary structures on the basis of thermodynamic stability, conservation, and/or covariance in sequence alignments. Neither the algorithms nor the information gained from the individual inputs have been independently assessed. Furthermore, due to issues in modelling background signal, it has been difficult to gauge the precision of these algorithms on a genomic scale, in which even a seemingly small false-positive rate can result in a vast excess of false discoveries. Results We developed a shuffling algorithm, shuffle-pair.pl, that simultaneously preserves dinucleotide frequency, gaps, and local conservation in pairwise sequence alignments. We used shuffle-pair.pl to assess precision and recall of six ncRNA search tools (MSARI, QRNA, ddbRNA, RNAz, Evofold, and several variants of simple thermodynamic stability on a test set of 3046 alignments of known ncRNAs. Relative to mononucleotide shuffling, preservation of dinucleotide content in shuffling the alignments resulted in a drastic increase in estimated false-positive detection rates for ncRNA elements, precluding evaluation of higher order alignments, which cannot not be adequately shuffled maintaining both dinucleotides and alignment structure. On pairwise alignments, none of the covariance-based tools performed markedly better than thermodynamic scoring alone. Although the high false-positive rates call into question the veracity of any individual predicted secondary structural element in our analysis, we nevertheless identified intriguing global trends in human genome alignments. The distribution of ncRNA prediction scores in 75-base windows overlapping UTRs, introns, and intergenic regions analyzed using both thermodynamic stability and EvoFold (which has no thermodynamic component was

The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.

Science.gov (United States)

Bui, Duy Duc An; Wyatt, Mathew; Cimino, James J

2017-11-01

Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be the gold standard approach but is tedious, expensive, slow, and impractical for use with large-scale clinical data. Automated or semi-automated de-identification using computer algorithms is a potentially promising alternative. The Informatics Institute of the University of Alabama at Birmingham is applying de-identification to clinical data drawn from the UAB hospital's electronic medical records system before releasing them for research. We participated in a shared task challenge by the Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID) at the de-identification regular track to gain experience developing our own automatic de-identification tool. We focused on the popular and successful methods from previous challenges: rule-based, dictionary-matching, and machine-learning approaches. We also explored new techniques such as disambiguation rules, term ambiguity measurement, and used multi-pass sieve framework at a micro level. For the challenge's primary measure (strict entity), our submissions achieved competitive results (f-measures: 87.3%, 87.1%, and 86.7%). For our preferred measure (binary token HIPAA), our submissions achieved superior results (f-measures: 93.7%, 93.6%, and 93%). With those encouraging results, we gain the confidence to improve and use the tool for the real de-identification task at the UAB Informatics Institute. Copyright © 2017 Elsevier Inc. All rights reserved.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

DEFF Research Database (Denmark)

Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

2013-01-01

available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about......BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking....... RESULTS: In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes...

Genome-scale metabolic models applied to human health and disease.

Science.gov (United States)

Cook, Daniel J; Nielsen, Jens

2017-11-01

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

Targeted and genome-scale methylomics reveals gene body signatures in human cell lines

Science.gov (United States)

Ball, Madeleine Price; Li, Jin Billy; Gao, Yuan; Lee, Je-Hyuk; LeProust, Emily; Park, In-Hyun; Xie, Bin; Daley, George Q.; Church, George M.

2012-01-01

Cytosine methylation, an epigenetic modification of DNA, is a target of growing interest for developing high throughput profiling technologies. Here we introduce two new, complementary techniques for cytosine methylation profiling utilizing next generation sequencing technology: bisulfite padlock probes (BSPPs) and methyl sensitive cut counting (MSCC). In the first method, we designed a set of ~10,000 BSPPs distributed over the ENCODE pilot project regions to take advantage of existing expression and chromatin immunoprecipitation data. We observed a pattern of low promoter methylation coupled with high gene body methylation in highly expressed genes. Using the second method, MSCC, we gathered genome-scale data for 1.4 million HpaII sites and confirmed that gene body methylation in highly expressed genes is a consistent phenomenon over the entire genome. Our observations highlight the usefulness of techniques which are not inherently or intentionally biased in favor of only profiling particular subsets like CpG islands or promoter regions. PMID:19329998

Proteomic strategy for the identification of critical actors in reorganization of the post-meiotic male genome.

Science.gov (United States)

Govin, Jerome; Gaucher, Jonathan; Ferro, Myriam; Debernardi, Alexandra; Garin, Jerome; Khochbin, Saadi; Rousseaux, Sophie

2012-01-01

After meiosis, during the final stages of spermatogenesis, the haploid male genome undergoes major structural changes, resulting in a shift from a nucleosome-based genome organization to the sperm-specific, highly compacted nucleoprotamine structure. Recent data support the idea that region-specific programming of the haploid male genome is of high importance for the post-fertilization events and for successful embryo development. Although these events constitute a unique and essential step in reproduction, the mechanisms by which they occur have remained completely obscure and the factors involved have mostly remained uncharacterized. Here, we sought a strategy to significantly increase our understanding of proteins controlling the haploid male genome reprogramming, based on the identification of proteins in two specific pools: those with the potential to bind nucleic acids (basic proteins) and proteins capable of binding basic proteins (acidic proteins). For the identification of acidic proteins, we developed an approach involving a transition-protein (TP)-based chromatography, which has the advantage of retaining not only acidic proteins due to the charge interactions, but also potential TP-interacting factors. A second strategy, based on an in-depth bioinformatic analysis of the identified proteins, was then applied to pinpoint within the lists obtained, male germ cells expressed factors relevant to the post-meiotic genome organization. This approach reveals a functional network of DNA-packaging proteins and their putative chaperones and sheds a new light on the way the critical transitions in genome organizations could take place. This work also points to a new area of research in male infertility and sperm quality assessments.

Genome profiling (GP method based classification of insects: congruence with that of classical phenotype-based one.

Directory of Open Access Journals (Sweden)

Shamim Ahmed

Full Text Available Ribosomal RNAs have been widely used for identification and classification of species, and have produced data giving new insights into phylogenetic relationships. Recently, multilocus genotyping and even whole genome sequencing-based technologies have been adopted in ambitious comparative biology studies. However, such technologies are still far from routine-use in species classification studies due to their high costs in terms of labor, equipment and consumables.Here, we describe a simple and powerful approach for species classification called genome profiling (GP. The GP method composed of random PCR, temperature gradient gel electrophoresis (TGGE and computer-aided gel image processing is highly informative and less laborious. For demonstration, we classified 26 species of insects using GP and 18S rDNA-sequencing approaches. The GP method was found to give a better correspondence to the classical phenotype-based approach than did 18S rDNA sequencing employing a congruence value. To our surprise, use of a single probe in GP was sufficient to identify the relationships between the insect species, making this approach more straightforward.The data gathered here, together with those of previous studies show that GP is a simple and powerful method that can be applied for actually universally identifying and classifying species. The current success supported our previous proposal that GP-based web database can be constructible and effective for the global identification/classification of species.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

DEFF Research Database (Denmark)

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim

2008-01-01

Background: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number...... to a genome scale metabolic model of A. oryzae. Results: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted...... model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion: A much enhanced annotation of the A. oryzae genome was performed and a genomescale metabolic model of A. oryzae was reconstructed. The model accurately predicted...

Genomic variation in Salmonella enterica core genes for epidemiological typing

DEFF Research Database (Denmark)

Leekitcharoenphon, Pimlapas; Lukjancenko, Oksana; Rundsten, Carsten Friis

2012-01-01

Background: Technological advances in high throughput genome sequencing are making whole genome sequencing (WGS) available as a routine tool for bacterial typing. Standardized procedures for identification of relevant genes and of variation are needed to enable comparison between studies and over...... genomes and evaluate their value as typing targets, comparing whole genome typing and traditional methods such as 16S and MLST. A consensus tree based on variation of core genes gives much better resolution than 16S and MLST; the pan-genome family tree is similar to the consensus tree, but with higher...... that there is a positive selection towards mutations leading to amino acid changes. Conclusions: Genomic variation within the core genome is useful for investigating molecular evolution and providing candidate genes for bacterial genome typing. Identification of genes with different degrees of variation is important...

Comparison on genomic predictions using GBLUP models and two single-step blending methods with different relationship matrices in the Nordic Holstein population

DEFF Research Database (Denmark)

Gao, Hongding; Christensen, Ole Fredslund; Madsen, Per

2012-01-01

Background A single-step blending approach allows genomic prediction using information of genotyped and non-genotyped animals simultaneously. However, the combined relationship matrix in a single-step method may need to be adjusted because marker-based and pedigree-based relationship matrices may...... not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare single-step blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16......) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step blending method. In the adjusted GBLUP and single-step methods, the genomic relationship matrix was adjusted...

In Silico Genome-Scale Reconstruction and Validation of the Staphylococcus aureus Metabolic Network

NARCIS (Netherlands)

Heinemann, Matthias; Kümmel, Anne; Ruinatscha, Reto; Panke, Sven

2005-01-01

A genome-scale metabolic model of the Gram-positive, facultative anaerobic opportunistic pathogen Staphylococcus aureus N315 was constructed based on current genomic data, literature, and physiological information. The model comprises 774 metabolic processes representing approximately 23% of all

Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models.

Science.gov (United States)

Hamilton, Joshua J; Dwivedi, Vivek; Reed, Jennifer L

2013-07-16

Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Genomes to Proteomes

Energy Technology Data Exchange (ETDEWEB)

Panisko, Ellen A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Grigoriev, Igor [USDOE Joint Genome Inst., Walnut Creek, CA (United States); Daly, Don S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Webb-Robertson, Bobbie-Jo [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Baker, Scott E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2009-03-01

Biologists are awash with genomic sequence data. In large part, this is due to the rapid acceleration in the generation of DNA sequence that occurred as public and private research institutes raced to sequence the human genome. In parallel with the large human genome effort, mostly smaller genomes of other important model organisms were sequenced. Projects following on these initial efforts have made use of technological advances and the DNA sequencing infrastructure that was built for the human and other organism genome projects. As a result, the genome sequences of many organisms are available in high quality draft form. While in many ways this is good news, there are limitations to the biological insights that can be gleaned from DNA sequences alone; genome sequences offer only a bird's eye view of the biological processes endemic to an organism or community. Fortunately, the genome sequences now being produced at such a high rate can serve as the foundation for other global experimental platforms such as proteomics. Proteomic methods offer a snapshot of the proteins present at a point in time for a given biological sample. Current global proteomics methods combine enzymatic digestion, separations, mass spectrometry and database searching for peptide identification. One key aspect of proteomics is the prediction of peptide sequences from mass spectrometry data. Global proteomic analysis uses computational matching of experimental mass spectra with predicted spectra based on databases of gene models that are often generated computationally. Thus, the quality of gene models predicted from a genome sequence is crucial in the generation of high quality peptide identifications. Once peptides are identified they can be assigned to their parent protein. Proteins identified as expressed in a given experiment are most useful when compared to other expressed proteins in a larger biological context or biochemical pathway. In this chapter we will discuss the automatic

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

Science.gov (United States)

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

DEFF Research Database (Denmark)

Kjeldsen, Kjeld Raunkjær; Nielsen, J.

2009-01-01

A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine...... yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate...

Genome-scale modeling of yeast: chronology, applications and critical perspectives.

Science.gov (United States)

Lopes, Helder; Rocha, Isabel

2017-08-01

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed. © FEMS 2017.

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

Science.gov (United States)

Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

A Genomic Survey of SCPP Family Genes in Fishes Provides Novel Insights into the Evolution of Fish Scales.

Science.gov (United States)

Lv, Yunyun; Kawasaki, Kazuhiko; Li, Jia; Li, Yanping; Bian, Chao; Huang, Yu; You, Xinxin; Shi, Qiong

2017-11-16

The family of secretory calcium-binding phosphoproteins (SCPPs) have been considered vital to skeletal tissue mineralization. However, most previous SCPP studies focused on phylogenetically distant animals but not on those closely related species. Here we provide novel insights into the coevolution of SCPP genes and fish scales in 10 species from Otophysi . According to their scale phenotypes, these fishes can be divided into three groups, i.e., scaled, sparsely scaled, and scaleless. We identified homologous SCPP genes in the genomes of these species and revealed an absence of some SCPP members in some genomes, suggesting an uneven evolutionary history of SCPP genes in fishes. In addition, most of these SCPP genes, with the exception of SPP1 , individually form one or two gene cluster(s) on each corresponding genome. Furthermore, we constructed phylogenetic trees using maximum likelihood method to estimate their evolution. The phylogenetic topology mostly supports two subclasses in some species, such as Cyprinus carpio , Sinocyclocheilus anshuiensis , S. grahamin , and S. rhinocerous , but not in the other examined fishes. By comparing the gene structures of recently reported candidate genes, SCPP1 and SCPP5 , for determining scale phenotypes, we found that the hypothesis is suitable for Astyanax mexicanus , but denied by S. anshuiensis , even though they are both sparsely scaled for cave adaptation. Thus, we conclude that, although different fish species display similar scale phenotypes, the underlying genetic changes however might be diverse. In summary, this paper accelerates the recognition of the SCPP family in teleosts for potential scale evolution.

iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.

Science.gov (United States)

Norsigian, Charles J; Kavvas, Erol; Seif, Yara; Palsson, Bernhard O; Monk, Jonathan M

2018-01-01

Acinetobacter baumannii has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen.

Numerical and Experimental Identification of Seven-Wire Strand Tensions Using Scale Energy Entropy Spectra of Ultrasonic Guided Waves

Directory of Open Access Journals (Sweden)

Ji Qian

2018-01-01

Full Text Available Accurate identification of tension in multiwire strands is a key issue to ensure structural safety and durability of prestressed concrete structures, cable-stayed bridges, and hoist elevators. This paper proposes a method to identify strand tensions based on scale energy entropy spectra of ultrasonic guided waves (UGWs. A numerical method was first developed to simulate UGW propagation in a seven-wire strand, employing the wavelet transform to extract UGW time-frequency energy distributions for different loadings. Mode separation and frequency band loss of L(0,1 were then found for increasing tension, and UGW scale energy entropy spectra were extracted to establish a tension identification index. A good linear relationship was found between the proposed identification index and tensile force, and effects of propagation distance and propagation path were analyzed. Finally, UGWs propagation was examined experimentally for a long seven-wire strand to investigate attenuation and long distance propagation. Numerical and experimental results verified that the proposed method not only can effectively identify strand tensions but can also adapt to long distance tests for practical engineering.

Hob Identification Methods

Directory of Open Access Journals (Sweden)

Andrzej Piotrowski

2018-03-01

Full Text Available In industrial practice, hobs are manufactured and used. The problem boils down to the identification of a hob with defining its profile, which depends on many design and technological parameters (such as the grinding wheel size, profile, type and positioning during machining. This makes the basis for the correct execution and sharpening of the tool. The accuracy of the hob determines the quality of gear wheel teeth being shaped. The article presents the hob identification methods that are possible to be used in industrial and laboratory practice.

Estimating phylogenetic trees from genome-scale data.

Science.gov (United States)

Liu, Liang; Xi, Zhenxiang; Wu, Shaoyuan; Davis, Charles C; Edwards, Scott V

2015-12-01

The heterogeneity of signals in the genomes of diverse organisms poses challenges for traditional phylogenetic analysis. Phylogenetic methods known as "species tree" methods have been proposed to directly address one important source of gene tree heterogeneity, namely the incomplete lineage sorting that occurs when evolving lineages radiate rapidly, resulting in a diversity of gene trees from a single underlying species tree. Here we review theory and empirical examples that help clarify conflicts between species tree and concatenation methods, and misconceptions in the literature about the performance of species tree methods. Considering concatenation as a special case of the multispecies coalescent model helps explain differences in the behavior of the two methods on phylogenomic data sets. Recent work suggests that species tree methods are more robust than concatenation approaches to some of the classic challenges of phylogenetic analysis, including rapidly evolving sites in DNA sequences and long-branch attraction. We show that approaches, such as binning, designed to augment the signal in species tree analyses can distort the distribution of gene trees and are inconsistent. Computationally efficient species tree methods incorporating biological realism are a key to phylogenetic analysis of whole-genome data. © 2015 New York Academy of Sciences.

Annotated Draft Genome Assemblies for the Northern Bobwhite (Colinus virginianus) and the Scaled Quail (Callipepla squamata) Reveal Disparate Estimates of Modern Genome Diversity and Historic Effective Population Size.

Science.gov (United States)

Oldeschulte, David L; Halley, Yvette A; Wilson, Miranda L; Bhattarai, Eric K; Brashear, Wesley; Hill, Joshua; Metz, Richard P; Johnson, Charles D; Rollins, Dale; Peterson, Markus J; Bickhart, Derek M; Decker, Jared E; Sewell, John F; Seabury, Christopher M

2017-09-07

Northern bobwhite ( Colinus virginianus ; hereafter bobwhite) and scaled quail ( Callipepla squamata ) populations have suffered precipitous declines across most of their US ranges. Illumina-based first- (v1.0) and second- (v2.0) generation draft genome assemblies for the scaled quail and the bobwhite produced N50 scaffold sizes of 1.035 and 2.042 Mb, thereby producing a 45-fold improvement in contiguity over the existing bobwhite assembly, and ≥90% of the assembled genomes were captured within 1313 and 8990 scaffolds, respectively. The scaled quail assembly (v1.0 = 1.045 Gb) was ∼20% smaller than the bobwhite (v2.0 = 1.254 Gb), which was supported by kmer-based estimates of genome size. Nevertheless, estimates of GC content (41.72%; 42.66%), genome-wide repetitive content (10.40%; 10.43%), and MAKER-predicted protein coding genes (17,131; 17,165) were similar for the scaled quail (v1.0) and bobwhite (v2.0) assemblies, respectively. BUSCO analyses utilizing 3023 single-copy orthologs revealed a high level of assembly completeness for the scaled quail (v1.0; 84.8%) and the bobwhite (v2.0; 82.5%), as verified by comparison with well-established avian genomes. We also detected 273 putative segmental duplications in the scaled quail genome (v1.0), and 711 in the bobwhite genome (v2.0), including some that were shared among both species. Autosomal variant prediction revealed ∼2.48 and 4.17 heterozygous variants per kilobase within the scaled quail (v1.0) and bobwhite (v2.0) genomes, respectively, and estimates of historic effective population size were uniformly higher for the bobwhite across all time points in a coalescent model. However, large-scale declines were predicted for both species beginning ∼15-20 KYA. Copyright © 2017 Oldeschulte et al.

Annotated Draft Genome Assemblies for the Northern Bobwhite (Colinus virginianus and the Scaled Quail (Callipepla squamata Reveal Disparate Estimates of Modern Genome Diversity and Historic Effective Population Size

Directory of Open Access Journals (Sweden)

David L. Oldeschulte

2017-09-01

Full Text Available Northern bobwhite (Colinus virginianus; hereafter bobwhite and scaled quail (Callipepla squamata populations have suffered precipitous declines across most of their US ranges. Illumina-based first- (v1.0 and second- (v2.0 generation draft genome assemblies for the scaled quail and the bobwhite produced N50 scaffold sizes of 1.035 and 2.042 Mb, thereby producing a 45-fold improvement in contiguity over the existing bobwhite assembly, and ≥90% of the assembled genomes were captured within 1313 and 8990 scaffolds, respectively. The scaled quail assembly (v1.0 = 1.045 Gb was ∼20% smaller than the bobwhite (v2.0 = 1.254 Gb, which was supported by kmer-based estimates of genome size. Nevertheless, estimates of GC content (41.72%; 42.66%, genome-wide repetitive content (10.40%; 10.43%, and MAKER-predicted protein coding genes (17,131; 17,165 were similar for the scaled quail (v1.0 and bobwhite (v2.0 assemblies, respectively. BUSCO analyses utilizing 3023 single-copy orthologs revealed a high level of assembly completeness for the scaled quail (v1.0; 84.8% and the bobwhite (v2.0; 82.5%, as verified by comparison with well-established avian genomes. We also detected 273 putative segmental duplications in the scaled quail genome (v1.0, and 711 in the bobwhite genome (v2.0, including some that were shared among both species. Autosomal variant prediction revealed ∼2.48 and 4.17 heterozygous variants per kilobase within the scaled quail (v1.0 and bobwhite (v2.0 genomes, respectively, and estimates of historic effective population size were uniformly higher for the bobwhite across all time points in a coalescent model. However, large-scale declines were predicted for both species beginning ∼15–20 KYA.

An SVD-based comparison of nine whole eukaryotic genomes supports a coelomate rather than ecdysozoan lineage

Directory of Open Access Journals (Sweden)

Stuart Gary W

2004-12-01

Full Text Available Abstract Background Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored. Results A non-alignment method based on the Singular Value Decomposition (SVD was used to compare the predicted protein complement of nine whole eukaryotic genomes ranging from yeast to man. This analysis resulted in the simultaneous identification and definition of a large number of well conserved motifs and gene families, and produced a species tree supporting one of two conflicting hypotheses of metazoan relationships. Conclusions Our SVD-based analysis of the entire protein complement of nine whole eukaryotic genomes suggests that highly conserved motifs and gene families can be identified and effectively compared in a single coherent definition space for the easy extraction of gene and species trees. While this occurs without the explicit definition of orthologs or homologous sites, the analysis can provide a basis for these definitions.

Conservation genetics and genomics of amphibians and reptiles.

Science.gov (United States)

Shaffer, H Bradley; Gidiş, Müge; McCartney-Melstad, Evan; Neal, Kevin M; Oyamaguchi, Hilton M; Tellez, Marisa; Toffelmier, Erin M

2015-01-01

Amphibians and reptiles as a group are often secretive, reach their greatest diversity often in remote tropical regions, and contain some of the most endangered groups of organisms on earth. Particularly in the past decade, genetics and genomics have been instrumental in the conservation biology of these cryptic vertebrates, enabling work ranging from the identification of populations subject to trade and exploitation, to the identification of cryptic lineages harboring critical genetic variation, to the analysis of genes controlling key life history traits. In this review, we highlight some of the most important ways that genetic analyses have brought new insights to the conservation of amphibians and reptiles. Although genomics has only recently emerged as part of this conservation tool kit, several large-scale data sources, including full genomes, expressed sequence tags, and transcriptomes, are providing new opportunities to identify key genes, quantify landscape effects, and manage captive breeding stocks of at-risk species.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

DEFF Research Database (Denmark)

Nielsen, Henrik Bjørn; Almeida, Mathieu; Juncker, Agnieszka

2014-01-01

of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify...

Genomic and proteomic identification of Late Holocene remains

DEFF Research Database (Denmark)

Biard, Vincent; Gol'din, Pavel; Gladilina, Elena

2017-01-01

A critical challenge of the 21st century is to understand and minimise the effects of human activities on biodiversity. Cetaceans are a prime concern in biodiversity research, as many species still suffer from human impacts despite decades of management and conservation efforts. Zooarchaeology...... sequencing approach. In addition, shotgun sequencing produced several complete ancient odontocete mitogenomes and auxiliary nuclear genomic data for further exploration in a population genetic context. In contrast, both morphological identification and Sanger sequencing lacked taxonomic resolution and....../or resulted in misclassification of samples. We found that the combination of ZooMS and shotgun sequencing provides a powerful tool in zooarchaeology, and here allowed for a deeper understanding of past marine resource use and its implication for current management and conservation of Black Sea odontocetes....

Predicting growth of the healthy infant using a genome scale metabolic model.

Science.gov (United States)

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

Using relational databases for improved sequence similarity searching and large-scale genomic analyses.

Science.gov (United States)

Mackey, Aaron J; Pearson, William R

2004-10-01

Relational databases are designed to integrate diverse types of information and manage large sets of search results, greatly simplifying genome-scale analyses. Relational databases are essential for management and analysis of large-scale sequence analyses, and can also be used to improve the statistical significance of similarity searches by focusing on subsets of sequence libraries most likely to contain homologs. This unit describes using relational databases to improve the efficiency of sequence similarity searching and to demonstrate various large-scale genomic analyses of homology-related data. This unit describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. These include basic use of the database to generate a novel sequence library subset, how to extend and use seqdb_demo for the storage of sequence similarity search results and making use of various kinds of stored search results to address aspects of comparative genomic analysis.

Mapping the space of genomic signatures.

Directory of Open Access Journals (Sweden)

Lila Kari

Full Text Available We propose a computational method to measure and visualize interrelationships among any number of DNA sequences allowing, for example, the examination of hundreds or thousands of complete mitochondrial genomes. An "image distance" is computed for each pair of graphical representations of DNA sequences, and the distances are visualized as a Molecular Distance Map: Each point on the map represents a DNA sequence, and the spatial proximity between any two points reflects the degree of structural similarity between the corresponding sequences. The graphical representation of DNA sequences utilized, Chaos Game Representation (CGR, is genome- and species-specific and can thus act as a genomic signature. Consequently, Molecular Distance Maps could inform species identification, taxonomic classifications and, to a certain extent, evolutionary history. The image distance employed, Structural Dissimilarity Index (DSSIM, implicitly compares the occurrences of oligomers of length up to k (herein k = 9 in DNA sequences. We computed DSSIM distances for more than 5 million pairs of complete mitochondrial genomes, and used Multi-Dimensional Scaling (MDS to obtain Molecular Distance Maps that visually display the sequence relatedness in various subsets, at different taxonomic levels. This general-purpose method does not require DNA sequence alignment and can thus be used to compare similar or vastly different DNA sequences, genomic or computer-generated, of the same or different lengths. We illustrate potential uses of this approach by applying it to several taxonomic subsets: phylum Vertebrata, (superkingdom Protista, classes Amphibia-Insecta-Mammalia, class Amphibia, and order Primates. This analysis of an extensive dataset confirms that the oligomer composition of full mtDNA sequences can be a source of taxonomic information. This method also correctly finds the mtDNA sequences most closely related to that of the anatomically modern human (the Neanderthal

Genome-Wide Gene Set Analysis for Identification of Pathways Associated with Alcohol Dependence

Science.gov (United States)

Biernacka, Joanna M.; Geske, Jennifer; Jenkins, Gregory D.; Colby, Colin; Rider, David N.; Karpyak, Victor M.; Choi, Doo-Sup; Fridley, Brooke L.

2013-01-01

It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the “Synthesis and Degradation of Ketone Bodies” pathway. Our results also support the potential involvement of the “Neuroactive Ligand Receptor Interaction” pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence. PMID:22717047

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

Science.gov (United States)

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

2017-08-01

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

Genome-wide identification, functional and evolutionary analysis of terpene synthases in pineapple.

