Apweiler, R; Gateau, A; Contrino, S; Martin, M J; Junker, V; O'Donovan, C; Lang, F; Mitaritonna, N; Kappus, S; Bairoch, A
SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation, a minimal level of redundancy and high level of integration with other databases. Ongoing genome sequencing projects have dramatically increased the number of protein sequences to be incorporated into SWISS-PROT. Since we do not want to dilute the quality standards of SWISS-PROT by incorporating sequences without proper sequence analysis and annotation, we cannot speed up the incorporation of new incoming data indefinitely. However, as we also want to make the sequences available as fast as possible, we introduced TREMBL (TRanslation of EMBL nucleotide sequence database), a supplement to SWISS-PROT. TREMBL consists of computer-annotated entries in SWISS-PROT format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except for CDS already included in SWISS-PROT. While TREMBL is already of immense value, its computer-generated annotation does not match the quality of SWISS-PROTs. The main difference is in the protein functional information attached to sequences. With this in mind, we are dedicating substantial effort to develop and apply computer methods to enhance the functional information attached to TREMBL entries.
Juncker, Agnieszka; Jensen, Lars J.; Pierleoni, Andrea
A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome....
Full Text Available Abstract With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1 searching local or remote biological databases for sequence similarities using Blast services, 2 predicting protein coding sequence from EST data and, 3 annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics.
Murvai, J; Vlahovicek, K; Barta, E; Pongor, S
SBASE 8.0 is the eighth release of the SBASE library of protein domain sequences that contains 294 898 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to most major sequence databases and sequence pattern collections. The entries are clustered into over 2005 statistically validated domain groups (SBASE-A) and 595 non-validated groups (SBASE-B), provided with several WWW-based search and browsing facilities for online use. A domain-search facility was developed, based on non-parametric pattern recognition methods, including artificial neural networks. SBASE 8.0 is freely available by anonymous 'ftp' file transfer from ftp.icgeb.trieste.it. Automated searching of SBASE can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/ and http://sbase.abc. hu/sbase/.
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.
Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.
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
Jaschob, Daniel; Davis, Trisha N; Riffle, Michael
Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke
Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http
Full Text Available List Contact us Gclust Server Amino acid sequences of predicted proteins and their annotation for 95 organis...m species. Data detail Data name Amino acid sequences of predicted proteins and their annotation for 95 orga...nism species. DOI 10.18908/lsdba.nbdc00464-001 Description of data contents Amino acid sequences of predicted proteins...Database Description Download License Update History of This Database Site Policy | Contact Us Amino acid sequences of predicted prot...eins and their annotation for 95 organism species. - Gclust Server | LSDB Archive ...
Rigoutsos, Isidore; Huynh, Tien; Floratos, Aris; Parida, Laxmi; Platt, Daniel
Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were
Ikram, Najmul; Qadir, Muhammad Abdul; Afzal, Muhammad Tanvir
Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic similarity methods, and in protein function prediction. In this research, we investigate the relationship between the two similarity methods. The results suggest absence of a strong correlation between sequence and semantic similarities. There is a large number of proteins with low sequence similarity and high semantic similarity. We observe that Pearson's correlation coefficient is not sufficient to explain the nature of this relationship. Interestingly, the term semantic similarity values above 0 and below 1 do not seem to play a role in improving the correlation. That is, the correlation coefficient depends only on the number of common GO terms in proteins under comparison, and the semantic similarity measurement method does not influence it. Semantic similarity and sequence similarity have a distinct behavior. These findings are of significant effect for future works on protein comparison, and will help understand the semantic similarity between proteins in a better way.
Natale, D A; Shankavaram, U T; Galperin, M Y; Wolf, Y I; Aravind, L; Koonin, E V
Standard archival sequence databases have not been designed as tools for genome annotation and are far from being optimal for this purpose. We used the database of Clusters of Orthologous Groups of proteins (COGs) to reannotate the genomes of two archaea, Aeropyrum pernix, the first member of the Crenarchaea to be sequenced, and Pyrococcus abyssi. A. pernix and P. abyssi proteins were assigned to COGs using the COGNITOR program; the results were verified on a case-by-case basis and augmented by additional database searches using the PSI-BLAST and TBLASTN programs. Functions were predicted for over 300 proteins from A. pernix, which could not be assigned a function using conventional methods with a conservative sequence similarity threshold, an approximately 50% increase compared to the original annotation. A. pernix shares most of the conserved core of proteins that were previously identified in the Euryarchaeota. Cluster analysis or distance matrix tree construction based on the co-occurrence of genomes in COGs showed that A. pernix forms a distinct group within the archaea, although grouping with the two species of Pyrococci, indicative of similar repertoires of conserved genes, was observed. No indication of a specific relationship between Crenarchaeota and eukaryotes was obtained in these analyses. Several proteins that are conserved in Euryarchaeota and most bacteria are unexpectedly missing in A. pernix, including the entire set of de novo purine biosynthesis enzymes, the GTPase FtsZ (a key component of the bacterial and euryarchaeal cell-division machinery), and the tRNA-specific pseudouridine synthase, previously considered universal. A. pernix is represented in 48 COGs that do not contain any euryarchaeal members. Many of these proteins are TCA cycle and electron transport chain enzymes, reflecting the aerobic lifestyle of A. pernix. Special-purpose databases organized on the basis of phylogenetic analysis and carefully curated with respect to known and
Gorbalenya Alexander E
Full Text Available Abstract Background A growing diversity of biological data is tagged with unique identifiers (UIDs associated with polynucleotides and proteins to ensure efficient computer-mediated data storage, maintenance, and processing. These identifiers, which are not informative for most people, are often substituted by biologically meaningful names in various presentations to facilitate utilization and dissemination of sequence-based knowledge. This substitution is commonly done manually that may be a tedious exercise prone to mistakes and omissions. Results Here we introduce SNAD (Sequence Name Annotation-based Designer that mediates automatic conversion of sequence UIDs (associated with multiple alignment or phylogenetic tree, or supplied as plain text list into biologically meaningful names and acronyms. This conversion is directed by precompiled or user-defined templates that exploit wealth of annotation available in cognate entries of external databases. Using examples, we demonstrate how this tool can be used to generate names for practical purposes, particularly in virology. Conclusion A tool for controllable annotation-based conversion of sequence UIDs into biologically meaningful names and acronyms has been developed and placed into service, fostering links between quality of sequence annotation, and efficiency of communication and knowledge dissemination among researchers.
Vujaklija, Ivan; Bielen, Ana; Paradžik, Tina; Biđin, Siniša; Goldstein, Pavle; Vujaklija, Dušica
The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. In this study, we describe an approach to decrease annotation errors of protein families characterized by low overall sequence similarity. The GDSL lipolytic family comprises proteins with multifunctional properties and high potential for pharmaceutical and industrial applications. The number of proteins assigned to this family has increased rapidly over the last few years. In particular, the natural abundance of GDSL enzymes reported recently in plants indicates that they could be a good source of novel GDSL enzymes. We noticed that a significant proportion of annotated sequences lack specific GDSL motif(s) or catalytic residue(s). Here, we applied motif-based sequence analyses to identify enzymes possessing conserved GDSL motifs in selected proteomes across the plant kingdom. Motif-based HMM scanning (Viterbi decoding-VD and posterior decoding-PD) and the here described PD/VD protocol were successfully applied on 12 selected plant proteomes to identify sequences with GDSL motifs. A significant number of identified GDSL sequences were novel. Moreover, our scanning approach successfully detected protein sequences lacking at least one of the essential motifs (171/820) annotated by Pfam profile search (PfamA) as GDSL. Based on these analyses we provide a curated list of GDSL enzymes from the selected plants. CLANS clustering and phylogenetic analysis helped us to gain a better insight into the evolutionary relationship of all identified GDSL sequences. Three novel GDSL subfamilies as well as unreported variations in GDSL motifs were discovered in this study. In addition, analyses of selected proteomes showed a remarkable expansion of GDSL enzymes in the lycophyte, Selaginella moellendorffii. Finally, we provide a general motif-HMM scanner which is easily accessible through
Wise, Michael J.
Protein Annotators' Assistant (PAA) is a software system which assists protein annotators in assigning functions to newly sequenced proteins. PAA employs a number of information retrieval techniques in a novel setting and is thus related to text categorization, where multiple categories may be suggested, except that in this case none of the…
Gille, Christoph; Fähling, Michael; Weyand, Birgit; Wieland, Thomas; Gille, Andreas
Alignment-Annotator is a novel web service designed to generate interactive views of annotated nucleotide and amino acid sequence alignments (i) de novo and (ii) embedded in other software. All computations are performed at server side. Interactivity is implemented in HTML5, a language native to web browsers. The alignment is initially displayed using default settings and can be modified with the graphical user interfaces. For example, individual sequences can be reordered or deleted using drag and drop, amino acid color code schemes can be applied and annotations can be added. Annotations can be made manually or imported (BioDAS servers, the UniProt, the Catalytic Site Atlas and the PDB). Some edits take immediate effect while others require server interaction and may take a few seconds to execute. The final alignment document can be downloaded as a zip-archive containing the HTML files. Because of the use of HTML the resulting interactive alignment can be viewed on any platform including Windows, Mac OS X, Linux, Android and iOS in any standard web browser. Importantly, no plugins nor Java are required and therefore Alignment-Anotator represents the first interactive browser-based alignment visualization. http://www.bioinformatics.org/strap/aa/ and http://strap.charite.de/aa/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Standley, Daron M; Toh, Hiroyuki; Nakamura, Haruki
A method to functionally annotate structural genomics targets, based on a novel structural alignment scoring function, is proposed. In the proposed score, position-specific scoring matrices are used to weight structurally aligned residue pairs to highlight evolutionarily conserved motifs. The functional form of the score is first optimized for discriminating domains belonging to the same Pfam family from domains belonging to different families but the same CATH or SCOP superfamily. In the optimization stage, we consider four standard weighting functions as well as our own, the "maximum substitution probability," and combinations of these functions. The optimized score achieves an area of 0.87 under the receiver-operating characteristic curve with respect to identifying Pfam families within a sequence-unique benchmark set of domain pairs. Confidence measures are then derived from the benchmark distribution of true-positive scores. The alignment method is next applied to the task of functionally annotating 230 query proteins released to the public as part of the Protein 3000 structural genomics project in Japan. Of these queries, 78 were found to align to templates with the same Pfam family as the query or had sequence identities > or = 30%. Another 49 queries were found to match more distantly related templates. Within this group, the template predicted by our method to be the closest functional relative was often not the most structurally similar. Several nontrivial cases are discussed in detail. Finally, 103 queries matched templates at the fold level, but not the family or superfamily level, and remain functionally uncharacterized. 2008 Wiley-Liss, Inc.
Brown Alfred L
Full Text Available Abstract Background Annotations that describe the function of sequences are enormously important to researchers during laboratory investigations and when making computational inferences. However, there has been little investigation into the data quality of sequence function annotations. Here we have developed a new method of estimating the error rate of curated sequence annotations, and applied this to the Gene Ontology (GO sequence database (GOSeqLite. This method involved artificially adding errors to sequence annotations at known rates, and used regression to model the impact on the precision of annotations based on BLAST matched sequences. Results We estimated the error rate of curated GO sequence annotations in the GOSeqLite database (March 2006 at between 28% and 30%. Annotations made without use of sequence similarity based methods (non-ISS had an estimated error rate of between 13% and 18%. Annotations made with the use of sequence similarity methodology (ISS had an estimated error rate of 49%. Conclusion While the overall error rate is reasonably low, it would be prudent to treat all ISS annotations with caution. Electronic annotators that use ISS annotations as the basis of predictions are likely to have higher false prediction rates, and for this reason designers of these systems should consider avoiding ISS annotations where possible. Electronic annotators that use ISS annotations to make predictions should be viewed sceptically. We recommend that curators thoroughly review ISS annotations before accepting them as valid. Overall, users of curated sequence annotations from the GO database should feel assured that they are using a comparatively high quality source of information.
Hestand, Matthew S.; Kalbfleisch, Theodore S.; Coleman, Stephen J.
Current gene annotation of the horse genome is largely derived from in silico predictions and cross-species alignments. Only a small number of genes are annotated based on equine EST and mRNA sequences. To expand the number of equine genes annotated from equine experimental evidence, we sequenced m...... and appear to be small errors in the equine reference genome, since they are also identified as homozygous variants by genomic DNA resequencing of the reference horse. Taken together, we provide a resource of equine mRNA structures and protein coding variants that will enhance equine and cross...
Full Text Available Hypothetical Proteins are the proteins that are predicted to be expressed from an open reading frame (ORF, constituting a substantial fraction of proteomes in both prokaryotes and eukaryotes. Genome projects have led to the identification of many therapeutic targets, the putative function of the protein and their interactions. In this review we have enlisted various methods. Annotation linked to structural and functional prediction of hypothetical proteins assist in the discovery of new structures and functions serving as markers and pharmacological targets for drug designing, discovery and screening. Mass spectrometry is an analytical technique for validating protein characterisation. Matrix-assisted laser desorption ionization–mass spectrometry (MALDI-MS is an efficient analytical method. Microarrays and Protein expression profiles help understanding the biological systems through a systems-wide study of proteins and their interactions with other proteins and non-proteinaceous molecules to control complex processes in cells and tissues and even whole organism. Next generation sequencing technology accelerates multiple areas of genomics research.
Folta Kevin M
Full Text Available Abstract Background Availability of DNA sequence information is vital for pursuing structural, functional and comparative genomics studies in plastids. Traditionally, the first step in mining the valuable information within a chloroplast genome requires sequencing a chloroplast plasmid library or BAC clones. These activities involve complicated preparatory procedures like chloroplast DNA isolation or identification of the appropriate BAC clones to be sequenced. Rolling circle amplification (RCA is being used currently to amplify the chloroplast genome from purified chloroplast DNA and the resulting products are sheared and cloned prior to sequencing. Herein we present a universal high-throughput, rapid PCR-based technique to amplify, sequence and assemble plastid genome sequence from diverse species in a short time and at reasonable cost from total plant DNA, using the large inverted repeat region from strawberry and peach as proof of concept. The method exploits the highly conserved coding regions or intergenic regions of plastid genes. Using an informatics approach, chloroplast DNA sequence information from 5 available eudicot plastomes was aligned to identify the most conserved regions. Cognate primer pairs were then designed to generate ~1 – 1.2 kb overlapping amplicons from the inverted repeat region in 14 diverse genera. Results 100% coverage of the inverted repeat region was obtained from Arabidopsis, tobacco, orange, strawberry, peach, lettuce, tomato and Amaranthus. Over 80% coverage was obtained from distant species, including Ginkgo, loblolly pine and Equisetum. Sequence from the inverted repeat region of strawberry and peach plastome was obtained, annotated and analyzed. Additionally, a polymorphic region identified from gel electrophoresis was sequenced from tomato and Amaranthus. Sequence analysis revealed large deletions in these species relative to tobacco plastome thus exhibiting the utility of this method for structural and
Friedberg, Iddo; Harder, Tim; Godzik, Adam
Annotations, or JAFA server. JAFA queries several function prediction servers with a protein sequence and assembles the returned predictions in a legible, non-redundant format. In this manner, JAFA combines the predictions of several servers to provide a comprehensive view of what are the predicted functions...
Huang, Daisie I; Cronk, Quentin C B
Plann automates the process of annotating a plastome sequence in GenBank format for either downstream processing or for GenBank submission by annotating a new plastome based on a similar, well-annotated plastome. Plann is a Perl script to be executed on the command line. Plann compares a new plastome sequence to the features annotated in a reference plastome and then shifts the intervals of any matching features to the locations in the new plastome. Plann's output can be used in the National Center for Biotechnology Information's tbl2asn to create a Sequin file for GenBank submission. Unlike Web-based annotation packages, Plann is a locally executable script that will accurately annotate a plastome sequence to a locally specified reference plastome. Because it executes from the command line, it is ready to use in other software pipelines and can be easily rerun as a draft plastome is improved.
Full Text Available Abstract Background Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. Results This paperpresents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifsis viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association isfound to be a very useful feature. We take advantageof the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correctassociation. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. Conclusions In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about thefunctions of newly discovered candidate protein motifs.
Genomic sequence data are often available well before the annotated sequence is published. We present a method for analysis of genomic DNA to identify coding sequences using the GeneScan algorithm and characterize these resultant sequences by BLAST. The routines are used to develop a system for automated ...
Mewes, H W; Amid, C; Arnold, R; Frishman, D; Güldener, U; Mannhaupt, G; Münsterkötter, M; Pagel, P; Strack, N; Stümpflen, V; Warfsmann, J; Ruepp, A
The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein-protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).
Profiti, Giuseppe; Martelli, Pier Luigi; Casadio, Rita
BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Li, Tao; Li, Qian-Zhong
RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.
Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Pareja, Eduardo; Tobes, Raquel
BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version – which is developed in Java, takes advantage of Amazon Web Services (AWS) cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future. PMID:23185310
Full Text Available BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version - which is developed in Java, takes advantage of Amazon Web Services (AWS cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future.
Sveinbjornsson, Gardar; Albrechtsen, Anders; Zink, Florian
The consensus approach to genome-wide association studies (GWAS) has been to assign equal prior probability of association to all sequence variants tested. However, some sequence variants, such as loss-of-function and missense variants, are more likely than others to affect protein function...... for the family-wise error rate (FWER), using as weights the enrichment of sequence annotations among association signals. We show that this weighted adjustment increases the power to detect association over the standard Bonferroni correction. We use the enrichment of associations by sequence annotation we have...
Faulon, Jean-Loup Michel; Heffelfinger, Grant S.
A novel experimental and computational technique based on multiple enzymatic digestion of a protein or protein mixture that reconstructs protein sequences from sequences of overlapping peptides is described in this SAND report. This approach, analogous to shotgun sequencing of DNA, is to be used to sequence alternative spliced proteins, to identify post-translational modifications, and to sequence genetically engineered proteins.
Md. Saiful Islam
Full Text Available In developing countries threat of cholera is a significant health concern whenever water purification and sewage disposal systems are inadequate. Vibrio cholerae is one of the responsible bacteria involved in cholera disease. The complete genome sequence of V. cholerae deciphers the presence of various genes and hypothetical proteins whose function are not yet understood. Hence analyzing and annotating the structure and function of hypothetical proteins is important for understanding the V. cholerae. V. cholerae O139 is the most common and pathogenic bacterial strain among various V. cholerae strains. In this study sequence of six hypothetical proteins of V. cholerae O139 has been annotated from NCBI. Various computational tools and databases have been used to determine domain family, protein-protein interaction, solubility of protein, ligand binding sites etc. The three dimensional structure of two proteins were modeled and their ligand binding sites were identified. We have found domains and families of only one protein. The analysis revealed that these proteins might have antibiotic resistance activity, DNA breaking-rejoining activity, integrase enzyme activity, restriction endonuclease, etc. Structural prediction of these proteins and detection of binding sites from this study would indicate a potential target aiding docking studies for therapeutic designing against cholera.
Daub, Jennifer; Eberhardt, Ruth Y; Tate, John G; Burge, Sarah W
The primary task of the Rfam database is to collate experimentally validated noncoding RNA (ncRNA) sequences from the published literature and facilitate the prediction and annotation of new homologues in novel nucleotide sequences. We group homologous ncRNA sequences into "families" and related families are further grouped into "clans." We collate and manually curate data cross-references for these families from other databases and external resources. Our Web site offers researchers a simple interface to Rfam and provides tools with which to annotate their own sequences using our covariance models (CMs), through our tools for searching, browsing, and downloading information on Rfam families. In this chapter, we will work through examples of annotating a query sequence, collating family information, and searching for data.
Bromberg, Yana; Yachdav, Guy; Ofran, Yanay; Schneider, Reinhard; Rost, Burkhard
The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.
Bolleman, Jerven T; Mungall, Christopher J; Strozzi, Francesco; Baran, Joachim; Dumontier, Michel; Bonnal, Raoul J P; Buels, Robert; Hoehndorf, Robert; Fujisawa, Takatomo; Katayama, Toshiaki; Cock, Peter J A
Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.
Ueno, Yutaka; Arita, Masanori; Kumagai, Toshitaka; Asai, Kiyoshi
The data processing language in a graphical software tool that manages sequence annotation data from genome databases should provide flexible functions for the tasks in molecular biology research. Among currently available languages we adopted the Lua programming language. It fulfills our requirements to perform computational tasks for sequence map layouts, i.e. the handling of data containers, symbolic reference to data, and a simple programming syntax. Upon importing a foreign file, the original data are first decomposed in the Lua language while maintaining the original data schema. The converted data are parsed by the Lua interpreter and the contents are stored in our data warehouse. Then, portions of annotations are selected and arranged into our catalog format to be depicted on the sequence map. Our sequence visualization program was successfully implemented, embedding the Lua language for processing of annotation data and layout script. The program is available at http://staff.aist.go.jp/yutaka.ueno/guppy/.
Boffelli, Dario; Weer, Claire V.; Weng, Li; Lewis, Keith D.; Shoukry, Malak I.; Pachter, Lior; Keys, David N.; Rubin, Edward M.
Analysis of sequence variation among members of a single species offers a potential approach to identify functional DNA elements responsible for biological features unique to that species. Due to its high rate of allelic polymorphism and ease of genetic manipulability, we chose the sea squirt, Ciona intestinalis, to explore intra-species sequence comparisons for genome annotation. A large number of C. intestinalis specimens were collected from four continents and a set of genomic intervals amplified, resequenced and analyzed to determine the mutation rates at each nucleotide in the sequence. We found that regions with low mutation rates efficiently demarcated functionally constrained sequences: these include a set of noncoding elements, which we showed in C intestinalis transgenic assays to act as tissue-specific enhancers, as well as the location of coding sequences. This illustrates that comparisons of multiple members of a species can be used for genome annotation, suggesting a path for the annotation of the sequenced genomes of organisms occupying uncharacterized phylogenetic branches of the animal kingdom and raises the possibility that the resequencing of a large number of Homo sapiens individuals might be used to annotate the human genome and identify sequences defining traits unique to our species. The sequence data from this study has been submitted to GenBank under accession nos. AY667278-AY667407.
Full Text Available Transposable elements (TEs are mobile, repetitive sequences that make up significant fractions of metazoan genomes. Despite their near ubiquity and importance in genome and chromosome biology, most efforts to annotate TEs in genome sequences rely on the results of a single computational program, RepeatMasker. In contrast, recent advances in gene annotation indicate that high-quality gene models can be produced from combining multiple independent sources of computational evidence. To elevate the quality of TE annotations to a level comparable to that of gene models, we have developed a combined evidence-model TE annotation pipeline, analogous to systems used for gene annotation, by integrating results from multiple homology-based and de novo TE identification methods. As proof of principle, we have annotated "TE models" in Drosophila melanogaster Release 4 genomic sequences using the combined computational evidence derived from RepeatMasker, BLASTER, TBLASTX, all-by-all BLASTN, RECON, TE-HMM and the previous Release 3.1 annotation. Our system is designed for use with the Apollo genome annotation tool, allowing automatic results to be curated manually to produce reliable annotations. The euchromatic TE fraction of D. melanogaster is now estimated at 5.3% (cf. 3.86% in Release 3.1, and we found a substantially higher number of TEs (n = 6,013 than previously identified (n = 1,572. Most of the new TEs derive from small fragments of a few hundred nucleotides long and highly abundant families not previously annotated (e.g., INE-1. We also estimated that 518 TE copies (8.6% are inserted into at least one other TE, forming a nest of elements. The pipeline allows rapid and thorough annotation of even the most complex TE models, including highly deleted and/or nested elements such as those often found in heterochromatic sequences. Our pipeline can be easily adapted to other genome sequences, such as those of the D. melanogaster heterochromatin or other
McCarthy Fiona M
Full Text Available Abstract Background The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned. Results We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology, we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines. Conclusion We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and
Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.