Science.gov (United States)

Chen, Xiaoe; Yang, Wei; Zhang, Liqin; Wu, Xianmiao; Cheng, Tian; Li, Guanglin

2017-10-01

Terpene synthases (TPSs) are vital for the biosynthesis of active terpenoids, which have important physiological, ecological and medicinal value. Although terpenoids have been reported in pineapple (Ananas comosus), genome-wide investigations of the TPS genes responsible for pineapple terpenoid synthesis are still lacking. By integrating pineapple genome and proteome data, twenty-one putative terpene synthase genes were found in pineapple and divided into five subfamilies. Tandem duplication is the cause of TPS gene family duplication. Furthermore, functional differentiation between each TPS subfamily may have occurred for several reasons. Sixty-two key amino acid sites were identified as being type-II functionally divergence between TPS-a and TPS-c subfamily. Finally, coevolution analysis indicated that multiple amino acid residues are involved in coevolutionary processes. In addition, the enzyme activity of two TPSs were tested. This genome-wide identification, functional and evolutionary analysis of pineapple TPS genes provide a new insight into understanding the roles of TPS family and lay the basis for further characterizing the function and evolution of TPS gene family. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Rapid and Reproducible Genomic DNA Extraction Protocol for Sequence-Based Identification of Archaea, Bacteria, Cyanobacteria, Diatoms, Fungi, and Green Algae

Directory of Open Access Journals (Sweden)

Farkhondeh Saba

2017-01-01

Full Text Available Background:  Sequence-based identification of various microorganisms including Archaea, Bacteria, Cyanobacteria, Diatoms, Fungi, and green algae necessitates an efficient and reproducible genome extraction procedure though which a pure template DNA is yielded and it can be used in polymerase chain reactions (PCR. Considering the fact that DNA extraction from these microorganisms is time consuming and laborious, we developed and standardized a safe, rapid and inexpensive miniprep protocol. Methods:  According to our results, amplification of various genomic regions including SSU, LSU, ITS, β-tubulin, actin, RPB2, and EF-1 resulted in a reproducible and efficient DNA extraction from a wide range of microorganisms yielding adequate pure genomic material for reproducible PCR-amplifications. Results:   This method relies on a temporary shock of increased concentrations of detergent which can be applied concomitant with multiple freeze-thaws to yield sufficient amount of DNA for PCR amplification of multiple or single fragments(s of the genome. As an advantage, the recipe seems very flexible, thus, various optional steps can be included depending on the samples used.Conclusion:   Having the needed flexibility in each step, this protocol is applicable on a very wide range of samples. Hence, various steps can be included depending on the desired quantity and quality.

A Rapid and Reproducible Genomic DNA Extraction Protocol for Sequence-Based Identification of Archaea, Bacteria, Cyanobacteria, Diatoms, Fungi, and Green Algae

Directory of Open Access Journals (Sweden)

Farkhondeh Saba

2016-09-01

Full Text Available Background:  Sequence-based identification of various microorganisms including Archaea, Bacteria, Cyanobacteria, Diatoms, Fungi, and green algae necessitates an efficient and reproducible genome extraction procedure though which a pure template DNA is yielded and it can be used in polymerase chain reactions (PCR. Considering the fact that DNA extraction from these microorganisms is time consuming and laborious, we developed and standardized a safe, rapid and inexpensive miniprep protocol. Methods:  According to our results, amplification of various genomic regions including SSU, LSU, ITS, β-tubulin, actin, RPB2, and EF-1 resulted in a reproducible and efficient DNA extraction from a wide range of microorganisms yielding adequate pure genomic material for reproducible PCR-amplifications. Results:   This method relies on a temporary shock of increased concentrations of detergent which can be applied concomitant with multiple freeze-thaws to yield sufficient amount of DNA for PCR amplification of multiple or single fragments(s of the genome. As an advantage, the recipe seems very flexible, thus, various optional steps can be included depending on the samples used.Conclusion:   Having the needed flexibility in each step, this protocol is applicable on a very wide range of samples. Hence, various steps can be included depending on the desired quantity and quality.

Genome-wide sequencing for the identification of rearrangements associated with Tourette syndrome and obsessive-compulsive disorder

Directory of Open Access Journals (Sweden)

Hooper Sean D

2012-12-01

Full Text Available Abstract Background Tourette Syndrome (TS is a neuropsychiatric disorder in children characterized by motor and verbal tics. Although several genes have been suggested in the etiology of TS, the genetic mechanisms remain poorly understood. Methods Using cytogenetics and FISH analysis, we identified an apparently balanced t(6,22(q16.2;p13 in a male patient with TS and obsessive-compulsive disorder (OCD. In order to map the breakpoints and to identify additional submicroscopic rearrangements, we performed whole genome mate-pair sequencing and CGH-array analysis on DNA from the proband. Results Sequence and CGH array analysis revealed a 400 kb deletion located 1.3 Mb telomeric of the chromosome 6q breakpoint, which has not been reported in controls. The deletion affects three genes (GPR63, NDUFA4 and KLHL32 and overlaps a region previously found deleted in a girl with autistic features and speech delay. The proband’s mother, also a carrier of the translocation, was diagnosed with OCD and shares the deletion. We also describe a further potentially related rearrangement which, while unmapped in Homo sapiens, was consistent with the chimpanzee genome. Conclusions We conclude that genome-wide sequencing at relatively low resolution can be used for the identification of submicroscopic rearrangements. We also show that large rearrangements may escape detection using standard analysis of whole genome sequencing data. Our findings further provide a candidate region for TS and OCD on chromosome 6q16.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Science.gov (United States)

Henson, Michael A

2015-12-01

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated. © 2015 Authors; published by Portland Press Limited.

Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

Directory of Open Access Journals (Sweden)

Kevin R Ramkissoon

Full Text Available The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

Science.gov (United States)

Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

Directory of Open Access Journals (Sweden)

Balagurunathan Balaji

2012-02-01

Full Text Available Abstract Background Fermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes. Results We present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation. Conclusion The genome-scale

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes.

Science.gov (United States)

Nielsen, H Bjørn; Almeida, Mathieu; Juncker, Agnieszka Sierakowska; Rasmussen, Simon; Li, Junhua; Sunagawa, Shinichi; Plichta, Damian R; Gautier, Laurent; Pedersen, Anders G; Le Chatelier, Emmanuelle; Pelletier, Eric; Bonde, Ida; Nielsen, Trine; Manichanh, Chaysavanh; Arumugam, Manimozhiyan; Batto, Jean-Michel; Quintanilha Dos Santos, Marcelo B; Blom, Nikolaj; Borruel, Natalia; Burgdorf, Kristoffer S; Boumezbeur, Fouad; Casellas, Francesc; Doré, Joël; Dworzynski, Piotr; Guarner, Francisco; Hansen, Torben; Hildebrand, Falk; Kaas, Rolf S; Kennedy, Sean; Kristiansen, Karsten; Kultima, Jens Roat; Léonard, Pierre; Levenez, Florence; Lund, Ole; Moumen, Bouziane; Le Paslier, Denis; Pons, Nicolas; Pedersen, Oluf; Prifti, Edi; Qin, Junjie; Raes, Jeroen; Sørensen, Søren; Tap, Julien; Tims, Sebastian; Ussery, David W; Yamada, Takuji; Renault, Pierre; Sicheritz-Ponten, Thomas; Bork, Peer; Wang, Jun; Brunak, Søren; Ehrlich, S Dusko

2014-08-01

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Genome-Independent Identification of RNA Editing by Mutual Information (GIREMI) | Informatics Technology for Cancer Research (ITCR)

Science.gov (United States)

Identification of single-nucleotide variants in RNA-seq data. Current version focuses on detection of RNA editing sites without requiring genome sequence data. New version is under development to separately identify RNA editing sites and genetic variants using RNA-seq data alone.

Exploring effect of segmentation scale on orient-based crop identification using HJ CCD data in Northeast China

International Nuclear Information System (INIS)

Cao, Xin; Zheng, Xinqi; Li, Qiangzi; Du, Xin; Zhang, Miao

2014-01-01

Crop identification and acreage estimation with remote sensing were the main issues for crop production estimation. Object-oriented classification has been involved in crop extraction from high spatial resolution images. However, different imagery segmentation scales for object-oriented classification always yield quite different crop identification accuracy. In this paper, multi-scale image segmentation was conducted to carry out crop identification using HJ CCD imagery in Red Star Farm in Heilongjiang province. Corn, soybean and wheat were identified as the final crop classes. Crop identification features at different segmentation scale were generated. Crop separability based on different feature-combinations was evaluated using class separation distance. Nearest Neighbour classifier (NN) was then used for crop identification. The results showed that the best segmentation scale was 8, and the overall crop identification accuracy was about 0.969 at that scale

Ultrafast comparison of personal genomes

OpenAIRE

Mauldin, Denise; Hood, Leroy; Robinson, Max; Glusman, Gustavo

2017-01-01

We present an ultra-fast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into 'genome fingerprints' that can be readily compared across sequencing technologies and reference versions. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. This enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative s...

Genome-wide identification of structural variants in genes encoding drug targets

DEFF Research Database (Denmark)

Rasmussen, Henrik Berg; Dahmcke, Christina Mackeprang

2012-01-01

The objective of the present study was to identify structural variants of drug target-encoding genes on a genome-wide scale. We also aimed at identifying drugs that are potentially amenable for individualization of treatments based on knowledge about structural variation in the genes encoding...

An approach to large scale identification of non-obvious structural similarities between proteins

Science.gov (United States)

Cherkasov, Artem; Jones, Steven JM

2004-01-01

Background A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy. Results The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors. The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity. Conclusion Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence. PMID:15147578

An approach to large scale identification of non-obvious structural similarities between proteins

Directory of Open Access Journals (Sweden)

Cherkasov Artem

2004-05-01

Full Text Available Abstract Background A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy. Results The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors. The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity. Conclusion Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence.

A tailing genome walking method suitable for genomes with high local GC content.

Science.gov (United States)

Liu, Taian; Fang, Yongxiang; Yao, Wenjuan; Guan, Qisai; Bai, Gang; Jing, Zhizhong

2013-10-15

The tailing genome walking strategies are simple and efficient. However, they sometimes can be restricted due to the low stringency of homo-oligomeric primers. Here we modified their conventional tailing step by adding polythymidine and polyguanine to the target single-stranded DNA (ssDNA). The tailed ssDNA was then amplified exponentially with a specific primer in the known region and a primer comprising 5' polycytosine and 3' polyadenosine. The successful application of this novel method for identifying integration sites mediated by φC31 integrase in goat genome indicates that the method is more suitable for genomes with high complexity and local GC content. Copyright © 2013 Elsevier Inc. All rights reserved.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

Science.gov (United States)

Mourad, Raphaël; Cuvier, Olivier

2016-05-01

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

DEFF Research Database (Denmark)

Sanchez, Benjamin J.; Nielsen, Jens

2015-01-01

Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are curre......Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published...... in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted....

Genome-based microbial ecology of anammox granules in a full-scale wastewater treatment system

NARCIS (Netherlands)

Speth, D.R.; Zandt, M.H. in 't; Guerrero Cruz, S.; Dutilh, B.E.; Jetten, M.S.M.

2016-01-01

Partial-nitritation anammox (PNA) is a novel wastewater treatment procedure for energy-efficient ammonium removal. Here we use genome-resolved metagenomics to build a genome-based ecological model of the microbial community in a full-scale PNA reactor. Sludge from the bioreactor examined here is

Barcode server: a visualization-based genome analysis system.

Directory of Open Access Journals (Sweden)

Fenglou Mao

Full Text Available We have previously developed a computational method for representing a genome as a barcode image, which makes various genomic features visually apparent. We have demonstrated that this visual capability has made some challenging genome analysis problems relatively easy to solve. We have applied this capability to a number of challenging problems, including (a identification of horizontally transferred genes, (b identification of genomic islands with special properties and (c binning of metagenomic sequences, and achieved highly encouraging results. These application results inspired us to develop this barcode-based genome analysis server for public service, which supports the following capabilities: (a calculation of the k-mer based barcode image for a provided DNA sequence; (b detection of sequence fragments in a given genome with distinct barcodes from those of the majority of the genome, (c clustering of provided DNA sequences into groups having similar barcodes; and (d homology-based search using Blast against a genome database for any selected genomic regions deemed to have interesting barcodes. The barcode server provides a job management capability, allowing processing of a large number of analysis jobs for barcode-based comparative genome analyses. The barcode server is accessible at http://csbl1.bmb.uga.edu/Barcode.

A BAC clone fingerprinting approach to the detection of human genome rearrangements

Science.gov (United States)

Krzywinski, Martin; Bosdet, Ian; Mathewson, Carrie; Wye, Natasja; Brebner, Jay; Chiu, Readman; Corbett, Richard; Field, Matthew; Lee, Darlene; Pugh, Trevor; Volik, Stas; Siddiqui, Asim; Jones, Steven; Schein, Jacquie; Collins, Collin; Marra, Marco

2007-01-01

We present a method, called fingerprint profiling (FPP), that uses restriction digest fingerprints of bacterial artificial chromosome clones to detect and classify rearrangements in the human genome. The approach uses alignment of experimental fingerprint patterns to in silico digests of the sequence assembly and is capable of detecting micro-deletions (1-5 kb) and balanced rearrangements. Our method has compelling potential for use as a whole-genome method for the identification and characterization of human genome rearrangements. PMID:17953769

Consumer-company Identification: Development and Validation of a Scale

Directory of Open Access Journals (Sweden)

Diogo Fajardo Nunes Hildebrand

2010-07-01

Full Text Available Consumer-Company Identification is a relatively new issue in the marketing academia. Bhattacharya and Sen(2003 explored the Social Identity theory and established Consumer-Company Identification as the primary psychological substrate for deep relationships between the organization and its customers. In the present study a new instrument was constructed and validated that permits the empirical verification of the phenomenon described by Bhattacharya and Sen (2003. The scale validated in the present study is the first to embrace the idiosyncrasies of the identification between consumers and organizations. The process was conducted through 3 independent data collections. The first one was collected using literature search and in-depth interviews with 12 undergraduate students and bachelors from different professional fields. The second data base was obtained from a survey of 226 undergraduate students from 3 universities in 2 big Brazilian cities. This data base was used for purification purposes using Explanatory Factorial Analysis. Finally, the Structural Equation Modeling technique was applied to analyze a third data base composed of 387 observations collected from the same 3 universities of the second study. The results confirm the content, convergent and discriminant validity of the new scale proposed.

Identification and insertion polymorphisms of short interspersed nuclear elements (SINEs) in Brassica genomes

International Nuclear Information System (INIS)

Nouroz, F.; Naveed, M.

2018-01-01

The non-LTR retrotransposons (retroposons) are abundant in plant genomes including members of Brassicaceae. Of the retroposons, long interspersed nuclear elements (LINEs) are more copious followed by short interspersed nuclear elements (SINEs) in sequenced eukaryotic genomes. The SINEs are short elements and ranged from 100-500 bps flanked by variable sized target site duplications, 5' tRNA region with polymerase III promoter, internal tRNA unrelated region, 3' LINEs derived region and a poly adenosine tail. Different computational approaches were used for the identification and characterization of SINEs, while PCR was used to detect the SINEs insertion polymorphisms in various Brassica genotypes. Ten previously unidentified families of SINEs were identified and characterized from Brassica genomes. The structural features of these SINEs were studied in detail, which showed typical SINE features displaying small sizes, target site duplications, head regions, internal regions (body) of variable sizes and a poly (A) tail at the 3' terminus. The elements from various families ranged from 206-558 bp, where BoSINE2 family displayed smallest SINE element (206 bp), while larger members belonged to BoSINE9 family (524-558 bp). The distribution and abundance of SINEs in various Brassica species and genotypes (40) at a particular site/locus were investigated by SINEs based PCR markers. Various SINE insertion polymorphisms were detected from different genotypes, where higher PCR bands amplified the SINE insertions, while lower bands amplified the pre-insertion sites (flanking regions). The analysis of Brassica SINEs copy numbers from 10 identified families revealed that around 860 and 1712 copies of SINEs were calculated from B. rapa and B. oleracea Whole-genome shotgun contigs (WGS) respectively. Analysis of insertion sites of Brassica SINEs revealed that the members from all 10 SINE families had shown an insertion preference in AT rich regions. The present

Reconstruction of genome-scale human metabolic models using omics data

DEFF Research Database (Denmark)

Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

2015-01-01

used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods......, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic...... refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model....

New families of human regulatory RNA structures identified by comparative analysis of vertebrate genomes

DEFF Research Database (Denmark)

Parker, Brian John; Moltke, Ida; Roth, Adam

2011-01-01

a comparative method, EvoFam, for genome-wide identification of families of regulatory RNA structures, based on primary sequence and secondary structure similarity. We apply EvoFam to a 41-way genomic vertebrate alignment. Genome-wide, we identify 220 human, high-confidence families outside protein...

Modeling Lactococcus lactis using a genome-scale flux model

Directory of Open Access Journals (Sweden)

Nielsen Jens

2005-06-01

Full Text Available Abstract Background Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA and minimization of metabolic adjustment (MOMA were used as modeling frameworks. Results The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. Conclusion The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.

De novo identification of replication-timing domains in the human genome by deep learning.

Science.gov (United States)

Liu, Feng; Ren, Chao; Li, Hao; Zhou, Pingkun; Bo, Xiaochen; Shu, Wenjie

2016-03-01

The de novo identification of the initiation and termination zones-regions that replicate earlier or later than their upstream and downstream neighbours, respectively-remains a key challenge in DNA replication. Building on advances in deep learning, we developed a novel hybrid architecture combining a pre-trained, deep neural network and a hidden Markov model (DNN-HMM) for the de novo identification of replication domains using replication timing profiles. Our results demonstrate that DNN-HMM can significantly outperform strong, discriminatively trained Gaussian mixture model-HMM (GMM-HMM) systems and other six reported methods that can be applied to this challenge. We applied our trained DNN-HMM to identify distinct replication domain types, namely the early replication domain (ERD), the down transition zone (DTZ), the late replication domain (LRD) and the up transition zone (UTZ), using newly replicated DNA sequencing (Repli-Seq) data across 15 human cells. A subsequent integrative analysis revealed that these replication domains harbour unique genomic and epigenetic patterns, transcriptional activity and higher-order chromosomal structure. Our findings support the 'replication-domain' model, which states (1) that ERDs and LRDs, connected by UTZs and DTZs, are spatially compartmentalized structural and functional units of higher-order chromosomal structure, (2) that the adjacent DTZ-UTZ pairs form chromatin loops and (3) that intra-interactions within ERDs and LRDs tend to be short-range and long-range, respectively. Our model reveals an important chromatin organizational principle of the human genome and represents a critical step towards understanding the mechanisms regulating replication timing. Our DNN-HMM method and three additional algorithms can be freely accessed at https://github.com/wenjiegroup/DNN-HMM The replication domain regions identified in this study are available in GEO under the accession ID GSE53984. shuwj@bmi.ac.cn or boxc

Genome Sequence of the Palaeopolyploid soybean

Energy Technology Data Exchange (ETDEWEB)

Schmutz, Jeremy; Cannon, Steven B.; Schlueter, Jessica; Ma, Jianxin; Mitros, Therese; Nelson, William; Hyten, David L.; Song, Qijian; Thelen, Jay J.; Cheng, Jianlin; Xu, Dong; Hellsten, Uffe; May, Gregory D.; Yu, Yeisoo; Sakura, Tetsuya; Umezawa, Taishi; Bhattacharyya, Madan K.; Sandhu, Devinder; Valliyodan, Babu; Lindquist, Erika; Peto, Myron; Grant, David; Shu, Shengqiang; Goodstein, David; Barry, Kerrie; Futrell-Griggs, Montona; Abernathy, Brian; Du, Jianchang; Tian, Zhixi; Zhu, Liucun; Gill, Navdeep; Joshi, Trupti; Libault, Marc; Sethuraman, Anand; Zhang, Xue-Cheng; Shinozaki, Kazuo; Nguyen, Henry T.; Wing, Rod A.; Cregan, Perry; Specht, James; Grimwood, Jane; Rokhsar, Dan; Stacey, Gary; Shoemaker, Randy C.; Jackson, Scott A.

2009-08-03

Soybean (Glycine max) is one of the most important crop plants for seed protein and oil content, and for its capacity to fix atmospheric nitrogen through symbioses with soil-borne microorganisms. We sequenced the 1.1-gigabase genome by a whole-genome shotgun approach and integrated it with physical and high-density genetic maps to create a chromosome-scale draft sequence assembly. We predict 46,430 protein-coding genes, 70percent more than Arabidopsis and similar to the poplar genome which, like soybean, is an ancient polyploid (palaeopolyploid). About 78percent of the predicted genes occur in chromosome ends, which comprise less than one-half of the genome but account for nearly all of the genetic recombination. Genome duplications occurred at approximately 59 and 13 million years ago, resulting in a highly duplicated genome with nearly 75percent of the genes present in multiple copies. The two duplication events were followed by gene diversification and loss, and numerous chromosome rearrangements. An accurate soybean genome sequence will facilitate the identification of the genetic basis of many soybean traits, and accelerate the creation of improved soybean varieties.

Genomic identification of founding haplotypes reveals the history of the selfing species Capsella rubella.

Directory of Open Access Journals (Sweden)

Yaniv Brandvain

Full Text Available The shift from outcrossing to self-fertilization is among the most common evolutionary transitions in flowering plants. Until recently, however, a genome-wide view of this transition has been obscured by both a dearth of appropriate data and the lack of appropriate population genomic methods to interpret such data. Here, we present a novel population genomic analysis detailing the origin of the selfing species, Capsella rubella, which recently split from its outcrossing sister, Capsella grandiflora. Due to the recency of the split, much of the variation within C. rubella is also found within C. grandiflora. We can therefore identify genomic regions where two C. rubella individuals have inherited the same or different segments of ancestral diversity (i.e. founding haplotypes present in C. rubella's founder(s. Based on this analysis, we show that C. rubella was founded by multiple individuals drawn from a diverse ancestral population closely related to extant C. grandiflora, that drift and selection have rapidly homogenized most of this ancestral variation since C. rubella's founding, and that little novel variation has accumulated within this time. Despite the extensive loss of ancestral variation, the approximately 25% of the genome for which two C. rubella individuals have inherited different founding haplotypes makes up roughly 90% of the genetic variation between them. To extend these findings, we develop a coalescent model that utilizes the inferred frequency of founding haplotypes and variation within founding haplotypes to estimate that C. rubella was founded by a potentially large number of individuals between 50 and 100 kya, and has subsequently experienced a twenty-fold reduction in its effective population size. As population genomic data from an increasing number of outcrossing/selfing pairs are generated, analyses like the one developed here will facilitate a fine-scaled view of the evolutionary and demographic impact of the

Analyses of Dynamics in Dairy Products and Identification of Lactic Acid Bacteria Population by Molecular Methods

Directory of Open Access Journals (Sweden)

Aytül Sofu

2017-01-01

Full Text Available Lactic acid bacteria (LAB with different ecological niches are widely seen in fermented meat, vegetables, dairy products and cereals as well as in fermented beverages. Lactic acid bacteria are the most important group of bacteria in dairy industry due to their probiotic characteristics and fermentation agents as starter culture. In the taxonomy of the lactic acid bacteria; by means of rep-PCR, which is the analysis of repetitive sequences that are based on 16S ribosomal RNA (rRNA gene sequence, it is possible to conduct structural microbial community analyses such as Restriction Fragment Length Polymorphism (RFLP analysis of DNA fragments of different sizes cut with enzymes, Random Amplified Polymorphic DNA (RAPD polymorphic DNA amplified randomly at low temperatures and Amplified Fragment-Length Polymorphism (AFLP-PCR of cut genomic DNA. Besides, in the recent years, non-culture-based molecular methods such as Pulse Field Gel Electrophoresis (PFGE, Denaturing Gradient Gel Electrophoresis (DGGE, Thermal Gradient Gel Electrophoresis (TGGE, and Fluorescence In-situ Hybridization (FISH have replaced classical methods once used for the identification of LAB. Identification of lactic acid bacteria culture independent regardless of the method will be one of the most important methods used in the future pyrosequencing as a Next Generation Sequencing (NGS techniques. This paper reviews molecular-method based studies conducted on the identification of LAB species in dairy products.

miRNAFold: a web server for fast miRNA precursor prediction in genomes.

Science.gov (United States)

Tav, Christophe; Tempel, Sébastien; Poligny, Laurent; Tahi, Fariza

2016-07-08

Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In particular, ab initio methods are usually required when predicting novel miRNAs. Here we present a web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. The web server is freely available at: http://EvryRNA.ibisc.univ-evry.fr/miRNAFold. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

Science.gov (United States)

Zhao, Shanrong; Prenger, Kurt; Smith, Lance; Messina, Thomas; Fan, Hongtao; Jaeger, Edward; Stephens, Susan

2013-06-27

Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Directory of Open Access Journals (Sweden)

Wen-Jing Shen

2017-03-01

Full Text Available This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB electric model by using a combination of particle swarm optimization (PSO and Levenberg-Marquardt (LM algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.

Identification and characterisation of Short Interspersed Nuclear Elements in the olive tree (Olea europaea L.) genome.

Science.gov (United States)

Barghini, Elena; Mascagni, Flavia; Natali, Lucia; Giordani, Tommaso; Cavallini, Andrea

2017-02-01

Short Interspersed Nuclear Elements (SINEs) are nonautonomous retrotransposons in the genome of most eukaryotic species. While SINEs have been intensively investigated in humans and other animal systems, SINE identification has been carried out only in a limited number of plant species. This lack of information is apparent especially in non-model plants whose genome has not been sequenced yet. The aim of this work was to produce a specific bioinformatics pipeline for analysing second generation sequence reads of a non-model species and identifying SINEs. We have identified, for the first time, 227 putative SINEs of the olive tree (Olea europaea), that constitute one of the few sets of such sequences in dicotyledonous species. The identified SINEs ranged from 140 to 362 bp in length and were characterised with regard to the occurrence of the tRNA domain in their sequence. The majority of identified elements resulted in single copy or very lowly repeated, often in association with genic sequences. Analysis of sequence similarity allowed us to identify two major groups of SINEs showing different abundances in the olive tree genome, the former with sequence similarity to SINEs of Scrophulariaceae and Solanaceae and the latter to SINEs of Salicaceae. A comparison of sequence conservation between olive SINEs and LTR retrotransposon families suggested that SINE expansion in the genome occurred especially in very ancient times, before LTR retrotransposon expansion, and presumably before the separation of the rosids (to which Oleaceae belong) from the Asterids. Besides providing data on olive SINEs, our results demonstrate the suitability of the pipeline employed for SINE identification. Applying this pipeline will favour further structural and functional analyses on these relatively unknown elements to be performed also in other plant species, even in the absence of a reference genome, and will allow establishing general evolutionary patterns for this kind of repeats in

Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites

KAUST Repository

Abkallo, Hussein M.

2016-10-01

Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

Multi-scale Material Parameter Identification Using LS-DYNA® and LS-OPT®

Energy Technology Data Exchange (ETDEWEB)

Stander, Nielen; Basudhar, Anirban; Basu, Ushnish; Gandikota, Imtiaz; Savic, Vesna; Sun, Xin; Choi, Kyoo Sil; Hu, Xiaohua; Pourboghrat, F.; Park, Taejoon; Mapar, Aboozar; Kumar, Shavan; Ghassemi-Armaki, Hassan; Abu-Farha, Fadi

2015-09-14

Ever-tightening regulations on fuel economy, and the likely future regulation of carbon emissions, demand persistent innovation in vehicle design to reduce vehicle mass. Classical methods for computational mass reduction include sizing, shape and topology optimization. One of the few remaining options for weight reduction can be found in materials engineering and material design optimization. Apart from considering different types of materials, by adding material diversity and composite materials, an appealing option in automotive design is to engineer steel alloys for the purpose of reducing plate thickness while retaining sufficient strength and ductility required for durability and safety. A project to develop computational material models for advanced high strength steel is currently being executed under the auspices of the United States Automotive Materials Partnership (USAMP) funded by the US Department of Energy. Under this program, new Third Generation Advanced High Strength Steel (i.e., 3GAHSS) are being designed, tested and integrated with the remaining design variables of a benchmark vehicle Finite Element model. The objectives of the project are to integrate atomistic, microstructural, forming and performance models to create an integrated computational materials engineering (ICME) toolkit for 3GAHSS. The mechanical properties of Advanced High Strength Steels (AHSS) are controlled by many factors, including phase composition and distribution in the overall microstructure, volume fraction, size and morphology of phase constituents as well as stability of the metastable retained austenite phase. The complex phase transformation and deformation mechanisms in these steels make the well-established traditional techniques obsolete, and a multi-scale microstructure-based modeling approach following the ICME [0]strategy was therefore chosen in this project. Multi-scale modeling as a major area of research and development is an outgrowth of the Comprehensive

[A accurate identification method for Chinese materia medica--systematic identification of Chinese materia medica].

Science.gov (United States)

Wang, Xue-Yong; Liao, Cai-Li; Liu, Si-Qi; Liu, Chun-Sheng; Shao, Ai-Juan; Huang, Lu-Qi

2013-05-01

This paper put forward a more accurate identification method for identification of Chinese materia medica (CMM), the systematic identification of Chinese materia medica (SICMM) , which might solve difficulties in CMM identification used the ordinary traditional ways. Concepts, mechanisms and methods of SICMM were systematically introduced and possibility was proved by experiments. The establishment of SICMM will solve problems in identification of Chinese materia medica not only in phenotypic characters like the mnorphous, microstructure, chemical constituents, but also further discovery evolution and classification of species, subspecies and population in medical plants. The establishment of SICMM will improve the development of identification of CMM and create a more extensive study space.

Component identification of electron transport chains in curdlan-producing Agrobacterium sp. ATCC 31749 and its genome-specific prediction using comparative genome and phylogenetic trees analysis.

Science.gov (United States)

Zhang, Hongtao; Setubal, Joao Carlos; Zhan, Xiaobei; Zheng, Zhiyong; Yu, Lijun; Wu, Jianrong; Chen, Dingqiang

2011-06-01

Agrobacterium sp. ATCC 31749 (formerly named Alcaligenes faecalis var. myxogenes) is a non-pathogenic aerobic soil bacterium used in large scale biotechnological production of curdlan. However, little is known about its genomic information. DNA partial sequence of electron transport chains (ETCs) protein genes were obtained in order to understand the components of ETC and genomic-specificity in Agrobacterium sp. ATCC 31749. Degenerate primers were designed according to ETC conserved sequences in other reported species. DNA partial sequences of ETC genes in Agrobacterium sp. ATCC 31749 were cloned by the PCR method using degenerate primers. Based on comparative genomic analysis, nine electron transport elements were ascertained, including NADH ubiquinone oxidoreductase, succinate dehydrogenase complex II, complex III, cytochrome c, ubiquinone biosynthesis protein ubiB, cytochrome d terminal oxidase, cytochrome bo terminal oxidase, cytochrome cbb (3)-type terminal oxidase and cytochrome caa (3)-type terminal oxidase. Similarity and phylogenetic analyses of these genes revealed that among fully sequenced Agrobacterium species, Agrobacterium sp. ATCC 31749 is closest to Agrobacterium tumefaciens C58. Based on these results a comprehensive ETC model for Agrobacterium sp. ATCC 31749 is proposed.

Genomic diversity within the Enterobacter cloacae complex.

Directory of Open Access Journals (Sweden)

Armand Paauw

Full Text Available BACKGROUND: Isolates of the Enterobacter cloacae complex have been increasingly isolated as nosocomial pathogens, but phenotypic identification of the E. cloacae complex is unreliable and irreproducible. Identification of species based on currently available genotyping tools is already superior to phenotypic identification, but the taxonomy of isolates belonging to this complex is cumbersome. METHODOLOGY/PRINCIPAL FINDINGS: This study shows that multilocus sequence analysis and comparative genomic hybridization based on a mixed genome array is a powerful method for studying species assignment within the E. cloacae complex. The E. cloacae complex is shown to be evolutionarily divided into two clades that are genetically distinct from each other. The younger first clade is genetically more homogenous, contains the Enterobacter hormaechei species and is the most frequently cultured Enterobacter species in hospitals. The second and older clade consists of several (subspecies that are genetically more heterogeneous. Genetic markers were identified that could discriminate between the two clades and cluster 1. CONCLUSIONS/SIGNIFICANCE: Based on genomic differences it is concluded that some previously defined (clonal and heterogenic (subspecies of the E. cloacae complex have to be redefined because of disagreements with known or proposed nomenclature. However, further improved identification of the redefined species will be possible based on novel markers presented here.

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

Science.gov (United States)

Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

2017-08-15

Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Identification of a contemporary human parechovirus type 1 by VIDISCA and characterisation of its full genome

Directory of Open Access Journals (Sweden)

Drexler Jan

2008-02-01

Full Text Available Abstract Background Enteritis is caused by a spectrum of viruses that is most likely not fully characterised. When testing stool samples by cell culture, virus isolates are sometimes obtained which cannot be typed by current methods. In this study we used VIDISCA, a virus identification method which has not yet been widely applied, on such an untyped virus isolate. Results We found a human parechovirus (HPeV type 1 (strain designation: BNI-788st. Because genomes of contemporary HPeV1 were not available, we determined its complete genome sequence. We found that the novel strain was likely the result of recombination between structural protein genes of an ancestor of contemporary HPeV1 strains and nonstructural protein genes from an unknown ancestor, most closely related to HPeV3. In contrast to the non-structural protein genes of other HPeV prototype strains, the non-structural protein genes of BNI-788st and HPeV3 prototype strains did not co-segregate in bootscan analysis with that of other prototype strains. Conclusion HPeV3 nonstructural protein genes may form a distinct element in a pool of circulating HPeV non-structural protein genes. More research into the complex HPeV evolution is required to connect virus ecology with disease patterns in humans.

Alignment-free genome tree inference by learning group-specific distance metrics.

Science.gov (United States)

Patil, Kaustubh R; McHardy, Alice C

2013-01-01

Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

Protein identification from two-dimensional gel electrophoresis analysis of Klebsiella pneumoniae by combined use of mass spectrometry data and raw genome sequences

Directory of Open Access Journals (Sweden)

Zeng An-Ping

2003-12-01

Full Text Available Abstract Separation of proteins by two-dimensional gel electrophoresis (2-DE coupled with identification of proteins through peptide mass fingerprinting (PMF by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS is the widely used technique for proteomic analysis. This approach relies, however, on the presence of the proteins studied in public-accessible protein databases or the availability of annotated genome sequences of an organism. In this work, we investigated the reliability of using raw genome sequences for identifying proteins by PMF without the need of additional information such as amino acid sequences. The method is demonstrated for proteomic analysis of Klebsiella pneumoniae grown anaerobically on glycerol. For 197 spots excised from 2-DE gels and submitted for mass spectrometric analysis 164 spots were clearly identified as 122 individual proteins. 95% of the 164 spots can be successfully identified merely by using peptide mass fingerprints and a strain-specific protein database (ProtKpn constructed from the raw genome sequences of K. pneumoniae. Cross-species protein searching in the public databases mainly resulted in the identification of 57% of the 66 high expressed protein spots in comparison to 97% by using the ProtKpn database. 10 dha regulon related proteins that are essential for the initial enzymatic steps of anaerobic glycerol metabolism were successfully identified using the ProtKpn database, whereas none of them could be identified by cross-species searching. In conclusion, the use of strain-specific protein database constructed from raw genome sequences makes it possible to reliably identify most of the proteins from 2-DE analysis simply through peptide mass fingerprinting.

Star identification methods, techniques and algorithms

CERN Document Server

Zhang, Guangjun

2017-01-01

This book summarizes the research advances in star identification that the author’s team has made over the past 10 years, systematically introducing the principles of star identification, general methods, key techniques and practicable algorithms. It also offers examples of hardware implementation and performance evaluation for the star identification algorithms. Star identification is the key step for celestial navigation and greatly improves the performance of star sensors, and as such the book include the fundamentals of star sensors and celestial navigation, the processing of the star catalog and star images, star identification using modified triangle algorithms, star identification using star patterns and using neural networks, rapid star tracking using star matching between adjacent frames, as well as implementation hardware and using performance tests for star identification. It is not only valuable as a reference book for star sensor designers and researchers working in pattern recognition and othe...

Identifying all moiety conservation laws in genome-scale metabolic networks.

Science.gov (United States)

De Martino, Andrea; De Martino, Daniele; Mulet, Roberto; Pagnani, Andrea

2014-01-01

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Identifying all moiety conservation laws in genome-scale metabolic networks.

Directory of Open Access Journals (Sweden)

Andrea De Martino

Full Text Available The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Identification of copy number variants defining genomic differences among major human groups.

Directory of Open Access Journals (Sweden)

Lluís Armengol

Full Text Available BACKGROUND: Understanding the genetic contribution to phenotype variation of human groups is necessary to elucidate differences in disease predisposition and response to pharmaceutical treatments in different human populations. METHODOLOGY/PRINCIPAL FINDINGS: We have investigated the genome-wide profile of structural variation on pooled samples from the three populations studied in the HapMap project by comparative genome hybridization (CGH in different array platforms. We have identified and experimentally validated 33 genomic loci that show significant copy number differences from one population to the other. Interestingly, we found an enrichment of genes related to environment adaptation (immune response, lipid metabolism and extracellular space within these regions and the study of expression data revealed that more than half of the copy number variants (CNVs translate into gene-expression differences among populations, suggesting that they could have functional consequences. In addition, the identification of single nucleotide polymorphisms (SNPs that are in linkage disequilibrium with the copy number alleles allowed us to detect evidences of population differentiation and recent selection at the nucleotide variation level. CONCLUSIONS: Overall, our results provide a comprehensive view of relevant copy number changes that might play a role in phenotypic differences among major human populations, and generate a list of interesting candidates for future studies.

Identification and classification of conserved RNA secondary structures in the human genome

DEFF Research Database (Denmark)

Pedersen, Jakob Skou; Bejerano, Gill; Siepel, Adam

2006-01-01

The discoveries of microRNAs and riboswitches, among others, have shown functional RNAs to be biologically more important and genomically more prevalent than previously anticipated. We have developed a general comparative genomics method based on phylogenetic stochastic context-free grammars...... for identifying functional RNAs encoded in the human genome and used it to survey an eight-way genome-wide alignment of the human, chimpanzee, mouse, rat, dog, chicken, zebra-fish, and puffer-fish genomes for deeply conserved functional RNAs. At a loose threshold for acceptance, this search resulted in a set......, the results nevertheless provide evidence for many new human functional RNAs and present specific predictions to facilitate their further characterization....

Subspace Barzilai-Borwein Gradient Method for Large-Scale Bound Constrained Optimization

International Nuclear Information System (INIS)

Xiao Yunhai; Hu Qingjie

2008-01-01

An active set subspace Barzilai-Borwein gradient algorithm for large-scale bound constrained optimization is proposed. The active sets are estimated by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the Barzilai-Borwein gradient method. In this work, a nonmonotone line search strategy that guarantees global convergence is used. Preliminary numerical results show that the proposed method is promising, and competitive with the well-known method SPG on a subset of bound constrained problems from CUTEr collection

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

In silico method for modelling metabolism and gene product expression at genome scale

Energy Technology Data Exchange (ETDEWEB)

Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernard O.

2012-07-03

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Implementation of Whole Genome Sequencing (WGS for Identification and Characterization of Shiga Toxin-Producing Escherichia coli (STEC in the United States

Directory of Open Access Journals (Sweden)

Rebecca L Lindsey

2016-05-01

Full Text Available Shiga toxin-producing Escherichia coli (STEC is an important foodborne pathogen capable of causing severe disease in humans. Rapid and accurate identification and characterization techniques are essential during outbreak investigations. Current methods for characterization of STEC are expensive and time-consuming. With the advent of rapid and cheap whole genome sequencing (WGS benchtop sequencers, the potential exists to replace traditional workflows with WGS. The aim of this study was to validate tools to do reference identification and characterization from WGS for STEC in a single workflow within an easy to use commercially available software platform. Publically available serotype, virulence, and antimicrobial resistance databases were downloaded from the Center for Genomic Epidemiology (CGE (www.genomicepidemiology.org and integrated into a genotyping plug-in with in silico PCR tools to confirm some of the virulence genes detected from WGS data. Additionally, down sampling experiments on the WGS sequence data were performed to determine a threshold for sequence coverage needed to accurately predict serotype and virulence genes using the established workflow. The serotype database was tested on a total of 228 genomes and correctly predicted from WGS for 96.1% of O serogroups and 96.5% of H serogroups identified by conventional testing techniques. A total of 59 genomes were evaluated to determine the threshold of coverage to detect the different WGS targets, 40 were evaluated for serotype and virulence gene detection and 19 for the stx gene subtypes. For serotype, 95% of the O and 100% of the H serogroups were detected at > 40x and ≥ 30x coverage, respectively. For virulence targets and stx gene subtypes, nearly all genes were detected at > 40x, though some targets were 100% detectable from genomes with coverage ≥20x. The resistance detection tool was 97% concordant with phenotypic testing results. With isolates sequenced to > 40x

Implementation of Whole Genome Sequencing (WGS) for Identification and Characterization of Shiga Toxin-Producing Escherichia coli (STEC) in the United States

Science.gov (United States)

Lindsey, Rebecca L.; Pouseele, Hannes; Chen, Jessica C.; Strockbine, Nancy A.; Carleton, Heather A.

2016-01-01

Shiga toxin-producing Escherichia coli (STEC) is an important foodborne pathogen capable of causing severe disease in humans. Rapid and accurate identification and characterization techniques are essential during outbreak investigations. Current methods for characterization of STEC are expensive and time-consuming. With the advent of rapid and cheap whole genome sequencing (WGS) benchtop sequencers, the potential exists to replace traditional workflows with WGS. The aim of this study was to validate tools to do reference identification and characterization from WGS for STEC in a single workflow within an easy to use commercially available software platform. Publically available serotype, virulence, and antimicrobial resistance databases were downloaded from the Center for Genomic Epidemiology (CGE) (www.genomicepidemiology.org) and integrated into a genotyping plug-in with in silico PCR tools to confirm some of the virulence genes detected from WGS data. Additionally, down sampling experiments on the WGS sequence data were performed to determine a threshold for sequence coverage needed to accurately predict serotype and virulence genes using the established workflow. The serotype database was tested on a total of 228 genomes and correctly predicted from WGS for 96.1% of O serogroups and 96.5% of H serogroups identified by conventional testing techniques. A total of 59 genomes were evaluated to determine the threshold of coverage to detect the different WGS targets, 40 were evaluated for serotype and virulence gene detection and 19 for the stx gene subtypes. For serotype, 95% of the O and 100% of the H serogroups were detected at > 40x and ≥ 30x coverage, respectively. For virulence targets and stx gene subtypes, nearly all genes were detected at > 40x, though some targets were 100% detectable from genomes with coverage ≥20x. The resistance detection tool was 97% concordant with phenotypic testing results. With isolates sequenced to > 40x coverage, the different

A protocol for generating a high-quality genome-scale metabolic reconstruction.

Science.gov (United States)

Thiele, Ines; Palsson, Bernhard Ø

2010-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

Science.gov (United States)

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes

Directory of Open Access Journals (Sweden)

Michael Strong

2009-12-01

Full Text Available As the number of completely sequenced microbial genomes continues to rise at an impressive rate, it is important to prepare students with the skills necessary to investigate microorganisms at the genomic level. As a part of the core curriculum for first-year graduate students in the biological sciences, we have implemented a web-based tutorial to introduce students to the fields of comparative and functional genomics. The tutorial focuses on recent computational methods for identifying functionally linked genes and proteins on a genome-wide scale and was used to introduce students to the Rosetta Stone, Phylogenetic Profile, conserved Gene Neighbor, and Operon computational methods. Students learned to use a number of publicly available web servers and databases to identify functionally linked genes in the Escherichia coli genome, with emphasis on genome organization and operon structure. The overall effectiveness of the tutorial was assessed based on student evaluations and homework assignments. The tutorial is available to other educators at http://www.doe-mbi.ucla.edu/~strong/m253.php.

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

OpenAIRE

Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...

Identification of genomic sites for CRISPR/Cas9-based genome editing in the Vitis vinifera genome

Science.gov (United States)

CRISPR/Cas9 has been recently demonstrated as an effective and popular genome editing tool for modifying genomes of human, animals, microorganisms, and plants. Success of such genome editing is highly dependent on the availability of suitable target sites in the genomes to be edited. Many specific t...

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

Directory of Open Access Journals (Sweden)

Chen Jiun-Ching

2007-05-01

Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

Identification of endogenous retroviral reading frames in the human genome

Directory of Open Access Journals (Sweden)

Wiuf Carsten

2004-10-01

Full Text Available Abstract Background Human endogenous retroviruses (HERVs comprise a large class of repetitive retroelements. Most HERVs are ancient and invaded our genome at least 25 million years ago, except for the evolutionary young HERV-K group. The far majority of the encoded genes are degenerate due to mutational decay and only a few non-HERV-K loci are known to retain intact reading frames. Additional intact HERV genes may exist, since retroviral reading frames have not been systematically annotated on a genome-wide scale. Results By clustering of hits from multiple BLAST searches using known retroviral sequences we have mapped 1.1% of the human genome as retrovirus related. The coding potential of all identified HERV regions were analyzed by annotating viral open reading frames (vORFs and we report 7836 loci as verified by protein homology criteria. Among 59 intact or almost-intact viral polyproteins scattered around the human genome we have found 29 envelope genes including two novel gammaretroviral types. One encodes a protein similar to a recently discovered zebrafish retrovirus (ZFERV while another shows partial, C-terminal, homology to Syncytin (HERV-W/FRD. Conclusions This compilation of HERV sequences and their coding potential provide a useful tool for pursuing functional analysis such as RNA expression profiling and effects of viral proteins, which may, in turn, reveal a role for HERVs in human health and disease. All data are publicly available through a database at http://www.retrosearch.dk.

Multi-scale coding of genomic information: From DNA sequence to genome structure and function

International Nuclear Information System (INIS)

Arneodo, Alain; Vaillant, Cedric; Audit, Benjamin; Argoul, Francoise; D'Aubenton-Carafa, Yves; Thermes, Claude

2011-01-01

Understanding how chromatin is spatially and dynamically organized in the nucleus of eukaryotic cells and how this affects genome functions is one of the main challenges of cell biology. Since the different orders of packaging in the hierarchical organization of DNA condition the accessibility of DNA sequence elements to trans-acting factors that control the transcription and replication processes, there is actually a wealth of structural and dynamical information to learn in the primary DNA sequence. In this review, we show that when using concepts, methodologies, numerical and experimental techniques coming from statistical mechanics and nonlinear physics combined with wavelet-based multi-scale signal processing, we are able to decipher the multi-scale sequence encoding of chromatin condensation-decondensation mechanisms that play a fundamental role in regulating many molecular processes involved in nuclear functions.

Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics.

Science.gov (United States)

Kelly, Benjamin J; Fitch, James R; Hu, Yangqiu; Corsmeier, Donald J; Zhong, Huachun; Wetzel, Amy N; Nordquist, Russell D; Newsom, David L; White, Peter

2015-01-20

While advances in genome sequencing technology make population-scale genomics a possibility, current approaches for analysis of these data rely upon parallelization strategies that have limited scalability, complex implementation and lack reproducibility. Churchill, a balanced regional parallelization strategy, overcomes these challenges, fully automating the multiple steps required to go from raw sequencing reads to variant discovery. Through implementation of novel deterministic parallelization techniques, Churchill allows computationally efficient analysis of a high-depth whole genome sample in less than two hours. The method is highly scalable, enabling full analysis of the 1000 Genomes raw sequence dataset in a week using cloud resources. http://churchill.nchri.org/.

Identification of genome-specific transcripts in wheat–rye translocation lines

Directory of Open Access Journals (Sweden)

Tong Geon Lee

2015-09-01

Full Text Available Studying gene expression in wheat–rye translocation lines is complicated due to the presence of homeologs in hexaploid wheat and high levels of synteny between wheat and rye genomes (Naranjo and Fernandez-Rueda, 1991 [1]; Devos et al., 1995 [2]; Lee et al., 2010 [3]; Lee et al., 2013 [4]. To overcome limitations of current gene expression studies on wheat–rye translocation lines and identify genome-specific transcripts, we developed a custom Roche NimbleGen Gene Expression microarray that contains probes derived from the sequence of hexaploid wheat, diploid rye and diploid progenitors of hexaploid wheat genome (Lee et al., 2014. Using the array developed, we identified genome-specific transcripts in a wheat–rye translocation line (Lee et al., 2014. Expression data are deposited in the NCBI Gene Expression Omnibus (GEO under accession number GSE58678. Here we report the details of the methods used in the array workflow and data analysis.

A contig-based strategy for the genome-wide discovery of microRNAs without complete genome resources.

Directory of Open Access Journals (Sweden)

Jun-Zhi Wen

Full Text Available MicroRNAs (miRNAs are important regulators of many cellular processes and exist in a wide range of eukaryotes. High-throughput sequencing is a mainstream method of miRNA identification through which it is possible to obtain the complete small RNA profile of an organism. Currently, most approaches to miRNA identification rely on a reference genome for the prediction of hairpin structures. However, many species of economic and phylogenetic importance are non-model organisms without complete genome sequences, and this limits miRNA discovery. Here, to overcome this limitation, we have developed a contig-based miRNA identification strategy. We applied this method to a triploid species of edible banana (GCTCV-119, Musa spp. AAA group and identified 180 pre-miRNAs and 314 mature miRNAs, which is three times more than those were predicted by the available dataset-based methods (represented by EST+GSS. Based on the recently published miRNA data set of Musa acuminate, the recall rate and precision of our strategy are estimated to be 70.6% and 92.2%, respectively, significantly better than those of EST+GSS-based strategy (10.2% and 50.0%, respectively. Our novel, efficient and cost-effective strategy facilitates the study of the functional and evolutionary role of miRNAs, as well as miRNA-based molecular breeding, in non-model species of economic or evolutionary interest.

Genome wide identification of aberrant alternative splicing events in myotonic dystrophy type 2.

Science.gov (United States)

Perfetti, Alessandra; Greco, Simona; Fasanaro, Pasquale; Bugiardini, Enrico; Cardani, Rosanna; Garcia-Manteiga, Jose M; Manteiga, Jose M Garcia; Riba, Michela; Cittaro, Davide; Stupka, Elia; Meola, Giovanni; Martelli, Fabio

2014-01-01

Myotonic dystrophy type 2 (DM2) is a genetic, autosomal dominant disease due to expansion of tetraplet (CCTG) repetitions in the first intron of the ZNF9/CNBP gene. DM2 is a multisystemic disorder affecting the skeletal muscle, the heart, the eye and the endocrine system. According to the proposed pathological mechanism, the expanded tetraplets have an RNA toxic effect, disrupting the splicing of many mRNAs. Thus, the identification of aberrantly spliced transcripts is instrumental for our understanding of the molecular mechanisms underpinning the disease. The aim of this study was the identification of new aberrant alternative splicing events in DM2 patients. By genome wide analysis of 10 DM2 patients and 10 controls (CTR), we identified 273 alternative spliced exons in 218 genes. While many aberrant splicing events were already identified in the past, most were new. A subset of these events was validated by qPCR assays in 19 DM2 and 15 CTR subjects. To gain insight into the molecular pathways involving the identified aberrantly spliced genes, we performed a bioinformatics analysis with Ingenuity system. This analysis indicated a deregulation of development, cell survival, metabolism, calcium signaling and contractility. In conclusion, our genome wide analysis provided a database of aberrant splicing events in the skeletal muscle of DM2 patients. The affected genes are involved in numerous pathways and networks important for muscle physio-pathology, suggesting that the identified variants may contribute to DM2 pathogenesis.

Rare and common regulatory variation in population-scale sequenced human genomes.

Directory of Open Access Journals (Sweden)

Stephen B Montgomery

2011-07-01

Full Text Available Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function.

Fuel number identification method and device

International Nuclear Information System (INIS)

Doi, Takami; Seno, Makoto; Kikuchi, Takashi; Sakamoto, Hiromi; Takahashi, Masaki; Tanaka, Keiji.

1997-01-01

The present invention provides a method of and a device for automatically identifying fuel numbers impressed on fuel assemblies disposed in a fuel reprocessing facility, power plant and a reactor core at a high speed and at a high identification rate. Namely, three or more character images are photographed for one fuel assembly as an object of the identification under different illumination conditions. As a result, different character images by the number of the illumination directions can be obtained for identical impressed characters. Learning on a neural network system is applied to the images of all of the characters impressed on the fuel assembly obtained under illumination of predetermined directions. Then, result of the identification by the number of the illumination directions can be obtained for each of the characters as an object of the identification. As a result, since the result of the identification is determined based on a theory of decision of majority, highly automatic identification can be realized. (I.S.)

Communications device identification methods, communications methods, wireless communications readers, wireless communications systems, and articles of manufacture

Science.gov (United States)

Steele, Kerry D [Kennewick, WA; Anderson, Gordon A [Benton City, WA; Gilbert, Ronald W [Morgan Hill, CA

2011-02-01

Communications device identification methods, communications methods, wireless communications readers, wireless communications systems, and articles of manufacture are described. In one aspect, a communications device identification method includes providing identification information regarding a group of wireless identification devices within a wireless communications range of a reader, using the provided identification information, selecting one of a plurality of different search procedures for identifying unidentified ones of the wireless identification devices within the wireless communications range, and identifying at least some of the unidentified ones of the wireless identification devices using the selected one of the search procedures.

Multiple independent identification decisions: a method of calibrating eyewitness identifications.

Science.gov (United States)

Pryke, Sean; Lindsay, R C L; Dysart, Jennifer E; Dupuis, Paul

2004-02-01

Two experiments (N = 147 and N = 90) explored the use of multiple independent lineups to identify a target seen live. In Experiment 1, simultaneous face, body, and sequential voice lineups were used. In Experiment 2, sequential face, body, voice, and clothing lineups were used. Both studies demonstrated that multiple identifications (by the same witness) from independent lineups of different features are highly diagnostic of suspect guilt (G. L. Wells & R. C. L. Lindsay, 1980). The number of suspect and foil selections from multiple independent lineups provides a powerful method of calibrating the accuracy of eyewitness identification. Implications for use of current methods are discussed. ((c) 2004 APA, all rights reserved)

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

Science.gov (United States)

Lara-Ramírez, Edgar E.; Salazar, Ma Isabel; López-López, María de Jesús; Salas-Benito, Juan Santiago; Sánchez-Varela, Alejandro

2014-01-01

The increasing number of dengue virus (DENV) genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4) has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC) with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3) as well as the effective number of codons (ENC, ENCp) versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA) and clustering analysis on relative synonymous codon usage (RSCU) within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution. PMID:25136631

Analysis of Genome-Scale Data

NARCIS (Netherlands)

Kemmeren, P.P.C.W.