We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...
Li, Minghui; Goncearenco, Alexander; Panchenko, Anna R
In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.
Singh, Harinder; Raghava, Gajendra P S
In the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search. A web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein. In summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb .
Schneider, Michel; Bairoch, Amos; Wu, Cathy H.; Apweiler, Rolf
The Swiss-Prot, TrEMBL, Protein Information Resource (PIR), and DNA Data Bank of Japan (DDBJ) protein database activities have united to form the Universal Protein Resource (UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase (UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/Swiss-Prot (fully manually curated entries) and UniProtKB/TrEMBL (automated annotation, classification and extensive cross-references). New releases are published fortnightly. A specific Plant Proteome Annotation Program (http://www.expasy.org/sprot/ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and nonplant model organisms. PMID:15888679
Schneider, Michel; Bairoch, Amos; Wu, Cathy H; Apweiler, Rolf
The Swiss-Prot, TrEMBL, Protein Information Resource (PIR), and DNA Data Bank of Japan (DDBJ) protein database activities have united to form the Universal Protein Resource (UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase (UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/Swiss-Prot (fully manually curated entries) and UniProtKB/TrEMBL (automated annotation, classification and extensive cross-references). New releases are published fortnightly. A specific Plant Proteome Annotation Program (http://www.expasy.org/sprot/ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and non-plant model organisms.
Lephoto, Tiisetso E; Gray, Vincent M
We present the annotation of the draft genome sequence of Serratia sp. strain TEL (GenBank accession number KP711410). This organism was isolated from entomopathogenic nematode Oscheius sp. strain TEL (GenBank accession number KM492926) collected from grassland soil and has a genome size of 5,000,541 bp and 542 subsystems. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession number LDEG00000000.
Tiisetso E. Lephoto
Full Text Available We present the annotation of the draft genome sequence of Serratia sp. strain TEL (GenBank accession number KP711410. This organism was isolated from entomopathogenic nematode Oscheius sp. strain TEL (GenBank accession number KM492926 collected from grassland soil and has a genome size of 5,000,541 bp and 542 subsystems. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession number LDEG00000000.
Lephoto, Tiisetso E.; Gray, Vincent M.
We present the annotation of the draft genome sequence of Serratia sp. strain TEL (GenBank accession number KP711410). This organism was isolated from entomopathogenic nematode Oscheius sp. strain TEL (GenBank accession number KM492926) collected from grassland soil and has a genome size of 5,000,541 bp and 542 subsystems. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession number LDEG00000000.
Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J
We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.
Full Text Available The automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database Ara- Cyc which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation.
Pesch, Robert; Lysenko, Artem; Hindle, Matthew; Hassani-Pak, Keywan; Thiele, Ralf; Rawlings, Christopher; Köhler, Jacob; Taubert, Jan
The automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database Ara-Cyc) which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation. The methods and algorithms presented in this publication are an integral part of the ONDEX system which is freely available from http://ondex.sf.net/.
Spraggins Thomas A
Full Text Available Abstract Background Cowpea [Vigna unguiculata (L. Walp.] is one of the most important food and forage legumes in the semi-arid tropics because of its ability to tolerate drought and grow on poor soils. It is cultivated mostly by poor farmers in developing countries, with 80% of production taking place in the dry savannah of tropical West and Central Africa. Cowpea is largely an underexploited crop with relatively little genomic information available for use in applied plant breeding. The goal of the Cowpea Genomics Initiative (CGI, funded by the Kirkhouse Trust, a UK-based charitable organization, is to leverage modern molecular genetic tools for gene discovery and cowpea improvement. One aspect of the initiative is the sequencing of the gene-rich region of the cowpea genome (termed the genespace recovered using methylation filtration technology and providing annotation and analysis of the sequence data. Description CGKB, Cowpea Genespace/Genomics Knowledge Base, is an annotation knowledge base developed under the CGI. The database is based on information derived from 298,848 cowpea genespace sequences (GSS isolated by methylation filtering of genomic DNA. The CGKB consists of three knowledge bases: GSS annotation and comparative genomics knowledge base, GSS enzyme and metabolic pathway knowledge base, and GSS simple sequence repeats (SSRs knowledge base for molecular marker discovery. A homology-based approach was applied for annotations of the GSS, mainly using BLASTX against four public FASTA formatted protein databases (NCBI GenBank Proteins, UniProtKB-Swiss-Prot, UniprotKB-PIR (Protein Information Resource, and UniProtKB-TrEMBL. Comparative genome analysis was done by BLASTX searches of the cowpea GSS against four plant proteomes from Arabidopsis thaliana, Oryza sativa, Medicago truncatula, and Populus trichocarpa. The possible exons and introns on each cowpea GSS were predicted using the HMM-based Genscan gene predication program and the
Hou, Qingzhen; De Geest, Paul F.G.; Vranken, Wim F.; Heringa, Jaap; Feenstra, K. Anton
Motivation: Genome sequencing is producing an ever-increasing amount of associated protein sequences. Few of these sequences have experimentally validated annotations, however, and computational predictions are becoming increasingly successful in producing such annotations. One key challenge remains
Du, Yushen; Wu, Nicholas C; Jiang, Lin; Zhang, Tianhao; Gong, Danyang; Shu, Sara; Wu, Ting-Ting; Sun, Ren
Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available. To fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is
Full Text Available Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp, we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.
Background The complete sequences of chloroplast genomes provide wealthy information regarding the evolutionary history of species. With the advance of next-generation sequencing technology, the number of completely sequenced chloroplast genomes is expected to increase exponentially, powerful computational tools annotating the genome sequences are in urgent need. Results We have developed a web server CPGAVAS. The server accepts a complete chloroplast genome sequence as input. First, it predicts protein-coding and rRNA genes based on the identification and mapping of the most similar, full-length protein, cDNA and rRNA sequences by integrating results from Blastx, Blastn, protein2genome and est2genome programs. Second, tRNA genes and inverted repeats (IR) are identified using tRNAscan, ARAGORN and vmatch respectively. Third, it calculates the summary statistics for the annotated genome. Fourth, it generates a circular map ready for publication. Fifth, it can create a Sequin file for GenBank submission. Last, it allows the extractions of protein and mRNA sequences for given list of genes and species. The annotation results in GFF3 format can be edited using any compatible annotation editing tools. The edited annotations can then be uploaded to CPGAVAS for update and re-analyses repeatedly. Using known chloroplast genome sequences as test set, we show that CPGAVAS performs comparably to another application DOGMA, while having several superior functionalities. Conclusions CPGAVAS allows the semi-automatic and complete annotation of a chloroplast genome sequence, and the visualization, editing and analysis of the annotation results. It will become an indispensible tool for researchers studying chloroplast genomes. The software is freely accessible from http://www.herbalgenomics.org/cpgavas. PMID:23256920
Full Text Available Abstract Background The complete sequences of chloroplast genomes provide wealthy information regarding the evolutionary history of species. With the advance of next-generation sequencing technology, the number of completely sequenced chloroplast genomes is expected to increase exponentially, powerful computational tools annotating the genome sequences are in urgent need. Results We have developed a web server CPGAVAS. The server accepts a complete chloroplast genome sequence as input. First, it predicts protein-coding and rRNA genes based on the identification and mapping of the most similar, full-length protein, cDNA and rRNA sequences by integrating results from Blastx, Blastn, protein2genome and est2genome programs. Second, tRNA genes and inverted repeats (IR are identified using tRNAscan, ARAGORN and vmatch respectively. Third, it calculates the summary statistics for the annotated genome. Fourth, it generates a circular map ready for publication. Fifth, it can create a Sequin file for GenBank submission. Last, it allows the extractions of protein and mRNA sequences for given list of genes and species. The annotation results in GFF3 format can be edited using any compatible annotation editing tools. The edited annotations can then be uploaded to CPGAVAS for update and re-analyses repeatedly. Using known chloroplast genome sequences as test set, we show that CPGAVAS performs comparably to another application DOGMA, while having several superior functionalities. Conclusions CPGAVAS allows the semi-automatic and complete annotation of a chloroplast genome sequence, and the visualization, editing and analysis of the annotation results. It will become an indispensible tool for researchers studying chloroplast genomes. The software is freely accessible from http://www.herbalgenomics.org/cpgavas.
Tedersoo, Leho; Abarenkov, Kessy; Nilsson, R Henrik; Schüssler, Arthur; Grelet, Gwen-Aëlle; Kohout, Petr; Oja, Jane; Bonito, Gregory M; Veldre, Vilmar; Jairus, Teele; Ryberg, Martin; Larsson, Karl-Henrik; Kõljalg, Urmas
Sequence analysis of the ribosomal RNA operon, particularly the internal transcribed spacer (ITS) region, provides a powerful tool for identification of mycorrhizal fungi. The sequence data deposited in the International Nucleotide Sequence Databases (INSD) are, however, unfiltered for quality and are often poorly annotated with metadata. To detect chimeric and low-quality sequences and assign the ectomycorrhizal fungi to phylogenetic lineages, fungal ITS sequences were downloaded from INSD, aligned within family-level groups, and examined through phylogenetic analyses and BLAST searches. By combining the fungal sequence database UNITE and the annotation and search tool PlutoF, we also added metadata from the literature to these accessions. Altogether 35,632 sequences belonged to mycorrhizal fungi or originated from ericoid and orchid mycorrhizal roots. Of these sequences, 677 were considered chimeric and 2,174 of low read quality. Information detailing country of collection, geographical coordinates, interacting taxon and isolation source were supplemented to cover 78.0%, 33.0%, 41.7% and 96.4% of the sequences, respectively. These annotated sequences are publicly available via UNITE (http://unite.ut.ee/) for downstream biogeographic, ecological and taxonomic analyses. In European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena/), the annotated sequences have a special link-out to UNITE. We intend to expand the data annotation to additional genes and all taxonomic groups and functional guilds of fungi.
Huo, Tong; Zhang, Yinjie; Lin, Jianping
The giant panda is one of the most critically endangered species due to the fragmentation and loss of its habitat. Studying the functions of proteins in this animal, especially specific trait-related proteins, is therefore necessary to protect the species. In this work, the functions of these proteins were investigated using the genome sequence of the giant panda. Data on 21,001 proteins and their functions were stored in the Giant Panda Protein Database, in which the proteins were divided in...
Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick
Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.
Bairoch, Amos; Boeckmann, Brigitte
SWISS-PROT is an annotated protein sequence database established in 1986 and maintained collaboratively, since 1988, by the Department of Medical Biochemistry of the University of Geneva and the EMBL Data Library
Alexandra M Schnoes
Full Text Available The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the "few articles - many proteins" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.
Jex, Aaron R; Littlewood, D Timothy; Gasser, Robin B
Mitochondrial (mt) genomics has significant implications in a range of fundamental areas of parasitology, including evolution, systematics, and population genetics as well as explorations of mt biochemistry, physiology, and function. Mt genomes also provide a rich source of markers to aid molecular epidemiological and ecological studies of key parasites. However, there is still a paucity of information on mt genomes for many metazoan organisms, particularly parasitic helminths, which has often related to challenges linked to sequencing from tiny amounts of material. The advent of next-generation sequencing (NGS) technologies has paved the way for low cost, high-throughput mt genomic research, but there have been obstacles, particularly in relation to post-sequencing assembly and analyses of large datasets. In this chapter, we describe protocols for the efficient amplification and sequencing of mt genomes from small portions of individual helminths, and highlight the utility of NGS platforms to expedite mt genomics. In addition, we recommend approaches for manual or semi-automated bioinformatic annotation and analyses to overcome the bioinformatic "bottleneck" to research in this area. Taken together, these approaches have demonstrated applicability to a range of parasites and provide prospects for using complete mt genomic sequence datasets for large-scale molecular systematic and epidemiological studies. In addition, these methods have broader utility and might be readily adapted to a range of other medium-sized molecular regions (i.e., 10-100 kb), including large genomic operons, and other organellar (e.g., plastid) and viral genomes.
Schnoes, Alexandra M.; Ream, David C.; Thorman, Alexander W.; Babbitt, Patricia C.; Friedberg, Iddo
The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. PMID:23737737
Huo, Tong; Zhang, Yinjie; Lin, Jianping
The giant panda is one of the most critically endangered species due to the fragmentation and loss of its habitat. Studying the functions of proteins in this animal, especially specific trait-related proteins, is therefore necessary to protect the species. In this work, the functions of these proteins were investigated using the genome sequence of the giant panda. Data on 21,001 proteins and their functions were stored in the Giant Panda Protein Database, in which the proteins were divided into two groups: 20,179 proteins whose functions can be predicted by GeneScan formed the known-function group, whereas 822 proteins whose functions cannot be predicted by GeneScan comprised the unknown-function group. For the known-function group, we further classified the proteins by molecular function, biological process, cellular component, and tissue specificity. For the unknown-function group, we developed a strategy in which the proteins were filtered by cross-Blast to identify panda-specific proteins under the assumption that proteins related to the panda-specific traits in the unknown-function group exist. After this filtering procedure, we identified 32 proteins (2 of which are membrane proteins) specific to the giant panda genome as compared against the dog and horse genomes. Based on their amino acid sequences, these 32 proteins were further analyzed by functional classification using SVM-Prot, motif prediction using MyHits, and interacting protein prediction using the Database of Interacting Proteins. Nineteen proteins were predicted to be zinc-binding proteins, thus affecting the activities of nucleic acids. The 32 panda-specific proteins will be further investigated by structural and functional analysis.
Bairoch, A; Boeckmann, B
SWISS-PROT is an annotated protein sequence database established in 1986 and maintained collaboratively, since 1988, by the Department of Medical Biochemistry of the University of Geneva and the EMBL Data Library. The SWISS-PROT protein sequence data bank consist of sequence entries. Sequence entries are composed of different lines types, each with their own format. For standardization purposes the format of SWISS-PROT follows as closely as possible that of the EMBL Nucleotide Sequence Databa...
Zhidkov, Ilia; Nagar, Tal; Mishmar, Dan; Rubin, Eitan
The use of Next-Generation Sequencing of mitochondrial DNA is becoming widespread in biological and clinical research. This, in turn, creates a need for a convenient tool that detects and analyzes heteroplasmy. Here we present MitoBamAnnotator, a user friendly web-based tool that allows maximum flexibility and control in heteroplasmy research. MitoBamAnnotator provides the user with a comprehensively annotated overview of mitochondrial genetic variation, allowing for an in-depth analysis with no prior knowledge in programming. Copyright Â© 2011 Elsevier B.V. and Mitochondria Research Society. All rights reserved. All rights reserved.
Full Text Available Helicobacter pylori is a pathogenic bacterium that colonizes the human epithelia, causing duodenal and gastric ulcers, and gastric cancer. The genome of H. pylori 26695 has been previously sequenced and annotated. In addition, two genome-scale metabolic models have been developed. In order to maintain accurate and relevant information on coding sequences (CDS and to retrieve new information, the assignment of new functions to Helicobacter pylori 26695s genes was performed in this work. The use of software tools, on-line databases and an annotation pipeline for inspecting each gene allowed the attribution of validated EC numbers and TC numbers to metabolic genes encoding enzymes and transport proteins, respectively. 1212 genes encoding proteins were identified in this annotation, being 712 metabolic genes and 500 non-metabolic, while 191 new functions were assignment to the CDS of this bacterium. This information provides relevant biological information for the scientific community dealing with this organism and can be used as the basis for a new metabolic model reconstruction.
Full Text Available A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.
Kolář, Michal; Lassig, M.; Berg, J.
Roč. 2, č. 90 (2008), e-e ISSN 1752-0509 Institutional research plan: CEZ:AV0Z50520514 Keywords : graph alignment * functional annotation * protein orthology Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.706, year: 2008
Full Text Available Abstract Background Campylobacter jejuni is the leading bacterial cause of human gastroenteritis in the developed world. To improve our understanding of this important human pathogen, the C. jejuni NCTC11168 genome was sequenced and published in 2000. The original annotation was a milestone in Campylobacter research, but is outdated. We now describe the complete re-annotation and re-analysis of the C. jejuni NCTC11168 genome using current database information, novel tools and annotation techniques not used during the original annotation. Results Re-annotation was carried out using sequence database searches such as FASTA, along with programs such as TMHMM for additional support. The re-annotation also utilises sequence data from additional Campylobacter strains and species not available during the original annotation. Re-annotation was accompanied by a full literature search that was incorporated into the updated EMBL file [EMBL: AL111168]. The C. jejuni NCTC11168 re-annotation reduced the total number of coding sequences from 1654 to 1643, of which 90.0% have additional information regarding the identification of new motifs and/or relevant literature. Re-annotation has led to 18.2% of coding sequence product functions being revised. Conclusions Major updates were made to genes involved in the biosynthesis of important surface structures such as lipooligosaccharide, capsule and both O- and N-linked glycosylation. This re-annotation will be a key resource for Campylobacter research and will also provide a prototype for the re-annotation and re-interpretation of other bacterial genomes.
Yang, Xiaohan; Tschaplinski, Timothy J.; Hurst, Gregory B.; Jawdy, Sara; Abraham, Paul E.; Lankford, Patricia K.; Adams, Rachel M.; Shah, Manesh B.; Hettich, Robert L.; Lindquist, Erika; Kalluri, Udaya C.; Gunter, Lee E.; Pennacchio, Christa; Tuskan, Gerald A.
Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.
Benkman Craig W
Full Text Available Abstract Background Massively parallel sequencing of cDNA is now an efficient route for generating enormous sequence collections that represent expressed genes. This approach provides a valuable starting point for characterizing functional genetic variation in non-model organisms, especially where whole genome sequencing efforts are currently cost and time prohibitive. The large and complex genomes of pines (Pinus spp. have hindered the development of genomic resources, despite the ecological and economical importance of the group. While most genomic studies have focused on a single species (P. taeda, genomic level resources for other pines are insufficiently developed to facilitate ecological genomic research. Lodgepole pine (P. contorta is an ecologically important foundation species of montane forest ecosystems and exhibits substantial adaptive variation across its range in western North America. Here we describe a sequencing study of expressed genes from P. contorta, including their assembly and annotation, and their potential for molecular marker development to support population and association genetic studies. Results We obtained 586,732 sequencing reads from a 454 GS XLR70 Titanium pyrosequencer (mean length: 306 base pairs. A combination of reference-based and de novo assemblies yielded 63,657 contigs, with 239,793 reads remaining as singletons. Based on sequence similarity with known proteins, these sequences represent approximately 17,000 unique genes, many of which are well covered by contig sequences. This sequence collection also included a surprisingly large number of retrotransposon sequences, suggesting that they are highly transcriptionally active in the tissues we sampled. We located and characterized thousands of simple sequence repeats and single nucleotide polymorphisms as potential molecular markers in our assembled and annotated sequences. High quality PCR primers were designed for a substantial number of the SSR loci
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.1278 >orf19.1278; Contig19-10104; complement(13162...4..>132028); ; conserved hypothetical protein; truncated protein IQNNKCSGCNLKLDFPVIHFKCKHSFHQKCLSTNLIATSTESS
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.4711 >orf19.4711; Contig19-10212; complement(29836...7..>300616); ; acidic repetitive protein; truncated protein DRSDYNEEDNNDFTRKLNEIQSKESNHEDLAQSEVQEGQKDEPDSVNQ
of alternative start codons. ( ii) Integration of sequences feature annotation - in particular, native support for working with files containing intron/ exon structure annotation. The software is available for both download and online use at http://www.cbs.dtu.dk/services/VirtualRibosome/....
Horn, Fabian; Habel, Andreas; Scharf, Daniel H.; Dworschak, Jan; Brakhage, Axel A.; Guthke, Reinhard; Hertweck, Christian; Linde, J?rg
Verticillium hemipterigenum (anamorph Torrubiella hemipterigena) is an entomopathogenic fungus and produces a broad range of secondary metabolites. Here, we present the draft genome sequence of the fungus, including gene structure and functional annotation. Genes were predicted incorporating RNA-Seq data and functionally annotated to provide the basis for further genome studies.
Full Text Available Liaoning cashmere goat is a famous goat breed for cashmere wool. In order to increase the transcriptome data and accelerate genetic improvement for this breed, we performed de novo transcriptome sequencing to generate the first expressed sequence tag dataset for the Liaoning cashmere goat, using next-generation sequencing technology.Transcriptome sequencing of Liaoning cashmere goat on a Roche 454 platform yielded 804,601 high-quality reads. Clustering and assembly of these reads produced a non-redundant set of 117,854 unigenes, comprising 13,194 isotigs and 104,660 singletons. Based on similarity searches with known proteins, 17,356 unigenes were assigned to 6,700 GO categories, and the terms were summarized into three main GO categories and 59 sub-categories. 3,548 and 46,778 unigenes had significant similarity to existing sequences in the KEGG and COG databases, respectively. Comparative analysis revealed that 42,254 unigenes were aligned to 17,532 different sequences in NCBI non-redundant nucleotide databases. 97,236 (82.51% unigenes were mapped to the 30 goat chromosomes. 35,551 (30.17% unigenes were matched to 11,438 reported goat protein-coding genes. The remaining non-matched unigenes were further compared with cattle and human reference genes, 67 putative new goat genes were discovered. Additionally, 2,781 potential simple sequence repeats were initially identified from all unigenes.The transcriptome of Liaoning cashmere goat was deep sequenced, de novo assembled, and annotated, providing abundant data to better understand the Liaoning cashmere goat transcriptome. The potential simple sequence repeats provide a material basis for future genetic linkage and quantitative trait loci analyses.
Full Text Available Common bean (Phaseolus vulgaris is an important legume crop grown and consumed worldwide. With the availability of the common bean genome sequence, the next challenge is to annotate the genome and characterize functional DNA elements. Transposable elements (TEs are the most abundant component of plant genomes and can dramatically affect genome evolution and genetic variation. Thus, it is pivotal to identify TEs in the common bean genome. In this study, we performed a genome-wide transposon annotation in common bean using a combination of homology and sequence structure-based methods. We developed a 2.12-Mb transposon database which includes 791 representative transposon sequences and is available upon request or from www.phytozome.org. Of note, nearly all transposons in the database are previously unrecognized TEs. More than 5,000 transposon-related expressed sequence tags (ESTs were detected which indicates that some transposons may be transcriptionally active. Two Ty1-copia retrotransposon families were found to encode the envelope-like protein which has rarely been identified in plant genomes. Also, we identified an extra open reading frame (ORF termed ORF2 from 15 Ty3-gypsy families that was located between the ORF encoding the retrotransposase and the 3’LTR. The ORF2 was in opposite transcriptional orientation to retrotransposase. Sequence homology searches and phylogenetic analysis suggested that the ORF2 may have an ancient origin, but its function is not clear. This transposon data provides a useful resource for understanding the genome organization and evolution and may be used to identify active TEs for developing transposon-tagging system in common bean and other related genomes.
Petersen, Thomas Nordahl; Lukjancenko, Oksana; Thomsen, Martin Christen Frølund
number of false positive species annotations are a problem unless thresholds or post-processing are applied to differentiate between correct and false annotations. MGmapper is a package to process raw next generation sequence data and perform reference based sequence assignment, followed by a post...... pipeline is freely available as a bitbucked package (https://bitbucket.org/genomicepidemiology/mgmapper). A web-version (https://cge.cbs.dtu.dk/services/MGmapper) provides the basic functionality for analysis of small fastq datasets....
Full Text Available Abstract Background The enormous throughput and low cost of second-generation sequencing platforms now allow research and clinical geneticists to routinely perform single experiments that identify tens of thousands to millions of variant sites. Existing methods to annotate variant sites using information from publicly available databases via web browsers are too slow to be useful for the large sequencing datasets being routinely generated by geneticists. Because sequence annotation of variant sites is required before functional characterization can proceed, the lack of a high-throughput pipeline to efficiently annotate variant sites can act as a significant bottleneck in genetics research. Results SeqAnt (Sequence Annotator is an open source web service and software package that rapidly annotates DNA sequence variants and identifies recessive or compound heterozygous loci in human, mouse, fly, and worm genome sequencing experiments. Variants are characterized with respect to their functional type, frequency, and evolutionary conservation. Annotated variants can be viewed on a web browser, downloaded in a tab-delimited text file, or directly uploaded in a BED format to the UCSC genome browser. To demonstrate the speed of SeqAnt, we annotated a series of publicly available datasets that ranged in size from 37 to 3,439,107 variant sites. The total time to completely annotate these data completely ranged from 0.17 seconds to 28 minutes 49.8 seconds. Conclusion SeqAnt is an open source web service and software package that overcomes a critical bottleneck facing research and clinical geneticists using second-generation sequencing platforms. SeqAnt will prove especially useful for those investigators who lack dedicated bioinformatics personnel or infrastructure in their laboratories.
Sen, Sanchayita; Young, Jasmine; Berrisford, John M; Chen, Minyu; Conroy, Matthew J; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A
The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100,000 structures contain more than 20,000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. © The Author(s) 2014. Published by Oxford University Press.
Thallinger Gerhard G
Full Text Available Abstract Background Structural data from crystallographic analyses contain a vast amount of information on protein-protein contacts. Knowledge on protein-protein interactions is essential for understanding many processes in living cells. The methods to investigate these interactions range from genetics to biophysics, crystallography, bioinformatics and computer modeling. Also crystal contact information can be useful to understand biologically relevant protein oligomerisation as they rely in principle on the same physico-chemical interaction forces. Visualization of crystal and biological contact data including different surface properties can help to analyse protein-protein interactions. Results VASCo is a program package for the calculation of protein surface properties and the visualization of annotated surfaces. Special emphasis is laid on protein-protein interactions, which are calculated based on surface point distances. The same approach is used to compare surfaces of two aligned molecules. Molecular properties such as electrostatic potential or hydrophobicity are mapped onto these surface points. Molecular surfaces and the corresponding properties are calculated using well established programs integrated into the package, as well as using custom developed programs. The modular package can easily be extended to include new properties for annotation. The output of the program is most conveniently displayed in PyMOL using a custom-made plug-in. Conclusion VASCo supplements other available protein contact visualisation tools and provides additional information on biological interactions as well as on crystal contacts. The tool provides a unique feature to compare surfaces of two aligned molecules based on point distances and thereby facilitates the visualization and analysis of surface differences.
Full Text Available High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks.
Vesth, Tammi Camilla
of this class have very little homology to other known genomes making functional annotation based on sequence similarity very difficult. Inspired in part by this analysis, an approach for comparative functional annotation was created based public sequenced genomes, CMGfunc. Functionally related groups......In November 2013, there was around 21.000 different prokaryotic genomes sequenced and publicly available, and the number is growing daily with another 20.000 or more genomes expected to be sequenced and deposited by the end of 2014. An important part of the analysis of this data is the functional...... annotation of genes – the descriptions assigned to genes that describe the likely function of the encoded proteins. This process is limited by several factors, including the definition of a function which can be more or less specific as well as how many genes can actually be assigned a function based...
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.3361 >orf19.3361; Contig19-10173; 157397..>158185;... YAT2*; carnitine acetyltransferase; gene family | truncated protein MSTYRFQETLEKLPIPDLVQTCNAYLEALKPLQTEQEHE
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.4748 >orf19.4748; Contig19-10215; complement(47336.....47731); MSL1*; U2 snRNA-associated protein; MPSTKRSSSTEYSHKDSKKKVKLDYVNLKPSQTLYVKNLNTKINKKILLHNLYLLFSAFGDIISINLQNGFAFIIFSNLNSATLALRNLKNQDFFDKPLVLNYAVKESKAISQEKQKLQDENDEEVMPSYE*
Holberg Blicher, Thomas; Gupta, Ramneek; Wesolowska, Agata
the differences between many individuals of the same species-humans in particular-the focus needs be on the functional impact of individual residue variation. To fulfil the promises of personal genomics, we need to start asking not only what is in a genome but also how millions of small differences between......Protein annotation provides a condensed and systematic view on the function of individual proteins. It has traditionally dealt with sorting proteins into functional categories, which for example has proven to be successful for the comparison of different species. However, if we are to understand...... individual genomes affect protein function and in turn human health. Copyright © 2010 Elsevier Ltd. All rights reserved....
Galperin, Michael Y; Makarova, Kira S; Wolf, Yuri I; Koonin, Eugene V
Microbial genome sequencing projects produce numerous sequences of deduced proteins, only a small fraction of which have been or will ever be studied experimentally. This leaves sequence analysis as the only feasible way to annotate these proteins and assign to them tentative functions. The Clusters of Orthologous Groups of proteins (COGs) database (http://www.ncbi.nlm.nih.gov/COG/), first created in 1997, has been a popular tool for functional annotation. Its success was largely based on (i) its reliance on complete microbial genomes, which allowed reliable assignment of orthologs and paralogs for most genes; (ii) orthology-based approach, which used the function(s) of the characterized member(s) of the protein family (COG) to assign function(s) to the entire set of carefully identified orthologs and describe the range of potential functions when there were more than one; and (iii) careful manual curation of the annotation of the COGs, aimed at detailed prediction of the biological function(s) for each COG while avoiding annotation errors and overprediction. Here we present an update of the COGs, the first since 2003, and a comprehensive revision of the COG annotations and expansion of the genome coverage to include representative complete genomes from all bacterial and archaeal lineages down to the genus level. This re-analysis of the COGs shows that the original COG assignments had an error rate below 0.5% and allows an assessment of the progress in functional genomics in the past 12 years. During this time, functions of many previously uncharacterized COGs have been elucidated and tentative functional assignments of many COGs have been validated, either by targeted experiments or through the use of high-throughput methods. A particularly important development is the assignment of functions to several widespread, conserved proteins many of which turned out to participate in translation, in particular rRNA maturation and tRNA modification. The new version of the
Huang, Daisie I.; Cronk, Quentin C. B.
Premise of the study: Plann automates the process of annotating a plastome sequence in GenBank format for either downstream processing or for GenBank submission by annotating a new plastome based on a similar, well-annotated plastome. Methods and Results: Plann is a Perl script to be executed on the command line. Plann compares a new plastome sequence to the features annotated in a reference plastome and then shifts the intervals of any matching features to the locations in the new plastome. Plann’s output can be used in the National Center for Biotechnology Information’s tbl2asn to create a Sequin file for GenBank submission. Conclusions: Unlike Web-based annotation packages, Plann is a locally executable script that will accurately annotate a plastome sequence to a locally specified reference plastome. Because it executes from the command line, it is ready to use in other software pipelines and can be easily rerun as a draft plastome is improved. PMID:26312193
Real, Fernando; Vidal, Ramon Oliveira; Carazzolle, Marcelo Falsarella; Mondego, Jorge Maurício Costa; Costa, Gustavo Gilson Lacerda; Herai, Roberto Hirochi; Würtele, Martin; de Carvalho, Lucas Miguel; e Ferreira, Renata Carmona; Mortara, Renato Arruda; Barbiéri, Clara Lucia; Mieczkowski, Piotr; da Silveira, José Franco; Briones, Marcelo Ribeiro da Silva; Pereira, Gonçalo Amarante Guimarães; Bahia, Diana
We present the sequencing and annotation of the Leishmania (Leishmania) amazonensis genome, an etiological agent of human cutaneous leishmaniasis in the Amazon region of Brazil. L. (L.) amazonensis shares features with Leishmania (L.) mexicana but also exhibits unique characteristics regarding geographical distribution and clinical manifestations of cutaneous lesions (e.g. borderline disseminated cutaneous leishmaniasis). Predicted genes were scored for orthologous gene families and conserved domains in comparison with other human pathogenic Leishmania spp. Carboxypeptidase, aminotransferase, and 3′-nucleotidase genes and ATPase, thioredoxin, and chaperone-related domains were represented more abundantly in L. (L.) amazonensis and L. (L.) mexicana species. Phylogenetic analysis revealed that these two species share groups of amastin surface proteins unique to the genus that could be related to specific features of disease outcomes and host cell interactions. Additionally, we describe a hypothetical hybrid interactome of potentially secreted L. (L.) amazonensis proteins and host proteins under the assumption that parasite factors mimic their mammalian counterparts. The model predicts an interaction between an L. (L.) amazonensis heat-shock protein and mammalian Toll-like receptor 9, which is implicated in important immune responses such as cytokine and nitric oxide production. The analysis presented here represents valuable information for future studies of leishmaniasis pathogenicity and treatment. PMID:23857904
Mewes, H W; Frishman, D; Güldener, U; Mannhaupt, G; Mayer, K; Mokrejs, M; Morgenstern, B; Münsterkötter, M; Rudd, S; Weil, B
The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz-Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91-93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155-158; Barker et al. (2001) Nucleic Acids Res., 29, 29-32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de).
Full Text Available We report draft genome sequence of Stenotrophomonas sp. strain SAM8, isolated from environmental water. The draft genome size is 3,665,538 bp with a G + C content of 67.2% and contains 6 rRNA sequence (single copies of 5S, 16S & 23S rRNA. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession no. LDAV00000000.
Full Text Available We report draft genome sequence of Proteus sp. strain SAS71, isolated from water spring in Aljouf region, Saudi Arabia. The draft genome size is 3,037,704 bp with a G + C content of 39.3% and contains 6 rRNA sequence (single copies of 5S, 16S & 23S rRNA. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession no. LDIU00000000.
Firth Andrew E
Full Text Available Abstract Background The many Hepadnaviridae sequences available have widely varied functional annotation. The genomes are very compact (~3.2 kb but contain multiple layers of functional regulatory elements in addition to coding regions. Key regions are subject to purifying selection, as mutations in these regions will produce non-functional viruses. Results These genomic sequences have been organized into a structured database to facilitate research at the molecular level. HBVRegDB is a comparative genomic analysis tool with an integrated underlying sequence database. The database contains genomic sequence data from representative viruses. In addition to INSDC and RefSeq annotation, HBVRegDB also contains expert and systematically calculated annotations (e.g. promoters and comparative genome analysis results (e.g. blastn, tblastx. It also contains analyses based on curated HBV alignments. Information about conserved regions – including primary conservation (e.g. CDS-Plotcon and RNA secondary structure predictions (e.g. Alidot – is integrated into the database. A large amount of data is graphically presented using the GBrowse (Generic Genome Browser adapted for analysis of viral genomes. Flexible query access is provided based on any annotated genomic feature. Novel regulatory motifs can be found by analysing the annotated sequences. Conclusion HBVRegDB serves as a knowledge database and as a comparative genomic analysis tool for molecular biologists investigating HBV. It is publicly available and complementary to other viral and HBV focused datasets and tools http://hbvregdb.otago.ac.nz. The availability of multiple and highly annotated sequences of viral genomes in one database combined with comparative analysis tools facilitates detection of novel genomic elements.
Kozomara, Ana; Griffiths-Jones, Sam
miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.
Kent, Stephen [University of Chicago
The objective of this research program was to prototype methods for the chemical synthesis of predicted protein molecules in annotated microbial genomes. High throughput chemical methods were to be used to make large numbers of predicted proteins and protein domains, based on microbial genome sequences. Microscale chemical synthesis methods for the parallel preparation of peptide-thioester building blocks were developed; these peptide segments are used for the parallel chemical synthesis of proteins and protein domains. Ultimately, it is envisaged that these synthetic molecules would be ‘printed’ in spatially addressable arrays. The unique ability of total synthesis to precision label protein molecules with dyes and with chemical or biochemical ‘tags’ can be used to facilitate novel assay technologies adapted from state-of-the art single molecule fluorescence detection techniques. In the future, in conjunction with modern laboratory automation this integrated set of techniques will enable high throughput experimental validation of the functional annotation of microbial genomes.
Mewes, H W; Frishman, D; Mayer, K F X; Münsterkötter, M; Noubibou, O; Pagel, P; Rattei, T; Oesterheld, M; Ruepp, A; Stümpflen, V
The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein-protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.gsf.de).
Muzny, D.M.; Bolund, Lars; As part of the Chinese Human Genome Sequencing Consortium, E.T.A.L.
as numerous loci involved in multiple human cancers such as the gene encoding FHIT, which contains the most common constitutive fragile site in the genome, FRA3B. Using genomic sequence from chimpanzee and rhesus macaque, we were able to characterize the breakpoints defining a large pericentric inversion...
Full Text Available We present the de novo draft genome sequence for a vertebrate mammalian herbivore, the desert woodrat (Neotoma lepida. This species is of ecological and evolutionary interest with respect to ingestion, microbial detoxification and hepatic metabolism of toxic plant secondary compounds from the highly toxic creosote bush (Larrea tridentata and the juniper shrub (Juniperus monosperma. The draft genome sequence and annotation have been deposited at GenBank under the accession LZPO01000000.
Piao, Hailan; Froula, Jeff; Du, Changbin; Kim, Tae-Wan; Hawley, Erik R; Bauer, Stefan; Wang, Zhong; Ivanova, Nathalia; Clark, Douglas S; Klenk, Hans-Peter; Hess, Matthias
Although recent nucleotide sequencing technologies have significantly enhanced our understanding of microbial genomes, the function of ∼35% of genes identified in a genome currently remains unknown. To improve the understanding of microbial genomes and consequently of microbial processes it will be crucial to assign a function to this "genomic dark matter." Due to the urgent need for additional carbohydrate-active enzymes for improved production of transportation fuels from lignocellulosic biomass, we screened the genomes of more than 5,500 microorganisms for hypothetical proteins that are located in the proximity of already known cellulases. We identified, synthesized and expressed a total of 17 putative cellulase genes with insufficient sequence similarity to currently known cellulases to be identified as such using traditional sequence annotation techniques that rely on significant sequence similarity. The recombinant proteins of the newly identified putative cellulases were subjected to enzymatic activity assays to verify their hydrolytic activity towards cellulose and lignocellulosic biomass. Eleven (65%) of the tested enzymes had significant activity towards at least one of the substrates. This high success rate highlights that a gene context-based approach can be used to assign function to genes that are otherwise categorized as "genomic dark matter" and to identify biomass-degrading enzymes that have little sequence similarity to already known cellulases. The ability to assign function to genes that have no related sequence representatives with functional annotation will be important to enhance our understanding of microbial processes and to identify microbial proteins for a wide range of applications. © 2014 Wiley Periodicals, Inc.
Smaili, Fatima Z.
The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.
Pearson, W.R. [Univ. of Virginia, Charlottesville, VA (United States). Dept. of Biochemistry
This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. This tutorial examines how the information conserved during the evolution of a protein molecule can be used to infer reliably homology, and thus a shared proteinfold and possibly a shared active site or function. The authors start by reviewing a geological/evolutionary time scale. Next they look at the evolution of several protein families. During the tutorial, these families will be used to demonstrate that homologous protein ancestry can be inferred with confidence. They also examine different modes of protein evolution and consider some hypotheses that have been presented to explain the very earliest events in protein evolution. The next part of the tutorial will examine the technical aspects of protein sequence comparison. Both optimal and heuristic algorithms and their associated parameters that are used to characterize protein sequence similarities are discussed. Perhaps more importantly, they survey the statistics of local similarity scores, and how these statistics can both be used to improve the selectivity of a search and to evaluate the significance of a match. They them examine distantly related members of three protein families, the serine proteases, the glutathione transferases, and the G-protein-coupled receptors (GCRs). Finally, the discuss how sequence similarity can be used to examine internal repeated or mosaic structures in proteins.
Full Text Available Abstract Background Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets. Results We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology
Yim, Soorin; Yu, Hasun; Jang, Dongjin; Lee, Doheon
Signaling pathways can be reconstructed by identifying 'effect types' (i.e. activation/inhibition) of protein-protein interactions (PPIs). Effect types are composed of 'directions' (i.e. upstream/downstream) and 'signs' (i.e. positive/negative), thereby requiring directions as well as signs of PPIs to predict signaling events from PPI networks. Here, we propose a computational method for systemically annotating effect types to PPIs using relations between functional information of proteins. We used regulates, positively regulates, and negatively regulates relations in Gene Ontology (GO) to predict directions and signs of PPIs. These relations indicate both directions and signs between GO terms so that we can project directions and signs between relevant GO terms to PPIs. Independent test results showed that our method is effective for predicting both directions and signs of PPIs. Moreover, our method outperformed a previous GO-based method that did not consider the relations between GO terms. We annotated effect types to human PPIs and validated several highly confident effect types against literature. The annotated human PPIs are available in Additional file 2 to aid signaling pathway reconstruction and network biology research. We annotated effect types to PPIs by using regulates, positively regulates, and negatively regulates relations in GO. We demonstrated that those relations are effective for predicting not only signs, but also directions of PPIs. The usefulness of those relations suggests their potential applications to other types of interactions such as protein-DNA interactions.
Carver, Tim; Berriman, Matthew; Tivey, Adrian; Patel, Chinmay; Böhme, Ulrike; Barrell, Barclay G; Parkhill, Julian; Rajandream, Marie-Adèle
Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences. Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text. Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/ http://www.sanger.ac.uk/Software/ACT/
Carver, Tim; Berriman, Matthew; Tivey, Adrian; Patel, Chinmay; Böhme, Ulrike; Barrell, Barclay G.; Parkhill, Julian; Rajandream, Marie-Adèle
Motivation: Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences. Results: Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text. Availability: Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/ http://www.sanger.ac.uk/Software/ACT/ Contact: email@example.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18845581
Kumar, Gayatri; Mudgal, Richa; Srinivasan, Narayanaswamy; Sandhya, Sankaran
Knowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure. We first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation. The results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as 'linkers', where natural linkers between distant proteins are unavailable. This article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian.
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.710 >orf19.710; Contig19-10065; complement(47186.....>47710); LSC2*; succinate-CoA ligase beta subunit; truncated protein | overlap LGFDDNASFRQEEVFSWRDPTQEDPQEAE
Mavromatis, Konstantinos; Land, Miriam L; Brettin, Thomas S; Quest, Daniel J; Copeland, Alex; Clum, Alicia; Goodwin, Lynne; Woyke, Tanja; Lapidus, Alla; Klenk, Hans Peter; Cottingham, Robert W; Kyrpides, Nikos C
The emergence of next generation sequencing (NGS) has provided the means for rapid and high throughput sequencing and data generation at low cost, while concomitantly creating a new set of challenges. The number of available assembled microbial genomes continues to grow rapidly and their quality reflects the quality of the sequencing technology used, but also of the analysis software employed for assembly and annotation. In this work, we have explored the quality of the microbial draft genomes across various sequencing technologies. We have compared the draft and finished assemblies of 133 microbial genomes sequenced at the Department of Energy-Joint Genome Institute and finished at the Los Alamos National Laboratory using a variety of combinations of sequencing technologies, reflecting the transition of the institute from Sanger-based sequencing platforms to NGS platforms. The quality of the public assemblies and of the associated gene annotations was evaluated using various metrics. Results obtained with the different sequencing technologies, as well as their effects on downstream processes, were analyzed. Our results demonstrate that the Illumina HiSeq 2000 sequencing system, the primary sequencing technology currently used for de novo genome sequencing and assembly at JGI, has various advantages in terms of total sequence throughput and cost, but it also introduces challenges for the downstream analyses. In all cases assembly results although on average are of high quality, need to be viewed critically and consider sources of errors in them prior to analysis. These data follow the evolution of microbial sequencing and downstream processing at the JGI from draft genome sequences with large gaps corresponding to missing genes of significant biological role to assemblies with multiple small gaps (Illumina) and finally to assemblies that generate almost complete genomes (Illumina+PacBio).
Pirooznia, Mehdi; Gong, Ping; Guan, Xin; Inouye, Laura S; Yang, Kuan; Perkins, Edward J; Deng, Youping
Background Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. Results A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363–EH672369 and EL515444–EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. Conclusion The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at . PMID:18047730
Wang, Juexin; Sheridan, Robert; Sumer, S Onur; Schultz, Nikolaus; Xu, Dong; Gao, Jianjiong
Accurately mapping and annotating genomic locations on 3D protein structures is a key step in structure-based analysis of genomic variants detected by recent large-scale sequencing efforts. There are several mapping resources currently available, but none of them provides a web API (Application Programming Interface) that support programmatic access. We present G2S, a real-time web API that provides automated mapping of genomic variants on 3D protein structures. G2S can align genomic locations of variants, protein locations, or protein sequences to protein structures and retrieve the mapped residues from structures. G2S API uses REST-inspired design conception and it can be used by various clients such as web browsers, command terminals, programming languages and other bioinformatics tools for bringing 3D structures into genomic variant analysis. The webserver and source codes are freely available at https://g2s.genomenexus.org. firstname.lastname@example.org. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com
K H Dhanyalakshmi
Full Text Available The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs. Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS, which also provides a web service API (Application Programming Interface for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.
Yu, Jia-Feng; Sui, Tian-Xiang; Wang, Hong-Mei; Wang, Chun-Ling; Jing, Li; Wang, Ji-Hua
Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. Project supported by the National Natural Science Foundation of China (Grant Nos. 61302186 and 61271378) and the Funding from the State Key Laboratory of Bioelectronics of Southeast University.