2005-01-01

The genetic material of every cell in an organism is stored inside DNA in the form of genes, which together form the genome. The information stored in the DNA is translated to RNA and subsequently to proteins, which form complex biological systems. The availability of whole genome sequences has

Incidental and clinically actionable genetic variants in 1005 whole exomes and genomes from Qatar

Directory of Open Access Journals (Sweden)

Abhinav Jain

2017-10-01

Full Text Available Next generation sequencing (NGS technologies such as whole genome and whole exome sequencing has enabled accurate diagnosis of genetic diseases through identification of variations at the genome wide level. While many large populations have been adequately covered in global sequencing efforts little is known on the genomic architecture of populations from Middle East, and South Asia and Africa. Incidental findings and their prevalence in populations have been extensively studied in populations of Caucasian descent. The recent emphasis on genomics and availability of genome-scale datasets in public domain for ethnic population in the Middle East prompted us to estimate the prevalence of incidental findings for this population. In this study, we used whole genome and exome data for a total 1005 non-related healthy individuals from Qatar population dataset which contained 20,930,177 variants. Systematic analysis of the variants in 59 genes recommended by the American College of Medical Genetics and Genomics for reporting of incidental findings revealed a total of 2 pathogenic and 2 likely pathogenic variants. Our analysis suggests the prevalence of incidental variants in population-scale datasets is approx. 0.6%, much lower than those reported for global populations. Our study underlines the essentiality to study population-scale genomes from ethnic groups to understand systematic differences in genetic variants associated with disease predisposition.

Data on the genome-wide identification of CNL R-genes in Setaria italica (L.) P. Beauv.

Science.gov (United States)

Andersen, Ethan J; Nepal, Madhav P

2017-08-01

We report data associated with the identification of 242 disease resistance genes (R-genes) in the genome of Setaria italica as presented in "Genetic diversity of disease resistance genes in foxtail millet ( Setaria italica L.)" (Andersen and Nepal, 2017) [1]. Our data describe the structure and evolution of the Coiled-coil, Nucleotide-binding site, Leucine-rich repeat (CNL) R-genes in foxtail millet. The CNL genes were identified through rigorous extraction and analysis of recently available plant genome sequences using cutting-edge analytical software. Data visualization includes gene structure diagrams, chromosomal syntenic maps, a chromosomal density plot, and a maximum-likelihood phylogenetic tree comparing Sorghum bicolor , Panicum virgatum , Setaria italica , and Arabidopsis thaliana . Compilation of InterProScan annotations, Gene Ontology (GO) annotations, and Basic Local Alignment Search Tool (BLAST) results for the 242 R-genes identified in the foxtail millet genome are also included in tabular format.

Simultaneous genomic identification and profiling of a single cell using semiconductor-based next generation sequencing

Directory of Open Access Journals (Sweden)

Manabu Watanabe

2014-09-01

Full Text Available Combining single-cell methods and next-generation sequencing should provide a powerful means to understand single-cell biology and obviate the effects of sample heterogeneity. Here we report a single-cell identification method and seamless cancer gene profiling using semiconductor-based massively parallel sequencing. A549 cells (adenocarcinomic human alveolar basal epithelial cell line were used as a model. Single-cell capture was performed using laser capture microdissection (LCM with an Arcturus® XT system, and a captured single cell and a bulk population of A549 cells (≈106 cells were subjected to whole genome amplification (WGA. For cell identification, a multiplex PCR method (AmpliSeq™ SNP HID panel was used to enrich 136 highly discriminatory SNPs with a genotype concordance probability of 1031–35. For cancer gene profiling, we used mutation profiling that was performed in parallel using a hotspot panel for 50 cancer-related genes. Sequencing was performed using a semiconductor-based bench top sequencer. The distribution of sequence reads for both HID and Cancer panel amplicons was consistent across these samples. For the bulk population of cells, the percentages of sequence covered at coverage of more than 100× were 99.04% for the HID panel and 98.83% for the Cancer panel, while for the single cell percentages of sequence covered at coverage of more than 100× were 55.93% for the HID panel and 65.96% for the Cancer panel. Partial amplification failure or randomly distributed non-amplified regions across samples from single cells during the WGA procedures or random allele drop out probably caused these differences. However, comparative analyses showed that this method successfully discriminated a single A549 cancer cell from a bulk population of A549 cells. Thus, our approach provides a powerful means to overcome tumor sample heterogeneity when searching for somatic mutations.

Applicability of SCAR markers to food genomics: olive oil traceability.

Science.gov (United States)

Pafundo, Simona; Agrimonti, Caterina; Maestri, Elena; Marmiroli, Nelson

2007-07-25

DNA analysis with molecular markers has opened a shortcut toward a genomic comprehension of complex organisms. The availability of micro-DNA extraction methods, coupled with selective amplification of the smallest extracted fragments with molecular markers, could equally bring a breakthrough in food genomics: the identification of original components in food. Amplified fragment length polymorphisms (AFLPs) have been instrumental in plant genomics because they may allow rapid and reliable analysis of multiple and potentially polymorphic sites. Nevertheless, their direct application to the analysis of DNA extracted from food matrixes is complicated by the low quality of DNA extracted: its high degradation and the presence of inhibitors of enzymatic reactions. The conversion of an AFLP fragment to a robust and specific single-locus PCR-based marker, therefore, could extend the use of molecular markers to large-scale analysis of complex agro-food matrixes. In the present study is reported the development of sequence characterized amplified regions (SCARs) starting from AFLP profiles of monovarietal olive oils analyzed on agarose gel; one of these was used to identify differences among 56 olive cultivars. All the developed markers were purposefully amplified in olive oils to apply them to olive oil traceability.

Performance comparison of two efficient genomic selection methods (gsbay & MixP) applied in aquacultural organisms

Science.gov (United States)

Su, Hailin; Li, Hengde; Wang, Shi; Wang, Yangfan; Bao, Zhenmin

2017-02-01

Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools MixP and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop ( Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction (GBLUP) method which has been applied widely. Our results showed that both MixP and gsbay could accurately estimate single-nucleotide polymorphism (SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values (GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by MixP; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by MixP and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with MixP the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by MixP and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.

Genome-based microbial ecology of anammox granules in a full-scale wastewater treatment system.

Science.gov (United States)

Speth, Daan R; In 't Zandt, Michiel H; Guerrero-Cruz, Simon; Dutilh, Bas E; Jetten, Mike S M

2016-03-31

Partial-nitritation anammox (PNA) is a novel wastewater treatment procedure for energy-efficient ammonium removal. Here we use genome-resolved metagenomics to build a genome-based ecological model of the microbial community in a full-scale PNA reactor. Sludge from the bioreactor examined here is used to seed reactors in wastewater treatment plants around the world; however, the role of most of its microbial community in ammonium removal remains unknown. Our analysis yielded 23 near-complete draft genomes that together represent the majority of the microbial community. We assign these genomes to distinct anaerobic and aerobic microbial communities. In the aerobic community, nitrifying organisms and heterotrophs predominate. In the anaerobic community, widespread potential for partial denitrification suggests a nitrite loop increases treatment efficiency. Of our genomes, 19 have no previously cultivated or sequenced close relatives and six belong to bacterial phyla without any cultivated members, including the most complete Omnitrophica (formerly OP3) genome to date.

Multiplexed precision genome editing with trackable genomic barcodes in yeast.

Science.gov (United States)

Roy, Kevin R; Smith, Justin D; Vonesch, Sibylle C; Lin, Gen; Tu, Chelsea Szu; Lederer, Alex R; Chu, Angela; Suresh, Sundari; Nguyen, Michelle; Horecka, Joe; Tripathi, Ashutosh; Burnett, Wallace T; Morgan, Maddison A; Schulz, Julia; Orsley, Kevin M; Wei, Wu; Aiyar, Raeka S; Davis, Ronald W; Bankaitis, Vytas A; Haber, James E; Salit, Marc L; St Onge, Robert P; Steinmetz, Lars M

2018-07-01

Our understanding of how genotype controls phenotype is limited by the scale at which we can precisely alter the genome and assess the phenotypic consequences of each perturbation. Here we describe a CRISPR-Cas9-based method for multiplexed accurate genome editing with short, trackable, integrated cellular barcodes (MAGESTIC) in Saccharomyces cerevisiae. MAGESTIC uses array-synthesized guide-donor oligos for plasmid-based high-throughput editing and features genomic barcode integration to prevent plasmid barcode loss and to enable robust phenotyping. We demonstrate that editing efficiency can be increased more than fivefold by recruiting donor DNA to the site of breaks using the LexA-Fkh1p fusion protein. We performed saturation editing of the essential gene SEC14 and identified amino acids critical for chemical inhibition of lipid signaling. We also constructed thousands of natural genetic variants, characterized guide mismatch tolerance at the genome scale, and ascertained that cryptic Pol III termination elements substantially reduce guide efficacy. MAGESTIC will be broadly useful to uncover the genetic basis of phenotypes in yeast.

GenoSets: visual analytic methods for comparative genomics.

Directory of Open Access Journals (Sweden)

Aurora A Cain

Full Text Available Many important questions in biology are, fundamentally, comparative, and this extends to our analysis of a growing number of sequenced genomes. Existing genomic analysis tools are often organized around literal views of genomes as linear strings. Even when information is highly condensed, these views grow cumbersome as larger numbers of genomes are added. Data aggregation and summarization methods from the field of visual analytics can provide abstracted comparative views, suitable for sifting large multi-genome datasets to identify critical similarities and differences. We introduce a software system for visual analysis of comparative genomics data. The system automates the process of data integration, and provides the analysis platform to identify and explore features of interest within these large datasets. GenoSets borrows techniques from business intelligence and visual analytics to provide a rich interface of interactive visualizations supported by a multi-dimensional data warehouse. In GenoSets, visual analytic approaches are used to enable querying based on orthology, functional assignment, and taxonomic or user-defined groupings of genomes. GenoSets links this information together with coordinated, interactive visualizations for both detailed and high-level categorical analysis of summarized data. GenoSets has been designed to simplify the exploration of multiple genome datasets and to facilitate reasoning about genomic comparisons. Case examples are included showing the use of this system in the analysis of 12 Brucella genomes. GenoSets software and the case study dataset are freely available at http://genosets.uncc.edu. We demonstrate that the integration of genomic data using a coordinated multiple view approach can simplify the exploration of large comparative genomic data sets, and facilitate reasoning about comparisons and features of interest.

A protocol for large scale genomic DNA isolation for cacao genetics ...

African Journals Online (AJOL)

Advances in DNA technology, such as marker assisted selection, detection of quantitative trait loci and genomic selection also require the isolation of DNA from a large number of samples and the preservation of tissue samples for future use in cacao genome studies. The present study proposes a method for the ...

Multiscale global identification of porous structures

Science.gov (United States)

Hatłas, Marcin; Beluch, Witold

2018-01-01

The paper is devoted to the evolutionary identification of the material constants of porous structures based on measurements conducted on a macro scale. Numerical homogenization with the RVE concept is used to determine the equivalent properties of a macroscopically homogeneous material. Finite element method software is applied to solve the boundary-value problem in both scales. Global optimization methods in form of evolutionary algorithm are employed to solve the identification task. Modal analysis is performed to collect the data necessary for the identification. A numerical example presenting the effectiveness of proposed attitude is attached.

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Directory of Open Access Journals (Sweden)

Tyler W. H. Backman

2018-01-01

Full Text Available Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1 systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2 automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression

DEFF Research Database (Denmark)

Ren, Shancheng; Wei, Gong-Hong; Liu, Dongbing

2018-01-01

BACKGROUND: Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE: To systematically explore the genomic complexity and define disease-driven genetic......-scale and comprehensive genomic data of prostate cancer from Asian population. Identification of these genetic alterations may help advance prostate cancer diagnosis, prognosis, and treatment....... alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS: The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME...

Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

KAUST Repository

Rapakoulia, Trisevgeni

2017-08-09

Motivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results: We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens.

Genome-Wide Identification and Expression Analysis of WRKY Gene Family in Capsicum annuum L.

Science.gov (United States)

Diao, Wei-Ping; Snyder, John C; Wang, Shu-Bin; Liu, Jin-Bing; Pan, Bao-Gui; Guo, Guang-Jun; Wei, Ge

2016-01-01

The WRKY family of transcription factors is one of the most important families of plant transcriptional regulators with members regulating multiple biological processes, especially in regulating defense against biotic and abiotic stresses. However, little information is available about WRKYs in pepper (Capsicum annuum L.). The recent release of completely assembled genome sequences of pepper allowed us to perform a genome-wide investigation for pepper WRKY proteins. In the present study, a total of 71 WRKY genes were identified in the pepper genome. According to structural features of their encoded proteins, the pepper WRKY genes (CaWRKY) were classified into three main groups, with the second group further divided into five subgroups. Genome mapping analysis revealed that CaWRKY were enriched on four chromosomes, especially on chromosome 1, and 15.5% of the family members were tandemly duplicated genes. A phylogenetic tree was constructed depending on WRKY domain' sequences derived from pepper and Arabidopsis. The expression of 21 selected CaWRKY genes in response to seven different biotic and abiotic stresses (salt, heat shock, drought, Phytophtora capsici, SA, MeJA, and ABA) was evaluated by quantitative RT-PCR; Some CaWRKYs were highly expressed and up-regulated by stress treatment. Our results will provide a platform for functional identification and molecular breeding studies of WRKY genes in pepper.

Time-Efficient Cloning Attacks Identification in Large-Scale RFID Systems

Directory of Open Access Journals (Sweden)

Ju-min Zhao

2017-01-01

Full Text Available Radio Frequency Identification (RFID is an emerging technology for electronic labeling of objects for the purpose of automatically identifying, categorizing, locating, and tracking the objects. But in their current form RFID systems are susceptible to cloning attacks that seriously threaten RFID applications but are hard to prevent. Existing protocols aimed at detecting whether there are cloning attacks in single-reader RFID systems. In this paper, we investigate the cloning attacks identification in the multireader scenario and first propose a time-efficient protocol, called the time-efficient Cloning Attacks Identification Protocol (CAIP to identify all cloned tags in multireaders RFID systems. We evaluate the performance of CAIP through extensive simulations. The results show that CAIP can identify all the cloned tags in large-scale RFID systems fairly fast with required accuracy.

Complete Mitochondrial Genomes of the Cherskii’s Sculpin and Siberian Taimen Reveal GenBank Entry Errors: Incorrect Species Identification and Recombinant Mitochondrial Genome

Directory of Open Access Journals (Sweden)

Evgeniy S Balakirev

2017-08-01

Full Text Available The complete mitochondrial (mt genome is sequenced in 2 individuals of the Cherskii’s sculpin Cottus czerskii . A surprisingly high level of sequence divergence (10.3% has been detected between the 2 genomes of C czerskii studied here and the GenBank mt genome of C czerskii (KJ956027. At the same time, a surprisingly low level of divergence (1.4% has been detected between the GenBank C czerskii (KJ956027 and the Amur sculpin Cottus szanaga (KX762049, KX762050. We argue that the observed discrepancies are due to incorrect taxonomic identification so that the GenBank accession number KJ956027 represents actually the mt genome of C szanaga erroneously identified as C czerskii . Our results are of consequence concerning the GenBank database quality, highlighting the potential negative consequences of entry errors, which once they are introduced tend to be propagated among databases and subsequent publications. We illustrate the premise with the data on recombinant mt genome of the Siberian taimen Hucho taimen (NCBI Reference Sequence Database NC_016426.1; GenBank accession number HQ897271.1, bearing 2 introgressed fragments (≈0.9 kb [kilobase] from 2 lenok subspecies, Brachymystax lenok and Brachymystax lenok tsinlingensis , submitted to GenBank on June 12, 2011. Since the time of submission, the H taimen recombinant mt genome leading to incorrect phylogenetic inferences was propagated in multiple subsequent publications despite the fact that nonrecombinant H taimen genomes were also available (submitted to GenBank on August 2, 2014; KJ711549, KJ711550. Other examples of recombinant sequences persisting in GenBank are also considered. A GenBank Entry Error Depositary is urgently needed to monitor and avoid a progressive accumulation of wrong biological information.

Effectiveness of ITS and sub-regions as DNA barcode markers for the identification of Basidiomycota (Fungi).

Science.gov (United States)

Badotti, Fernanda; de Oliveira, Francislon Silva; Garcia, Cleverson Fernando; Vaz, Aline Bruna Martins; Fonseca, Paula Luize Camargos; Nahum, Laila Alves; Oliveira, Guilherme; Góes-Neto, Aristóteles

2017-02-23

Fungi are among the most abundant and diverse organisms on Earth. However, a substantial amount of the species diversity, relationships, habitats, and life strategies of these microorganisms remain to be discovered and characterized. One important factor hindering progress is the difficulty in correctly identifying fungi. Morphological and molecular characteristics have been applied in such tasks. Later, DNA barcoding has emerged as a new method for the rapid and reliable identification of species. The nrITS region is considered the universal barcode of Fungi, and the ITS1 and ITS2 sub-regions have been applied as metabarcoding markers. In this study, we performed a large-scale analysis of all the available Basidiomycota sequences from GenBank. We carried out a rigorous trimming of the initial dataset based in methodological principals of DNA Barcoding. Two different approaches (PCI and barcode gap) were used to determine the performance of the complete ITS region and sub-regions. For most of the Basidiomycota genera, the three genomic markers performed similarly, i.e., when one was considered a good marker for the identification of a genus, the others were also; the same results were observed when the performance was insufficient. However, based on barcode gap analyses, we identified genomic markers that had a superior identification performance than the others and genomic markers that were not indicated for the identification of some genera. Notably, neither the complete ITS nor the sub-regions were useful in identifying 11 of the 113 Basidiomycota genera. The complex phylogenetic relationships and the presence of cryptic species in some genera are possible explanations of this limitation and are discussed. Knowledge regarding the efficiency and limitations of the barcode markers that are currently used for the identification of organisms is crucial because it benefits research in many areas. Our study provides information that may guide researchers in choosing

Data on the genome-wide identification of CNL R-genes in Setaria italica (L. P. Beauv.

Directory of Open Access Journals (Sweden)

Ethan J. Andersen

2017-08-01

Full Text Available We report data associated with the identification of 242 disease resistance genes (R-genes in the genome of Setaria italica as presented in “Genetic diversity of disease resistance genes in foxtail millet (Setaria italica L.” (Andersen and Nepal, 2017 [1]. Our data describe the structure and evolution of the Coiled-coil, Nucleotide-binding site, Leucine-rich repeat (CNL R-genes in foxtail millet. The CNL genes were identified through rigorous extraction and analysis of recently available plant genome sequences using cutting-edge analytical software. Data visualization includes gene structure diagrams, chromosomal syntenic maps, a chromosomal density plot, and a maximum-likelihood phylogenetic tree comparing Sorghum bicolor, Panicum virgatum, Setaria italica, and Arabidopsis thaliana. Compilation of InterProScan annotations, Gene Ontology (GO annotations, and Basic Local Alignment Search Tool (BLAST results for the 242 R-genes identified in the foxtail millet genome are also included in tabular format.

A systematic study of genome context methods: calibration, normalization and combination

Directory of Open Access Journals (Sweden)

Dale Joseph M

2010-10-01

Full Text Available Abstract Background Genome context methods have been introduced in the last decade as automatic methods to predict functional relatedness between genes in a target genome using the patterns of existence and relative locations of the homologs of those genes in a set of reference genomes. Much work has been done in the application of these methods to different bioinformatics tasks, but few papers present a systematic study of the methods and their combination necessary for their optimal use. Results We present a thorough study of the four main families of genome context methods found in the literature: phylogenetic profile, gene fusion, gene cluster, and gene neighbor. We find that for most organisms the gene neighbor method outperforms the phylogenetic profile method by as much as 40% in sensitivity, being competitive with the gene cluster method at low sensitivities. Gene fusion is generally the worst performing of the four methods. A thorough exploration of the parameter space for each method is performed and results across different target organisms are presented. We propose the use of normalization procedures as those used on microarray data for the genome context scores. We show that substantial gains can be achieved from the use of a simple normalization technique. In particular, the sensitivity of the phylogenetic profile method is improved by around 25% after normalization, resulting, to our knowledge, on the best-performing phylogenetic profile system in the literature. Finally, we show results from combining the various genome context methods into a single score. When using a cross-validation procedure to train the combiners, with both original and normalized scores as input, a decision tree combiner results in gains of up to 20% with respect to the gene neighbor method. Overall, this represents a gain of around 15% over what can be considered the state of the art in this area: the four original genome context methods combined using a

Genome-Wide Association Studies In Plant Pathosystems: Toward an Ecological Genomics Approach

Directory of Open Access Journals (Sweden)

Claudia Bartoli

2017-05-01

Full Text Available The emergence and re-emergence of plant pathogenic microorganisms are processes that imply perturbations in both host and pathogen ecological niches. Global change is largely assumed to drive the emergence of new etiological agents by altering the equilibrium of the ecological habitats which in turn places hosts more in contact with pathogen reservoirs. In this context, the number of epidemics is expected to increase dramatically in the next coming decades both in wild and crop plants. Under these considerations, the identification of the genetic variants underlying natural variation of resistance is a pre-requisite to estimate the adaptive potential of wild plant populations and to develop new breeding resistant cultivars. On the other hand, the prediction of pathogen's genetic determinants underlying disease emergence can help to identify plant resistance alleles. In the genomic era, whole genome sequencing combined with the development of statistical methods led to the emergence of Genome Wide Association (GWA mapping, a powerful tool for detecting genomic regions associated with natural variation of disease resistance in both wild and cultivated plants. However, GWA mapping has been less employed for the detection of genetic variants associated with pathogenicity in microbes. Here, we reviewed GWA studies performed either in plants or in pathogenic microorganisms (bacteria, fungi and oomycetes. In addition, we highlighted the benefits and caveats of the emerging joint GWA mapping approach that allows for the simultaneous identification of genes interacting between genomes of both partners. Finally, based on co-evolutionary processes in wild populations, we highlighted a phenotyping-free joint GWA mapping approach as a promising tool for describing the molecular landscape underlying plant - microbe interactions.

Roadmap for annotating transposable elements in eukaryote genomes.

Science.gov (United States)

Permal, Emmanuelle; Flutre, Timothée; Quesneville, Hadi

2012-01-01

Current high-throughput techniques have made it feasible to sequence even the genomes of non-model organisms. However, the annotation process now represents a bottleneck to genome analysis, especially when dealing with transposable elements (TE). Combined approaches, using both de novo and knowledge-based methods to detect TEs, are likely to produce reasonably comprehensive and sensitive results. This chapter provides a roadmap for researchers involved in genome projects to address this issue. At each step of the TE annotation process, from the identification of TE families to the annotation of TE copies, we outline the tools and good practices to be used.

Identification, characterization and distribution of transposable elements in the flax (Linum usitatissimum L. genome

Directory of Open Access Journals (Sweden)

González Leonardo Galindo

2012-11-01

Full Text Available Abstract Background Flax (Linum usitatissimum L. is an important crop for the production of bioproducts derived from its seed and stem fiber. Transposable elements (TEs are widespread in plant genomes and are a key component of their evolution. The availability of a genome assembly of flax (Linum usitatissimum affords new opportunities to explore the diversity of TEs and their relationship to genes and gene expression. Results Four de novo repeat identification algorithms (PILER, RepeatScout, LTR_finder and LTR_STRUC were applied to the flax genome assembly. The resulting library of flax repeats was combined with the RepBase Viridiplantae division and used with RepeatMasker to identify TEs coverage in the genome. LTR retrotransposons were the most abundant TEs (17.2% genome coverage, followed by Long Interspersed Nuclear Element (LINE retrotransposons (2.10% and Mutator DNA transposons (1.99%. Comparison of putative flax TEs to flax transcript databases indicated that TEs are not highly expressed in flax. However, the presence of recent insertions, defined by 100% intra-element LTR similarity, provided evidence for recent TE activity. Spatial analysis showed TE-rich regions, gene-rich regions as well as regions with similar genes and TE density. Monte Carlo simulations for the 71 largest scaffolds (≥ 1 Mb each did not show any regional differences in the frequency of TE overlap with gene coding sequences. However, differences between TE superfamilies were found in their proximity to genes. Genes within TE-rich regions also appeared to have lower transcript expression, based on EST abundance. When LTR elements were compared, Copia showed more diversity, recent insertions and conserved domains than the Gypsy, demonstrating their importance in genome evolution. Conclusions The calculated 23.06% TE coverage of the flax WGS assembly is at the low end of the range of TE coverages reported in other eudicots, although this estimate does not include

Identification, characterization and distribution of transposable elements in the flax (Linum usitatissimum L.) genome.

Science.gov (United States)

González, Leonardo Galindo; Deyholos, Michael K

2012-11-21

Flax (Linum usitatissimum L.) is an important crop for the production of bioproducts derived from its seed and stem fiber. Transposable elements (TEs) are widespread in plant genomes and are a key component of their evolution. The availability of a genome assembly of flax (Linum usitatissimum) affords new opportunities to explore the diversity of TEs and their relationship to genes and gene expression. Four de novo repeat identification algorithms (PILER, RepeatScout, LTR_finder and LTR_STRUC) were applied to the flax genome assembly. The resulting library of flax repeats was combined with the RepBase Viridiplantae division and used with RepeatMasker to identify TEs coverage in the genome. LTR retrotransposons were the most abundant TEs (17.2% genome coverage), followed by Long Interspersed Nuclear Element (LINE) retrotransposons (2.10%) and Mutator DNA transposons (1.99%). Comparison of putative flax TEs to flax transcript databases indicated that TEs are not highly expressed in flax. However, the presence of recent insertions, defined by 100% intra-element LTR similarity, provided evidence for recent TE activity. Spatial analysis showed TE-rich regions, gene-rich regions as well as regions with similar genes and TE density. Monte Carlo simulations for the 71 largest scaffolds (≥ 1 Mb each) did not show any regional differences in the frequency of TE overlap with gene coding sequences. However, differences between TE superfamilies were found in their proximity to genes. Genes within TE-rich regions also appeared to have lower transcript expression, based on EST abundance. When LTR elements were compared, Copia showed more diversity, recent insertions and conserved domains than the Gypsy, demonstrating their importance in genome evolution. The calculated 23.06% TE coverage of the flax WGS assembly is at the low end of the range of TE coverages reported in other eudicots, although this estimate does not include TEs likely found in unassembled repetitive regions of

Inverse PCR-based method for isolating novel SINEs from genome.

Science.gov (United States)

Han, Yawei; Chen, Liping; Guan, Lihong; He, Shunping

2014-04-01

Short interspersed elements (SINEs) are moderately repetitive DNA sequences in eukaryotic genomes. Although eukaryotic genomes contain numerous SINEs copy, it is very difficult and laborious to isolate and identify them by the reported methods. In this study, the inverse PCR was successfully applied to isolate SINEs from Opsariichthys bidens genome in Eastern Asian Cyprinid. A group of SINEs derived from tRNA(Ala) molecular had been identified, which were named Opsar according to Opsariichthys. SINEs characteristics were exhibited in Opsar, which contained a tRNA(Ala)-derived region at the 5' end, a tRNA-unrelated region, and AT-rich region at the 3' end. The tRNA-derived region of Opsar shared 76 % sequence similarity with tRNA(Ala) gene. This result indicated that Opsar could derive from the inactive or pseudogene of tRNA(Ala). The reliability of method was tested by obtaining C-SINE, Ct-SINE, and M-SINEs from Ctenopharyngodon idellus, Megalobrama amblycephala, and Cyprinus carpio genomes. This method is simpler than the previously reported, which successfully omitted many steps, such as preparation of probes, construction of genomic libraries, and hybridization.

Identification methods of irradiated food

International Nuclear Information System (INIS)

Raffi, J.J.

1991-01-01

After a general review of the different possible methods, the stress is put upon the ones close to application: electron spin resonance, thermoluminescence and method of lipids. The problem of the specificity of each method is discussed (proof or presumption): they are then placed in the context of the programme of identification of irradiated foods just co-organized by the author with the Community Bureau of Reference (CEC) [fr

Plant Transporter Identification

DEFF Research Database (Denmark)

Larsen, Bo

Membrane transport proteins (transporters) play a critical role for numerous biological processes, by controlling the movements of ions and molecules in and out of cells. In plants, transporters thus function as gatekeepers between the plant and its surrounding environment and between organs......, tissues, cells and intracellular compartments. Since plants are highly compartmentalized organisms with complex transportation infrastructures, they consequently have many transporters. However, the vast majority of predicted transporters have not yet been experimentally verified to have transport...... activity. This project contains a review of the implemented methods, which have led to plant transporter identification, and present our progress on creating a high-throughput functional genomics transporter identification platform....

Evaluation of Quality Assessment Protocols for High Throughput Genome Resequencing Data.

Science.gov (United States)

Chiara, Matteo; Pavesi, Giulio

2017-01-01

Large-scale initiatives aiming to recover the complete sequence of thousands of human genomes are currently being undertaken worldwide, concurring to the generation of a comprehensive catalog of human genetic variation. The ultimate and most ambitious goal of human population scale genomics is the characterization of the so-called human "variome," through the identification of causal mutations or haplotypes. Several research institutions worldwide currently use genotyping assays based on Next-Generation Sequencing (NGS) for diagnostics and clinical screenings, and the widespread application of such technologies promises major revolutions in medical science. Bioinformatic analysis of human resequencing data is one of the main factors limiting the effectiveness and general applicability of NGS for clinical studies. The requirement for multiple tools, to be combined in dedicated protocols in order to accommodate different types of data (gene panels, exomes, or whole genomes) and the high variability of the data makes difficult the establishment of a ultimate strategy of general use. While there already exist several studies comparing sensitivity and accuracy of bioinformatic pipelines for the identification of single nucleotide variants from resequencing data, little is known about the impact of quality assessment and reads pre-processing strategies. In this work we discuss major strengths and limitations of the various genome resequencing protocols are currently used in molecular diagnostics and for the discovery of novel disease-causing mutations. By taking advantage of publicly available data we devise and suggest a series of best practices for the pre-processing of the data that consistently improve the outcome of genotyping with minimal impacts on computational costs.

Comparative sequence analysis of Sordaria macrospora and Neurospora crassa as a means to improve genome annotation.

Science.gov (United States)

Nowrousian, Minou; Würtz, Christian; Pöggeler, Stefanie; Kück, Ulrich

2004-03-01

One of the most challenging parts of large scale sequencing projects is the identification of functional elements encoded in a genome. Recently, studies of genomes of up to six different Saccharomyces species have demonstrated that a comparative analysis of genome sequences from closely related species is a powerful approach to identify open reading frames and other functional regions within genomes [Science 301 (2003) 71, Nature 423 (2003) 241]. Here, we present a comparison of selected sequences from Sordaria macrospora to their corresponding Neurospora crassa orthologous regions. Our analysis indicates that due to the high degree of sequence similarity and conservation of overall genomic organization, S. macrospora sequence information can be used to simplify the annotation of the N. crassa genome.

Techniques for Large-Scale Bacterial Genome Manipulation and Characterization of the Mutants with Respect to In Silico Metabolic Reconstructions.

Science.gov (United States)

diCenzo, George C; Finan, Turlough M

2018-01-01

The rate at which all genes within a bacterial genome can be identified far exceeds the ability to characterize these genes. To assist in associating genes with cellular functions, a large-scale bacterial genome deletion approach can be employed to rapidly screen tens to thousands of genes for desired phenotypes. Here, we provide a detailed protocol for the generation of deletions of large segments of bacterial genomes that relies on the activity of a site-specific recombinase. In this procedure, two recombinase recognition target sequences are introduced into known positions of a bacterial genome through single cross-over plasmid integration. Subsequent expression of the site-specific recombinase mediates recombination between the two target sequences, resulting in the excision of the intervening region and its loss from the genome. We further illustrate how this deletion system can be readily adapted to function as a large-scale in vivo cloning procedure, in which the region excised from the genome is captured as a replicative plasmid. We next provide a procedure for the metabolic analysis of bacterial large-scale genome deletion mutants using the Biolog Phenotype MicroArray™ system. Finally, a pipeline is described, and a sample Matlab script is provided, for the integration of the obtained data with a draft metabolic reconstruction for the refinement of the reactions and gene-protein-reaction relationships in a metabolic reconstruction.

Genome-wide evolutionary dynamics of influenza B viruses on a global scale.

Directory of Open Access Journals (Sweden)

Pinky Langat

2017-12-01

Full Text Available The global-scale epidemiology and genome-wide evolutionary dynamics of influenza B remain poorly understood compared with influenza A viruses. We compiled a spatio-temporally comprehensive dataset of influenza B viruses, comprising over 2,500 genomes sampled worldwide between 1987 and 2015, including 382 newly-sequenced genomes that fill substantial gaps in previous molecular surveillance studies. Our contributed data increase the number of available influenza B virus genomes in Europe, Africa and Central Asia, improving the global context to study influenza B viruses. We reveal Yamagata-lineage diversity results from co-circulation of two antigenically-distinct groups that also segregate genetically across the entire genome, without evidence of intra-lineage reassortment. In contrast, Victoria-lineage diversity stems from geographic segregation of different genetic clades, with variability in the degree of geographic spread among clades. Differences between the lineages are reflected in their antigenic dynamics, as Yamagata-lineage viruses show alternating dominance between antigenic groups, while Victoria-lineage viruses show antigenic drift of a single lineage. Structural mapping of amino acid substitutions on trunk branches of influenza B gene phylogenies further supports these antigenic differences and highlights two potential mechanisms of adaptation for polymerase activity. Our study provides new insights into the epidemiological and molecular processes shaping influenza B virus evolution globally.

Genome-wide evolutionary dynamics of influenza B viruses on a global scale

Science.gov (United States)

Langat, Pinky; Bowden, Thomas A.; Edwards, Stephanie; Gall, Astrid; Rambaut, Andrew; Daniels, Rodney S.; Russell, Colin A.; Pybus, Oliver G.; McCauley, John

2017-01-01

The global-scale epidemiology and genome-wide evolutionary dynamics of influenza B remain poorly understood compared with influenza A viruses. We compiled a spatio-temporally comprehensive dataset of influenza B viruses, comprising over 2,500 genomes sampled worldwide between 1987 and 2015, including 382 newly-sequenced genomes that fill substantial gaps in previous molecular surveillance studies. Our contributed data increase the number of available influenza B virus genomes in Europe, Africa and Central Asia, improving the global context to study influenza B viruses. We reveal Yamagata-lineage diversity results from co-circulation of two antigenically-distinct groups that also segregate genetically across the entire genome, without evidence of intra-lineage reassortment. In contrast, Victoria-lineage diversity stems from geographic segregation of different genetic clades, with variability in the degree of geographic spread among clades. Differences between the lineages are reflected in their antigenic dynamics, as Yamagata-lineage viruses show alternating dominance between antigenic groups, while Victoria-lineage viruses show antigenic drift of a single lineage. Structural mapping of amino acid substitutions on trunk branches of influenza B gene phylogenies further supports these antigenic differences and highlights two potential mechanisms of adaptation for polymerase activity. Our study provides new insights into the epidemiological and molecular processes shaping influenza B virus evolution globally. PMID:29284042

Substructure identification for shear structures: cross-power spectral density method

International Nuclear Information System (INIS)

Zhang, Dongyu; Johnson, Erik A

2012-01-01

In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage. (paper)

Large scale identification and categorization of protein sequences using structured logistic regression.

Directory of Open Access Journals (Sweden)

Bjørn P Pedersen

Full Text Available BACKGROUND: Structured Logistic Regression (SLR is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well-suited for this task. The classification of P-type ATPases, a large family of ATP-driven membrane pumps transporting essential cations, was selected as a test-case that would generate important biological information as well as provide a proof-of-concept for the application of SLR to a large scale bioinformatics problem. RESULTS: Using SLR, we have built classifiers to identify and automatically categorize P-type ATPases into one of 11 pre-defined classes. The SLR-classifiers are compared to a Hidden Markov Model approach and shown to be highly accurate and scalable. Representing the bulk of currently known sequences, we analysed 9.3 million sequences in the UniProtKB and attempted to classify a large number of P-type ATPases. To examine the distribution of pumps on organisms, we also applied SLR to 1,123 complete genomes from the Entrez genome database. Finally, we analysed the predicted membrane topology of the identified P-type ATPases. CONCLUSIONS: Using the SLR-based classification tool we are able to run a large scale study of P-type ATPases. This study provides proof-of-concept for the application of SLR to a bioinformatics problem and the analysis of P-type ATPases pinpoints new and interesting targets for further biochemical characterization and structural analysis.

Discovery, genotyping and characterization of structural variation and novel sequence at single nucleotide resolution from de novo genome assemblies on a population scale

DEFF Research Database (Denmark)

Liu, Siyang; Huang, Shujia; Rao, Junhua

2015-01-01

present a novel approach implemented in a single software package, AsmVar, to discover, genotype and characterize different forms of structural variation and novel sequence from population-scale de novo genome assemblies up to nucleotide resolution. Application of AsmVar to several human de novo genome......) as well as large deletions. However, these approaches consistently display a substantial bias against the recovery of complex structural variants and novel sequence in individual genomes and do not provide interpretation information such as the annotation of ancestral state and formation mechanism. We...... assemblies captures a wide spectrum of structural variants and novel sequences present in the human population in high sensitivity and specificity. Our method provides a direct solution for investigating structural variants and novel sequences from de novo genome assemblies, facilitating the construction...

Genome Partitioner: A web tool for multi-level partitioning of large-scale DNA constructs for synthetic biology applications.

Science.gov (United States)

Christen, Matthias; Del Medico, Luca; Christen, Heinz; Christen, Beat

2017-01-01

Recent advances in lower-cost DNA synthesis techniques have enabled new innovations in the field of synthetic biology. Still, efficient design and higher-order assembly of genome-scale DNA constructs remains a labor-intensive process. Given the complexity, computer assisted design tools that fragment large DNA sequences into fabricable DNA blocks are needed to pave the way towards streamlined assembly of biological systems. Here, we present the Genome Partitioner software implemented as a web-based interface that permits multi-level partitioning of genome-scale DNA designs. Without the need for specialized computing skills, biologists can submit their DNA designs to a fully automated pipeline that generates the optimal retrosynthetic route for higher-order DNA assembly. To test the algorithm, we partitioned a 783 kb Caulobacter crescentus genome design. We validated the partitioning strategy by assembling a 20 kb test segment encompassing a difficult to synthesize DNA sequence. Successful assembly from 1 kb subblocks into the 20 kb segment highlights the effectiveness of the Genome Partitioner for reducing synthesis costs and timelines for higher-order DNA assembly. The Genome Partitioner is broadly applicable to translate DNA designs into ready to order sequences that can be assembled with standardized protocols, thus offering new opportunities to harness the diversity of microbial genomes for synthetic biology applications. The Genome Partitioner web tool can be accessed at https://christenlab.ethz.ch/GenomePartitioner.

Genome Partitioner: A web tool for multi-level partitioning of large-scale DNA constructs for synthetic biology applications.

Directory of Open Access Journals (Sweden)

Matthias Christen

Full Text Available Recent advances in lower-cost DNA synthesis techniques have enabled new innovations in the field of synthetic biology. Still, efficient design and higher-order assembly of genome-scale DNA constructs remains a labor-intensive process. Given the complexity, computer assisted design tools that fragment large DNA sequences into fabricable DNA blocks are needed to pave the way towards streamlined assembly of biological systems. Here, we present the Genome Partitioner software implemented as a web-based interface that permits multi-level partitioning of genome-scale DNA designs. Without the need for specialized computing skills, biologists can submit their DNA designs to a fully automated pipeline that generates the optimal retrosynthetic route for higher-order DNA assembly. To test the algorithm, we partitioned a 783 kb Caulobacter crescentus genome design. We validated the partitioning strategy by assembling a 20 kb test segment encompassing a difficult to synthesize DNA sequence. Successful assembly from 1 kb subblocks into the 20 kb segment highlights the effectiveness of the Genome Partitioner for reducing synthesis costs and timelines for higher-order DNA assembly. The Genome Partitioner is broadly applicable to translate DNA designs into ready to order sequences that can be assembled with standardized protocols, thus offering new opportunities to harness the diversity of microbial genomes for synthetic biology applications. The Genome Partitioner web tool can be accessed at https://christenlab.ethz.ch/GenomePartitioner.

Harnessing CRISPR-Cas systems for bacterial genome editing.

Science.gov (United States)

Selle, Kurt; Barrangou, Rodolphe

2015-04-01

Manipulation of genomic sequences facilitates the identification and characterization of key genetic determinants in the investigation of biological processes. Genome editing via clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) constitutes a next-generation method for programmable and high-throughput functional genomics. CRISPR-Cas systems are readily reprogrammed to induce sequence-specific DNA breaks at target loci, resulting in fixed mutations via host-dependent DNA repair mechanisms. Although bacterial genome editing is a relatively unexplored and underrepresented application of CRISPR-Cas systems, recent studies provide valuable insights for the widespread future implementation of this technology. This review summarizes recent progress in bacterial genome editing and identifies fundamental genetic and phenotypic outcomes of CRISPR targeting in bacteria, in the context of tool development, genome homeostasis, and DNA repair. Copyright © 2015 Elsevier Ltd. All rights reserved.

RegPrecise 3.0--a resource for genome-scale exploration of transcriptional regulation in bacteria.

Science.gov (United States)

Novichkov, Pavel S; Kazakov, Alexey E; Ravcheev, Dmitry A; Leyn, Semen A; Kovaleva, Galina Y; Sutormin, Roman A; Kazanov, Marat D; Riehl, William; Arkin, Adam P; Dubchak, Inna; Rodionov, Dmitry A

2013-11-01

Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in prokaryotes is one of the critical tasks of modern genomics. Bacteria from different taxonomic groups, whose lifestyles and natural environments are substantially different, possess highly diverged transcriptional regulatory networks. The comparative genomics approaches are useful for in silico reconstruction of bacterial regulons and networks operated by both transcription factors (TFs) and RNA regulatory elements (riboswitches). RegPrecise (http://regprecise.lbl.gov) is a web resource for collection, visualization and analysis of transcriptional regulons reconstructed by comparative genomics. We significantly expanded a reference collection of manually curated regulons we introduced earlier. RegPrecise 3.0 provides access to inferred regulatory interactions organized by phylogenetic, structural and functional properties. Taxonomy-specific collections include 781 TF regulogs inferred in more than 160 genomes representing 14 taxonomic groups of Bacteria. TF-specific collections include regulogs for a selected subset of 40 TFs reconstructed across more than 30 taxonomic lineages. Novel collections of regulons operated by RNA regulatory elements (riboswitches) include near 400 regulogs inferred in 24 bacterial lineages. RegPrecise 3.0 provides four classifications of the reference regulons implemented as controlled vocabularies: 55 TF protein families; 43 RNA motif families; ~150 biological processes or metabolic pathways; and ~200 effectors or environmental signals. Genome-wide visualization of regulatory networks and metabolic pathways covered by the reference regulons are available for all studied genomes. A separate section of RegPrecise 3.0 contains draft regulatory networks in 640 genomes obtained by an conservative propagation of the reference regulons to closely related genomes. RegPrecise 3.0 gives access to the transcriptional regulons reconstructed in

Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks.

Science.gov (United States)

Raisaro, Jean Louis; Tramèr, Florian; Ji, Zhanglong; Bu, Diyue; Zhao, Yongan; Carey, Knox; Lloyd, David; Sofia, Heidi; Baker, Dixie; Flicek, Paul; Shringarpure, Suyash; Bustamante, Carlos; Wang, Shuang; Jiang, Xiaoqian; Ohno-Machado, Lucila; Tang, Haixu; Wang, XiaoFeng; Hubaux, Jean-Pierre

2017-07-01

The Global Alliance for Genomics and Health (GA4GH) created the Beacon Project as a means of testing the willingness of data holders to share genetic data in the simplest technical context-a query for the presence of a specified nucleotide at a given position within a chromosome. Each participating site (or "beacon") is responsible for assuring that genomic data are exposed through the Beacon service only with the permission of the individual to whom the data pertains and in accordance with the GA4GH policy and standards.While recognizing the inference risks associated with large-scale data aggregation, and the fact that some beacons contain sensitive phenotypic associations that increase privacy risk, the GA4GH adjudged the risk of re-identification based on the binary yes/no allele-presence query responses as acceptable. However, recent work demonstrated that, given a beacon with specific characteristics (including relatively small sample size and an adversary who possesses an individual's whole genome sequence), the individual's membership in a beacon can be inferred through repeated queries for variants present in the individual's genome.In this paper, we propose three practical strategies for reducing re-identification risks in beacons. The first two strategies manipulate the beacon such that the presence of rare alleles is obscured; the third strategy budgets the number of accesses per user for each individual genome. Using a beacon containing data from the 1000 Genomes Project, we demonstrate that the proposed strategies can effectively reduce re-identification risk in beacon-like datasets. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

Directory of Open Access Journals (Sweden)

Edgar E. Lara-Ramírez

2014-01-01

Full Text Available The increasing number of dengue virus (DENV genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4 has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3 as well as the effective number of codons (ENC, ENCp versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA and clustering analysis on relative synonymous codon usage (RSCU within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution.

TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile.

Science.gov (United States)

Huang, Yen-Tsung; Hsu, Thomas; Christiani, David C

2014-01-01

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X (2) distributions that can be obtained using permutation with scaled X (2) approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (number data, and causal mechanisms of the five pathways require further study.

Genome-based microbial ecology of anammox granules in a full-scale wastewater treatment system

OpenAIRE

Speth, D.R.; Zandt, M.H. in 't; Guerrero Cruz, S.; Dutilh, B.E.; Jetten, M.S.M.

2016-01-01

Partial-nitritation anammox (PNA) is a novel wastewater treatment procedure for energy-efficient ammonium removal. Here we use genome-resolved metagenomics to build a genome-based ecological model of the microbial community in a full-scale PNA reactor. Sludge from the bioreactor examined here is used to seed reactors in wastewater treatment plants around the world; however, the role of most of its microbial community in ammonium removal remains unknown. Our analysis yielded 23 near-complete d...

Full-scale experimental validation of decentralized damage identification using wireless smart sensors

International Nuclear Information System (INIS)

Jang, Shinae; Sim, Sung-Han; Jo, Hongki; Spencer Jr, Billie F

2012-01-01

Wireless smart sensor networks (WSSN) facilitate a new paradigm for structural health monitoring (SHM) of civil infrastructure. Conventionally, SHM systems employing wired sensors and centralized data acquisition have been used to characterize the state of a structure; however, widespread implementation has been limited due to high costs and difficulties in installation. WSSN offer a unique opportunity to overcome such difficulties. Recent developments have realized low-cost, smart sensors with on-board computation and wireless communication capabilities, making deployment of a dense array of sensors on large civil structures both economical and feasible. Wireless smart sensors (WSS) have shown their tremendous potential for SHM in recent full-scale bridge monitoring examples. However, structural damage identification using on-board computation capability in a WSSN, a primary objective of SHM, has yet to reach its full potential. This paper presents full-scale validation of a damage identification strategy using a decentralized network of Imote2 nodes on a historic steel truss bridge. A total of 24 WSS nodes with 144 sensor channels are deployed on the bridge to validate the developed damage identification software. The performance of this decentralized damage identification strategy is demonstrated on the WSSN by comparing its results with those from the traditional centralized approach, as well as visual inspection. (paper)

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

Science.gov (United States)

Cui, Peng; Zhong, Tingyan; Wang, Zhuo; Wang, Tao; Zhao, Hongyu; Liu, Chenglin; Lu, Hui

2018-06-01

Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene identification algorithms have been developed, large numbers of false positives and low coverage are still major problems in this field. In this study we constructed a novel computational framework for circadian gene identification using deep neural networks (DNN) - a deep learning algorithm which can represent the raw form of data patterns without imposing assumptions on the expression distribution. Firstly, we transformed time-course gene expression data into categorical-state data to denote the changing trend of gene expression. Two distinct expression patterns emerged after clustering of the state data for circadian genes from our manually created learning dataset. DNN was then applied to discriminate the aperiodic genes and the two subtypes of periodic genes. In order to assess the performance of DNN, four commonly used machine learning methods including k-nearest neighbors, logistic regression, naïve Bayes, and support vector machines were used for comparison. The results show that the DNN model achieves the best balanced precision and recall. Next, we conducted large scale circadian gene detection using the trained DNN model for the remaining transcription profiles. Comparing with JTK_CYCLE and a study performed by Möller-Levet et al. (doi: https://doi.org/10.1073/pnas.1217154110), we identified 1132 novel periodic genes. Through the functional analysis of these novel circadian genes, we found that the GTPase superfamily exhibits distinct circadian expression patterns and may provide a molecular switch of circadian control of the functioning of the immune system in human blood. Our study provides novel insights into both the circadian gene identification field and the study of complex circadian-driven biological

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)-A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes.

Science.gov (United States)

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare . However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq—A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes

Directory of Open Access Journals (Sweden)

Karolina Chwialkowska

2017-11-01

Full Text Available Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq. We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare. However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation

Metabolite coupling in genome-scale metabolic networks

Directory of Open Access Journals (Sweden)

Palsson Bernhard Ø

2006-03-01

Full Text Available Abstract Background Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. Results Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual

Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

Directory of Open Access Journals (Sweden)

Man Zhu

2017-03-01

Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

Genome-wide screen for universal individual identification SNPs based on the HapMap and 1000 Genomes databases.

Science.gov (United States)

Huang, Erwen; Liu, Changhui; Zheng, Jingjing; Han, Xiaolong; Du, Weian; Huang, Yuanjian; Li, Chengshi; Wang, Xiaoguang; Tong, Dayue; Ou, Xueling; Sun, Hongyu; Zeng, Zhaoshu; Liu, Chao

2018-04-03

Differences among SNP panels for individual identification in SNP-selecting and populations led to few common SNPs, compromising their universal applicability. To screen all universal SNPs, we performed a genome-wide SNP mining in multiple populations based on HapMap and 1000Genomes databases. SNPs with high minor allele frequencies (MAF) in 37 populations were selected. With MAF from ≥0.35 to ≥0.43, the number of selected SNPs decreased from 2769 to 0. A total of 117 SNPs with MAF ≥0.39 have no linkage disequilibrium with each other in every population. For 116 of the 117 SNPs, cumulative match probability (CMP) ranged from 2.01 × 10-48 to 1.93 × 10-50 and cumulative exclusion probability (CEP) ranged from 0.9999999996653 to 0.9999999999945. In 134 tested Han samples, 110 of the 117 SNPs remained within high MAF and conformed to Hardy-Weinberg equilibrium, with CMP = 4.70 × 10-47 and CEP = 0.999999999862. By analyzing the same number of autosomal SNPs as in the HID-Ion AmpliSeq Identity Panel, i.e. 90 randomized out of the 110 SNPs, our panel yielded preferable CMP and CEP. Taken together, the 110-SNPs panel is advantageous for forensic test, and this study provided plenty of highly informative SNPs for compiling final universal panels.

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

DEFF Research Database (Denmark)

King, Zachary A.; Lu, Justin; Dräger, Andreas

2016-01-01

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repo...

A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

Directory of Open Access Journals (Sweden)

Hojung Nam

2014-09-01

Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

Proteome scale identification, classification and structural analysis of iron-binding proteins in bread wheat.

Science.gov (United States)

Verma, Shailender Kumar; Sharma, Ankita; Sandhu, Padmani; Choudhary, Neha; Sharma, Shailaja; Acharya, Vishal; Akhter, Yusuf

2017-05-01

Bread wheat is one of the major staple foods of worldwide population and iron plays a significant role in growth and development of the plant. In this report, we are presenting the genome wide identification of iron-binding proteins in bread wheat. The wheat genome derived putative proteome was screened for identification of iron-binding sequence motifs. Out of 602 putative iron-binding proteins, 130 were able to produce reliable structural models by homology techniques and further analyzed for the presence of iron-binding structural motifs. The computationally identified proteins appear to bind to ferrous and ferric ions and showed diverse coordination geometries. Glu, His, Asp and Cys amino acid residues were found to be mostly involved in iron binding. We have classified these proteins on the basis of their localization in the different cellular compartments. The identified proteins were further classified into their protein folds, families and functional classes ranging from structure maintenance of cellular components, regulation of gene expression, post translational modification, membrane proteins, enzymes, signaling and storage proteins. This comprehensive report regarding structural iron binding proteome provides useful insights into the diversity of iron binding proteins of wheat plants and further utilized to study their roles in plant growth, development and physiology. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of Genome-Scale Data

OpenAIRE

Kemmeren, P.P.C.W.

2005-01-01

The genetic material of every cell in an organism is stored inside DNA in the form of genes, which together form the genome. The information stored in the DNA is translated to RNA and subsequently to proteins, which form complex biological systems. The availability of whole genome sequences has given rise to the parallel development of other high-throughput approaches such as determining mRNA expression level changes, gene-deletion phenotypes, chromosomal location of DNA binding proteins, cel...

Identification of the key ecological factors influencing vegetation degradation in semi-arid agro-pastoral ecotone considering spatial scales

Science.gov (United States)

Peng, Yu; Wang, Qinghui; Fan, Min

2017-11-01

When assessing re-vegetation project performance and optimizing land management, identification of the key ecological factors inducing vegetation degradation has crucial implications. Rainfall, temperature, elevation, slope, aspect, land use type, and human disturbance are ecological factors affecting the status of vegetation index. However, at different spatial scales, the key factors may vary. Using Helin County, Inner-Mongolia, China as the study site and combining remote sensing image interpretation, field surveying, and mathematical methods, this study assesses key ecological factors affecting vegetation degradation under different spatial scales in a semi-arid agro-pastoral ecotone. It indicates that the key factors are different at various spatial scales. Elevation, rainfall, and temperature are identified as crucial for all spatial extents. Elevation, rainfall and human disturbance are key factors for small-scale quadrats of 300 m × 300 m and 600 m × 600 m, temperature and land use type are key factors for a medium-scale quadrat of 1 km × 1 km, and rainfall, temperature, and land use are key factors for large-scale quadrats of 2 km × 2 km and 5 km × 5 km. For this region, human disturbance is not the key factor for vegetation degradation across spatial scales. It is necessary to consider spatial scale for the identification of key factors determining vegetation characteristics. The eco-restoration programs at various spatial scales should identify key influencing factors according their scales so as to take effective measurements. The new understanding obtained in this study may help to explore the forces which driving vegetation degradation in the degraded regions in the world.