Yu Jia-Feng; Sui Tian-Xiang; Wang Ji-Hua; Wang Hong-Mei; Wang Chun-Ling; Jing Li
Agrobacterium tumefaciens strain C58 is a type of pathogen that can cause tumors in some dicotyledonous plants. Ever since the genome of A. tumefaciens strain C58 was sequenced, the quality of annotation of its protein-coding genes has been queried continually, because the annotation varies greatly among different databases. In this paper, the questionable hypothetical genes were re-predicted by integrating the TN curve and Z curve methods. As a result, 30 genes originally annotated as “hypothetical” were discriminated as being non-coding sequences. By testing the re-prediction program 10 times on data sets composed of the function-known genes, the mean accuracy of 99.99% and mean Matthews correlation coefficient value of 0.9999 were obtained. Further sequence analysis and COG analysis showed that the re-annotation results were very reliable. This work can provide an efficient tool and data resources for future studies of A. tumefaciens strain C58. (special topic)
Timothy I Shaw
Full Text Available The Jamaican fruit bat (Artibeus jamaicensis is one of the most common bats in the tropical Americas. It is thought to be a potential reservoir host of Tacaribe virus, an arenavirus closely related to the South American hemorrhagic fever viruses. We performed transcriptome sequencing and annotation from lung, kidney and spleen tissues using 454 and Illumina platforms to develop this species as an animal model. More than 100,000 contigs were assembled, with 25,000 genes that were functionally annotated. Of the remaining unannotated contigs, 80% were found within bat genomes or transcriptomes. Annotated genes are involved in a broad range of activities ranging from cellular metabolism to genome regulation through ncRNAs. Reciprocal BLAST best hits yielded 8,785 sequences that are orthologous to mouse, rat, cattle, horse and human. Species tree analysis of sequences from 2,378 loci was used to achieve 95% bootstrap support for the placement of bat as sister to the clade containing horse, dog, and cattle. Through substitution rate estimation between bat and human, 32 genes were identified with evidence for positive selection. We also identified 466 immune-related genes, which may be useful for studying Tacaribe virus infection of this species. The Jamaican fruit bat transcriptome dataset is a resource that should provide additional candidate markers for studying bat evolution and ecology, and tools for analysis of the host response and pathology of disease.
Nowrousian, Minou; Würtz, Christian; Pöggeler, Stefanie; Kück, Ulrich
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.
Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug
Mohd Zakihalani A. Halim
Full Text Available Here, we report the draft genome sequence and annotation of a multidrug resistant Mycobacterium tuberculosis strain PR10 (MDR-TB PR10 isolated from a patient diagnosed with tuberculosis. The size of the draft genome MDR-TB PR10 is 4.34 Mbp with 65.6% of G + C content and consists of 4637 predicted genes. The determinants were categorized by RAST into 400 subsystems with 4286 coding sequences and 50 RNAs. The whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number CP010968. Keywords: Mycobacterium tuberculosis, Genome, MDR, Extrapulmonary
Georges, Arthur; Li, Qiye; Lian, Jinmin; O'Meally, Denis; Deakin, Janine; Wang, Zongji; Zhang, Pei; Fujita, Matthew; Patel, Hardip R; Holleley, Clare E; Zhou, Yang; Zhang, Xiuwen; Matsubara, Kazumi; Waters, Paul; Graves, Jennifer A Marshall; Sarre, Stephen D; Zhang, Guojie
The lizards of the family Agamidae are one of the most prominent elements of the Australian reptile fauna. Here, we present a genomic resource built on the basis of a wild-caught male ZZ central bearded dragon Pogona vitticeps. The genomic sequence for P. vitticeps, generated on the Illumina HiSeq 2000 platform, comprised 317 Gbp (179X raw read depth) from 13 insert libraries ranging from 250 bp to 40 kbp. After filtering for low-quality and duplicated reads, 146 Gbp of data (83X) was available for assembly. Exceptionally high levels of heterozygosity (0.85 % of single nucleotide polymorphisms plus sequence insertions or deletions) complicated assembly; nevertheless, 96.4 % of reads mapped back to the assembled scaffolds, indicating that the assembly included most of the sequenced genome. Length of the assembly was 1.8 Gbp in 545,310 scaffolds (69,852 longer than 300 bp), the longest being 14.68 Mbp. N50 was 2.29 Mbp. Genes were annotated on the basis of de novo prediction, similarity to the green anole Anolis carolinensis, Gallus gallus and Homo sapiens proteins, and P. vitticeps transcriptome sequence assemblies, to yield 19,406 protein-coding genes in the assembly, 63 % of which had intact open reading frames. Our assembly captured 99 % (246 of 248) of core CEGMA genes, with 93 % (231) being complete. The quality of the P. vitticeps assembly is comparable or superior to that of other published squamate genomes, and the annotated P. vitticeps genome can be accessed through a genome browser available at https://genomics.canberra.edu.au.
Full Text Available The previous survey identified 70 basic helix-loop-helix (bHLH proteins, but it was proved to be incomplete, and the functional information and regulatory networks of frog bHLH transcription factors were not fully known. Therefore, we conducted an updated genome-wide survey in the Xenopus tropicalis genome project databases and identified 105 bHLH sequences. Among the retrieved 105 sequences, phylogenetic analyses revealed that 103 bHLH proteins belonged to 43 families or subfamilies with 46, 26, 11, 3, 15, and 4 members in the corresponding supergroups. Next, gene ontology (GO enrichment analyses showed 65 significant GO annotations of biological processes and molecular functions and KEGG pathways counted in frequency. To explore the functional pathways, regulatory gene networks, and/or related gene groups coding for Xenopus tropicalis bHLH proteins, the identified bHLH genes were put into the databases KOBAS and STRING to get the signaling information of pathways and protein interaction networks according to available public databases and known protein interactions. From the genome annotation and pathway analysis using KOBAS, we identified 16 pathways in the Xenopus tropicalis genome. From the STRING interaction analysis, 68 hub proteins were identified, and many hub proteins created a tight network or a functional module within the protein families.
Matthew R Henn
Full Text Available BACKGROUND: Bacterial viruses (phages play a critical role in shaping microbial populations as they influence both host mortality and horizontal gene transfer. As such, they have a significant impact on local and global ecosystem function and human health. Despite their importance, little is known about the genomic diversity harbored in phages, as methods to capture complete phage genomes have been hampered by the lack of knowledge about the target genomes, and difficulties in generating sufficient quantities of genomic DNA for sequencing. Of the approximately 550 phage genomes currently available in the public domain, fewer than 5% are marine phage. METHODOLOGY/PRINCIPAL FINDINGS: To advance the study of phage biology through comparative genomic approaches we used marine cyanophage as a model system. We compared DNA preparation methodologies (DNA extraction directly from either phage lysates or CsCl purified phage particles, and sequencing strategies that utilize either Sanger sequencing of a linker amplification shotgun library (LASL or of a whole genome shotgun library (WGSL, or 454 pyrosequencing methods. We demonstrate that genomic DNA sample preparation directly from a phage lysate, combined with 454 pyrosequencing, is best suited for phage genome sequencing at scale, as this method is capable of capturing complete continuous genomes with high accuracy. In addition, we describe an automated annotation informatics pipeline that delivers high-quality annotation and yields few false positives and negatives in ORF calling. CONCLUSIONS/SIGNIFICANCE: These DNA preparation, sequencing and annotation strategies enable a high-throughput approach to the burgeoning field of phage genomics.
Fitzgerald, Lisa A; Graves, Michael V; Li, Xiao; Feldblyum, Tamara; Hartigan, James; Van Etten, James L
Viruses MT325 and FR483, members of the family Phycodnaviridae, genus Chlorovirus, infect the fresh water, unicellular, eukaryotic, chlorella-like green alga, Chlorella Pbi. The 314,335-bp genome of MT325 and the 321,240-bp genome of FR483 are the first viruses that infect Chlorella Pbi to have their genomes sequenced and annotated. Furthermore, these genomes are the two smallest chlorella virus genomes sequenced to date, MT325 has 331 putative protein-encoding and 10 tRNA-encoding genes and FR483 has 335 putative protein-encoding and 9 tRNA-encoding genes. The protein-encoding genes are almost evenly distributed on both strands, and intergenic space is minimal. Approximately 40% of the viral gene products resemble entries in public databases, including some that are the first of their kind to be detected in a virus. For example, these unique gene products include an aquaglyceroporin in MT325, a potassium ion transporter protein and an alkyl sulfatase in FR483, and a dTDP-glucose pyrophosphorylase in both viruses. Comparison of MT325 and FR483 protein-encoding genes with the prototype chlorella virus PBCV-1 indicates that approximately 82% of the genes are present in all three viruses.
Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially
He, Zihuai; Xu, Bin; Lee, Seunggeun; Ionita-Laza, Iuliana
Substantial progress has been made in the functional annotation of genetic variation in the human genome. Integrative analysis that incorporates such functional annotations into sequencing studies can aid the discovery of disease-associated genetic variants, especially those with unknown function and located outside protein-coding regions. Direct incorporation of one functional annotation as weight in existing dispersion and burden tests can suffer substantial loss of power when the functional annotation is not predictive of the risk status of a variant. Here, we have developed unified tests that can utilize multiple functional annotations simultaneously for integrative association analysis with efficient computational techniques. We show that the proposed tests significantly improve power when variant risk status can be predicted by functional annotations. Importantly, when functional annotations are not predictive of risk status, the proposed tests incur only minimal loss of power in relation to existing dispersion and burden tests, and under certain circumstances they can even have improved power by learning a weight that better approximates the underlying disease model in a data-adaptive manner. The tests can be constructed with summary statistics of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of multiple studies without sharing individual-level data. We applied the proposed tests to a meta-analysis of noncoding rare variants in Metabochip data on 12,281 individuals from eight studies for lipid traits. By incorporating the Eigen functional score, we detected significant associations between noncoding rare variants in SLC22A3 and low-density lipoprotein and total cholesterol, associations that are missed by standard dispersion and burden tests. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Young, Jasmine Y.; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M.
Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org PMID:24291661
Full Text Available BACKGROUND: Celery is an increasing popular vegetable species, but limited transcriptome and genomic data hinder the research to it. In addition, a lack of celery molecular markers limits the process of molecular genetic breeding. High-throughput transcriptome sequencing is an efficient method to generate a large transcriptome sequence dataset for gene discovery, molecular marker development and marker-assisted selection breeding. PRINCIPAL FINDINGS: Celery transcriptomes from four tissues were sequenced using Illumina paired-end sequencing technology. De novo assembling was performed to generate a collection of 42,280 unigenes (average length of 502.6 bp that represent the first transcriptome of the species. 78.43% and 48.93% of the unigenes had significant similarity with proteins in the National Center for Biotechnology Information (NCBI non-redundant protein database (Nr and Swiss-Prot database respectively, and 10,473 (24.77% unigenes were assigned to Clusters of Orthologous Groups (COG. 21,126 (49.97% unigenes harboring Interpro domains were annotated, in which 15,409 (36.45% were assigned to Gene Ontology(GO categories. Additionally, 7,478 unigenes were mapped onto 228 pathways using the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG. Large numbers of simple sequence repeats (SSRs were indentified, and then the rate of successful amplication and polymorphism were investigated among 31 celery accessions. CONCLUSIONS: This study demonstrates the feasibility of generating a large scale of sequence information by Illumina paired-end sequencing and efficient assembling. Our results provide a valuable resource for celery research. The developed molecular markers are the foundation of further genetic linkage analysis and gene localization, and they will be essential to accelerate the process of breeding.
Fernandez-Valverde, Selene L; Calcino, Andrew D; Degnan, Bernard M
The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence. Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3' and 5' untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3' UTRs. The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.
Hooper, Cornelia M; Castleden, Ian R; Aryamanesh, Nader; Jacoby, Richard P; Millar, A Harvey
Barley, wheat, rice and maize provide the bulk of human nutrition and have extensive industrial use as agricultural products. The genomes of these crops each contains >40,000 genes encoding proteins; however, the major genome databases for these species lack annotation information of protein subcellular location for >80% of these gene products. We address this gap, by constructing the compendium of crop protein subcellular locations called crop Proteins with Annotated Locations (cropPAL). Subcellular location is most commonly determined by fluorescent protein tagging of live cells or mass spectrometry detection in subcellular purifications, but can also be predicted from amino acid sequence or protein expression patterns. The cropPAL database collates 556 published studies, from >300 research institutes in >30 countries that have been previously published, as well as compiling eight pre-computed subcellular predictions for all Hordeum vulgare, Triticum aestivum, Oryza sativa and Zea mays protein sequences. The data collection including metadata for proteins and published studies can be accessed through a search portal http://crop-PAL.org. The subcellular localization information housed in cropPAL helps to depict plant cells as compartmentalized protein networks that can be investigated for improving crop yield and quality, and developing new biotechnological solutions to agricultural challenges. © The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: firstname.lastname@example.org.
Oliver Stephen G
Full Text Available Abstract Background Pfam is a general-purpose database of protein domain alignments and profile Hidden Markov Models (HMMs, which is very popular for the annotation of sequence data produced by genome sequencing projects. Pfam provides models that are often very general in terms of the taxa that they cover and it has previously been suggested that such general models may lack some of the specificity or selectivity that would be provided by kingdom-specific models. Results Here we present a general approach to create domain libraries of HMMs for sub-taxa of a kingdom. Taking fungal species as an example, we construct a domain library of HMMs (called Fungal Pfam or FPfam using sequences from 30 genomes, consisting of 24 species from the ascomycetes group and two basidiomycetes, Ustilago maydis, a fungal pathogen of maize, and the white rot fungus Phanerochaete chrysosporium. In addition, we include the Microsporidion Encephalitozoon cuniculi, an obligate intracellular parasite, and two non-fungal species, the oomycetes Phytophthora sojae and Phytophthora ramorum, both plant pathogens. We evaluate the performance in terms of coverage against the original 30 genomes used in training FPfam and against five more recently sequenced fungal genomes that can be considered as an independent test set. We show that kingdom-specific models such as FPfam can find instances of both novel and well characterized domains, increases overall coverage and detects more domains per sequence with typically higher bitscores than Pfam for the same domain families. An evaluation of the effect of changing E-values on the coverage shows that the performance of FPfam is consistent over the range of E-values applied. Conclusion Kingdom-specific models are shown to provide improved coverage. However, as the models become more specific, some sequences found by Pfam may be missed by the models in FPfam and some of the families represented in the test set are not present in FPfam
Full Text Available Bacillus species 062011 msu is a harmful pathogenic strain responsible for causing abscessation in sheep and goat population studied by Mariappan et al. (2012 . The organism specifically targets the female sheep and goat population and results in the reduction of milk and meat production. In the present study, we have performed the whole genome sequencing of the pathogenic isolate using the Ion Torrent sequencing platform and generated 458,944 raw reads with an average length of 198.2 bp. The genome sequence was assembled, annotated and analysed for the genetic islands, metabolic pathways, orthologous groups, virulence factors and antibiotic resistance genes associated with the pathogen. Simultaneously the 16S rRNA sequencing study and genome sequence comparison data confirmed that the strain belongs to the species Bacillus cereus and exhibits 99% sequence homo;logy with the genomes of B. cereus ATCC 10987 and B. cereus FRI-35. Hence, we have renamed the organism as Bacillus cereus 062011msu. The Whole Genome Shotgun (WGS project has been deposited at DDBJ/ENA/GenBank under the accession NTMF00000000 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA404036(SAMN07629099. Keywords: Bacillus cereus, Genome sequencing, Abscessation, Virulence factors
Full Text Available Over the course of HIV infection, virus replication is facilitated by the phosphorylation of HIV proteins by human ERK1 and ERK2 mitogen-activated protein kinases (MAPKs. MAPKs are known to phosphorylate their substrates by first binding with them at a docking site. Docking site interactions could be viable drug targets because the sequences guiding them are more specific than phosphorylation consensus sites. In this study we use multiple bioinformatics tools to discover candidate MAPK docking site motifs on HIV proteins known to be phosphorylated by MAPKs, and we discuss the possibility of targeting docking sites with drugs. Using sequence alignments of HIV proteins of different subtypes, we show that MAPK docking patterns previously described for human proteins appear on the HIV matrix, Tat, and Vif proteins in a strain dependent manner, but are absent from HIV Rev and appear on all HIV Nef strains. We revise the regular expressions of previously annotated MAPK docking patterns in order to provide a subtype independent motif that annotates all HIV proteins. One revision is based on a documented human variant of one of the substrate docking motifs, and the other reduces the number of required basic amino acids in the standard docking motifs from two to one. The proposed patterns are shown to be consistent with in silico docking between ERK1 and the HIV matrix protein. The motif usage on HIV proteins is sufficiently different from human proteins in amino acid sequence similarity to allow for HIV specific targeting using small-molecule drugs.
Full Text Available BACKGROUND: As a major stored-product pest insect, Liposcelis entomophila has developed high levels of resistance to various insecticides in grain storage systems. However, the molecular mechanisms underlying resistance and environmental stress have not been characterized. To date, there is a lack of genomic information for this species. Therefore, studies aimed at profiling the L. entomophila transcriptome would provide a better understanding of the biological functions at the molecular levels. METHODOLOGY/PRINCIPAL FINDINGS: We applied Illumina sequencing technology to sequence the transcriptome of L. entomophila. A total of 54,406,328 clean reads were obtained and that de novo assembled into 54,220 unigenes, with an average length of 571 bp. Through a similarity search, 33,404 (61.61% unigenes were matched to known proteins in the NCBI non-redundant (Nr protein database. These unigenes were further functionally annotated with gene ontology (GO, cluster of orthologous groups of proteins (COG, and Kyoto Encyclopedia of Genes and Genomes (KEGG databases. A large number of genes potentially involved in insecticide resistance were manually curated, including 68 putative cytochrome P450 genes, 37 putative glutathione S-transferase (GST genes, 19 putative carboxyl/cholinesterase (CCE genes, and other 126 transcripts to contain target site sequences or encoding detoxification genes representing eight types of resistance enzymes. Furthermore, to gain insight into the molecular basis of the L. entomophila toward thermal stresses, 25 heat shock protein (Hsp genes were identified. In addition, 1,100 SSRs and 57,757 SNPs were detected and 231 pairs of SSR primes were designed for investigating the genetic diversity in future. CONCLUSIONS/SIGNIFICANCE: We developed a comprehensive transcriptomic database for L. entomophila. These sequences and putative molecular markers would further promote our understanding of the molecular mechanisms underlying
Full Text Available Abstract Background Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. Description AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of ~90% and average precision of ~80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of ~70% and average precision of ~30%, correctly localizing binding sites for small molecules in ~95% of its predictions. Conclusion The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/.
Full Text Available Typhoid fever is a major cause of illness in most developing countries, including Bangladesh. In quest of new potential drug against Typhoid fever, the current study was designed to elucidate structural and functional details of S. typhi hypothetical protein (HP R_27. HP R_27 has the primary amino acid sequences available only. The structural annotation was determined by ProtParam, SOPMA, and CELLO. The three-dimensional (3D structure of HP R_27 predicted through homology modeling by using Phyre2. The 3D structure then refined and verified by ModRefiner, PROCHECK, ERRAT, QMEAN. The functional annotation was also performed by InterProScan, SMART, Pfam, NCBI-CDD and found Phospholipase D-like and DNA repair activity. Multiple sequence alignment also supported the existence of PLD-like domain and DNA repair protein domain in the selected hypothetical protein sequences. Finally, the cavity of drug binding was also identified to assist further molecular docking study and potent inhibitor identification. This in silico approach can be further utilized in molecular drug design for other clinically significant pathogens.
Full Text Available We report the 9.0-Mb draft genome of Amycolatopsis vancoresmycina strain DSM 44592T, isolated from Indian soil sample; produces antibiotic vancoresmycin. Draft genome of strain DSM44592T consists of 9,037,069 bp with a G+C content of 71.79% and 8340 predicted protein coding genes and 57 RNAs. RAST annotation indicates that strains Streptomyces sp. AA4 (score 521, Saccharomonospora viridis DSM 43017 (score 400 and Actinosynnema mirum DSM 43827 (score 372 are the closest neighbors of the strain DSM 44592T.
Brian R. King
Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.
Huerta-Cepas, J.; Szklarczyk, D.; Forslund, K.; Cook, H.; Heller, D.; Walter, M.C.; Rattei, T.; Mende, D.R.; Sunagawa, S.; Kuhn, M.; Jensen, L.J.; von Mering, C.; Bork, P.
eggNOG is a public resource that provides Orthologous Groups (OGs) of proteins at different taxonomic levels, each with integrated and summarized functional annotations. Developments since the latest public release include changes to the algorithm for creating OGs across taxonomic levels, making nested groups hierarchically consistent. This allows for a better propagation of functional terms across nested OGs and led to the novel annotation of 95 890 previously uncharacterized OGs, increasing...
Kozomara, Ana; Griffiths-Jones, Sam
We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.
Tetko, Igor V; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Frishman, Goar; Montrone, Corinna; Fobo, Gisela; Ruepp, Andreas; Antonov, Alexey V; Surmeli, Dimitrij; Mewes, Hans-Wernen
Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. BFAB is available at http://mips.gsf.de/proj/bfab
Young-Rae Cho; Yanan Xin; Speegle, Greg
Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.
Each protein is characterized by its unique sequential order of amino acids, the so-called protein sequence. Biology”s paradigm is that this order of amino acids determines the protein”s architecture and function. In this thesis, we introduce novel algorithms to analyze protein sequences. Chapter 1
Ong, Wen Dee; Voo, Lok-Yung Christopher; Kumar, Vijay Subbiah
Pineapple (Ananas comosus var. comosus), is an important tropical non-climacteric fruit with high commercial potential. Understanding the mechanism and processes underlying fruit ripening would enable scientists to enhance the improvement of quality traits such as, flavor, texture, appearance and fruit sweetness. Although, the pineapple is an important fruit, there is insufficient transcriptomic or genomic information that is available in public databases. Application of high throughput transcriptome sequencing to profile the pineapple fruit transcripts is therefore needed. To facilitate this, we have performed transcriptome sequencing of ripe yellow pineapple fruit flesh using Illumina technology. About 4.7 millions Illumina paired-end reads were generated and assembled using the Velvet de novo assembler. The assembly produced 28,728 unique transcripts with a mean length of approximately 200 bp. Sequence similarity search against non-redundant NCBI database identified a total of 16,932 unique transcripts (58.93%) with significant hits. Out of these, 15,507 unique transcripts were assigned to gene ontology terms. Functional annotation against Kyoto Encyclopedia of Genes and Genomes pathway database identified 13,598 unique transcripts (47.33%) which were mapped to 126 pathways. The assembly revealed many transcripts that were previously unknown. The unique transcripts derived from this work have rapidly increased of the number of the pineapple fruit mRNA transcripts as it is now available in public databases. This information can be further utilized in gene expression, genomics and other functional genomics studies in pineapple.