Comparative genomics and prediction of conditionally dispensable sequences in legume-infecting Fusarium oxysporum formae speciales facilitates identification of candidate effectors.

Science.gov (United States)

Williams, Angela H; Sharma, Mamta; Thatcher, Louise F; Azam, Sarwar; Hane, James K; Sperschneider, Jana; Kidd, Brendan N; Anderson, Jonathan P; Ghosh, Raju; Garg, Gagan; Lichtenzveig, Judith; Kistler, H Corby; Shea, Terrance; Young, Sarah; Buck, Sally-Anne G; Kamphuis, Lars G; Saxena, Rachit; Pande, Suresh; Ma, Li-Jun; Varshney, Rajeev K; Singh, Karam B

2016-03-05

Soil-borne fungi of the Fusarium oxysporum species complex cause devastating wilt disease on many crops including legumes that supply human dietary protein needs across many parts of the globe. We present and compare draft genome assemblies for three legume-infecting formae speciales (ff. spp.): F. oxysporum f. sp. ciceris (Foc-38-1) and f. sp. pisi (Fop-37622), significant pathogens of chickpea and pea respectively, the world's second and third most important grain legumes, and lastly f. sp. medicaginis (Fom-5190a) for which we developed a model legume pathosystem utilising Medicago truncatula. Focusing on the identification of pathogenicity gene content, we leveraged the reference genomes of Fusarium pathogens F. oxysporum f. sp. lycopersici (tomato-infecting) and F. solani (pea-infecting) and their well-characterised core and dispensable chromosomes to predict genomic organisation in the newly sequenced legume-infecting isolates. Dispensable chromosomes are not essential for growth and in Fusarium species are known to be enriched in host-specificity and pathogenicity-associated genes. Comparative genomics of the publicly available Fusarium species revealed differential patterns of sequence conservation across F. oxysporum formae speciales, with legume-pathogenic formae speciales not exhibiting greater sequence conservation between them relative to non-legume-infecting formae speciales, possibly indicating the lack of a common ancestral source for legume pathogenicity. Combining predicted dispensable gene content with in planta expression in the model legume-infecting isolate, we identified small conserved regions and candidate effectors, four of which shared greatest similarity to proteins from another legume-infecting ff. spp. We demonstrate that distinction of core and potential dispensable genomic regions of novel F. oxysporum genomes is an effective tool to facilitate effector discovery and the identification of gene content possibly linked to host

Core Genome Multilocus Sequence Typing for Identification of Globally Distributed Clonal Groups and Differentiation of Outbreak Strains of Listeria monocytogenes.

Science.gov (United States)

Chen, Yi; Gonzalez-Escalona, Narjol; Hammack, Thomas S; Allard, Marc W; Strain, Errol A; Brown, Eric W

2016-10-15

Many listeriosis outbreaks are caused by a few globally distributed clonal groups, designated clonal complexes or epidemic clones, of Listeria monocytogenes, several of which have been defined by classic multilocus sequence typing (MLST) schemes targeting 6 to 8 housekeeping or virulence genes. We have developed and evaluated core genome MLST (cgMLST) schemes and applied them to isolates from multiple clonal groups, including those associated with 39 listeriosis outbreaks. The cgMLST clusters were congruent with MLST-defined clonal groups, which had various degrees of diversity at the whole-genome level. Notably, cgMLST could distinguish among outbreak strains and epidemiologically unrelated strains of the same clonal group, which could not be achieved using classic MLST schemes. The precise selection of cgMLST gene targets may not be critical for the general identification of clonal groups and outbreak strains. cgMLST analyses further identified outbreak strains, including those associated with recent outbreaks linked to contaminated French-style cheese, Hispanic-style cheese, stone fruit, caramel apple, ice cream, and packaged leafy green salad, as belonging to major clonal groups. We further developed lineage-specific cgMLST schemes, which can include accessory genes when core genomes do not possess sufficient diversity, and this provided additional resolution over species-specific cgMLST. Analyses of isolates from different common-source listeriosis outbreaks revealed various degrees of diversity, indicating that the numbers of allelic differences should always be combined with cgMLST clustering and epidemiological evidence to define a listeriosis outbreak. Classic multilocus sequence typing (MLST) schemes targeting internal fragments of 6 to 8 genes that define clonal complexes or epidemic clones have been widely employed to study L. monocytogenes biodiversity and its relation to pathogenicity potential and epidemiology. We demonstrated that core genome MLST

Expressed Peptide Tags: An additional layer of data for genome annotation

Energy Technology Data Exchange (ETDEWEB)

Savidor, Alon [ORNL; Donahoo, Ryan S [ORNL; Hurtado-Gonzales, Oscar [University of Tennessee, Knoxville (UTK); Verberkmoes, Nathan C [ORNL; Shah, Manesh B [ORNL; Lamour, Kurt H [ORNL; McDonald, W Hayes [ORNL

2006-01-01

While genome sequencing is becoming ever more routine, genome annotation remains a challenging process. Identification of the coding sequences within the genomic milieu presents a tremendous challenge, especially for eukaryotes with their complex gene architectures. Here we present a method to assist the annotation process through the use of proteomic data and bioinformatics. Mass spectra of digested protein preparations of the organism of interest were acquired and searched against a protein database created by a six frame translation of the genome. The identified peptides were mapped back to the genome, compared to the current annotation, and then categorized as supporting or extending the current genome annotation. We named the classified peptides Expressed Peptide Tags (EPTs). The well annotated bacterium Rhodopseudomonas palustris was used as a control for the method and showed high degree of correlation between EPT mapping and the current annotation, with 86% of the EPTs confirming existing gene calls and less than 1% of the EPTs expanding on the current annotation. The eukaryotic plant pathogens Phytophthora ramorum and Phytophthora sojae, whose genomes have been recently sequenced and are much less well annotated, were also subjected to this method. A series of algorithmic steps were taken to increase the confidence of EPT identification for these organisms, including generation of smaller sub-databases to be searched against, and definition of EPT criteria that accommodates the more complex eukaryotic gene architecture. As expected, the analysis of the Phytophthora species showed less correlation between EPT mapping and their current annotation. While ~77% of Phytophthora EPTs supported the current annotation, a portion of them (7.2% and 12.6% for P. ramorum and P. sojae, respectively) suggested modification to current gene calls or identified novel genes that were missed by the current genome annotation of these organisms.

Genome-wide engineering of an infectious clone of herpes simplex virus type 1 using synthetic genomics assembly methods.

Science.gov (United States)

Oldfield, Lauren M; Grzesik, Peter; Voorhies, Alexander A; Alperovich, Nina; MacMath, Derek; Najera, Claudia D; Chandra, Diya Sabrina; Prasad, Sanjana; Noskov, Vladimir N; Montague, Michael G; Friedman, Robert M; Desai, Prashant J; Vashee, Sanjay

2017-10-17

Here, we present a transformational approach to genome engineering of herpes simplex virus type 1 (HSV-1), which has a large DNA genome, using synthetic genomics tools. We believe this method will enable more rapid and complex modifications of HSV-1 and other large DNA viruses than previous technologies, facilitating many useful applications. Yeast transformation-associated recombination was used to clone 11 fragments comprising the HSV-1 strain KOS 152 kb genome. Using overlapping sequences between the adjacent pieces, we assembled the fragments into a complete virus genome in yeast, transferred it into an Escherichia coli host, and reconstituted infectious virus following transfection into mammalian cells. The virus derived from this yeast-assembled genome, KOS YA , replicated with kinetics similar to wild-type virus. We demonstrated the utility of this modular assembly technology by making numerous modifications to a single gene, making changes to two genes at the same time and, finally, generating individual and combinatorial deletions to a set of five conserved genes that encode virion structural proteins. While the ability to perform genome-wide editing through assembly methods in large DNA virus genomes raises dual-use concerns, we believe the incremental risks are outweighed by potential benefits. These include enhanced functional studies, generation of oncolytic virus vectors, development of delivery platforms of genes for vaccines or therapy, as well as more rapid development of countermeasures against potential biothreats.

Ensembl Genomes 2016: more genomes, more complexity.

Science.gov (United States)

Kersey, Paul Julian; Allen, James E; Armean, Irina; Boddu, Sanjay; Bolt, Bruce J; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Falin, Lee J; Grabmueller, Christoph; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Aranganathan, Naveen K; Langridge, Nicholas; Lowy, Ernesto; McDowall, Mark D; Maheswari, Uma; Nuhn, Michael; Ong, Chuang Kee; Overduin, Bert; Paulini, Michael; Pedro, Helder; Perry, Emily; Spudich, Giulietta; Tapanari, Electra; Walts, Brandon; Williams, Gareth; Tello-Ruiz, Marcela; Stein, Joshua; Wei, Sharon; Ware, Doreen; Bolser, Daniel M; Howe, Kevin L; Kulesha, Eugene; Lawson, Daniel; Maslen, Gareth; Staines, Daniel M

2016-01-04

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including reference sequence, gene models, transcriptional data, genetic variation and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments. These include the development of new analyses and views to represent polyploid genomes (of which bread wheat is the primary exemplar); and the continued up-scaling of the resource, which now includes over 23 000 bacterial genomes, 400 fungal genomes and 100 protist genomes, in addition to 55 genomes from invertebrate metazoa and 39 genomes from plants. This dramatic increase in the number of included genomes is one part of a broader effort to automate the integration of archival data (genome sequence, but also associated RNA sequence data and variant calls) within the context of reference genomes and make it available through the Ensembl user interfaces. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Molecular analysis of single oocyst of Eimeria by whole genome amplification (WGA) based nested PCR.

Science.gov (United States)

Wang, Yunzhou; Tao, Geru; Cui, Yujuan; Lv, Qiyao; Xie, Li; Li, Yuan; Suo, Xun; Qin, Yinghe; Xiao, Lihua; Liu, Xianyong

2014-09-01

PCR-based molecular tools are widely used for the identification and characterization of protozoa. Here we report the molecular analysis of Eimeria species using combined methods of whole genome amplification (WGA) and nested PCR. Single oocyst of Eimeria stiedai or Eimeriamedia was directly used for random amplification of the genomic DNA with either primer extension preamplification (PEP) or multiple displacement amplification (MDA), and then the WGA product was used as template in nested PCR with species-specific primers for ITS-1, 18S rDNA and 23S rDNA of E. stiedai and E. media. WGA-based PCR was successful for the amplification of these genes from single oocyst. For the species identification of single oocyst isolated from mixed E. stiedai or E. media, the results from WGA-based PCR were exactly in accordance with those from morphological identification, suggesting the availability of this method in molecular analysis of eimerian parasites at the single oocyst level. WGA-based PCR method can also be applied for the identification and genetic characterization of other protists. Copyright © 2014 Elsevier Inc. All rights reserved.

Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model

NARCIS (Netherlands)

Teusink, B.; Wiersma, A.; Molenaar, D.; Francke, C.; Vos, de W.M.; Siezen, R.J.; Smid, E.J.

2006-01-01

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for

VESPA: Very large-scale Evolutionary and Selective Pressure Analyses

Directory of Open Access Journals (Sweden)

Andrew E. Webb

2017-06-01

Full Text Available Background Large-scale molecular evolutionary analyses of protein coding sequences requires a number of preparatory inter-related steps from finding gene families, to generating alignments and phylogenetic trees and assessing selective pressure variation. Each phase of these analyses can represent significant challenges, particularly when working with entire proteomes (all protein coding sequences in a genome from a large number of species. Methods We present VESPA, software capable of automating a selective pressure analysis using codeML in addition to the preparatory analyses and summary statistics. VESPA is written in python and Perl and is designed to run within a UNIX environment. Results We have benchmarked VESPA and our results show that the method is consistent, performs well on both large scale and smaller scale datasets, and produces results in line with previously published datasets. Discussion Large-scale gene family identification, sequence alignment, and phylogeny reconstruction are all important aspects of large-scale molecular evolutionary analyses. VESPA provides flexible software for simplifying these processes along with downstream selective pressure variation analyses. The software automatically interprets results from codeML and produces simplified summary files to assist the user in better understanding the results. VESPA may be found at the following website: http://www.mol-evol.org/VESPA.

Predicting human height by Victorian and genomic methods.

Science.gov (United States)

Aulchenko, Yurii S; Struchalin, Maksim V; Belonogova, Nadezhda M; Axenovich, Tatiana I; Weedon, Michael N; Hofman, Albert; Uitterlinden, Andre G; Kayser, Manfred; Oostra, Ben A; van Duijn, Cornelia M; Janssens, A Cecile J W; Borodin, Pavel M

2009-08-01

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

Genome-Based Identification of Active Prophage Regions by Next Generation Sequencing in Bacillus licheniformis DSM13

Science.gov (United States)

Hertel, Robert; Rodríguez, David Pintor; Hollensteiner, Jacqueline; Dietrich, Sascha; Leimbach, Andreas; Hoppert, Michael; Liesegang, Heiko; Volland, Sonja

2015-01-01

Prophages are viruses, which have integrated their genomes into the genome of a bacterial host. The status of the prophage genome can vary from fully intact with the potential to form infective particles to a remnant state where only a few phage genes persist. Prophages have impact on the properties of their host and are therefore of great interest for genomic research and strain design. Here we present a genome- and next generation sequencing (NGS)-based approach for identification and activity evaluation of prophage regions. Seven prophage or prophage-like regions were identified in the genome of Bacillus licheniformis DSM13. Six of these regions show similarity to members of the Siphoviridae phage family. The remaining region encodes the B. licheniformis orthologue of the PBSX prophage from Bacillus subtilis. Analysis of isolated phage particles (induced by mitomycin C) from the wild-type strain and prophage deletion mutant strains revealed activity of the prophage regions BLi_Pp2 (PBSX-like), BLi_Pp3 and BLi_Pp6. In contrast to BLi_Pp2 and BLi_Pp3, neither phage DNA nor phage particles of BLi_Pp6 could be visualized. However, the ability of prophage BLi_Pp6 to generate particles could be confirmed by sequencing of particle-protected DNA mapping to prophage locus BLi_Pp6. The introduced NGS-based approach allows the investigation of prophage regions and their ability to form particles. Our results show that this approach increases the sensitivity of prophage activity analysis and can complement more conventional approaches such as transmission electron microscopy (TEM). PMID:25811873

Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study.

Directory of Open Access Journals (Sweden)

Paul S de Vries

Full Text Available An increasing number of genome-wide association (GWA studies are now using the higher resolution 1000 Genomes Project reference panel (1000G for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8, the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

SNIT: SNP identification for strain typing

Directory of Open Access Journals (Sweden)

Reifman Jaques

2011-09-01

Full Text Available Abstract With ever-increasing numbers of microbial genomes being sequenced, efficient tools are needed to perform strain-level identification of any newly sequenced genome. Here, we present the SNP identification for strain typing (SNIT pipeline, a fast and accurate software system that compares a newly sequenced bacterial genome with other genomes of the same species to identify single nucleotide polymorphisms (SNPs and small insertions/deletions (indels. Based on this information, the pipeline analyzes the polymorphic loci present in all input genomes to identify the genome that has the fewest differences with the newly sequenced genome. Similarly, for each of the other genomes, SNIT identifies the input genome with the fewest differences. Results from five bacterial species show that the SNIT pipeline identifies the correct closest neighbor with 75% to 100% accuracy. The SNIT pipeline is available for download at http://www.bhsai.org/snit.html

SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale

Directory of Open Access Journals (Sweden)

Paccanaro Alberto

2010-03-01

Full Text Available Abstract Background An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community. Results SCPS (Spectral Clustering of Protein Sequences is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database (14,183 sequences and the complete genome of the yeast Saccharomyces cerevisiae (6,690 sequences. Conclusions Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein

SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale.

Science.gov (United States)

Nepusz, Tamás; Sasidharan, Rajkumar; Paccanaro, Alberto

2010-03-09

An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community. SCPS (Spectral Clustering of Protein Sequences) is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database (14,183 sequences) and the complete genome of the yeast Saccharomyces cerevisiae (6,690 sequences). Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein descriptions using GI numbers from NCBI, it interfaces with

Chlamydiaceae Genomics Reveals Interspecies Admixture and the Recent Evolution of Chlamydia abortus Infecting Lower Mammalian Species and Humans

OpenAIRE

Joseph, Sandeep J.; Marti, Hanna; Didelot, Xavier; Castillo-Ramirez, Santiago; Read, Timothy D.; Dean, Deborah

2015-01-01

Chlamydiaceae are obligate intracellular bacteria that cause a diversity of severe infections among humans and livestock on a global scale. Identification of new species since 1989 and emergence of zoonotic infections, including abortion in women, underscore the need for genome sequencing of multiple strains of each species to advance our knowledge of evolutionary dynamics across Chlamydiaceae. Here, we genome sequenced isolates from avian, lower mammalian and human hosts. Based on core gene ...

Identification and characterization of nuclear genes involved in photosynthesis in Populus

Science.gov (United States)

2014-01-01

Background The gap between the real and potential photosynthetic rate under field conditions suggests that photosynthesis could potentially be improved. Nuclear genes provide possible targets for improving photosynthetic efficiency. Hence, genome-wide identification and characterization of the nuclear genes affecting photosynthetic traits in woody plants would provide key insights on genetic regulation of photosynthesis and identify candidate processes for improvement of photosynthesis. Results Using microarray and bulked segregant analysis strategies, we identified differentially expressed nuclear genes for photosynthesis traits in a segregating population of poplar. We identified 515 differentially expressed genes in this population (FC ≥ 2 or FC ≤ 0.5, P photosynthesis by the nuclear genome mainly involves transport, metabolism and response to stimulus functions. Conclusions This study provides new genome-scale strategies for the discovery of potential candidate genes affecting photosynthesis in Populus, and for identification of the functions of genes involved in regulation of photosynthesis. This work also suggests that improving photosynthetic efficiency under field conditions will require the consideration of multiple factors, such as stress responses. PMID:24673936

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)—A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes

Science.gov (United States)

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare. However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop

A quantitative comparison of single-cell whole genome amplification methods.

Directory of Open Access Journals (Sweden)

Charles F A de Bourcy

Full Text Available Single-cell sequencing is emerging as an important tool for studies of genomic heterogeneity. Whole genome amplification (WGA is a key step in single-cell sequencing workflows and a multitude of methods have been introduced. Here, we compare three state-of-the-art methods on both bulk and single-cell samples of E. coli DNA: Multiple Displacement Amplification (MDA, Multiple Annealing and Looping Based Amplification Cycles (MALBAC, and the PicoPLEX single-cell WGA kit (NEB-WGA. We considered the effects of reaction gain on coverage uniformity, error rates and the level of background contamination. We compared the suitability of the different WGA methods for the detection of copy-number variations, for the detection of single-nucleotide polymorphisms and for de-novo genome assembly. No single method performed best across all criteria and significant differences in characteristics were observed; the choice of which amplifier to use will depend strongly on the details of the type of question being asked in any given experiment.

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus

NARCIS (Netherlands)

C. Zeng (Chenjie); Guo, X. (Xingyi); J. Long (Jirong); K.B. Kuchenbaecker (Karoline); A. Droit (Arnaud); K. Michailidou (Kyriaki); M. Ghoussaini (Maya); S. Kar (Siddhartha); Freeman, A. (Adam); J.L. Hopper (John); R.L. Milne (Roger); M.K. Bolla (Manjeet K.); Wang, Q. (Qin); J. Dennis (Joe); S. Agata (Simona); S. Ahmed (Shahana); K. Aittomäki (Kristiina); I.L. Andrulis (Irene); H. Anton-Culver (Hoda); Antonenkova, N.N. (Natalia N.); A. Arason (Adalgeir); Arndt, V. (Volker); B.K. Arun (Banu); B. Arver (Brita Wasteson); F. Bacot (Francois); D. Barrowdale (Daniel); Baynes, C. (Caroline); A. Beeghly-Fadiel (Alicia); J. Benítez (Javier); M. Bermisheva (Marina); C. Blomqvist (Carl); W.J. Blot (William); N.V. Bogdanova (Natalia); S.E. Bojesen (Stig); B. Bonnani (Bernardo); A.-L. Borresen-Dale (Anne-Lise); J.S. Brand (Judith S.); H. Brauch (Hiltrud); P. Brennan (Paul); H. Brenner (Hermann); A. Broeks (Annegien); T. Brüning (Thomas); B. Burwinkel (Barbara); S.S. Buys (Saundra); Q. Cai (Qiuyin); T. Caldes (Trinidad); I. Campbell (Ian); T.A. Carpenter (Adrian); J. Chang-Claude (Jenny); Choi, J.-Y. (Ji-Yeob); K.B.M. Claes (Kathleen B.M.); C. Clarke (Christine); A. Cox (Angela); S.S. Cross (Simon); K. Czene (Kamila); M.B. Daly (Mary B.); M. de La Hoya (Miguel); K. De Leeneer (Kim); P. Devilee (Peter); O. Díez (Orland); S.M. Domchek (Susan); M. Doody (Michele); C.M. Dorfling (Cecilia); T. Dörk (Thilo); I. dos Santos Silva (Isabel); M. Dumont (Martine); M. Dwek (Miriam); Dworniczak, B. (Bernd); K.M. Egan (Kathleen); U. Eilber (Ursula); Z. Einbeigi (Zakaria); B. Ejlertsen (Bent); S.D. Ellis (Steve); D. Frost (Debra); F. Lalloo (Fiona); P.A. Fasching (Peter); J.D. Figueroa (Jonine); H. Flyger (Henrik); M. Friedlander (Michael); E. Friedman (Eitan); Gambino, G. (Gaetana); Gao, Y.-T. (Yu-Tang); J. Garber (Judy); M. García-Closas (Montserrat); P.A. Gehrig (Paola A.); F. Damiola (Francesca); F. Lesueur (Fabienne); S. Mazoyer (Sylvie); D. Stoppa-Lyonnet (Dominique); Giles, G.G. (Graham G.); A.K. Godwin (Andrew K.); D. Goldgar (David); A. González-Neira (Anna); M.H. Greene (Mark H.); P. Guénel (Pascal); L. Haeberle (Lothar); C.A. Haiman (Christopher A.); Hallberg, E. (Emily); U. Hamann (Ute); T.V.O. Hansen (Thomas); S. Hart (Stewart); J.M. Hartikainen (J.); J.M. Hartman (Joost); N. Hassan (Norhashimah); S. Healey (Sue); F.B.L. Hogervorst (Frans); S. Verhoef; Hendricks, C.B. (Carolyn B.); P. Hillemanns (Peter); A. Hollestelle (Antoinette); P.J. Hulick (Peter); D. Hunter (David); E.N. Imyanitov (Evgeny); C. Isaacs (Claudine); H. Ito (Hidemi); A. Jakubowska (Anna); R. Janavicius (Ramunas); Jaworska-Bieniek, K. (Katarzyna); U.B. Jensen; E.M. John (Esther); Joly Beauparlant, C. (Charles); M. Jones (Michael); M. Kabisch (Maria); D. Kang (Daehee); Karlan, B.Y. (Beth Y.); S. Kauppila (Saila); M. Kerin (Michael); S. Khan (Sofia); E.K. Khusnutdinova (Elza); J.A. Knight (Julia); I. Konstantopoulou (I.); P. Kraft (Peter); A. Kwong (Ava); Y. Laitman (Yael); Lambrechts, D. (Diether); C. Lazaro (Conxi); L. Le Marchand (Loic); C.N. Lee (Chuen); M.H. Lee (Min Hyuk); K.J. Lester (Kathryn); J. Li (Jingmei); A. Liljegren (Annelie); A. Lindblom (Annika); A. Lophatananon (Artitaya); J. Lubinski (Jan); P.L. Mai (Phuong); A. Mannermaa (Arto); S. Manoukian (Siranoush); S. Margolin (Sara); Marme, F. (Frederik); K. Matsuo (Keitaro); L. McGuffog (Lesley); A. Meindl (Alfons); F. Menegaux (Florence); M. Montagna (Marco); K.R. Muir (K.); A.-M. Mulligan (Anna-Marie); K.L. Nathanson (Katherine); S.L. Neuhausen (Susan); H. Nevanlinna (Heli); P. Newcomb (Polly); S. Nord (Silje); R.L. Nussbaum (Robert L.); K. Offit (Kenneth); E. Olah; O.I. Olopade (Olufunmilayo I.); C. Olswold (Curtis); A. Osorio (Ana); L. Papi (Laura); T.-W. Park-Simon; Paulsson-Karlsson, Y. (Ylva); S.T.H. Peeters (Stephanie); B. Peissel (Bernard); P. Peterlongo (Paolo); J. Peto (Julian); G. Pfeiler (Georg); C. Phelan (Catherine); Presneau, N. (Nadege); P. Radice (Paolo); N. Rahman (Nazneen); S.J. Ramus (Susan); M.U. Rashid (Muhammad); G. Rennert (Gad); K. Rhiem (Kerstin); Rudolph, A. (Anja); R. Salani (Ritu); Sangrajrang, S. (Suleeporn); E.J. Sawyer (Elinor); M.K. Schmidt (Marjanka); R.K. Schmutzler (Rita); M. Schoemaker (Minouk); P. Schürmann (Peter); C.M. Seynaeve (Caroline); C.-Y. Shen (Chen-Yang); M. Shrubsole (Martha); X.-O. Shu (Xiao-Ou); A.J. Sigurdson (Alice); C.F. Singer (Christian); S. Slager (Susan); Soucy, P. (Penny); M.C. Southey (Melissa); D. Steinemann (Doris); A.J. Swerdlow (Anthony ); C. Szabo (Csilla); Tchatchou, S. (Sandrine); P.J. Teixeira; S.-H. Teo (Soo-Hwang); M.B. Terry (Mary Beth); D.C. Tessier (Daniel C.); A. Teulé (A.); M. Thomassen (Mads); L. Tihomirova (Laima); M. Tischkowitz (Marc); A.E. Toland (Amanda); N. Tung (Nadine); C. Turnbull (Clare); A.M.W. van den Ouweland (Ans); E.J. van Rensburg (Elizabeth); ven den Berg, D. (David); J. Vijai (Joseph); S. Wang-Gohrke (Shan); J.N. Weitzel (Jeffrey); A.S. Whittemore (Alice); R. Winqvist (Robert); Wong, T.Y. (Tien Y.); A.H. Wu (Anna); Yannoukakos, D. (Drakoulis); J-C. Yu (Jyh-Cherng); P.D.P. Pharoah (Paul); P. Hall (Per); G. Chenevix-Trench (Georgia); A.M. Dunning (Alison); J. Simard (Jacques); F.J. Couch (Fergus); A.C. Antoniou (Antonis C.); D.F. Easton (Douglas F.); W. Zheng (Wei)

2016-01-01

textabstractBackground: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk. Method: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more

Fungal Genomics Program

Energy Technology Data Exchange (ETDEWEB)

Grigoriev, Igor

2012-03-12

The JGI Fungal Genomics Program aims to scale up sequencing and analysis of fungal genomes to explore the diversity of fungi important for energy and the environment, and to promote functional studies on a system level. Combining new sequencing technologies and comparative genomics tools, JGI is now leading the world in fungal genome sequencing and analysis. Over 120 sequenced fungal genomes with analytical tools are available via MycoCosm (www.jgi.doe.gov/fungi), a web-portal for fungal biologists. Our model of interacting with user communities, unique among other sequencing centers, helps organize these communities, improves genome annotation and analysis work, and facilitates new larger-scale genomic projects. This resulted in 20 high-profile papers published in 2011 alone and contributing to the Genomics Encyclopedia of Fungi, which targets fungi related to plant health (symbionts, pathogens, and biocontrol agents) and biorefinery processes (cellulose degradation, sugar fermentation, industrial hosts). Our next grand challenges include larger scale exploration of fungal diversity (1000 fungal genomes), developing molecular tools for DOE-relevant model organisms, and analysis of complex systems and metagenomes.

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

KAUST Repository

Magana-Mora, Arturo

2017-08-15

BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.

A genome-wide, fine-scale map of natural pigmentation variation in Drosophila melanogaster.

Directory of Open Access Journals (Sweden)

Héloïse Bastide

2013-06-01

Full Text Available Various approaches can be applied to uncover the genetic basis of natural phenotypic variation, each with their specific strengths and limitations. Here, we use a replicated genome-wide association approach (Pool-GWAS to fine-scale map genomic regions contributing to natural variation in female abdominal pigmentation in Drosophila melanogaster, a trait that is highly variable in natural populations and highly heritable in the laboratory. We examined abdominal pigmentation phenotypes in approximately 8000 female European D. melanogaster, isolating 1000 individuals with extreme phenotypes. We then used whole-genome Illumina sequencing to identify single nucleotide polymorphisms (SNPs segregating in our sample, and tested these for associations with pigmentation by contrasting allele frequencies between replicate pools of light and dark individuals. We identify two small regions near the pigmentation genes tan and bric-à-brac 1, both corresponding to known cis-regulatory regions, which contain SNPs showing significant associations with pigmentation variation. While the Pool-GWAS approach suffers some limitations, its cost advantage facilitates replication and it can be applied to any non-model system with an available reference genome.

A genome-wide, fine-scale map of natural pigmentation variation in Drosophila melanogaster.

Science.gov (United States)

Bastide, Héloïse; Betancourt, Andrea; Nolte, Viola; Tobler, Raymond; Stöbe, Petra; Futschik, Andreas; Schlötterer, Christian

2013-06-01

Various approaches can be applied to uncover the genetic basis of natural phenotypic variation, each with their specific strengths and limitations. Here, we use a replicated genome-wide association approach (Pool-GWAS) to fine-scale map genomic regions contributing to natural variation in female abdominal pigmentation in Drosophila melanogaster, a trait that is highly variable in natural populations and highly heritable in the laboratory. We examined abdominal pigmentation phenotypes in approximately 8000 female European D. melanogaster, isolating 1000 individuals with extreme phenotypes. We then used whole-genome Illumina sequencing to identify single nucleotide polymorphisms (SNPs) segregating in our sample, and tested these for associations with pigmentation by contrasting allele frequencies between replicate pools of light and dark individuals. We identify two small regions near the pigmentation genes tan and bric-à-brac 1, both corresponding to known cis-regulatory regions, which contain SNPs showing significant associations with pigmentation variation. While the Pool-GWAS approach suffers some limitations, its cost advantage facilitates replication and it can be applied to any non-model system with an available reference genome.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

Science.gov (United States)

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Comparative genomics allowed the identification of drug targets against human fungal pathogens

Directory of Open Access Journals (Sweden)

Martins Natalia F

2011-01-01

Full Text Available Abstract Background The prevalence of invasive fungal infections (IFIs has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. Results In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6 relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum. Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24-sterol C-methyltransferase. Conclusions Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of

A strategy for implementing genomics into nursing practice informed by three behaviour change theories.

Science.gov (United States)

Leach, Verity; Tonkin, Emma; Lancastle, Deborah; Kirk, Maggie

2016-06-01

Genomics is an ever increasing aspect of nursing practice, with focus being directed towards improving health. The authors present an implementation strategy for the incorporation of genomics into nursing practice within the UK, based on three behaviour change theories and the identification of individuals who are likely to provide support for change. Individuals identified as Opinion Leaders and Adopters of genomics illustrate how changes in behaviour might occur among the nursing profession. The core philosophy of the strategy is that genomic nurse Adopters and Opinion Leaders who have direct interaction with their peers in practice will be best placed to highlight the importance of genomics within the nursing role. The strategy discussed in this paper provides scope for continued nursing education and development of genomics within nursing practice on a larger scale. The recommendations might be of particular relevance for senior staff and management. © 2016 John Wiley & Sons Australia, Ltd.

Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints.

Science.gov (United States)

Yen, Jiun Y; Nazem-Bokaee, Hadi; Freedman, Benjamin G; Athamneh, Ahmad I M; Senger, Ryan S

2013-05-01

Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H2 production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design "fine-tuned" metabolic engineering strategies in silico that can be implemented directly with available genomic tools. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

LocateP: Genome-scale subcellular-location predictor for bacterial proteins

Directory of Open Access Journals (Sweden)

Zhou Miaomiao

2008-03-01

Full Text Available Abstract Background In the past decades, various protein subcellular-location (SCL predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. Results LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms

New Waste Beverage Cans Identification Method

Directory of Open Access Journals (Sweden)

Firmansyah Burlian

2016-05-01

Full Text Available The primary emphasis of this work is on the development of a new waste beverage cans identification method for automated beverage cans sorting systems known as the SVS system. The method described involved window-based subdivision of the image into X-cells, construction of X-candidate template for N-cells, calculation of matching scores of reference templates for the N-cells image, and application of matching score to identify the grade of the object. The SVS system performance for correct beverage cans grade identification is 95.17% with estimated throughput of 21,600 objects per hour with a conveyor belt width of 18˝. The weight of the throughput depends on the size and type of the objects.

Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

Science.gov (United States)

LaBudde, Robert A; Harnly, James M

2012-01-01

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

Data on the genome-wide identification of CNL R-genes in Setaria italica (L.) P. Beauv.

OpenAIRE

Andersen, Ethan J.; Nepal, Madhav P.

2017-01-01

We report data associated with the identification of 242 disease resistance genes (R-genes) in the genome of Setaria italica as presented in “Genetic diversity of disease resistance genes in foxtail millet (Setaria italica L.)” (Andersen and Nepal, 2017) [1]. Our data describe the structure and evolution of the Coiled-coil, Nucleotide-binding site, Leucine-rich repeat (CNL) R-genes in foxtail millet. The CNL genes were identified through rigorous extraction and analysis of recently available ...

Development of a Method to Implement Whole-Genome Bisulfite Sequencing of cfDNA from Cancer Patients and a Mouse Tumor Model

Directory of Open Access Journals (Sweden)

Elaine C. Maggi

2018-01-01

Full Text Available The goal of this study was to develop a method for whole genome cell-free DNA (cfDNA methylation analysis in humans and mice with the ultimate goal to facilitate the identification of tumor derived DNA methylation changes in the blood. Plasma or serum from patients with pancreatic neuroendocrine tumors or lung cancer, and plasma from a murine model of pancreatic adenocarcinoma was used to develop a protocol for cfDNA isolation, library preparation and whole-genome bisulfite sequencing of ultra low quantities of cfDNA, including tumor-specific DNA. The protocol developed produced high quality libraries consistently generating a conversion rate >98% that will be applicable for the analysis of human and mouse plasma or serum to detect tumor-derived changes in DNA methylation.

Applications of Genomic Sequencing in Pediatric CNS Tumors.

Science.gov (United States)

Bavle, Abhishek A; Lin, Frank Y; Parsons, D Williams

2016-05-01

Recent advances in genome-scale sequencing methods have resulted in a significant increase in our understanding of the biology of human cancers. When applied to pediatric central nervous system (CNS) tumors, these remarkable technological breakthroughs have facilitated the molecular characterization of multiple tumor types, provided new insights into the genetic basis of these cancers, and prompted innovative strategies that are changing the management paradigm in pediatric neuro-oncology. Genomic tests have begun to affect medical decision making in a number of ways, from delineating histopathologically similar tumor types into distinct molecular subgroups that correlate with clinical characteristics, to guiding the addition of novel therapeutic agents for patients with high-risk or poor-prognosis tumors, or alternatively, reducing treatment intensity for those with a favorable prognosis. Genomic sequencing has also had a significant impact on translational research strategies in pediatric CNS tumors, resulting in wide-ranging applications that have the potential to direct the rational preclinical screening of novel therapeutic agents, shed light on tumor heterogeneity and evolution, and highlight differences (or similarities) between pediatric and adult CNS tumors. Finally, in addition to allowing the identification of somatic (tumor-specific) mutations, the analysis of patient-matched constitutional (germline) DNA has facilitated the detection of pathogenic germline alterations in cancer genes in patients with CNS tumors, with critical implications for genetic counseling and tumor surveillance strategies for children with familial predisposition syndromes. As our understanding of the molecular landscape of pediatric CNS tumors continues to advance, innovative applications of genomic sequencing hold significant promise for further improving the care of children with these cancers.

GMATA: An Integrated Software Package for Genome-Scale SSR Mining, Marker Development and Viewing.

Science.gov (United States)

Wang, Xuewen; Wang, Le

2016-01-01

Simple sequence repeats (SSRs), also referred to as microsatellites, are highly variable tandem DNAs that are widely used as genetic markers. The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA) integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at http://sourceforge.net/projects/gmata/?source=navbar.

Genomics: The Science and Technology Behind the Human Genome Project (by Charles R. Cantor and Cassandra L. Smith)

Science.gov (United States)

Serra, Reviewed By Martin J.

2000-01-01

analysis of error in sequencing and current bottlenecks in the sequencing effort. The next chapter describes the steps necessary to scale current technologies for the sequencing of entire genomes. Chapter 12 examines alternate methods for DNA sequencing. Initially, methods of single-molecule sequencing and sequencing by microscopy are introduced; the majority of the chapter is devoted to the development of DNA sequencing methods using chip microarrays and hybridization. The remaining chapters (13-15) consider the uses and analysis of DNA sequence information. The initial focus is on the identification of genes. Several examples are given of the use of DNA sequence information for diagnosis of inherited or infectious diseases. The sequence-specific manipulation of DNA is discussed in Chapter 14. The final chapter deals with the implications of large-scale sequencing, including methods for identifying genes and finding errors in DNA sequences, to the development of computer algorithms for the interpretation of DNA sequence information. The text figures are black and white line drawings that, although clearly done, seem a bit primitive for 1999. While I appreciated the simplicity of the drawings, many students accustomed to more colorful presentations will find them wanting. The four color figures in the center of the text seem an afterthought and add little to the text's clarity. Each chapter has a set of additional reading sources, mostly primary sources. Often, specialized topics are offset into boxes that provide clarification and amplification without cluttering the text. An appendix includes a list of the Web-based database resources. As an undergraduate instructor who has previously taught biochemistry, molecular biology, and a course on the human genome, I found many interesting tidbits and amplifications throughout the text. I would recommend this book as a text for an advanced undergraduate or beginning graduate course in genomics. Although the text works though

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction.

Directory of Open Access Journals (Sweden)

Katsunori Yoshikawa

Full Text Available Arthrospira (Spirulina platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(PH dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

System Identification Methods for Aircraft Flight Control Development and Validation

Science.gov (United States)

1995-10-01

System-identification methods compose a mathematical model, or series of models, : from measurements of inputs and outputs of dynamic systems. This paper : discusses the use of frequency-domain system-identification methods for the : development and ...

VirSorter: mining viral signal from microbial genomic data

Directory of Open Access Journals (Sweden)

Simon Roux

2015-05-01

Full Text Available Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome, new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter’s prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages. Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in “reverse” to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made

VirSorter: mining viral signal from microbial genomic data

Science.gov (United States)

Roux, Simon; Enault, Francois; Hurwitz, Bonnie L.

2015-01-01

Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter’s prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in “reverse” to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the i

Portraits of Benvenuto Cellini and Anthropological Methods of Their Identification

Science.gov (United States)

Nasobin, Oleg

2016-01-01

Modern methods of biometric identification are increasingly applied in order to attribute works of art. They are based on developments in the 19th century anthropological methods. So, this article describes how the successional anthropological methods were applied for the identification of Benvenuto Cellini's portraits. Objective comparison of…

Genome-wide identification of aquaporin encoding genes in Brassica oleracea and their phylogenetic sequence comparison to Brassica crops and Arabidopsis

Science.gov (United States)

Diehn, Till A.; Pommerrenig, Benjamin; Bernhardt, Nadine; Hartmann, Anja; Bienert, Gerd P.

2015-01-01

Aquaporins (AQPs) are essential channel proteins that regulate plant water homeostasis and the uptake and distribution of uncharged solutes such as metalloids, urea, ammonia, and carbon dioxide. Despite their importance as crop plants, little is known about AQP gene and protein function in cabbage (Brassica oleracea) and other Brassica species. The recent releases of the genome sequences of B. oleracea and Brassica rapa allow comparative genomic studies in these species to investigate the evolution and features of Brassica genes and proteins. In this study, we identified all AQP genes in B. oleracea by a genome-wide survey. In total, 67 genes of four plant AQP subfamilies were identified. Their full-length gene sequences and locations on chromosomes and scaffolds were manually curated. The identification of six additional full-length AQP sequences in the B. rapa genome added to the recently published AQP protein family of this species. A phylogenetic analysis of AQPs of Arabidopsis thaliana, B. oleracea, B. rapa allowed us to follow AQP evolution in closely related species and to systematically classify and (re-) name these isoforms. Thirty-three groups of AQP-orthologous genes were identified between B. oleracea and Arabidopsis and their expression was analyzed in different organs. The two selectivity filters, gene structure and coding sequences were highly conserved within each AQP subfamily while sequence variations in some introns and untranslated regions were frequent. These data suggest a similar substrate selectivity and function of Brassica AQPs compared to Arabidopsis orthologs. The comparative analyses of all AQP subfamilies in three Brassicaceae species give initial insights into AQP evolution in these taxa. Based on the genome-wide AQP identification in B. oleracea and the sequence analysis and reprocessing of Brassica AQP information, our dataset provides a sequence resource for further investigations of the physiological and molecular functions of

Effective identification of Lactobacillus casei group species: genome-based selection of the gene mutL as the target of a novel multiplex PCR assay.

Science.gov (United States)

Bottari, Benedetta; Felis, Giovanna E; Salvetti, Elisa; Castioni, Anna; Campedelli, Ilenia; Torriani, Sandra; Bernini, Valentina; Gatti, Monica

2017-07-01

Lactobacillus casei,Lactobacillus paracasei and Lactobacillusrhamnosus form a closely related taxonomic group (the L. casei group) within the facultatively heterofermentative lactobacilli. Strains of these species have been used for a long time as probiotics in a wide range of products, and they represent the dominant species of nonstarter lactic acid bacteria in ripened cheeses, where they contribute to flavour development. The close genetic relationship among those species, as well as the similarity of biochemical properties of the strains, hinders the development of an adequate selective method to identify these bacteria. Despite this being a hot topic, as demonstrated by the large amount of literature about it, the results of different proposed identification methods are often ambiguous and unsatisfactory. The aim of this study was to develop a more robust species-specific identification assay for differentiating the species of the L. casei group. A taxonomy-driven comparative genomic analysis was carried out to select the potential target genes whose similarity could better reflect genome-wide diversity. The gene mutL appeared to be the most promising one and, therefore, a novel species-specific multiplex PCR assay was developed to rapidly and effectively distinguish L. casei, L. paracasei and L. rhamnosus strains. The analysis of a collection of 76 wild dairy isolates, previously identified as members of the L. casei group combining the results of multiple approaches, revealed that the novel designed primers, especially in combination with already existing ones, were able to improve the discrimination power at the species level and reveal previously undiscovered intraspecific biodiversity.

Gene design, cloning and protein-expression methods for high-value targets at the Seattle Structural Genomics Center for Infectious Disease

International Nuclear Information System (INIS)

Raymond, Amy; Haffner, Taryn; Ng, Nathan; Lorimer, Don; Staker, Bart; Stewart, Lance

2011-01-01

An overview of one salvage strategy for high-value SSGCID targets is given. Any structural genomics endeavor, particularly ambitious ones such as the NIAID-funded Seattle Structural Genomics Center for Infectious Disease (SSGCID) and Center for Structural Genomics of Infectious Disease (CSGID), face technical challenges at all points of the production pipeline. One salvage strategy employed by SSGCID is combined gene engineering and structure-guided construct design to overcome challenges at the levels of protein expression and protein crystallization. Multiple constructs of each target are cloned in parallel using Polymerase Incomplete Primer Extension cloning and small-scale expressions of these are rapidly analyzed by capillary electrophoresis. Using the methods reported here, which have proven particularly useful for high-value targets, otherwise intractable targets can be resolved

Sexagesimal scale for mapping human genome Escala sexagesimal para mapear el genoma humano

Directory of Open Access Journals (Sweden)

RICARDO CRUZ-COKE

2001-03-01

Full Text Available In a previous work I designed a diagram of the human genome based on a circular ideogram of the haploid set of chromosomes, using a low resolution scale of Megabase units. The purpose of this work is to draft a new scale to measure the physical map of the human genome at the highest resolution level. The entire length of the haploid genome of males is deployed in a circumference, marked with a sexagesimal scale with 360 degrees and 1296000 arc seconds. The radio of this circunference displays a semilogaritmic metric scale from 1 m up to the nanometer level. The base pair level of DNA sequences, 10-9 of this circunsference, is measured in milliarsec unit (mas, equivalent to a thousand of arcsecond. The "mas" unit, correspond to 1.27 nanometers (nm or 0.427 base pair (bp and it is the framework for measure DNA sequences. Thus the three billion base pairs of the human genome may be identified by 1296000000 "mas" units in continous correlation from number 1 to number 1296000000. This sexagesimal scale covers all the levels of the nuclear genetic material, from nucleotides to chromosomes. The locations of every codon and every gene may be numbered in the physical map of chomosome regions according to this new scale, instead of the partial kilobase and Megabase scales used today. The advantage of the new scale is the unification of the set of chromosomes under a continous scale of measurement at the DNA level, facilitating the correlation with the phenotypes of man and other speciesEn un trabajo anterior yo diseñé un diagrama del genoma humano basado en un ideograma circular del conjunto haploide de cromosomas, usando una escala de baja resolución en megabases. El propósito de este trabajo es el de diseñar una nueva escala para medir el mapa físico del genoma humano al más alto nivel de resolución. La longitud completa del genoma haploide del varon es extendido en una circunsferencia, marcada con una escala sexagesimal de 360 grados y 1296000

An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes

DEFF Research Database (Denmark)

Henri, Clementine; Leekitcharoenphon, Pimlapas; Carleton, Heather A.

2017-01-01

Background/objectives: Whole genome sequencing (WGS) has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic......MLPPST) or pan genome (wgMLPPST). Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L monocytogenes.Methods: The clustering methods were evaluated...... on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses.Results: The backward comparability between conventional typing methods and genomic methods revealed a near...

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs

Directory of Open Access Journals (Sweden)

Jieliang Shen

2018-01-01

Full Text Available The establishment of the Aircraft Dynamic Model (ADM constitutes the prerequisite for the design of the navigation and control system, but the aerodynamic parameters in the model could not be readily obtained especially for small-scaled fixed-wing UAVs. In this paper, the procedure of computing the aerodynamic parameters is developed. All the longitudinal and lateral aerodynamic derivatives are firstly calculated through semi-empirical method based on the aerodynamics, rather than the wind tunnel tests or fluid dynamics software analysis. Secondly, the residuals of each derivative are proposed to be identified or estimated further via Extended Kalman Filter(EKF, with the observations of the attitude and velocity from the airborne integrated navigation system. Meanwhile, the observability of the targeted parameters is analyzed and strengthened through multiple maneuvers. Based on a small-scaled fixed-wing aircraft driven by propeller, the airborne sensors are chosen and the model of the actuators are constructed. Then, real flight tests are implemented to verify the calculation and identification process. Test results tell the rationality of the semi-empirical method and show the improvement of accuracy of ADM after the compensation of the parameters.

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs.

Science.gov (United States)

Shen, Jieliang; Su, Yan; Liang, Qing; Zhu, Xinhua

2018-01-13

The establishment of the Aircraft Dynamic Model(ADM) constitutes the prerequisite for the design of the navigation and control system, but the aerodynamic parameters in the model could not be readily obtained especially for small-scaled fixed-wing UAVs. In this paper, the procedure of computing the aerodynamic parameters is developed. All the longitudinal and lateral aerodynamic derivatives are firstly calculated through semi-empirical method based on the aerodynamics, rather than the wind tunnel tests or fluid dynamics software analysis. Secondly, the residuals of each derivative are proposed to be identified or estimated further via Extended Kalman Filter(EKF), with the observations of the attitude and velocity from the airborne integrated navigation system. Meanwhile, the observability of the targeted parameters is analyzed and strengthened through multiple maneuvers. Based on a small-scaled fixed-wing aircraft driven by propeller, the airborne sensors are chosen and the model of the actuators are constructed. Then, real flight tests are implemented to verify the calculation and identification process. Test results tell the rationality of the semi-empirical method and show the improvement of accuracy of ADM after the compensation of the parameters.

The development of small-scale mechanization means positioning algorithm using radio frequency identification technology in industrial plants

Science.gov (United States)

Astafiev, A.; Orlov, A.; Privezencev, D.

2018-01-01

The article is devoted to the development of technology and software for the construction of positioning and control systems for small mechanization in industrial plants based on radio frequency identification methods, which will be the basis for creating highly efficient intelligent systems for controlling the product movement in industrial enterprises. The main standards that are applied in the field of product movement control automation and radio frequency identification are considered. The article reviews modern publications and automation systems for the control of product movement developed by domestic and foreign manufacturers. It describes the developed algorithm for positioning of small-scale mechanization means in an industrial enterprise. Experimental studies in laboratory and production conditions have been conducted and described in the article.

Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity

Science.gov (United States)

Tycko, Josh; Myer, Vic E.; Hsu, Patrick D.

2016-01-01

Summary Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance. PMID:27494557

Impact of identity theft on methods of identification.

Science.gov (United States)

McLemore, Jerri; Hodges, Walker; Wyman, Amy

2011-06-01

Responsibility for confirming a decedent's identity commonly falls on the shoulders of the coroner or medical examiner. Misidentification of bodies results in emotional turmoil for the next-of-kin and can negatively impact the coroner's or medical examiner's career. To avoid such mishaps, the use of scientific methods to establish a positive identification is advocated. The use of scientific methods of identification may not be reliable in cases where the decedent had assumed the identity of another person. Case studies of erroneously identified bodies due to identity theft from the state medical examiner offices in Iowa and New Mexico are presented. This article discusses the scope and major concepts of identity theft and how identity theft prevents the guarantee of a positive identification.

Assembly of viral genomes from metagenomes

Directory of Open Access Journals (Sweden)

Saskia L Smits

2014-12-01

Full Text Available Viral infections remain a serious global health issue. Metagenomic approaches are increasingly used in the detection of novel viral pathogens but also to generate complete genomes of uncultivated viruses. In silico identification of complete viral genomes from sequence data would allow rapid phylogenetic characterization of these new viruses. Often, however, complete viral genomes are not recovered, but rather several distinct contigs derived from a single entity, some of which have no sequence homology to any known proteins. De novo assembly of single viruses from a metagenome is challenging, not only because of the lack of a reference genome, but also because of intrapopulation variation and uneven or insufficient coverage. Here we explored different assembly algorithms, remote homology searches, genome-specific sequence motifs, k-mer frequency ranking, and coverage profile binning to detect and obtain viral target genomes from metagenomes. All methods were tested on 454-generated sequencing datasets containing three recently described RNA viruses with a relatively large genome which were divergent to previously known viruses from the viral families Rhabdoviridae and Coronaviridae. Depending on specific characteristics of the target virus and the metagenomic community, different assembly and in silico gap closure strategies were successful in obtaining near complete viral genomes.

Genome-scale modeling of the protein secretory machinery in yeast

DEFF Research Database (Denmark)

Feizi, Amir; Österlund, Tobias; Petranovic, Dina

2013-01-01

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking....... Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm...

Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis.

Science.gov (United States)

Koo, Byoung-Mo; Kritikos, George; Farelli, Jeremiah D; Todor, Horia; Tong, Kenneth; Kimsey, Harvey; Wapinski, Ilan; Galardini, Marco; Cabal, Angelo; Peters, Jason M; Hachmann, Anna-Barbara; Rudner, David Z; Allen, Karen N; Typas, Athanasios; Gross, Carol A

2017-03-22

A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Meta-Analysis of Heterogeneous Data Sources for Genome-Scale Identification of Risk Genes in Complex Phenotypes

DEFF Research Database (Denmark)

Pers, Tune Hannes; Hansen, Niclas Tue; Hansen, Kasper Lage

2011-01-01

Meta‐analyses of large‐scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome‐wide association (GWA) studies, protein......) with an odds ratio of 1.28 [1.12–1.48], which replicates a previous case‐control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available...

Genome-wide identification, characterization, and expression profile of aquaporin gene family in flax (Linum usitatissimum).

Science.gov (United States)

Shivaraj, S M; Deshmukh, Rupesh K; Rai, Rhitu; Bélanger, Richard; Agrawal, Pawan K; Dash, Prasanta K

2017-04-27

Membrane intrinsic proteins (MIPs) form transmembrane channels and facilitate transport of myriad substrates across the cell membrane in many organisms. Majority of plant MIPs have water transporting ability and are commonly referred as aquaporins (AQPs). In the present study, we identified aquaporin coding genes in flax by genome-wide analysis, their structure, function and expression pattern by pan-genome exploration. Cross-genera phylogenetic analysis with known aquaporins from rice, arabidopsis, and poplar showed five subgroups of flax aquaporins representing 16 plasma membrane intrinsic proteins (PIPs), 17 tonoplast intrinsic proteins (TIPs), 13 NOD26-like intrinsic proteins (NIPs), 2 small basic intrinsic proteins (SIPs), and 3 uncharacterized intrinsic proteins (XIPs). Amongst aquaporins, PIPs contained hydrophilic aromatic arginine (ar/R) selective filter but TIP, NIP, SIP and XIP subfamilies mostly contained hydrophobic ar/R selective filter. Analysis of RNA-seq and microarray data revealed high expression of PIPs in multiple tissues, low expression of NIPs, and seed specific expression of TIP3 in flax. Exploration of aquaporin homologs in three closely related Linum species bienne, grandiflorum and leonii revealed presence of 49, 39 and 19 AQPs, respectively. The genome-wide identification of aquaporins, first in flax, provides insight to elucidate their physiological and developmental roles in flax.

A genomic background based method for association analysis in related individuals.