Full Text Available Abstract Background Models for the simulation of metabolic networks require the accurate prediction of enzyme function. Based on a genomic sequence, enzymatic functions of gene products are today mainly predicted by sequence database searching and operon analysis. Other methods can support these techniques: We have developed an automatic method "BrEPS" that creates highly specific sequence patterns for the functional annotation of enzymes. Results The enzymes in the UniprotKB are identified and their sequences compared against each other with BLAST. The enzymes are then clustered into a number of trees, where each tree node is associated with a set of EC-numbers. The enzyme sequences in the tree nodes are aligned with ClustalW. The conserved columns of the resulting multiple alignments are used to construct sequence patterns. In the last step, we verify the quality of the patterns by computing their specificity. Patterns with low specificity are omitted and recomputed further down in the tree. The final high-quality patterns can be used for functional annotation. We ran our protocol on a recent Swiss-Prot release and show statistics, as well as a comparison to PRIAM, a probabilistic method that is also specialized on the functional annotation of enzymes. We determine the amount of true positive annotations for five common microorganisms with data from BRENDA and AMENDA serving as standard of truth. BrEPS is almost on par with PRIAM, a fact which we discuss in the context of five manually investigated cases. Conclusions Our protocol computes highly specific sequence patterns that can be used to support the functional annotation of enzymes. The main advantages of our method are that it is automatic and unsupervised, and quite fast once the patterns are evaluated. The results show that BrEPS can be a valuable addition to the reconstruction of metabolic networks.
Full Text Available Abstract Background The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB, thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm, has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. Results The evaluation of CMASA shows that the CMASA is highly accurate (0.96, sensitive (0.86, and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function and SPASM (a local structure alignment method; and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods. The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA. Conclusions The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the
Prakash, Ananth; Jeffryes, Matt; Bateman, Alex; Finn, Robert D
Protein sequence similarity search is one of the most commonly used bioinformatics methods for identifying evolutionarily related proteins. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software, which in the latest version (v3.1) uses a combination of sophisticated acceleration heuristics and mathematical and computational optimizations to enable the use of profile hidden Markov models (HMMs) for sequence analysis. The HMMER Web server provides a common platform by linking the HMMER algorithms to databases, thereby enabling the search for homologs, as well as providing sequence and functional annotation by linking external databases. This unit describes three basic protocols and two alternate protocols that explain how to use the HMMER Web server using various input formats and user defined parameters. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.
Full Text Available Abstract Background The most abundant family of insect cuticular proteins, the CPR family, is recognized by the R&R Consensus, a domain of about 64 amino acids that binds to chitin and is present throughout arthropods. Several species have now been shown to have more than 100 CPR genes, inviting speculation as to the functional importance of this large number and diversity. Results We have identified 156 genes in Anopheles gambiae that code for putative cuticular proteins in this CPR family, over 1% of the total number of predicted genes in this species. Annotation was verified using several criteria including identification of TATA boxes, INRs, and DPEs plus support from proteomic and gene expression analyses. Two previously recognized CPR classes, RR-1 and RR-2, form separate, well-supported clades with the exception of a small set of genes with long branches whose relationships are poorly resolved. Several of these outliers have clear orthologs in other species. Although both clades are under purifying selection, the RR-1 variant of the R&R Consensus is evolving at twice the rate of the RR-2 variant and is structurally more labile. In contrast, the regions flanking the R&R Consensus have diversified in amino-acid composition to a much greater extent in RR-2 genes compared with RR-1 genes. Many genes are found in compact tandem arrays that may include similar or dissimilar genes but always include just one of the two classes. Tandem arrays of RR-2 genes frequently contain subsets of genes coding for highly similar proteins (sequence clusters. Properties of the proteins indicated that each cluster may serve a distinct function in the cuticle. Conclusion The complete annotation of this large gene family provides insight on the mechanisms of gene family evolution and clues about the need for so many CPR genes. These data also should assist annotation of other Anopheles genes.
Full Text Available Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes, electrochemical potential-driven transporters (33 genes, and primary active transporters (15 genes. To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.
Raethong, Nachon; Wong-Ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa
Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H(+)-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.
Han, Joon-Hee; Chon, Jae-Kyung; Ahn, Jong-Hwa; Choi, Ik-Young; Lee, Yong-Hwan; Kim, Kyoung Su
Colletotrichum acutatum is a destructive fungal pathogen which causes anthracnose in a wide range of crops. Here we report the whole genome sequence and annotation of C. acutatum strain KC05, isolated from an infected pepper in Kangwon, South Korea. Genomic DNA from the KC05 strain was used for the whole genome sequencing using a PacBio sequencer and the MiSeq system. The KC05 genome was determined to be 52,190,760 bp in size with a G + C content of 51.73% in 27 scaffolds and to contain 13,559 genes with an average length of 1516 bp. Gene prediction and annotation were performed by incorporating RNA-Seq data. The genome sequence of the KC05 was deposited at DDBJ/ENA/GenBank under the accession number LUXP00000000.
Full Text Available Abstract Background Sheep scab is caused by Psoroptes ovis and is arguably the most important ectoparasitic disease affecting sheep in the UK. The disease is highly contagious and causes and considerable pruritis and irritation and is therefore a major welfare concern. Current methods of treatment are unsustainable and in order to elucidate novel methods of disease control a more comprehensive understanding of the parasite is required. To date, no full genomic DNA sequence or large scale transcript datasets are available and prior to this study only 484 P. ovis expressed sequence tags (ESTs were accessible in public databases. Results In order to further expand upon the transcriptomic coverage of P. ovis thus facilitating novel insights into the mite biology we undertook a larger scale EST approach, incorporating newly generated and previously described P. ovis transcript data and representing the largest collection of P. ovis ESTs to date. We sequenced 1,574 ESTs and assembled these along with 484 previously generated P. ovis ESTs, which resulted in the identification of 1,545 unique P. ovis sequences. BLASTX searches identified 961 ESTs with significant hits (E-value P. ovis ESTs. Gene Ontology (GO analysis allowed the functional annotation of 880 ESTs and included predictions of signal peptide and transmembrane domains; allowing the identification of potential P. ovis excreted/secreted factors, and mapping of metabolic pathways. Conclusions This dataset currently represents the largest collection of P. ovis ESTs, all of which are publicly available in the GenBank EST database (dbEST (accession numbers FR748230 - FR749648. Functional analysis of this dataset identified important homologues, including house dust mite allergens and tick salivary factors. These findings offer new insights into the underlying biology of P. ovis, facilitating further investigations into mite biology and the identification of novel methods of intervention.
Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.
Full Text Available HIV proteins target host hub proteins for transient binding interactions. The presence of viral proteins in the infected cell results in out-competition of host proteins in their interaction with hub proteins, drastically affecting cell physiology. Functional genomics and interactome datasets can be used to quantify the sequence hotspots on the HIV proteome mediating interactions with host hub proteins. In this study, we used the HIV and human interactome databases to identify HIV targeted host hub proteins and their host binding partners (H2. We developed a high throughput computational procedure utilizing motif discovery algorithms on sets of protein sequences, including sequences of HIV and H2 proteins. We identified as HIV sequence hotspots those linear motifs that are highly conserved on HIV sequences and at the same time have a statistically enriched presence on the sequences of H2 proteins. The HIV protein motifs discovered in this study are expressed by subsets of H2 host proteins potentially outcompeted by HIV proteins. A large subset of these motifs is involved in cleavage, nuclear localization, phosphorylation, and transcription factor binding events. Many such motifs are clustered on an HIV sequence in the form of hotspots. The sequential positions of these hotspots are consistent with the curated literature on phenotype altering residue mutations, as well as with existing binding site data. The hotspot map produced in this study is the first global portrayal of HIV motifs involved in altering the host protein network at highly connected hub nodes.
Ryan, Michael; Diekhans, Mark; Lien, Stephanie; Liu, Yun; Karchin, Rachel
LS-SNP/PDB is a new WWW resource for genome-wide annotation of human non-synonymous (amino acid changing) SNPs. It serves high-quality protein graphics rendered with UCSF Chimera molecular visualization software. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features. LS-SNP/PDB is available at (http://ls-snp.icm.jhu.edu/ls-snp-pdb) and via links from protein data bank (PDB) biology and chemistry tabs, UCSC Genome Browser Gene Details and SNP Details pages and PharmGKB Gene Variants Downloads/Cross-References pages.
Islamaj Dogan, Rezarta; Kim, Sun; Chatr-Aryamontri, Andrew; Chang, Christie S; Oughtred, Rose; Rust, Jennifer; Wilbur, W John; Comeau, Donald C; Dolinski, Kara; Tyers, Mike
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future
Dutta, Shuchismita; Burkhardt, Kyle; Young, Jasmine; Swaminathan, Ganesh J; Matsuura, Takanori; Henrick, Kim; Nakamura, Haruki; Berman, Helen M
The Protein Data Bank (PDB) is the repository for three-dimensional structures of biological macromolecules, determined by experimental methods. The data in the archive is free and easily available via the Internet from any of the worldwide centers managing this global archive. These data are used by scientists, researchers, bioinformatics specialists, educators, students, and general audiences to understand biological phenomenon at a molecular level. Analysis of this structural data also inspires and facilitates new discoveries in science. This chapter describes the tools and methods currently used for deposition, processing, and release of data in the PDB. References to future enhancements are also included.
Zhang, Yuanwei; Zang, Qiguang; Zhang, Huan; Ban, Rongjun; Yang, Yifan; Iqbal, Furhan; Li, Ao; Shi, Qinghua
Small RNA (sRNA) Sequencing technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent variations from their canonical sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). However, integrated tool to precisely detect and systematically annotate isomiRs from sRNA sequencing data is still in great demand. Here, we present an online tool, DeAnnIso (Detection and Annotation of IsomiRs from sRNA sequencing data). DeAnnIso can detect all the isomiRs in an uploaded sample, and can extract the differentially expressing isomiRs from paired or multiple samples. Once the isomiRs detection is accomplished, detailed annotation information, including isomiRs expression, isomiRs classification, SNPs in miRNAs and tissue specific isomiR expression are provided to users. Furthermore, DeAnnIso provides a comprehensive module of target analysis and enrichment analysis for the selected isomiRs. Taken together, DeAnnIso is convenient for users to screen for isomiRs of their interest and useful for further functional studies. The server is implemented in PHP + Perl + R and available to all users for free at: http://mcg.ustc.edu.cn/bsc/deanniso/ and http://mcg2.ustc.edu.cn/bsc/deanniso/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
Full Text Available Abstract Background Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. Discovering such patterns is time-consuming because abundant combinations exist when long gaps (a gap consists of one or more successive wildcards are considered. Mining algorithms often employ constraints to narrow down the search space in order to increase efficiency. However, improper constraint models might degrade the sensitivity and specificity of the motifs discovered by computational methods. We previously proposed a new constraint model to handle large wildcard regions for discovering functional motifs of proteins. The patterns that satisfy the proposed constraint model are called W-patterns. A W-pattern is a structured motif that groups motif symbols into pattern blocks interleaved with large irregular gaps. Considering large gaps reflects the fact that functional residues are not always from a single region of protein sequences, and restricting motif symbols into clusters corresponds to the observation that short motifs are frequently present within protein families. To efficiently discover W-patterns for large-scale sequence annotation and function prediction, this paper first formally introduces the problem to solve and proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions that incorporates several pruning strategies to largely reduce the mining cost. Results WildSpan is shown to efficiently find W-patterns containing conserved residues that are far separated in sequences. We conducted experiments with two mining strategies, protein-based and family-based mining, to evaluate the usefulness of W-patterns and performance of WildSpan. The protein-based mining mode
Inês C Conceição
Full Text Available BACKGROUND: Analysis of genomic sequence allows characterization of genome content and organization, and access beyond gene-coding regions for identification of functional elements. BAC libraries, where relatively large genomic regions are made readily available, are especially useful for species without a fully sequenced genome and can increase genomic coverage of phylogenetic and biological diversity. For example, no butterfly genome is yet available despite the unique genetic and biological properties of this group, such as diversified wing color patterns. The evolution and development of these patterns is being studied in a few target species, including Bicyclus anynana, where a whole-genome BAC library allows targeted access to large genomic regions. METHODOLOGY/PRINCIPAL FINDINGS: We characterize ∼1.3 Mb of genomic sequence around 11 selected genes expressed in B. anynana developing wings. Extensive manual curation of in silico predictions, also making use of a large dataset of expressed genes for this species, identified repetitive elements and protein coding sequence, and highlighted an expansion of Alcohol dehydrogenase genes. Comparative analysis with orthologous regions of the lepidopteran reference genome allowed assessment of conservation of fine-scale synteny (with detection of new inversions and translocations and of DNA sequence (with detection of high levels of conservation of non-coding regions around some, but not all, developmental genes. CONCLUSIONS: The general properties and organization of the available B. anynana genomic sequence are similar to the lepidopteran reference, despite the more than 140 MY divergence. Our results lay the groundwork for further studies of new interesting findings in relation to both coding and non-coding sequence: 1 the Alcohol dehydrogenase expansion with higher similarity between the five tandemly-repeated B. anynana paralogs than with the corresponding B. mori orthologs, and 2 the high
Wernersson, Rasmus; Rapacki, Krzysztof; Stærfeldt, Hans Henrik
FeatureMap3D is a web-based tool that maps protein features onto 3D structures. The user provides sequences annotated with any feature of interest, such as post-translational modifications, protease cleavage sites or exonic structure and FeatureMap3D will then search the Protein Data Bank (PDB) f...
Mewes, H W; Albermann, K; Heumann, K; Liebl, S; Pfeiffer, F
The MIPS group (Martinsried Institute for Protein Sequences) at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, collects, processes and distributes protein sequence data within the framework of the tripartite association of the PIR-International Protein Sequence Database (,). MIPS contributes nearly 50% of the data input to the PIR-International Protein Sequence Database. The database is distributed on CD-ROM together with PATCHX, an exhaustive supplement of unique, unverified protein sequences from external sources compiled by MIPS. Through its WWW server (http://www.mips.biochem.mpg.de/ ) MIPS permits internet access to sequence databases, homology data and to yeast genome information. (i) Sequence similarity results from the FASTA program () are stored in the FASTA database for all proteins from PIR-International and PATCHX. The database is dynamically maintained and permits instant access to FASTA results. (ii) Starting with FASTA database queries, proteins have been classified into families and superfamilies (PROT-FAM). (iii) The HPT (hashed position tree) data structure () developed at MIPS is a new approach for rapid sequence and pattern searching. (iv) MIPS provides access to the sequence and annotation of the complete yeast genome (), the functional classification of yeast genes (FunCat) and its graphical display, the 'Genome Browser' (). A CD-ROM based on the JAVA programming language providing dynamic interactive access to the yeast genome and the related protein sequences has been compiled and is available on request. PMID:9016498
Kaplan, Noam; Vaaknin, Avishay; Linial, Michal
Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.
Full Text Available Identifying the small number of rare causal variants contributing to disease has beena major focus of investigation in recent years, but represents a formidable statisticalchallenge due to the rare frequencies with which these variants are observed. In thiscommentary we draw attention to a formal statistical framework, namely hierarchicalmodeling, to combine functional genomic annotations with sequencing data with theobjective of enhancing our ability to identify rare causal variants. Using simulations weshow that in all configurations studied, the hierarchical modeling approach has superiordiscriminatory ability compared to a recently proposed aggregate measure of deleteriousness,the Combined Annotation-Dependent Depletion (CADD score, supportingour premise that aggregate functional genomic measures can more accurately identifycausal variants when used in conjunction with sequencing data through a hierarchicalmodeling approach
Full Text Available List Contact us AcEST AcEST(EST sequences of Adiantum capillus-veneris and their annotation) Data detail Dat...a name AcEST(EST sequences of Adiantum capillus-veneris and their annotation) DOI 10.18908/lsdba.nbdc00839-0...01 Description of data contents EST sequence of Adiantum capillus-veneris and its annotation (clone ID, libr...le search URL http://togodb.biosciencedbc.jp/togodb/view/archive_acest#en Data acquisition method Capillary ...ainst UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases) Number of data entries Adiantum capillus-veneris
Engelhardt, Barbara E; Jordan, Michael I; Repo, Susanna T; Brenner, Steven E
It is now easier to discover thousands of protein sequences in a new microbial genome than it is to biochemically characterize the specific activity of a single protein of unknown function. The molecular functions of protein sequences have typically been predicted using homology-based computational methods, which rely on the principle that homologous proteins share a similar function. However, some protein families include groups of proteins with different molecular functions. A phylogenetic approach for predicting molecular function (sometimes called 'phylogenomics') is an effective means to predict protein molecular function. These methods incorporate functional evidence from all members of a family that have functional characterizations using the evolutionary history of the protein family to make robust predictions for the uncharacterized proteins. However, they are often difficult to apply on a genome-wide scale because of the time-consuming step of reconstructing the phylogenies of each protein to be annotated. Our automated approach for function annotation using phylogeny, the SIFTER (Statistical Inference of Function Through Evolutionary Relationships) methodology, uses a statistical graphical model to compute the probabilities of molecular functions for unannotated proteins. Our benchmark tests showed that SIFTER provides accurate functional predictions on various protein families, outperforming other available methods.
Kosinski, Jan; Barbato, Alessandro; Tramontano, Anna
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.7258 >orf19.7258; Contig19-2507; 88880..89851; DDI1*; response to DNA alkyl...ation; MQLTISLDHSGDIISVDVPDSLCLEDFKAYLSAETGLEASVQVLKFNGRELVGNATLSELQIHDNDLLQLSKKQVA
Full Text Available Ca19AnnotatedDec2004aaSeq orf19.2370 >orf19.2370; Contig19-10147; complement(50671..52716); DSL1*; retrogra...de ER-to-golgi transport; MPSIEQQLEDQELYLKDIEQNINKTLSKINKTTLENDNDFRKQFEEIPQDSNTTESN
Full Text Available ruitment factor; MAKTRSKSAATAAATSPKASPTAAKVTKNKVTKPSTASPSKTTKTKAVKKTTTKKATPKKEEEEKK... Ca19AnnotatedDec2004aaSeq orf19.124 >orf19.124; Contig19-10035; 67601..68698; CIC1*; protease substrate rec
Wang, Tianmin; Mori, Hiroshi; Zhang, Chong; Kurokawa, Ken; Xing, Xin-Hui; Yamada, Takuji
Computational predictions of catalytic function are vital for in-depth understanding of enzymes. Because several novel approaches performing better than the common BLAST tool are rarely applied in research, we hypothesized that there is a large gap between the number of known annotated enzymes and the actual number in the protein universe, which significantly limits our ability to extract additional biologically relevant functional information from the available sequencing data. To reliably expand the enzyme space, we developed DomSign, a highly accurate domain signature-based enzyme functional prediction tool to assign Enzyme Commission (EC) digits. DomSign is a top-down prediction engine that yields results comparable, or superior, to those from many benchmark EC number prediction tools, including BLASTP, when a homolog with an identity >30% is not available in the database. Performance tests showed that DomSign is a highly reliable enzyme EC number annotation tool. After multiple tests, the accuracy is thought to be greater than 90%. Thus, DomSign can be applied to large-scale datasets, with the goal of expanding the enzyme space with high fidelity. Using DomSign, we successfully increased the percentage of EC-tagged enzymes from 12% to 30% in UniProt-TrEMBL. In the Kyoto Encyclopedia of Genes and Genomes bacterial database, the percentage of EC-tagged enzymes for each bacterial genome could be increased from 26.0% to 33.2% on average. Metagenomic mining was also efficient, as exemplified by the application of DomSign to the Human Microbiome Project dataset, recovering nearly one million new EC-labeled enzymes. Our results offer preliminarily confirmation of the existence of the hypothesized huge number of "hidden enzymes" in the protein universe, the identification of which could substantially further our understanding of the metabolisms of diverse organisms and also facilitate bioengineering by providing a richer enzyme resource. Furthermore, our results
Köser, Claudio U.
Since its publication in 1998, the genome sequence of the Mycobacterium tuberculosis H37Rv laboratory strain has acted as the cornerstone for the study of tuberculosis. In this review we address some of the practical aspects that have come to light relating to the use of H37Rv throughout the past decade which are of relevance for the ongoing genomic and laboratory studies of this pathogen. These include errors in the genome reference sequence and its annotation, as well as the recently detected variation amongst isolates of H37Rv from different laboratories. © 2011 Elsevier B.V..
Ghorbani, Abozar; Izadpanah, Keramatollah; Dietzgen, Ralf G
Maize Iranian mosaic virus (MIMV) is a negative-sense single-stranded RNA virus that is classified in the genus Nucleorhabdovirus, family Rhabdoviridae. The MIMV genome contains six open reading frames (ORFs) that encode in 3΄ to 5΄ order the nucleocapsid protein (N), phosphoprotein (P), putative movement protein (P3), matrix protein (M), glycoprotein (G) and RNA-dependent RNA polymerase (L). In this study, we determined the first complete genome sequence of MIMV using Illumina RNA-Seq and 3'/5' RACE. MIMV genome ('Fars' isolate) is 12,426 nucleotides in length. Unexpectedly, the predicted N gene ORF of this isolate and of four other Iranian isolates is 143 nucleotides shorter than that of the MIMV coding-complete reference isolate 'Shiraz 1' (Genbank NC_011542), possibly due to a minor error in the previous sequence. Genetic variability among the N, P, P3 and G ORFs of Iranian MIMV isolates was limited, but highest in the G gene ORF. Phylogenetic analysis of complete nucleorhabdovirus genomes demonstrated a close evolutionary relationship between MIMV, maize mosaic virus and taro vein chlorosis virus.
Drummy, L.; Koerner, H; Phillips, D; McAuliffe, J; Kumar, M; Farmer, B; Vaia, R; Naik, R
Recombinant protein-inorganic nanocomposites comprised of exfoliated Na+ montmorillonite (MMT) in a recombinant protein matrix based on silk-like and elastin-like amino acid motifs (silk elastin-like protein (SELP)) were formed via a solution blending process. Charged residues along the protein backbone are shown to dominate long-range interactions, whereas the SELP repeat sequence leads to local protein/MMT compatibility. Up to a 50% increase in room temperature modulus and a comparable decrease in high temperature coefficient of thermal expansion occur for cast films containing 2-10 wt.% MMT.
Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us tRNAD... tRNA sequence data, annotation data and curation data - tRNADB-CE | LSDB Archive ...
Alexander, Roger P; Fang, Gang; Rozowsky, Joel; Snyder, Michael; Gerstein, Mark B
Most of the human genome consists of non-protein-coding DNA. Recently, progress has been made in annotating these non-coding regions through the interpretation of functional genomics experiments and comparative sequence analysis. One can conceptualize functional genomics analysis as involving a sequence of steps: turning the output of an experiment into a 'signal' at each base pair of the genome; smoothing this signal and segmenting it into small blocks of initial annotation; and then clustering these small blocks into larger derived annotations and networks. Finally, one can relate functional genomics annotations to conserved units and measures of conservation derived from comparative sequence analysis.
Büssow, Konrad; Hoffmann, Steve; Sievert, Volker
Functional genomics involves the parallel experimentation with large sets of proteins. This requires management of large sets of open reading frames as a prerequisite of the cloning and recombinant expression of these proteins. A Java program was developed for retrieval of protein and nucleic acid sequences and annotations from NCBI GenBank, using the XML sequence format. Annotations retrieved by ORFer include sequence name, organism and also the completeness of the sequence. The program has a graphical user interface, although it can be used in a non-interactive mode. For protein sequences, the program also extracts the open reading frame sequence, if available, and checks its correct translation. ORFer accepts user input in the form of single or lists of GenBank GI identifiers or accession numbers. It can be used to extract complete sets of open reading frames and protein sequences from any kind of GenBank sequence entry, including complete genomes or chromosomes. Sequences are either stored with their features in a relational database or can be exported as text files in Fasta or tabulator delimited format. The ORFer program is freely available at http://www.proteinstrukturfabrik.de/orfer. The ORFer program allows for fast retrieval of DNA sequences, protein sequences and their open reading frames and sequence annotations from GenBank. Furthermore, storage of sequences and features in a relational database is supported. Such a database can supplement a laboratory information system (LIMS) with appropriate sequence information.
Paul, Sandip; Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V; Chattopadhyay, Sujay
A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing the pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen - a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for a species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars - Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. Copyright © 2015 Elsevier Inc. All rights reserved.
Bhardwaj, Archana; Bag, Sumit K; Sokurenko, Evgeni V.
A large amount of genomic data, especially from multiple isolates of a single species, has opened new vistas for microbial genomics analysis. Analyzing pan-genome (i.e. the sum of genetic repertoire) of microbial species is crucial in understanding the dynamics of molecular evolution, where virulence evolution is of major interest. Here we present PanCoreGen – a standalone application for pan- and core-genomic profiling of microbial protein-coding genes. PanCoreGen overcomes key limitations of the existing pan-genomic analysis tools, and develops an integrated annotation-structure for species-specific pan-genomic profile. It provides important new features for annotating draft genomes/contigs and detecting unidentified genes in annotated genomes. It also generates user-defined group-specific datasets within the pan-genome. Interestingly, analyzing an example-set of Salmonella genomes, we detect potential footprints of adaptive convergence of horizontally transferred genes in two human-restricted pathogenic serovars – Typhi and Paratyphi A. Overall, PanCoreGen represents a state-of-the-art tool for microbial phylogenomics and pathogenomics study. PMID:26456591
Yang, Jian-Hua; Qu, Liang-Hu
Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.
Tobes, Raquel; Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Kovach, Evdokim; Alekhin, Alexey; Pareja, Eduardo
New massive sequencing technologies are providing many bacterial genome sequences from diverse taxa but a refined annotation of these genomes is crucial for obtaining scientific findings and new knowledge. Thus, bacterial genome annotation has emerged as a key point to investigate in bacteria. Any efficient tool designed specifically to annotate bacterial genomes sequenced with massively parallel technologies has to consider the specific features of bacterial genomes (absence of introns and scarcity of nonprotein-coding sequence) and of next-generation sequencing (NGS) technologies (presence of errors and not perfectly assembled genomes). These features make it convenient to focus on coding regions and, hence, on protein sequences that are the elements directly related with biological functions. In this chapter we describe how to annotate bacterial genomes with BG7, an open-source tool based on a protein-centered gene calling/annotation paradigm. BG7 is specifically designed for the annotation of bacterial genomes sequenced with NGS. This tool is sequence error tolerant maintaining their capabilities for the annotation of highly fragmented genomes or for annotating mixed sequences coming from several genomes (as those obtained through metagenomics samples). BG7 has been designed with scalability as a requirement, with a computing infrastructure completely based on cloud computing (Amazon Web Services).
Nicholas J Schurch
Full Text Available The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct and complete annotation in addition to the underlying genomic sequence is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3' untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3' polyadenylation sites to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1 gene and 3' UTR re-annotation (including extension of one 3' UTR by 5.9 kb; (2 disentangling of gene expression in complex regions; (3 clearer interpretation of small RNA expression and (4 identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental data.
Wyrwicz, Lucjan S; Koczyk, Grzegorz; Rychlewski, Leszek; Plewczynski, Dariusz
The annotation of protein folds within newly sequenced genomes is the main target for semi-automated protein structure prediction (virtual structural genomics). A large number of automated methods have been developed recently with very good results in the case of single-domain proteins. Unfortunately, most of these automated methods often fail to properly predict the distant homology between a given multi-domain protein query and structural templates. Therefore a multi-domain protein should be split into domains in order to overcome this limitation. ProteinSplit is designed to identify protein domain boundaries using a novel algorithm that predicts disordered regions in protein sequences. The software utilizes various sequence characteristics to assess the local propensity of a protein to be disordered or ordered in terms of local structure stability. These disordered parts of a protein are likely to create interdomain spacers. Because of its speed and portability, the method was successfully applied to several genome-wide fold annotation experiments. The user can run an automated analysis of sets of proteins or perform semi-automated multiple user projects (saving the results on the server). Additionally the sequences of predicted domains can be sent to the Bioinfo.PL Protein Structure Prediction Meta-Server for further protein three-dimensional structure and function prediction. The program is freely accessible as a web service at http://lucjan.bioinfo.pl/proteinsplit together with detailed benchmark results on the critical assessment of a fully automated structure prediction (CAFASP) set of sequences. The source code of the local version of protein domain boundary prediction is available upon request from the authors
Full Text Available Malaria Journal Open AcceReview Heterologous expression of plasmodial proteins for structural studies and functional annotation Lyn-Marie Birkholtz1, Gregory Blatch2, Theresa L Coetzer3, Heinrich C Hoppe1,4, Esmaré Human1, Elizabeth J Morris1,5, Zoleka Ngcete..., Kwadlangezwa, South Africa Email: Lyn-Marie Birkholtz - email@example.com; Gregory Blatch - G.Blatch@ru.ac.za; Theresa L Coetzer - firstname.lastname@example.org; Heinrich C Hoppe - email@example.com; Esmaré Human - firstname.lastname@example.org; Elizabeth J Morris...
Guo, Shaogui; Liu, Jingan; Zheng, Yi; Huang, Mingyun; Zhang, Haiying; Gong, Guoyi; He, Hongju; Ren, Yi; Zhong, Silin; Fei, Zhangjun; Xu, Yong
Cultivated watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai var. lanatus] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues. We performed half Roche/454 GS-FLX run for each of the four watermelon fruit developmental stages (immature white, white-pink flesh, red flesh and over-ripe) and obtained 577,023 high quality ESTs with an average length of 302.8 bp. De novo assembly of these ESTs together with 11,786 watermelon ESTs collected from GenBank produced 75,068 unigenes with a total length of approximately 31.8 Mb. Overall 54.9% of the unigenes showed significant similarities to known sequences in GenBank non-redundant (nr) protein database and around two-thirds of them matched proteins of cucumber, the most closely-related species with a sequenced genome. The unigenes were further assigned with gene ontology (GO) terms and mapped to biochemical pathways. More than 5,000 SSRs were identified from the EST collection. Furthermore we carried out digital gene expression analysis of these ESTs and identified 3,023 genes that were differentially expressed during watermelon fruit development and ripening, which provided novel insights into watermelon fruit biology and a comprehensive resource of candidate genes for future functional analysis. We then generated profiles of several interesting metabolites that are important to fruit quality including pigmentation and sweetness. Integrative analysis of metabolite and digital gene expression
Marais Gabriel AB
Full Text Available Abstract Background The genus Silene is widely used as a model system for addressing ecological and evolutionary questions in plants, but advances in using the genus as a model system are impeded by the lack of available resources for studying its genome. Massively parallel sequencing cDNA has recently developed into an efficient method for characterizing the transcriptomes of non-model organisms, generating massive amounts of data that enable the study of multiple species in a comparative framework. The sequences generated provide an excellent resource for identifying expressed genes, characterizing functional variation and developing molecular markers, thereby laying the foundations for future studies on gene sequence and gene expression divergence. Here, we report the results of a comparative transcriptome sequencing study of eight individuals representing four Silene and one Dianthus species as outgroup. All sequences and annotations have been deposited in a newly developed and publicly available database called SiESTa, the Silene EST annotation database. Results A total of 1,041,122 EST reads were generated in two runs on a Roche GS-FLX 454 pyrosequencing platform. EST reads were analyzed separately for all eight individuals sequenced and were assembled into contigs using TGICL. These were annotated with results from BLASTX searches and Gene Ontology (GO terms, and thousands of single-nucleotide polymorphisms (SNPs were characterized. Unassembled reads were kept as singletons and together with the contigs contributed to the unigenes characterized in each individual. The high quality of unigenes is evidenced by the proportion (49% that have significant hits in similarity searches with the A. thaliana proteome. The SiESTa database is accessible at http://www.siesta.ethz.ch. Conclusion The sequence collections established in the present study provide an important genomic resource for four Silene and one Dianthus species and will help to
Background The genus Silene is widely used as a model system for addressing ecological and evolutionary questions in plants, but advances in using the genus as a model system are impeded by the lack of available resources for studying its genome. Massively parallel sequencing cDNA has recently developed into an efficient method for characterizing the transcriptomes of non-model organisms, generating massive amounts of data that enable the study of multiple species in a comparative framework. The sequences generated provide an excellent resource for identifying expressed genes, characterizing functional variation and developing molecular markers, thereby laying the foundations for future studies on gene sequence and gene expression divergence. Here, we report the results of a comparative transcriptome sequencing study of eight individuals representing four Silene and one Dianthus species as outgroup. All sequences and annotations have been deposited in a newly developed and publicly available database called SiESTa, the Silene EST annotation database. Results A total of 1,041,122 EST reads were generated in two runs on a Roche GS-FLX 454 pyrosequencing platform. EST reads were analyzed separately for all eight individuals sequenced and were assembled into contigs using TGICL. These were annotated with results from BLASTX searches and Gene Ontology (GO) terms, and thousands of single-nucleotide polymorphisms (SNPs) were characterized. Unassembled reads were kept as singletons and together with the contigs contributed to the unigenes characterized in each individual. The high quality of unigenes is evidenced by the proportion (49%) that have significant hits in similarity searches with the A. thaliana proteome. The SiESTa database is accessible at http://www.siesta.ethz.ch. Conclusion The sequence collections established in the present study provide an important genomic resource for four Silene and one Dianthus species and will help to further develop Silene as a
Kuczmarski Thomas A
Full Text Available Abstract Background MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. Description MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. Conclusion MannDB comprises a large number of genomes and comprehensive protein
Schulz, Wade L; Tormey, Christopher A; Torres, Richard
Next generation sequencing (NGS) has become a common technology in the clinical laboratory, particularly for the analysis of malignant neoplasms. However, most mutations identified by NGS are variants of unknown clinical significance (VOUS). Although the approach to define these variants differs by institution, software algorithms that predict variant effect on protein function may be used. However, these algorithms commonly generate conflicting results, potentially adding uncertainty to interpretation. In this review, we examine several computational tools used to predict whether a variant has clinical significance. In addition to describing the role of these tools in clinical diagnostics, we assess their efficacy in analyzing known pathogenic and benign variants in hematologic malignancies. Copyright© by the American Society for Clinical Pathology (ASCP).
Full Text Available Abstract Background The population of HIV replicating within a host consists of independently evolving and interacting sub-populations that can be genetically distinct within anatomical compartments. HIV replicating within the brain causes neurocognitive disorders in up to 20-30% of infected individuals and is a viral sanctuary site for the development of drug resistance. The primary determinant of HIV neurotropism is macrophage tropism, which is primarily determined by the viral envelope (env gene. However, studies of genetic aspects of HIV replicating in the brain are hindered because existing repositories of HIV sequences are not focused on neurotropic virus nor annotated with neurocognitive and neuropathological status. To address this need, we constructed the HIV Brain Sequence Database. Results The HIV Brain Sequence Database is a public database of HIV envelope sequences, directly sequenced from brain and other tissues from the same patients. Sequences are annotated with clinical data including viral load, CD4 count, antiretroviral status, neurocognitive impairment, and neuropathological diagnosis, all curated from the original publication. Tissue source is coded using an anatomical ontology, the Foundational Model of Anatomy, to capture the maximum level of detail available, while maintaining ontological relationships between tissues and their subparts. 44 tissue types are represented within the database, grouped into 4 categories: (i brain, brainstem, and spinal cord; (ii meninges, choroid plexus, and CSF; (iii blood and lymphoid; and (iv other (bone marrow, colon, lung, liver, etc. Patient coding is correlated across studies, allowing sequences from the same patient to be grouped to increase statistical power. Using Cytoscape, we visualized relationships between studies, patients and sequences, illustrating interconnections between studies and the varying depth of sequencing, patient number, and tissue representation across studies
Full Text Available Protein-protein interactions (PPIs are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π, a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users
Gao, Jian; Li, Qiye; Wang, Zongji; Zhou, Yang; Martelli, Paolo; Li, Fang; Xiong, Zijun; Wang, Jian; Yang, Huanming; Zhang, Guojie
The Chinese crocodile lizard, Shinisaurus crocodilurus, is the only living representative of the monotypic family Shinisauridae under the order Squamata. It is an obligate semi-aquatic, viviparous, diurnal species restricted to specific portions of mountainous locations in southwestern China and northeastern Vietnam. However, in the past several decades, this species has undergone a rapid decrease in population size due to illegal poaching and habitat disruption, making this unique reptile species endangered and listed in the Convention on International Trade in Endangered Species of Wild Fauna and Flora Appendix II since 1990. A proposal to uplist it to Appendix I was passed at the Convention on International Trade in Endangered Species of Wild Fauna and Flora Seventeenth meeting of the Conference of the Parties in 2016. To promote the conservation of this species, we sequenced the genome of a male Chinese crocodile lizard using a whole-genome shotgun strategy on the Illumina HiSeq 2000 platform. In total, we generated ∼291 Gb of raw sequencing data (×149 depth) from 13 libraries with insert sizes ranging from 250 bp to 40 kb. After filtering for polymerase chain reaction-duplicated and low-quality reads, ∼137 Gb of clean data (×70 depth) were obtained for genome assembly. We yielded a draft genome assembly with a total length of 2.24 Gb and an N50 scaffold size of 1.47 Mb. The assembled genome was predicted to contain 20 150 protein-coding genes and up to 1114 Mb (49.6%) of repetitive elements. The genomic resource of the Chinese crocodile lizard will contribute to deciphering the biology of this organism and provides an essential tool for conservation efforts. It also provides a valuable resource for future study of squamate evolution. © The Authors 2017. Published by Oxford University Press.
Lange, Philipp F; Huesgen, Pitter F; Nguyen, Karen; Overall, Christopher M
A goal of the Chromosome-centric Human Proteome Project is to identify all human protein species. With 3844 proteins annotated as "missing", this is challenging. Moreover, proteolytic processing generates new protein species with characteristic neo-N termini that are frequently accompanied by altered half-lives, function, interactions, and location. Enucleated and largely void of internal membranes and organelles, erythrocytes are simple yet proteomically challenging cells due to the high hemoglobin content and wide dynamic range of protein concentrations that impedes protein identification. Using the N-terminomics procedure TAILS, we identified 1369 human erythrocyte natural and neo-N-termini and 1234 proteins. Multiple semitryptic N-terminal peptides exhibited improved mass spectrometric identification properties versus the intact tryptic peptide enabling identification of 281 novel erythrocyte proteins and six missing proteins identified for the first time in the human proteome. With an improved bioinformatics workflow, we developed a new classification system and the Terminus Cluster Score. Thereby we described a new stabilizing N-end rule for processed protein termini, which discriminates novel protein species from degradation remnants, and identified protein domain hot spots susceptible to cleavage. Strikingly, 68% of the N-termini were within genome-encoded protein sequences, revealing alternative translation initiation sites, pervasive endoproteolytic processing, and stabilization of protein fragments in vivo. The mass spectrometry proteomics data have been deposited to ProteomeXchange with the data set identifier .
Liu, Junyan; Li, Lin; Peters, Brian M; Li, Bing; Deng, Yang; Xu, Zhenbo; Shirtliff, Mark E
Lactobacillus acetotolerans is a hard-to-culture beer-spoilage bacterium capable of entering into the viable putative nonculturable (VPNC) state. As part of an initial strategy to investigate the phenotypic behavior of L. acetotolerans, draft genome sequencing was performed. Results demonstrated a total of 1824 predicted annotated genes, with several potential VPNC- and beer-spoilage-associated genes identified. Importantly, this is the first genome sequence of L. acetotolerans as beer-spoilage bacteria and it may aid in further analysis of L. acetotolerans and other beer-spoilage bacteria, with direct implications for food safety control in the beer brewing industry. © FEMS 2016. All rights reserved. For permissions, please e-mail: email@example.com.
Full Text Available Abstract Background The expressed sequence tag (EST methodology is an attractive option for the generation of sequence data for species for which no completely sequenced genome is available. The annotation and comparative analysis of such datasets poses a formidable challenge for research groups that do not have the bioinformatics infrastructure of major genome sequencing centres. Therefore, there is a need for user-friendly tools to facilitate the annotation of non-model species EST datasets with well-defined ontologies that enable meaningful cross-species comparisons. To address this, we have developed annot8r, a platform for the rapid annotation of EST datasets with GO-terms, EC-numbers and KEGG-pathways. Results annot8r automatically downloads all files relevant for the annotation process and generates a reference database that stores UniProt entries, their associated Gene Ontology (GO, Enzyme Commission (EC and Kyoto Encyclopaedia of Genes and Genomes (KEGG annotation and additional relevant data. For each of GO, EC and KEGG, annot8r extracts a specific sequence subset from the UniProt dataset based on the information stored in the reference database. These three subsets are then formatted for BLAST searches. The user provides the protein or nucleotide sequences to be annotated and annot8r runs BLAST searches against these three subsets. The BLAST results are parsed and the corresponding annotations retrieved from the reference database. The annotations are saved both as flat files and also in a relational postgreSQL results database to facilitate more advanced searches within the results. annot8r is integrated with the PartiGene suite of EST analysis tools. Conclusion annot8r is a tool that assigns GO, EC and KEGG annotations for data sets resulting from EST sequencing projects both rapidly and efficiently. The benefits of an underlying relational database, flexibility and the ease of use of the program make it ideally suited for non
Weinberg, Zasha; Ruzzo, Walter L
Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CM's accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be. In this paper we introduce profile HMM-based heuristic filters. We show that their accuracy is usually superior to heuristics based on BLAST. Moreover, we compared our heuristics with those used in tRNAscan-SE, whose heuristics incorporate a significant amount of work specific to tRNAs, where our heuristics are generic to any ncRNA. Performance was roughly comparable, so we expect that our heuristics provide a high-quality solution that--unlike family-specific solutions--can scale to hundreds of ncRNA families. The source code is available under GNU Public License at the supplementary web site.
Huang Yan Zhao
To clarify the randomness of protein sequences, we make a detailed analysis of a set of typical protein sequences representing each structural classes by using nonlinear prediction method. No deterministic structures are found in these protein sequences and this implies that they behave as random sequences. We also give an explanation to the controversial results obtained in previous investigations.
This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large
Damte, Dereje; Suh, Joo-Won; Lee, Seung-Jin; Yohannes, Sileshi Belew; Hossain, Md Akil; Park, Seung-Chun
In the present study, a computational comparative and subtractive genomic/proteomic analysis aimed at the identification of putative therapeutic target and vaccine candidate proteins from Kyoto Encyclopedia of Genes and Genomes (KEGG) annotated metabolic pathways of Mycoplasma hyopneumoniae was performed for drug design and vaccine production pipelines against M.hyopneumoniae. The employed comparative genomic and metabolic pathway analysis with a predefined computational systemic workflow extracted a total of 41 annotated metabolic pathways from KEGG among which five were unique to M. hyopneumoniae. A total of 234 proteins were identified to be involved in these metabolic pathways. Although 125 non homologous and predicted essential proteins were found from the total that could serve as potential drug targets and vaccine candidates, additional prioritizing parameters characterize 21 proteins as vaccine candidate while druggability of each of the identified proteins evaluated by the DrugBank database prioritized 42 proteins suitable for drug targets. Copyright © 2013 Elsevier Inc. All rights reserved.
Full Text Available Abstract Background Understanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site http://www.sblest.org/ and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest. Results Users are able to submit their own queries or use a structure already in the PDB. Currently the databases that a user can query include the popular structural datasets ASTRAL 40 v1.69, ASTRAL 95 v1.69, CLUSTER50, CLUSTER70 and CLUSTER90 and PDBSELECT25. The results can be downloaded directly from the site and include function prediction, analysis of the most conserved environments and automated annotation of query proteins. These results reflect both the hits found with PSI-BLAST, HMMer and with S-BLEST. We have evaluated how well annotation transfer can be performed on SCOP ID's, Gene Ontology (GO ID's and EC Numbers. The method is very efficient and totally automated, generally taking around fifteen minutes for a 400 residue protein. Conclusion With structural genomics initiatives determining structures with little, if any, functional characterization
Sarver Aaron L
Full Text Available Abstract Background Next generation sequencing approaches applied to the analyses of transposon insertion junction fragments generated in high throughput forward genetic screens has created the need for clear informatics and statistical approaches to deal with the massive amount of data currently being generated. Previous approaches utilized to 1 map junction fragments within the genome and 2 identify Common Insertion Sites (CISs within the genome are not practical due to the volume of data generated by current sequencing technologies. Previous approaches applied to this problem also required significant manual annotation. Results We describe Transposon Annotation Poisson Distribution Association Network Connectivity Environment (TAPDANCE software, which automates the identification of CISs within transposon junction fragment insertion data. Starting with barcoded sequence data, the software identifies and trims sequences and maps putative genomic sequence to a reference genome using the bowtie short read mapper. Poisson distribution statistics are then applied to assess and rank genomic regions showing significant enrichment for transposon insertion. Novel methods of counting insertions are used to ensure that the results presented have the expected characteristics of informative CISs. A persistent mySQL database is generated and utilized to keep track of sequences, mappings and common insertion sites. Additionally, associations between phenotypes and CISs are also identified using Fisher’s exact test with multiple testing correction. In a case study using previously published data we show that the TAPDANCE software identifies CISs as previously described, prioritizes them based on p-value, allows holistic visualization of the data within genome browser software and identifies relationships present in the structure of the data. Conclusions The TAPDANCE process is fully automated, performs similarly to previous labor intensive approaches
Full Text Available Abstract Background Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. Results We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. Conclusion We have developed a novel and useful approach for
Singh, Swati; Gupta, Sanchita; Mani, Ashutosh; Chaturvedi, Anoop
Humulus lupulus is commonly known as hops, a member of the family moraceae. Currently many projects are underway leading to the accumulation of voluminous genomic and expressed sequence tag sequences in public databases. The genetically characterized domains in these databases are limited due to non-availability of reliable molecular markers. The large data of EST sequences are available in hops. The simple sequence repeat markers extracted from EST data are used as molecular markers for genetic characterization, in the present study. 25,495 EST sequences were examined and assembled to get full-length sequences. Maximum frequency distribution was shown by mononucleotide SSR motifs i.e. 60.44% in contig and 62.16% in singleton where as minimum frequency are observed for hexanucleotide SSR in contig (0.09%) and pentanucleotide SSR in singletons (0.12%). Maximum trinucleotide motifs code for Glutamic acid (GAA) while AT/TA were the most frequent repeat of dinucleotide SSRs. Flanking primer pairs were designed in-silico for the SSR containing sequences. Functional categorization of SSRs containing sequences was done through gene ontology terms like biological process, cellular component and molecular function. PMID:22368382
Lijin K. Gopi
Full Text Available Current era of functional genomics is enriched with good quality draft genomes and annotations for many thousands of species and varieties with the support of the advancements in the next generation sequencing technologies (NGS. Around 25,250 genomes, of the organisms from various kingdoms, are submitted in the NCBI genome resource till date. Each of these genomes was annotated using various tools and knowledge-bases that were available during the period of the annotation. It is obvious that these annotations will be improved if the same genome is annotated using improved tools and knowledge-bases. Here we present a new genome annotation pipeline, strengthened with various tools and knowledge-bases that are capable of producing better quality annotations from the consensus of the predictions from different tools. This resource also perform various additional annotations, apart from the usual gene predictions and functional annotations, which involve SSRs, novel repeats, paralogs, proteins with transmembrane helices, signal peptides etc. This new annotation resource is trained to evaluate and integrate all the predictions together to resolve the overlaps and ambiguities of the boundaries. One of the important highlights of this resource is the capability of predicting the phylogenetic relations of the repeats using the evolutionary trace analysis and orthologous gene clusters. We also present a case study, of the pipeline, in which we upgrade the genome annotation of Nelumbo nucifera (sacred lotus. It is demonstrated that this resource is capable of producing an improved annotation for a better understanding of the biology of various organisms.