Directory of Open Access Journals (Sweden)

Najaf Amin

Full Text Available BACKGROUND: Feasibility of genotyping of hundreds and thousands of single nucleotide polymorphisms (SNPs in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population. Of the available methods that account for relatedness, the Measured Genotype (MG approach is considered the 'gold standard'. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. METHODOLOGY: In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GC approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect genealogy. CONCLUSION: Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the 'gold standard' MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis

Complete Mitochondrial Genomes of the Cherskii's Sculpin Cottus czerskii and Siberian Taimen Hucho taimen Reveal GenBank Entry Errors: Incorrect Species Identification and Recombinant Mitochondrial Genome.

Science.gov (United States)

Balakirev, Evgeniy S; Saveliev, Pavel A; Ayala, Francisco J

2017-01-01

The complete mitochondrial (mt) genome is sequenced in 2 individuals of the Cherskii's sculpin Cottus czerskii . A surprisingly high level of sequence divergence (10.3%) has been detected between the 2 genomes of C czerskii studied here and the GenBank mt genome of C czerskii (KJ956027). At the same time, a surprisingly low level of divergence (1.4%) has been detected between the GenBank C czerskii (KJ956027) and the Amur sculpin Cottus szanaga (KX762049, KX762050). We argue that the observed discrepancies are due to incorrect taxonomic identification so that the GenBank accession number KJ956027 represents actually the mt genome of C szanaga erroneously identified as C czerskii . Our results are of consequence concerning the GenBank database quality, highlighting the potential negative consequences of entry errors, which once they are introduced tend to be propagated among databases and subsequent publications. We illustrate the premise with the data on recombinant mt genome of the Siberian taimen Hucho taimen (NCBI Reference Sequence Database NC_016426.1; GenBank accession number HQ897271.1), bearing 2 introgressed fragments (≈0.9 kb [kilobase]) from 2 lenok subspecies, Brachymystax lenok and Brachymystax lenok tsinlingensis , submitted to GenBank on June 12, 2011. Since the time of submission, the H taimen recombinant mt genome leading to incorrect phylogenetic inferences was propagated in multiple subsequent publications despite the fact that nonrecombinant H taimen genomes were also available (submitted to GenBank on August 2, 2014; KJ711549, KJ711550). Other examples of recombinant sequences persisting in GenBank are also considered. A GenBank Entry Error Depositary is urgently needed to monitor and avoid a progressive accumulation of wrong biological information.

Genomics protocols [Methods in molecular biology, v. 175

National Research Council Canada - National Science Library

Starkey, Michael P; Elaswarapu, Ramnath

2001-01-01

... exploiting the potential of gene therapy. Highlights include methods for the analysis of differential gene expression, SNP detection, comparative genomic hybridization, and the functional analysis of genes, as well as the use of bio...

Contributions to In Silico Genome Annotation

KAUST Repository

Kalkatawi, Manal M.

2017-11-30

Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation, while identifying functions of these regions is considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and timeconsuming. This study contributes to genome annotation by introducing several novel bioinformatics methods, some based on machine learning (ML) approaches. First, we present Dragon PolyA Spotter (DPS), a method for accurate identification of the polyadenylation signals (PAS) within human genomic DNA sequences. For this, we derived a novel feature-set able to characterize properties of the genomic region surrounding the PAS, enabling development of high accuracy optimized ML predictive models. DPS considerably outperformed the state-of-the-art results. The second contribution concerns developing generic models for structural annotation, i.e., the recognition of different genomic signals and regions (GSR) within eukaryotic DNA. We developed DeepGSR, a systematic framework that facilitates generating ML models to predict GSR with high accuracy. To the best of our knowledge, no available generic and automated method exists for such task that could facilitate the studies of newly sequenced organisms. The prediction module of DeepGSR uses deep learning algorithms to derive highly abstract features that depend mainly on proper data representation and hyperparameters calibration. DeepGSR, which was evaluated on recognition of PAS and translation initiation sites (TIS) in different organisms, yields a simpler and more precise representation of the problem under study, compared to some other hand-tailored models, while producing high accuracy prediction results. Finally

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

Science.gov (United States)

Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

2017-01-01

As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Process identification method based on the Z transformation; Methode d'identification de processus par la transformation en Z

Energy Technology Data Exchange (ETDEWEB)

Zwingelstein, G [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires

1968-07-01

A simple method is described for identifying the transfer function of a linear retard-less system, based on the inversion of the Z transformation of the transmittance using a computer. It is assumed in this study that the signals at the entrance and at the exit of the circuit considered are of the deterministic type. The study includes: the theoretical principle of the inversion of the Z transformation, details about programming simulation, and identification of filters whose degrees vary from the first to the fifth order. (authors) [French] On decrit une methode simple d'identification de fonction de transfert d'un systeme lineaire sans retard, qui repose sur l'inversion de la transformee en Z de la transmittance a l'aide d'un calculateur. On suppose dans cette etude, que les signaux a l'entree et a la sortie du circuit considere sont de type deterministe. L'etude comporte: le principe theorique de l'inversion de la transformation en Z, les details de la programmation, la simulation et l'identification de filtres dont le degre varie du premier au cinquieme ordre. (auteurs)

An overview of recent developments in genomics and associated statistical methods.

Science.gov (United States)

Bickel, Peter J; Brown, James B; Huang, Haiyan; Li, Qunhua

2009-11-13

The landscape of genomics has changed drastically in the last two decades. Increasingly inexpensive sequencing has shifted the primary focus from the acquisition of biological sequences to the study of biological function. Assays have been developed to study many intricacies of biological systems, and publicly available databases have given rise to integrative analyses that combine information from many sources to draw complex conclusions. Such research was the focus of the recent workshop at the Isaac Newton Institute, 'High dimensional statistics in biology'. Many computational methods from modern genomics and related disciplines were presented and discussed. Using, as much as possible, the material from these talks, we give an overview of modern genomics: from the essential assays that make data-generation possible, to the statistical methods that yield meaningful inference. We point to current analytical challenges, where novel methods, or novel applications of extant methods, are presently needed.

Experience from large scale use of the EuroGenomics custom SNP chip in cattle

DEFF Research Database (Denmark)

Boichard, Didier A; Boussaha, Mekki; Capitan, Aurélien

2018-01-01

This article presents the strategy to evaluate candidate mutations underlying QTL or responsible for genetic defects, based upon the design and large-scale use of the Eurogenomics custom SNP chip set up for bovine genomic selection. Some variants under study originated from mapping genetic defect...

Nanobody®-based chromatin immunoprecipitation/micro-array analysis for genome-wide identification of transcription factor DNA binding sites

Science.gov (United States)

Nguyen-Duc, Trong; Peeters, Eveline; Muyldermans, Serge; Charlier, Daniel; Hassanzadeh-Ghassabeh, Gholamreza

2013-01-01

Nanobodies® are single-domain antibody fragments derived from camelid heavy-chain antibodies. Because of their small size, straightforward production in Escherichia coli, easy tailoring, high affinity, specificity, stability and solubility, nanobodies® have been exploited in various biotechnological applications. A major challenge in the post-genomics and post-proteomics era is the identification of regulatory networks involving nucleic acid–protein and protein–protein interactions. Here, we apply a nanobody® in chromatin immunoprecipitation followed by DNA microarray hybridization (ChIP-chip) for genome-wide identification of DNA–protein interactions. The Lrp-like regulator Ss-LrpB, arguably one of the best-studied specific transcription factors of the hyperthermophilic archaeon Sulfolobus solfataricus, was chosen for this proof-of-principle nanobody®-assisted ChIP. Three distinct Ss-LrpB-specific nanobodies®, each interacting with a different epitope, were generated for ChIP. Genome-wide ChIP-chip with one of these nanobodies® identified the well-established Ss-LrpB binding sites and revealed several unknown target sequences. Furthermore, these ChIP-chip profiles revealed auxiliary operator sites in the open reading frame of Ss-lrpB. Our work introduces nanobodies® as a novel class of affinity reagents for ChIP. Taking into account the unique characteristics of nanobodies®, in particular, their short generation time, nanobody®-based ChIP is expected to further streamline ChIP-chip and ChIP-Seq experiments, especially in organisms with no (or limited) possibility of genetic manipulation. PMID:23275538

Identification methods for irradiated wheat

International Nuclear Information System (INIS)

Zhu Shengtao; Kume, Tamikazu; Ishigaki, Isao.

1992-02-01

The effect of irradiation on wheat seeds was examined using various kinds of analytical methods for the identification of irradiated seeds. In germination test, the growth of sprouts was markedly inhibited at 500Gy, which was not affected by storage. The decrease in germination percentage was detected at 3300Gy. The results of enzymatic activity change in the germ measured by Vita-Scope germinator showed that the seeds irradiated at 10kGy could be identified. The content of amino acids in ungerminated and germinated seeds were analyzed. Irradiation at 10kGy caused the decrease of lysine content but the change was small which need very careful operation to detect it. The chemiluminescence intensity increased with radiation dose and decreased during storage. The wheat irradiated at 10kGy could be identified even after 3 months storage. In the electron spin resonance (ESR) spectrum analysis, the signal intensity with the g value f 2.0055 of skinned wheat seeds increased with radiation dose. Among these methods, germination test was the most sensitive and effective for identification of irradiated wheat. (author)

A distributed computational search strategy for the identification of diagnostics targets: Application to finding aptamer targets for methicillin-resistant staphylococci

Directory of Open Access Journals (Sweden)

Flanagan Keith

2014-06-01

Full Text Available The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.

A distributed computational search strategy for the identification of diagnostics targets: application to finding aptamer targets for methicillin-resistant staphylococci.

Science.gov (United States)

Flanagan, Keith; Cockell, Simon; Harwood, Colin; Hallinan, Jennifer; Nakjang, Sirintra; Lawry, Beth; Wipat, Anil

2014-06-30

The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.

Genome-scale model guided design of Propionibacterium for enhanced propionic acid production

Directory of Open Access Journals (Sweden)

Laura Navone

2018-06-01

Full Text Available Production of propionic acid by fermentation of propionibacteria has gained increasing attention in the past few years. However, biomanufacturing of propionic acid cannot compete with the current oxo-petrochemical synthesis process due to its well-established infrastructure, low oil prices and the high downstream purification costs of microbial production. Strain improvement to increase propionic acid yield is the best alternative to reduce downstream purification costs. The recent generation of genome-scale models for a number of Propionibacterium species facilitates the rational design of metabolic engineering strategies and provides a new opportunity to explore the metabolic potential of the Wood-Werkman cycle. Previous strategies for strain improvement have individually targeted acid tolerance, rate of propionate production or minimisation of by-products. Here we used the P. freudenreichii subsp. shermanii and the pan-Propionibacterium genome-scale metabolic models (GEMs to simultaneously target these combined issues. This was achieved by focussing on strategies which yield higher energies and directly suppress acetate formation. Using P. freudenreichii subsp. shermanii, two strategies were assessed. The first tested the ability to manipulate the redox balance to favour propionate production by over-expressing the first two enzymes of the pentose-phosphate pathway (PPP, Zwf (glucose-6-phosphate 1-dehydrogenase and Pgl (6-phosphogluconolactonase. Results showed a 4-fold increase in propionate to acetate ratio during the exponential growth phase. Secondly, the ability to enhance the energy yield from propionate production by over-expressing an ATP-dependent phosphoenolpyruvate carboxykinase (PEPCK and sodium-pumping methylmalonyl-CoA decarboxylase (MMD was tested, which extended the exponential growth phase. Together, these strategies demonstrate that in silico design strategies are predictive and can be used to reduce by-product formation in

A universal genomic coordinate translator for comparative genomics.

Science.gov (United States)

Zamani, Neda; Sundström, Görel; Meadows, Jennifer R S; Höppner, Marc P; Dainat, Jacques; Lantz, Henrik; Haas, Brian J; Grabherr, Manfred G

2014-06-30

Genomic duplications constitute major events in the evolution of species, allowing paralogous copies of genes to take on fine-tuned biological roles. Unambiguously identifying the orthology relationship between copies across multiple genomes can be resolved by synteny, i.e. the conserved order of genomic sequences. However, a comprehensive analysis of duplication events and their contributions to evolution would require all-to-all genome alignments, which increases at N2 with the number of available genomes, N. Here, we introduce Kraken, software that omits the all-to-all requirement by recursively traversing a graph of pairwise alignments and dynamically re-computing orthology. Kraken scales linearly with the number of targeted genomes, N, which allows for including large numbers of genomes in analyses. We first evaluated the method on the set of 12 Drosophila genomes, finding that orthologous correspondence computed indirectly through a graph of multiple synteny maps comes at minimal cost in terms of sensitivity, but reduces overall computational runtime by an order of magnitude. We then used the method on three well-annotated mammalian genomes, human, mouse, and rat, and show that up to 93% of protein coding transcripts have unambiguous pairwise orthologous relationships across the genomes. On a nucleotide level, 70 to 83% of exons match exactly at both splice junctions, and up to 97% on at least one junction. We last applied Kraken to an RNA-sequencing dataset from multiple vertebrates and diverse tissues, where we confirmed that brain-specific gene family members, i.e. one-to-many or many-to-many homologs, are more highly correlated across species than single-copy (i.e. one-to-one homologous) genes. Not limited to protein coding genes, Kraken also identifies thousands of newly identified transcribed loci, likely non-coding RNAs that are consistently transcribed in human, chimpanzee and gorilla, and maintain significant correlation of expression levels across

Savant Genome Browser 2: visualization and analysis for population-scale genomics.

Science.gov (United States)

Fiume, Marc; Smith, Eric J M; Brook, Andrew; Strbenac, Dario; Turner, Brian; Mezlini, Aziz M; Robinson, Mark D; Wodak, Shoshana J; Brudno, Michael

2012-07-01

High-throughput sequencing (HTS) technologies are providing an unprecedented capacity for data generation, and there is a corresponding need for efficient data exploration and analysis capabilities. Although most existing tools for HTS data analysis are developed for either automated (e.g. genotyping) or visualization (e.g. genome browsing) purposes, such tools are most powerful when combined. For example, integration of visualization and computation allows users to iteratively refine their analyses by updating computational parameters within the visual framework in real-time. Here we introduce the second version of the Savant Genome Browser, a standalone program for visual and computational analysis of HTS data. Savant substantially improves upon its predecessor and existing tools by introducing innovative visualization modes and navigation interfaces for several genomic datatypes, and synergizing visual and automated analyses in a way that is powerful yet easy even for non-expert users. We also present a number of plugins that were developed by the Savant Community, which demonstrate the power of integrating visual and automated analyses using Savant. The Savant Genome Browser is freely available (open source) at www.savantbrowser.com.

Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods

International Nuclear Information System (INIS)

Xia, Bing; Zhao, Xin; Callafon, Raymond de; Garnier, Hugues; Nguyen, Truong; Mi, Chris

2016-01-01

Highlights: • Continuous-time system identification is applied in Lithium-ion battery modeling. • Continuous-time and discrete-time identification methods are compared in detail. • The instrumental variable method is employed to further improve the estimation. • Simulations and experiments validate the advantages of continuous-time methods. - Abstract: The modeling of Lithium-ion batteries usually utilizes discrete-time system identification methods to estimate parameters of discrete models. However, in real applications, there is a fundamental limitation of the discrete-time methods in dealing with sensitivity when the system is stiff and the storage resolutions are limited. To overcome this problem, this paper adopts direct continuous-time system identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete-time system identification methods, the continuous-time system identification methods provide more accurate estimates to both fast and slow dynamics in battery systems and are less sensitive to disturbances. A case of a 2"n"d-order equivalent circuit model is studied which shows that the continuous-time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional continuous-time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the continuous-time system identification methods in battery applications.

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

Science.gov (United States)

Rzhetsky, A; Gomez, S M

2001-10-01

Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. ar345@columbia.edu.

The Sequenced Angiosperm Genomes and Genome Databases.

Science.gov (United States)

Chen, Fei; Dong, Wei; Zhang, Jiawei; Guo, Xinyue; Chen, Junhao; Wang, Zhengjia; Lin, Zhenguo; Tang, Haibao; Zhang, Liangsheng

2018-01-01

Angiosperms, the flowering plants, provide the essential resources for human life, such as food, energy, oxygen, and materials. They also promoted the evolution of human, animals, and the planet earth. Despite the numerous advances in genome reports or sequencing technologies, no review covers all the released angiosperm genomes and the genome databases for data sharing. Based on the rapid advances and innovations in the database reconstruction in the last few years, here we provide a comprehensive review for three major types of angiosperm genome databases, including databases for a single species, for a specific angiosperm clade, and for multiple angiosperm species. The scope, tools, and data of each type of databases and their features are concisely discussed. The genome databases for a single species or a clade of species are especially popular for specific group of researchers, while a timely-updated comprehensive database is more powerful for address of major scientific mysteries at the genome scale. Considering the low coverage of flowering plants in any available database, we propose construction of a comprehensive database to facilitate large-scale comparative studies of angiosperm genomes and to promote the collaborative studies of important questions in plant biology.

Draft Genome Sequence of Lactobacillus rhamnosus 2166.

OpenAIRE

Karlyshev, Andrey V.; Melnikov, Vyacheslav G.; Kosarev, Igor V.; Abramov, Vyacheslav M.

2014-01-01

In this report, we present a draft sequence of the genome of Lactobacillus rhamnosus strain 2166, a potential novel probiotic. Genome annotation and read mapping onto a reference genome of L. rhamnosus strain GG allowed for the identification of the differences and similarities in the genomic contents and gene arrangements of these strains.

Genomic treasure troves: complete genome sequencing of herbarium and insect museum specimens.

Science.gov (United States)

Staats, Martijn; Erkens, Roy H J; van de Vossenberg, Bart; Wieringa, Jan J; Kraaijeveld, Ken; Stielow, Benjamin; Geml, József; Richardson, James E; Bakker, Freek T

2013-01-01

Unlocking the vast genomic diversity stored in natural history collections would create unprecedented opportunities for genome-scale evolutionary, phylogenetic, domestication and population genomic studies. Many researchers have been discouraged from using historical specimens in molecular studies because of both generally limited success of DNA extraction and the challenges associated with PCR-amplifying highly degraded DNA. In today's next-generation sequencing (NGS) world, opportunities and prospects for historical DNA have changed dramatically, as most NGS methods are actually designed for taking short fragmented DNA molecules as templates. Here we show that using a standard multiplex and paired-end Illumina sequencing approach, genome-scale sequence data can be generated reliably from dry-preserved plant, fungal and insect specimens collected up to 115 years ago, and with minimal destructive sampling. Using a reference-based assembly approach, we were able to produce the entire nuclear genome of a 43-year-old Arabidopsis thaliana (Brassicaceae) herbarium specimen with high and uniform sequence coverage. Nuclear genome sequences of three fungal specimens of 22-82 years of age (Agaricus bisporus, Laccaria bicolor, Pleurotus ostreatus) were generated with 81.4-97.9% exome coverage. Complete organellar genome sequences were assembled for all specimens. Using de novo assembly we retrieved between 16.2-71.0% of coding sequence regions, and hence remain somewhat cautious about prospects for de novo genome assembly from historical specimens. Non-target sequence contaminations were observed in 2 of our insect museum specimens. We anticipate that future museum genomics projects will perhaps not generate entire genome sequences in all cases (our specimens contained relatively small and low-complexity genomes), but at least generating vital comparative genomic data for testing (phylo)genetic, demographic and genetic hypotheses, that become increasingly more horizontal

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.

Identification of genetic loci in Lactobacillus plantarum that modulate the immune response of dendritic cells using comparative genome hybridization.

Directory of Open Access Journals (Sweden)

Marjolein Meijerink

Full Text Available BACKGROUND: Probiotics can be used to stimulate or regulate epithelial and immune cells of the intestinal mucosa and generate beneficial mucosal immunomodulatory effects. Beneficial effects of specific strains of probiotics have been established in the treatment and prevention of various intestinal disorders, including allergic diseases and diarrhea. However, the precise molecular mechanisms and the strain-dependent factors involved are poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we aimed to identify gene loci in the model probiotic organism Lactobacillus plantarum WCFS1 that modulate the immune response of host dendritic cells. The amounts of IL-10 and IL-12 secreted by dendritic cells (DCs after stimulation with 42 individual L. plantarum strains were measured and correlated with the strain-specific genomic composition using comparative genome hybridisation and the Random Forest algorithm. This in silico "gene-trait matching" approach led to the identification of eight candidate genes in the L. plantarum genome that might modulate the DC cytokine response to L. plantarum. Six of these genes were involved in bacteriocin production or secretion, one encoded a bile salt hydrolase and one encoded a transcription regulator of which the exact function is unknown. Subsequently, gene deletions mutants were constructed in L. plantarum WCFS1 and compared to the wild-type strain in DC stimulation assays. All three bacteriocin mutants as well as the transcription regulator (lp_2991 had the predicted effect on cytokine production confirming their immunomodulatory effect on the DC response to L. plantarum. Transcriptome analysis and qPCR data showed that transcript level of gtcA3, which is predicted to be involved in glycosylation of cell wall teichoic acids, was substantially increased in the lp_2991 deletion mutant (44 and 29 fold respectively. CONCLUSION: Comparative genome hybridization led to the identification of gene loci in L

Approaches for Comparative Genomics in Aspergillus and Penicillium

DEFF Research Database (Denmark)

Rasmussen, Jane Lind Nybo; Theobald, Sebastian; Brandl, Julian

2016-01-01

and applicable for many types of studies. In this chapter, we provide an overview of the state-of-the-art of comparative genomics in these fungi, along with recommended methods. The chapter describes databases for fungal comparative genomics. Based on experience, we suggest strategies for multiple types...... of comparative genomics, ranging from analysis of single genes, over gene clusters and CaZymes to genome-scale comparative genomics. Furthermore, we have examined published comparative genomics papers to summarize the preferred bioinformatic methods and parameters for a given type of analysis, highly useful...... comparative genomics to the development in bacterial genomics, where the comparison of hundreds of genomes has been performed for a while....

Identification of neural outgrowth genes using genome-wide RNAi.

Directory of Open Access Journals (Sweden)

Katharine J Sepp

2008-07-01

Full Text Available While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new

ALF: a strategy for identification of unauthorized GMOs in complex mixtures by a GW-NGS method and dedicated bioinformatics analysis.

Science.gov (United States)

Košir, Alexandra Bogožalec; Arulandhu, Alfred J; Voorhuijzen, Marleen M; Xiao, Hongmei; Hagelaar, Rico; Staats, Martijn; Costessi, Adalberto; Žel, Jana; Kok, Esther J; Dijk, Jeroen P van

2017-10-26

The majority of feed products in industrialised countries contains materials derived from genetically modified organisms (GMOs). In parallel, the number of reports of unauthorised GMOs (UGMOs) is gradually increasing. There is a lack of specific detection methods for UGMOs, due to the absence of detailed sequence information and reference materials. In this research, an adapted genome walking approach was developed, called ALF: Amplification of Linearly-enriched Fragments. Coupling of ALF to NGS aims for simultaneous detection and identification of all GMOs, including UGMOs, in one sample, in a single analysis. The ALF approach was assessed on a mixture made of DNA extracts from four reference materials, in an uneven distribution, mimicking a real life situation. The complete insert and genomic flanking regions were known for three of the included GMO events, while for MON15985 only partial sequence information was available. Combined with a known organisation of elements, this GMO served as a model for a UGMO. We successfully identified sequences matching with this organisation of elements serving as proof of principle for ALF as new UGMO detection strategy. Additionally, this study provides a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known GM elements.

Comparison of System Identification Methods using Ambient Bridge Test Data

DEFF Research Database (Denmark)

Andersen, P.; Brincker, Rune; Peeters, B.

1999-01-01

In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method, the stocha......In this paper the performance of four different system identification methods is compared using operational data obtained from an ambient vibration test of the Swiss Z24 highway bridge. The four methods are the frequency domain based peak-picking methods, the polyreference LSCE method...

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

Science.gov (United States)

Xu, Kelin; Jin, Li; Xiong, Momiao

2017-05-18

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction

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.

A review of output-only structural mode identification literature employing blind source separation methods

Science.gov (United States)

Sadhu, A.; Narasimhan, S.; Antoni, J.

2017-09-01

Output-only modal identification has seen significant activity in recent years, especially in large-scale structures where controlled input force generation is often difficult to achieve. This has led to the development of new system identification methods which do not require controlled input. They often work satisfactorily if they satisfy some general assumptions - not overly restrictive - regarding the stochasticity of the input. Hundreds of papers covering a wide range of applications appear every year related to the extraction of modal properties from output measurement data in more than two dozen mechanical, aerospace and civil engineering journals. In little more than a decade, concepts of blind source separation (BSS) from the field of acoustic signal processing have been adopted by several researchers and shown that they can be attractive tools to undertake output-only modal identification. Originally intended to separate distinct audio sources from a mixture of recordings, mathematical equivalence to problems in linear structural dynamics have since been firmly established. This has enabled many of the developments in the field of BSS to be modified and applied to output-only modal identification problems. This paper reviews over hundred articles related to the application of BSS and their variants to output-only modal identification. The main contribution of the paper is to present a literature review of the papers which have appeared on the subject. While a brief treatment of the basic ideas are presented where relevant, a comprehensive and critical explanation of their contents is not attempted. Specific issues related to output-only modal identification and the relative advantages and limitations of BSS methods both from theoretical and application standpoints are discussed. Gap areas requiring additional work are also summarized and the paper concludes with possible future trends in this area.

Computational methods for protein identification from mass spectrometry data.

Directory of Open Access Journals (Sweden)

Leo McHugh

2008-02-01

Full Text Available Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.

GAAP: Genome-organization-framework-Assisted Assembly Pipeline for prokaryotic genomes.

Science.gov (United States)

Yuan, Lina; Yu, Yang; Zhu, Yanmin; Li, Yulai; Li, Changqing; Li, Rujiao; Ma, Qin; Siu, Gilman Kit-Hang; Yu, Jun; Jiang, Taijiao; Xiao, Jingfa; Kang, Yu

2017-01-25

Next-generation sequencing (NGS) technologies have greatly promoted the genomic study of prokaryotes. However, highly fragmented assemblies due to short reads from NGS are still a limiting factor in gaining insights into the genome biology. Reference-assisted tools are promising in genome assembly, but tend to result in false assembly when the assigned reference has extensive rearrangements. Herein, we present GAAP, a genome assembly pipeline for scaffolding based on core-gene-defined Genome Organizational Framework (cGOF) described in our previous study. Instead of assigning references, we use the multiple-reference-derived cGOFs as indexes to assist in order and orientation of the scaffolds and build a skeleton structure, and then use read pairs to extend scaffolds, called local scaffolding, and distinguish between true and chimeric adjacencies in the scaffolds. In our performance tests using both empirical and simulated data of 15 genomes in six species with diverse genome size, complexity, and all three categories of cGOFs, GAAP outcompetes or achieves comparable results when compared to three other reference-assisted programs, AlignGraph, Ragout and MeDuSa. GAAP uses both cGOF and pair-end reads to create assemblies in genomic scale, and performs better than the currently available reference-assisted assembly tools as it recovers more assemblies and makes fewer false locations, especially for species with extensive rearranged genomes. Our method is a promising solution for reconstruction of genome sequence from short reads of NGS.

Identification and authentication. Common biometric methods review