Magness Charles L
a closely related species. Conclusion The number of different genes represented on microarrays for unfinished genomes can be greatly increased by matching known gene transcript annotations from a closely related species with sequence data from the unfinished genome. Signal intensity on both EST- and genome-derived arrays was highly correlated with probe distance from the 3' UTR, information often missing from ESTs yet present in early-stage genome projects.
Kim, Sun; Chatr-aryamontri, Andrew; Chang, Christie S.; Oughtred, Rose; Rust, Jennifer; Wilbur, W. John; Comeau, Donald C.; Dolinski, Kara; Tyers, Mike
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future
Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.
Rakhashiya, Purvi M; Patel, Pooja P; Thaker, Vrinda S
Actinobaceria, Micrococcus luteus SUBG006 was isolated from infected leaves of Mangifera indica L. vr. Nylon in Rajkot, (22.30°N, 70.78°E), Gujarat, India. The genome size is 3.86 Mb with G + C content of 69.80% and contains 112 rRNA sequences (5S, 16S and 23S). The whole genome sequencing has been deposited in DDBJ/EMBL/GenBank under the accession number JOKP00000000.
Full Text Available Abstract Background Magnaporthe oryzae, the causal agent of blast disease of rice, is the most destructive disease of rice worldwide. The genome of this fungal pathogen has been sequenced and an automated annotation has recently been updated to Version 6 http://www.broad.mit.edu/annotation/genome/magnaporthe_grisea/MultiDownloads.html. However, a comprehensive manual curation remains to be performed. Gene Ontology (GO annotation is a valuable means of assigning functional information using standardized vocabulary. We report an overview of the GO annotation for Version 5 of M. oryzae genome assembly. Methods A similarity-based (i.e., computational GO annotation with manual review was conducted, which was then integrated with a literature-based GO annotation with computational assistance. For similarity-based GO annotation a stringent reciprocal best hits method was used to identify similarity between predicted proteins of M. oryzae and GO proteins from multiple organisms with published associations to GO terms. Significant alignment pairs were manually reviewed. Functional assignments were further cross-validated with manually reviewed data, conserved domains, or data determined by wet lab experiments. Additionally, biological appropriateness of the functional assignments was manually checked. Results In total, 6,286 proteins received GO term assignment via the homology-based annotation, including 2,870 hypothetical proteins. Literature-based experimental evidence, such as microarray, MPSS, T-DNA insertion mutation, or gene knockout mutation, resulted in 2,810 proteins being annotated with GO terms. Of these, 1,673 proteins were annotated with new terms developed for Plant-Associated Microbe Gene Ontology (PAMGO. In addition, 67 experiment-determined secreted proteins were annotated with PAMGO terms. Integration of the two data sets resulted in 7,412 proteins (57% being annotated with 1,957 distinct and specific GO terms. Unannotated proteins
Deng, Youping; Dong, Yinghua; Thodima, Venkata; Clem, Rollie J; Passarelli, A Lorena
Background Little is known about the genome sequences of lepidopteran insects, although this group of insects has been studied extensively in the fields of endocrinology, development, immunity, and pathogen-host interactions. In addition, cell lines derived from Spodoptera frugiperda and other lepidopteran insects are routinely used for baculovirus foreign gene expression. This study reports the results of an expressed sequence tag (EST) sequencing project in cells from the lepidopteran insect S. frugiperda, the fall armyworm. Results We have constructed an EST database using two cDNA libraries from the S. frugiperda-derived cell line, SF-21. The database consists of 2,367 ESTs which were assembled into 244 contigs and 951 singlets for a total of 1,195 unique sequences. Conclusion S. frugiperda is an agriculturally important pest insect and genomic information will be instrumental for establishing initial transcriptional profiling and gene function studies, and for obtaining information about genes manipulated during infections by insect pathogens such as baculoviruses. PMID:17052344
Markus H Antwerpen
Full Text Available The zoonotic disease tularemia is caused by the bacterium Francisella tularensis. This pathogen is considered as a category A select agent with potential to be misused in bioterrorism. Molecular typing based on DNA-sequence like canSNP-typing or MLVA has become the accepted standard for this organism. Due to the organism's highly clonal nature, the current typing methods have reached their limit of discrimination for classifying closely related subpopulations within the subspecies F. tularensis ssp. holarctica. We introduce a new gene-by-gene approach, MLST+, based on whole genome data of 15 sequenced F. tularensis ssp. holarctica strains and apply this approach to investigate an epidemic of lethal tularemia among non-human primates in two animal facilities in Germany. Due to the high resolution of MLST+ we are able to demonstrate that three independent clones of this highly infectious pathogen were responsible for these spatially and temporally restricted outbreaks.
Nicholas T. Ingolia
Full Text Available Ribosome profiling suggests that ribosomes occupy many regions of the transcriptome thought to be noncoding, including 5′ UTRs and long noncoding RNAs (lncRNAs. Apparent ribosome footprints outside of protein-coding regions raise the possibility of artifacts unrelated to translation, particularly when they occupy multiple, overlapping open reading frames (ORFs. Here, we show hallmarks of translation in these footprints: copurification with the large ribosomal subunit, response to drugs targeting elongation, trinucleotide periodicity, and initiation at early AUGs. We develop a metric for distinguishing between 80S footprints and nonribosomal sources using footprint size distributions, which validates the vast majority of footprints outside of coding regions. We present evidence for polypeptide production beyond annotated genes, including the induction of immune responses following human cytomegalovirus (HCMV infection. Translation is pervasive on cytosolic transcripts outside of conserved reading frames, and direct detection of this expanded universe of translated products enables efforts at understanding how cells manage and exploit its consequences.
Huntemann, Marcel; Ivanova, Natalia N; Mavromatis, Konstantinos; Tripp, H James; Paez-Espino, David; Palaniappan, Krishnaveni; Szeto, Ernest; Pillay, Manoj; Chen, I-Min A; Pati, Amrita; Nielsen, Torben; Markowitz, Victor M; Kyrpides, Nikos C
The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.
Full Text Available The yeast two-hybrid (Y2H system exploits host cell genetics in order to display binary protein-protein interactions (PPIs via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS, and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine.
Austin, Christopher M; Tan, Mun Hua; Harrisson, Katherine A; Lee, Yin Peng; Croft, Laurence J; Sunnucks, Paul; Pavlova, Alexandra; Gan, Han Ming
One of the most iconic Australian fish is the Murray cod, Maccullochella peelii (Mitchell 1838), a freshwater species that can grow to ∼1.8 metres in length and live to age ≥48 years. The Murray cod is of a conservation concern as a result of strong population contractions, but it is also popular for recreational fishing and is of growing aquaculture interest. In this study, we report the whole genome sequence of the Murray cod to support ongoing population genetics, conservation, and management research, as well as to better understand the evolutionary ecology and history of the species. A draft Murray cod genome of 633 Mbp (N50 = 109 974bp; BUSCO and CEGMA completeness of 94.2% and 91.9%, respectively) with an estimated 148 Mbp of putative repetitive sequences was assembled from the combined sequencing data of 2 fish individuals with an identical maternal lineage; 47.2 Gb of Illumina HiSeq data and 804 Mb of Nanopore data were generated from the first individual while 23.2 Gb of Illumina MiSeq data were generated from the second individual. The inclusion of Nanopore reads for scaffolding followed by subsequent gap-closing using Illumina data led to a 29% reduction in the number of scaffolds and a 55% and 54% increase in the scaffold and contig N50, respectively. We also report the first transcriptome of Murray cod that was subsequently used to annotate the Murray cod genome, leading to the identification of 26 539 protein-coding genes. We present the whole genome of the Murray cod and anticipate this will be a catalyst for a range of genetic, genomic, and phylogenetic studies of the Murray cod and more generally other fish species of the Percichthydae family. © The Authors 2017. Published by Oxford University Press.
Jessen, Leon Ivar; Hoof, Ilka; Lund, Ole
Site does not require any pre-definition of subgroups or binary classification. Input is a set of protein sequences where each sequence has an associated real number, quantifying a given phenotype. SigniSite will then identify which amino acid residues are significantly associated with the data set......) using a set of human immunodeficiency virus protease-inhibitor genotype–phenotype data and corresponding resistance mutation scores from the Stanford University HIV Drug Resistance Database, and a data set of protein families with experimentally annotated SDPs. For both data sets, SigniSite was found...
Functional profiling is a key technique to characterize and compare the functional potential of entire genomes. The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families. Based on machine learning techniques for Pfam domain detection, the UFO web server for ultra-fast functional profiling allows researchers to process large protein sequence collections instantaneously. Besides the frequencies of Pfam and GO categories, the user also obtains the sequence specific assignments to Pfam domain families. In addition, a comparison with existing genomes provides dissimilarity scores with respect to 821 reference proteomes. Considering the underlying UFO domain detection, the results on 206 test genomes indicate a high sensitivity of the approach. In comparison with current state-of-the-art HMMs, the runtime measurements show a considerable speed up in the range of four orders of magnitude. For an average size prokaryotic genome, the computation of a functional profile together with its comparison typically requires about 10 seconds of processing time. For the first time the UFO web server makes it possible to get a quick overview on the functional inventory of newly sequenced organisms. The genome scale comparison with a large number of precomputed profiles allows a first guess about functionally related organisms. The service is freely available and does not require user registration or specification of a valid email address.
Full Text Available A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.. Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity. Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low
Millenbaugh, Bonnie A; Pangilinan, Jasmyn L.; Torriani, Stefano F.F.; Goodwin, Stephen B.; Kema, Gert H.J.; McDonald, Bruce A.
The mitochondrial genomes of two isolates of the wheat pathogen Mycosphaerella graminicola were sequenced completely and compared to identify polymorphic regions. This organism is of interest because it is phylogenetically distant from other fungi with sequenced mitochondrial genomes and it has shown discordant patterns of nuclear and mitochondrial diversity. The mitochondrial genome of M. graminicola is a circular molecule of approximately 43,960 bp containing the typical genes coding for 14 proteins related to oxidative phosphorylation, one RNA polymerase, two rRNA genes and a set of 27 tRNAs. The mitochondrial DNA of M. graminicola lacks the gene encoding the putative ribosomal protein (rps5-like), commonly found in fungal mitochondrial genomes. Most of the tRNA genes were clustered with a gene order conserved with many other ascomycetes. A sample of thirty-five additional strains representing the known global mt diversity was partially sequenced to measure overall mitochondrial variability within the species. Little variation was found, confirming previous RFLP-based findings of low mitochondrial diversity. The mitochondrial sequence of M. graminicola is the first reported from the family Mycosphaerellaceae or the order Capnodiales. The sequence also provides a tool to better understand the development of fungicide resistance and the conflicting pattern of high nuclear and low mitochondrial diversity in global populations of this fungus.
Huo, Tong; Liu, Wei; Guo, Yu; Yang, Cheng; Lin, Jianping; Rao, Zihe
Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease. We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by 'interolog' method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins. This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.
Full Text Available Phylogenetic trees are used to represent the evolutionary relationship among various groups of species. In this paper, a novel method for inferring prokaryotic phylogenies using multiple genomic information is proposed. The method is called CGCPhy and based on the distance matrix of orthologous gene clusters between whole-genome pairs. CGCPhy comprises four main steps. First, orthologous genes are determined by sequence similarity, genomic function, and genomic structure information. Second, genes involving potential HGT events are eliminated, since such genes are considered to be the highly conserved genes across different species and the genes located on fragments with abnormal genome barcode. Third, we calculate the distance of the orthologous gene clusters between each genome pair in terms of the number of orthologous genes in conserved clusters. Finally, the neighbor-joining method is employed to construct phylogenetic trees across different species. CGCPhy has been examined on different datasets from 617 complete single-chromosome prokaryotic genomes and achieved applicative accuracies on different species sets in agreement with Bergey's taxonomy in quartet topologies. Simulation results show that CGCPhy achieves high average accuracy and has a low standard deviation on different datasets, so it has an applicative potential for phylogenetic analysis.
Full Text Available Abstract Background The structural and functional features associated with Simple Sequence Proteins (SSPs are non-globularity, disease states, signaling and post-translational modification. SSPs are also an important source of genetic and possibly phenotypic variation. Analysis of 249 prokaryotic proteomes offers a new opportunity to examine the genomic properties of SSPs. Results SSPs are a minority but they grow with proteome size. This relationship is exhibited across species varying in genomic GC, mutational bias, life style, and pathogenicity. Their proportion in each proteome is strongly influenced by genomic base compositional bias. In most species simple duplications is favoured, but in a few cases such as Mycobacteria, large families of duplications occur. Amino acid preference in SSPs exhibits a trend towards low cost of biosynthesis. In SSPs and in non-SSPs, Alanine, Glycine, Leucine, and Valine are abundant in species widely varying in genomic GC whereas Isoleucine and Lysine are rich only in organisms with low genomic GC. Arginine is abundant in SSPs of two species and in the non-SSPs of Xanthomonas oryzae. Asparagine is abundant only in SSPs of low GC species. Aspartic acid is abundant only in the non-SSPs of Halobacterium sp NRC1. The abundance of Serine in SSPs of 62 species extends over a broader range compared to that of non-SSPs. Threonine(T is abundant only in SSPs of a couple of species. SSPs exhibit preferential association with Cell surface, Cell membrane and Transport functions and a negative association with Metabolism. Mesophiles and Thermophiles display similar ranges in the content of SSPs. Conclusion Although SSPs are a minority, the genomic forces of base compositional bias and duplications influence their growth and pattern in each species. The preferences and abundance of amino acids are governed by low biosynthetic cost, evolutionary age and base composition of codons. Abundance of charged amino acids Arginine
Smaili, Fatima Z.
operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching
Mewes, H W; Dietmann, S; Frishman, D; Gregory, R; Mannhaupt, G; Mayer, K F X; Münsterkötter, M; Ruepp, A; Spannagl, M; Stümpflen, V; Rattei, T
The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).
Full Text Available Abstract Background Apicomplexan parasites are causative agents of various diseases including malaria and have been targets of extensive genomic sequencing. We generated 5'-EST collections for six apicomplexa parasites using our full-length oligo-capping cDNA library method. To improve upon the current genome annotations, as well as to validate the importance for physical cDNA clone resources, we generated a large-scale collection of full-length cDNAs for several apicomplexa parasites. Results In this study, we used a total of 61,056 5'-end-single-pass cDNA sequences from Plasmodium falciparum, P. vivax, P. yoelii, P. berghei, Cryptosporidium parvum, and Toxoplasma gondii. We compared these partially sequenced cDNA sequences with the currently annotated gene models and observed significant inconsistencies between the two datasets. In particular, we found that on average 14% of the exons in the current gene models were not supported by any cDNA evidence, and that 16% of the current gene models may contain at least one mis-annotation and should be re-evaluated. We also identified a large number of transcripts that had been previously unidentified. For 732 cDNAs in T. gondii, the entire sequences were determined in order to evaluate the annotated gene models at the complete full-length transcript level. We found that 41% of the T. gondii gene models contained at least one inconsistency. We also identified and confirmed by RT-PCR 140 previously unidentified transcripts found in the intergenic regions of the current gene annotations. We show that the majority of these discrepancies are due to questionable predictions of one or two extra exons in the upstream or downstream regions of the genes. Conclusion Our data indicates that the current gene models are likely to still be incomplete and have much room for improvement. Our unique full-length cDNA information is especially useful for further refinement of the annotations for the genomes of
Shahbaaz, Mohd; Ahmad, Faizan; Imtaiyaz Hassan, Md
Haemophilus influenzae is a small pleomorphic Gram-negative bacteria which causes several chronic diseases, including bacteremia, meningitis, cellulitis, epiglottitis, septic arthritis, pneumonia, and empyema. Here we extensively analyzed the sequenced genome of H. influenzae strain Rd KW20 using protein family databases, protein structure prediction, pathways and genome context methods to assign a precise function to proteins whose functions are unknown. These proteins are termed as hypothetical proteins (HPs), for which no experimental information is available. Function prediction of these proteins would surely be supportive to precisely understand the biochemical pathways and mechanism of pathogenesis of Haemophilus influenzae. During the extensive analysis of H. influenzae genome, we found the presence of eight HPs showing lyase activity. Subsequently, we modeled and analyzed three-dimensional structure of all these HPs to determine their functions more precisely. We found these HPs possess cystathionine-β-synthase, cyclase, carboxymuconolactone decarboxylase, pseudouridine synthase A and C, D-tagatose-1,6-bisphosphate aldolase and aminodeoxychorismate lyase-like features, indicating their corresponding functions in the H. influenzae. Lyases are actively involved in the regulation of biosynthesis of various hormones, metabolic pathways, signal transduction, and DNA repair. Lyases are also considered as a key player for various biological processes. These enzymes are critically essential for the survival and pathogenesis of H. influenzae and, therefore, these enzymes may be considered as a potential target for structure-based rational drug design. Our structure-function relationship analysis will be useful to search and design potential lead molecules based on the structure of these lyases, for drug design and discovery.
Prlic, Andreas; Kalro, Tara; Bhattacharya, Roshni; Christie, Cole; Burley, Stephen K; Rose, Peter W
The Protein Data Bank (PDB) now contains more than 120,000 three-dimensional (3D) structures of biological macromolecules. To allow an interpretation of how PDB data relates to other publicly available annotations, we developed a novel data integration platform that maps 3D structural information across various datasets. This integration bridges from the human genome across protein sequence to 3D structure space. We developed novel software solutions for data management and visualization, while incorporating new libraries for web-based visualization using SVG graphics. The new views are available from http://www.rcsb.org and software is available from https://github.com/rcsb/. firstname.lastname@example.orgSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
Bada, Michael; Eckert, Miriam; Evans, Donald; Garcia, Kristin; Shipley, Krista; Sitnikov, Dmitry; Baumgartner, William A; Cohen, K Bretonnel; Verspoor, Karin; Blake, Judith A; Hunter, Lawrence E
Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.
Sharpton, Thomas J; Jospin, Guillaume; Wu, Dongying; Langille, Morgan G I; Pollard, Katherine S; Eisen, Jonathan A
New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences. We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as "Sifting Families," or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology-based analyses. We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/).
The author has used the sequence analysis of radioactive proteins and peptides to approach several problems during the past few years. They, in collaboration with others, have mapped precisely several adenovirus proteins with respect to the nucleotide sequence of the adenovirus genome; identified hitherto missed proteins encoded by bacteriophage MS2 and by simian virus 40; analyzed the aminoterminal maturation of several virus proteins; determined the cleavage sites for processing of the poliovirus polyprotein; and analyzed the mechanism of frameshifting by excess normal tRNAs during cell-free protein synthesis. This chapter is designed to aid those without prior experience at protein sequence determinations. It is based primarily on the experience gained in the studies cited above, which made use of the Beckman 890 series automated protein sequencers
Torkamani, Ali; Scott-Van Zeeland, Ashley A; Topol, Eric J; Schork, Nicholas J
Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants. Copyright © 2011 Elsevier Inc. All rights reserved.
Torkamani, Ali; Scott-Van Zeeland, Ashley A.; Topol, Eric J.; Schork, Nicholas J.
Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely to amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants. PMID:21839162
Hortsch, M; Labeit, S; Meyer, D I
Docking protein (DP, or SRP receptor) is a rough endoplasmic reticulum (ER)-associated protein essential for the targeting and translocation of nascent polypeptides across this membrane. It specifically interacts with a cytoplasmic ribonucleoprotein complex, the signal recognition particle (SRP). The nucleotide sequence of cDNA encoding the entire human DP and its deduced amino acid sequence are given.
Stamm, Marcus; Staritzbichler, René; Khafizov, Kamil; Forrest, Lucy R.
We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe PMID:24753425
Mewes, H W; Hani, J; Pfeiffer, F; Frishman, D
The MIPS group [Munich Information Center for Protein Sequences of the German National Center for Environment and Health (GSF)] at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, is involved in a number of data collection activities, including a comprehensive database of the yeast genome, a database reflecting the progress in sequencing the Arabidopsis thaliana genome, the systematic analysis of other small genomes and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). Through its WWW server (http://www.mips.biochem.mpg.de ) MIPS provides access to a variety of generic databases, including a database of protein families as well as automatically generated data by the systematic application of sequence analysis algorithms. The yeast genome sequence and its related information was also compiled on CD-ROM to provide dynamic interactive access to the 16 chromosomes of the first eukaryotic genome unraveled. PMID:9399795
Macklin, Derek; Egan, Rob; Wang, Zhong
Metagenome sequencing efforts have provided a large pool of billions of genes for identifying enzymes with desirable biochemical traits. However, homology search with billions of genes in a rapidly growing database has become increasingly computationally impractical. Here we present our pilot efforts to develop a novel alignment-free algorithm for homology search. Specifically, we represent individual proteins as feature vectors that denote the presence or absence of short kmers in the protein sequence. Similarity between feature vectors is then computed using the Tanimoto score, a distance metric that can be rapidly computed on bit string representations of feature vectors. Preliminary results indicate good correlation with optimal alignment algorithms (Spearman r of 0.87, ~;;1,000,000 proteins from Pfam), as well as with heuristic algorithms such as BLAST (Spearman r of 0.86, ~;;1,000,000 proteins). Furthermore, a prototype of FASTERp implemented in Python runs approximately four times faster than BLAST on a small scale dataset (~;;1000 proteins). We are optimizing and scaling to improve FASTERp to enable rapid homology searches against billion-protein databases, thereby enabling more comprehensive gene annotation efforts.
Background The currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its “dark matter”. Results Here we suggest that true size of “dark matter” is much larger than stated by current definitions. We propose an approach to reducing the size of “dark matter” by identifying and subtracting regions in protein sequences that are not likely to contain any domain. Conclusions Recent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of “dark matter”; however, its absolute size increases substantially with the growth of sequence data. PMID:23157439
Full Text Available Abstract Background The currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its “dark matter”. Results Here we suggest that true size of “dark matter” is much larger than stated by current definitions. We propose an approach to reducing the size of “dark matter” by identifying and subtracting regions in protein sequences that are not likely to contain any domain. Conclusions Recent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of “dark matter”; however, its absolute size increases substantially with the growth of sequence data.
Burkhardt, K.; Schneider, Bohdan; Ory, J.
Roč. 2, č. 10 (2006), s. 1186-1189 ISSN 1553-734X Institutional research plan: CEZ:AV0Z40550506 Keywords : PDB * RCSB * annotation Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 4.914, year: 2006
Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.
Huang Yanzhao [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China); Li Mingfeng [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China); Xiao Yi [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China)]. E-mail: email@example.com
Many multi-domain proteins have repetitive three-dimensional structures but nearly-random amino acid sequences. In the present paper, by using a modified recurrence plot proposed by us previously, we show that these amino acid sequences have hidden repetitions in fact. These results indicate that the repetitive domain structures are encoded by the repetitive sequences. This also gives a method to detect the repetitive domain structures directly from amino acid sequences.
Haas, B J; Salzberg, S L; Zhu, W; Pertea, M; Allen, J E; Orvis, J; White, O; Buell, C R; Wortman, J R
EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.
IIS--Integrated Interactome System: a web-based platform for the annotation, analysis and visualization of protein-metabolite-gene-drug interactions by integrating a variety of data sources and tools.
Carazzolle, Marcelo Falsarella; de Carvalho, Lucas Miguel; Slepicka, Hugo Henrique; Vidal, Ramon Oliveira; Pereira, Gonçalo Amarante Guimarães; Kobarg, Jörg; Meirelles, Gabriela Vaz
High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two
Mitochondrial Disease Sequence Data Resource (MSeqDR): A global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities
M.J. Falk (Marni J.); L. Shen (Lishuang); M. Gonzalez (Michael); J. Leipzig (Jeremy); M.T. Lott (Marie T.); A.P.M. Stassen (Alphons P.M.); M.A. Diroma (Maria Angela); D. Navarro-Gomez (Daniel); P. Yeske (Philip); R. Bai (Renkui); R.G. Boles (Richard G.); V. Brilhante (Virginia); D. Ralph (David); J.T. DaRe (Jeana T.); R. Shelton (Robert); S.F. Terry (Sharon); Z. Zhang (Zhe); W.C. Copeland (William C.); M. van Oven (Mannis); H. Prokisch (Holger); D.C. Wallace; M. Attimonelli (Marcella); D. Krotoski (Danuta); S. Zuchner (Stephan); X. Gai (Xiaowu); S. Bale (Sherri); J. Bedoyan (Jirair); D.M. Behar (Doron); P. Bonnen (Penelope); L. Brooks (Lisa); C. Calabrese (Claudia); S. Calvo (Sarah); P.F. Chinnery (Patrick); J. Christodoulou (John); D. Church (Deanna); R. Clima (Rosanna); B.H. Cohen (Bruce H.); R.G.H. Cotton (Richard); I.F.M. de Coo (René); O. Derbenevoa (Olga); J.T. den Dunnen (Johan); D. Dimmock (David); G. Enns (Gregory); G. Gasparre (Giuseppe); A. Goldstein (Amy); I. Gonzalez (Iris); K. Gwinn (Katrina); S. Hahn (Sihoun); R.H. Haas (Richard H.); H. Hakonarson (Hakon); M. Hirano (Michio); D. Kerr (Douglas); D. Li (Dong); M. Lvova (Maria); F. Macrae (Finley); D. Maglott (Donna); E. McCormick (Elizabeth); G. Mitchell (Grant); V.K. Mootha (Vamsi K.); Y. Okazaki (Yasushi); A. Pujol (Aurora); M. Parisi (Melissa); J.C. Perin (Juan Carlos); E.A. Pierce (Eric A.); V. Procaccio (Vincent); S. Rahman (Shamima); H. Reddi (Honey); H. Rehm (Heidi); E. Riggs (Erin); R.J.T. Rodenburg (Richard); Y. Rubinstein (Yaffa); R. Saneto (Russell); M. Santorsola (Mariangela); C. Scharfe (Curt); C. Sheldon (Claire); E.A. Shoubridge (Eric); D. Simone (Domenico); B. Smeets (Bert); J.A.M. Smeitink (Jan); C. Stanley (Christine); A. Suomalainen (Anu); M.A. Tarnopolsky (Mark); I. Thiffault (Isabelle); D.R. Thorburn (David R.); J.V. Hove (Johan Van); L. Wolfe (Lynne); L.-J. Wong (Lee-Jun)
textabstractSuccess rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires
Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction
Lewis, Tony E; Sillitoe, Ian; Andreeva, Antonina; Blundell, Tom L; Buchan, Daniel W A; Chothia, Cyrus; Cuff, Alison; Dana, Jose M; Filippis, Ioannis; Gough, Julian; Hunter, Sarah; Jones, David T; Kelley, Lawrence A; Kleywegt, Gerard J; Minneci, Federico; Mitchell, Alex; Murzin, Alexey G; Ochoa-Montaño, Bernardo; Rackham, Owen J L; Smith, James; Sternberg, Michael J E; Velankar, Sameer; Yeats, Corin; Orengo, Christine
Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker's yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).
Jan 20, 2013 ... 1976; Karrer and Gall 1976; Vogt and Braun 1976) and (iii) in the formation of hairpin loops in the newly transcribed RNA. Palindromic sequences are observed in various classes of proteins like histones (Cheng et al. 1989), prion proteins (Sulkowski 1992; Kazim 1993),. DNA-binding proteins (Suzuki 1992; ...
Shahib, Ali Al-; Gilbert, David; Breitling, Rainer
Much work has been done to identify species-specific proteins in sequenced genomes and hence to determine their function. We assumed that such proteins have specific physico-chemical properties that will discriminate them from proteins in other species. In this paper, we examine the validity of this
Haznedaroglu Berat Z
Full Text Available Abstract Background The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG ortholog identifiers (KOIs, and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly. Results Analyses of single k-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of k-mers (k-19 to k-63. For each k-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other k-mer assemblies. Producing a non-redundant CA of k-mers 19 to 63 resulted in a more complete functional annotation than any single k-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs in the assemblies of individual k-mers (k-19 to k-63 that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented. Conclusions This study demonstrated that different k-mer choices result in various quantities
Ghosh, Pritha; Mathew, Oommen K; Sowdhamini, Ramanathan
RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information. The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search. RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential
Gaston K Mazandu
Full Text Available With the advancement of new high throughput sequencing technologies, there has been an increase in the number of genome sequencing projects worldwide, which has yielded complete genome sequences of human, animals and plants. Subsequently, several labs have focused on genome annotation, consisting of assigning functions to gene products, mostly using Gene Ontology (GO terms. As a consequence, there is an increased heterogeneity in annotations across genomes due to different approaches used by different pipelines to infer these annotations and also due to the nature of the GO structure itself. This makes a curator's task difficult, even if they adhere to the established guidelines for assessing these protein annotations. Here we develop a genome-scale approach for integrating GO annotations from different pipelines using semantic similarity measures. We used this approach to identify inconsistencies and similarities in functional annotations between orthologs of human and Drosophila melanogaster, to assess the quality of GO annotations derived from InterPro2GO mappings compared to manually annotated GO annotations for the Drosophila melanogaster proteome from a FlyBase dataset and human, and to filter GO annotation data for these proteomes. Results obtained indicate that an efficient integration of GO annotations eliminates redundancy up to 27.08 and 22.32% in the Drosophila melanogaster and human GO annotation datasets, respectively. Furthermore, we identified lack of and missing annotations for some orthologs, and annotation mismatches between InterPro2GO and manual pipelines in these two proteomes, thus requiring further curation. This simplifies and facilitates tasks of curators in assessing protein annotations, reduces redundancy and eliminates inconsistencies in large annotation datasets for ease of comparative functional genomics.
Full Text Available Abstract Background Obtaining the gene structure for a given protein encoding gene is an important step in many analyses. A software suited for this task should be readily accessible, accurate, easy to handle and should provide the user with a coherent representation of the most probable gene structure. It should be rigorous enough to optimise features on the level of single bases and at the same time flexible enough to allow for cross-species searches. Results WebScipio, a web interface to the Scipio software, allows a user to obtain the corresponding coding sequence structure of a here given a query protein sequence that belongs to an already assembled eukaryotic genome. The resulting gene structure is presented in various human readable formats like a schematic representation, and a detailed alignment of the query and the target sequence highlighting any discrepancies. WebScipio can also be used to identify and characterise the gene structures of homologs in related organisms. In addition, it offers a web service for integration with other programs. Conclusion WebScipio is a tool that allows users to get a high-quality gene structure prediction from a protein query. It offers more than 250 eukaryotic genomes that can be searched and produces predictions that are close to what can be achieved by manual annotation, for in-species and cross-species searches alike. WebScipio is freely accessible at http://www.webscipio.org.
Full Text Available Abstract Background Polypeptides are composed of amino acids covalently bonded via a peptide bond. The majority of peptide bonds in proteins is found to occur in the trans conformation. In spite of their infrequent occurrence, cis peptide bonds play a key role in the protein structure and function, as well as in many significant biological processes. Results We perform a systematic analysis of regions in protein sequences that contain a proline cis peptide bond in order to discover non-random associations between the primary sequence and the nature of proline cis/trans isomerization. For this purpose an efficient pattern discovery algorithm is employed which discovers regular expression-type patterns that are overrepresented (i.e. appear frequently repeated in a set of sequences. Four types of pattern discovery are performed: i exact pattern discovery, ii pattern discovery using a chemical equivalency set, iii pattern discovery using a structural equivalency set and iv pattern discovery using certain amino acids' physicochemical properties. The extracted patterns are carefully validated using a specially implemented scoring function and a significance measure (i.e. log-probability estimate indicative of their specificity. The score threshold for the first three types of pattern discovery is 0.90 while for the last type of pattern discovery 0.80. Regarding the significance measure, all patterns yielded values in the range [-9, -31] which ensure that the derived patterns are highly unlikely to have emerged by chance. Among the highest scoring patterns, most of them are consistent with previous investigations concerning the neighborhood of cis proline peptide bonds, and many new ones are identified. Finally, the extracted patterns are systematically compared against the PROSITE database, in order to gain insight into the functional implications of cis prolyl bonds. Conclusion Cis patterns with matches in the PROSITE database fell mostly into two
Gacesa, Ranko; Zucko, Jurica; Petursdottir, Solveig K; Gudmundsdottir, Elisabet Eik; Fridjonsson, Olafur H; Diminic, Janko; Long, Paul F; Cullum, John; Hranueli, Daslav; Hreggvidsson, Gudmundur O; Starcevic, Antonio
The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya . The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.
Panchenko Anna R
Full Text Available Abstract Background In general, the length of a protein sequence is determined by its function and the wide variance in the lengths of an organism's proteins reflects the diversity of specific functional roles for these proteins. However, additional evolutionary forces that affect the length of a protein may be revealed by studying the length distributions of proteins evolving under weaker functional constraints. Results We performed sequence comparisons to distinguish highly conserved and poorly conserved proteins from the bacterium Escherichia coli, the archaeon Archaeoglobus fulgidus, and the eukaryotes Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. For all organisms studied, the conserved and nonconserved proteins have strikingly different length distributions. The conserved proteins are, on average, longer than the poorly conserved ones, and the length distributions for the poorly conserved proteins have a relatively narrow peak, in contrast to the conserved proteins whose lengths spread over a wider range of values. For the two prokaryotes studied, the poorly conserved proteins approximate the minimal length distribution expected for a diverse range of structural folds. Conclusions There is a relationship between protein conservation and sequence length. For all the organisms studied, there seems to be a significant evolutionary trend favoring shorter proteins in the absence of other, more specific functional constraints.
Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan
A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. firstname.lastname@example.org. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Bana, Nóra Á; Nyiri, Anna; Nagy, János; Frank, Krisztián; Nagy, Tibor; Stéger, Viktor; Schiller, Mátyás; Lakatos, Péter; Sugár, László; Horn, Péter; Barta, Endre; Orosz, László
We present here the de novo genome assembly CerEla1.0 for the red deer, Cervus elaphus, an emblematic member of the natural megafauna of the Northern Hemisphere. Humans spread the species in the South. Today, the red deer is also a farm-bred animal and is becoming a model animal in biomedical and population studies. Stag DNA was sequenced at 74× coverage by Illumina technology. The ALLPATHS-LG assembly of the reads resulted in 34.7 × 10 3 scaffolds, 26.1 × 10 3 of which were utilized in Cer.Ela1.0. The assembly spans 3.4 Gbp. For building the red deer pseudochromosomes, a pre-established genetic map was used for main anchor points. A nearly complete co-linearity was found between the mapmarker sequences of the deer genetic map and the order and orientation of the orthologous sequences in the syntenic bovine regions. Syntenies were also conserved at the in-scaffold level. The cM distances corresponded to 1.34 Mbp uniformly along the deer genome. Chromosomal rearrangements between deer and cattle were demonstrated. 2.8 × 10 6 SNPs, 365 × 10 3 indels and 19368 protein-coding genes were identified in CerEla1.0, along with positions for centromerons. CerEla1.0 demonstrates the utilization of dual references, i.e., when a target genome (here C. elaphus) already has a pre-established genetic map, and is combined with the well-established whole genome sequence of a closely related species (here Bos taurus). Genome-wide association studies (GWAS) that CerEla1.0 (NCBI, MKHE00000000) could serve for are discussed.
Cocco, Simona; Feinauer, Christoph; Figliuzzi, Matteo; Monasson, Rémi; Weigt, Martin
In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.
Aligning protein sequences using a score matrix has became a routine but valuable method in modern biological ..... the amino acids according to their substitution behaviour ...... which may cause great change (e.g. prolonging the helix) in.
Full Text Available Abstract Background Functional profiling is a key technique to characterize and compare the functional potential of entire genomes. The estimation of profiles according to an assignment of sequences to functional categories is a computationally expensive task because it requires the comparison of all protein sequences from a genome with a usually large database of annotated sequences or sequence families. Description Based on machine learning techniques for Pfam domain detection, the UFO web server for ultra-fast functional profiling allows researchers to process large protein sequence collections instantaneously. Besides the frequencies of Pfam and GO categories, the user also obtains the sequence specific assignments to Pfam domain families. In addition, a comparison with existing genomes provides dissimilarity scores with respect to 821 reference proteomes. Considering the underlying UFO domain detection, the results on 206 test genomes indicate a high sensitivity of the approach. In comparison with current state-of-the-art HMMs, the runtime measurements show a considerable speed up in the range of four orders of magnitude. For an average size prokaryotic genome, the computation of a functional profile together with its comparison typically requires about 10 seconds of processing time. Conclusion For the first time the UFO web server makes it possible to get a quick overview on the functional inventory of newly sequenced organisms. The genome scale comparison with a large number of precomputed profiles allows a first guess about functionally related organisms. The service is freely available and does not require user registration or specification of a valid email address.
Lang B Franz
Full Text Available Abstract Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1 analyzes nucleotide and protein sequence data; (2 determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3 assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4 generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at http://megasun.bch.umontreal.ca/Software/AutoFACT.htm.
Daniels Noah M
Full Text Available Abstract Background The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult. Results We present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD. Conclusions Considering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.
Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.
Nilsson, R. Henrik; Taylor, Andy F. S.; Adams, Rachel I.; Baschien, Christiane; Johan Bengtsson-Palme; Cangren, Patrik; Coleine, Claudia; Heide-Marie Daniel; Glassman, Sydney I.; Hirooka, Yuuri; Irinyi, Laszlo; Reda Iršėnaitė; Pedro M. Martin-Sanchez; Meyer, Wieland; Seung-Yoon Oh; Jose Paulo Sampaio; Seifert, Keith A.; Sklenář, Frantisek; Dirk Stubbe; Suh, Sung-Oui; Summerbell, Richard; Svantesson, Sten; Martin Unterseher; Cobus M. Visagie; Weiss, Michael; Woudenberg, Joyce HC; Christian Wurzbacher; den Wyngaert, Silke Van; Yilmaz, Neriman; Andrey Yurkov; Kõljalg, Urmas; Abarenkov, Kessy
Abstract Recent DNA-based studies have shown that the built environment is surprisingly rich in fungi. These indoor fungi – whether transient visitors or more persistent residents – may hold clues to the rising levels of human allergies and other medical and building-related health problems observed globally. The taxonomic identity of these fungi is crucial in such pursuits. Molecular identification of the built mycobiome is no trivial undertaking, however, given the large number of unidentified, misidentified, and technically compromised fungal sequences in public sequence databases. In addition, the sequence metadata required to make informed taxonomic decisions – such as country and host/substrate of collection – are often lacking even from reference and ex-type sequences. Here we report on a taxonomic annotation workshop (April 10–11, 2017) organized at the James Hutton Institute/University of Aberdeen (UK) to facilitate reproducible studies of the built mycobiome. The 32 participants went through public fungal ITS barcode sequences related to the built mycobiome for taxonomic and nomenclatural correctness, technical quality, and metadata availability. A total of 19,508 changes – including 4,783 name changes, 14,121 metadata annotations, and the removal of 99 technically compromised sequences – were implemented in the UNITE database for molecular identification of fungi (https://unite.ut.ee/) and shared with a range of other databases and downstream resources. Among the genera that saw the largest number of changes were Penicillium, Talaromyces, Cladosporium, Acremonium, and Alternaria, all of them of significant importance in both culture-based and culture-independent surveys of the built environment. PMID:29559822
R. Henrik Nilsson
Full Text Available Recent DNA-based studies have shown that the built environment is surprisingly rich in fungi. These indoor fungi – whether transient visitors or more persistent residents – may hold clues to the rising levels of human allergies and other medical and building-related health problems observed globally. The taxonomic identity of these fungi is crucial in such pursuits. Molecular identification of the built mycobiome is no trivial undertaking, however, given the large number of unidentified, misidentified, and technically compromised fungal sequences in public sequence databases. In addition, the sequence metadata required to make informed taxonomic decisions – such as country and host/substrate of collection – are often lacking even from reference and ex-type sequences. Here we report on a taxonomic annotation workshop (April 10–11, 2017 organized at the James Hutton Institute/University of Aberdeen (UK to facilitate reproducible studies of the built mycobiome. The 32 participants went through public fungal ITS barcode sequences related to the built mycobiome for taxonomic and nomenclatural correctness, technical quality, and metadata availability. A total of 19,508 changes – including 4,783 name changes, 14,121 metadata annotations, and the removal of 99 technically compromised sequences – were implemented in the UNITE database for molecular identification of fungi (https://unite.ut.ee/ and shared with a range of other databases and downstream resources. Among the genera that saw the largest number of changes were Penicillium, Talaromyces, Cladosporium, Acremonium, and Alternaria, all of them of significant importance in both culture-based and culture-independent surveys of the built environment.
Debora S Marks
Full Text Available The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues, including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7-4.8 Å C(α-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org. This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of
Full Text Available Abstract Background Oil palm is the second largest source of edible oil which contributes to approximately 20% of the world's production of oils and fats. In order to understand the molecular biology involved in in vitro propagation, flowering, efficient utilization of nitrogen sources and root diseases, we have initiated an expressed sequence tag (EST analysis on oil palm. Results In this study, six cDNA libraries from oil palm zygotic embryos, suspension cells, shoot apical meristems, young flowers, mature flowers and roots, were constructed. We have generated a total of 14537 expressed sequence tags (ESTs from these libraries, from which 6464 tentative unique contigs (TUCs and 2129 singletons were obtained. Approximately 6008 of these tentative unique genes (TUGs have significant matches to the non-redundant protein database, from which 2361 were assigned to one or more Gene Ontology categories. Predominant transcripts and differentially expressed genes were identified in multiple oil palm tissues. Homologues of genes involved in many aspects of flower development were also identified among the EST collection, such as CONSTANS-like, AGAMOUS-like (AGL2, AGL20, LFY-like, SQUAMOSA, SQUAMOSA binding protein (SBP etc. Majority of them are the first representatives in oil palm, providing opportunities to explore the cause of epigenetic homeotic flowering abnormality in oil palm, given the importance of flowering in fruit production. The transcript levels of two flowering-related genes, EgSBP and EgSEP were analysed in the flower tissues of various developmental stages. Gene homologues for enzymes involved in oil biosynthesis, utilization of nitrogen sources, and scavenging of oxygen radicals, were also uncovered among the oil palm ESTs. Conclusion The EST sequences generated will allow comparative genomic studies between oil palm and other monocotyledonous and dicotyledonous plants, development of gene-targeted markers for the reference genetic map
Full Text Available Abstract Background Many of the world's most important food crops have either polyploid genomes or homeologous regions derived from segmental shuffling following polyploid formation. The soybean (Glycine max genome has been shown to be composed of approximately four thousand short interspersed homeologous regions with 1, 2 or 4 copies per haploid genome by RFLP analysis, microsatellite anchors to BACs and by contigs formed from BAC fingerprints. Despite these similar regions,, the genome has been sequenced by whole genome shotgun sequence (WGS. Here the aim was to use BAC end sequences (BES derived from three minimum tile paths (MTP to examine the extent and homogeneity of polyploid-like regions within contigs and the extent of correlation between the polyploid-like regions inferred from fingerprinting and the polyploid-like sequences inferred from WGS matches. Results Results show that when sequence divergence was 1–10%, the copy number of homeologous regions could be identified from sequence variation in WGS reads overlapping BES. Homeolog sequence variants (HSVs were single nucleotide polymorphisms (SNPs; 89% and single nucleotide indels (SNIs 10%. Larger indels were rare but present (1%. Simulations that had predicted fingerprints of homeologous regions could be separated when divergence exceeded 2% were shown to be false. We show that a 5–10% sequence divergence is necessary to separate homeologs by fingerprinting. BES compared to WGS traces showed polyploid-like regions with less than 1% sequence divergence exist at 2.3% of the locations assayed. Conclusion The use of HSVs like SNPs and SNIs to characterize BACs wil improve contig building methods. The implications for bioinformatic and functional annotation of polyploid and paleopolyploid genomes show that a combined approach of BAC fingerprint based physical maps, WGS sequence and HSV-based partitioning of BAC clones from homeologous regions to separate contigs will allow reliable de
Konopka, Bogumił M; Marciniak, Marta; Dyrka, Witold
The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.
Andrade-Navarro Miguel A
Full Text Available Abstract Background Phylogenetic analyses of protein families are used to define the evolutionary relationships between homologous proteins. The interpretation of protein-sequence phylogenetic trees requires the examination of the taxonomic properties of the species associated to those sequences. However, there is no online tool to facilitate this interpretation, for example, by automatically attaching taxonomic information to the nodes of a tree, or by interactively colouring the branches of a tree according to any combination of taxonomic divisions. This is especially problematic if the tree contains on the order of hundreds of sequences, which, given the accelerated increase in the size of the protein sequence databases, is a situation that is becoming common. Results We have developed PhyloView, a web based tool for colouring phylogenetic trees upon arbitrary taxonomic properties of the species represented in a protein sequence phylogenetic tree. Provided that the tree contains SwissProt, SpTrembl, or GenBank protein identifiers, the tool retrieves the taxonomic information from the corresponding database. A colour picker displays a summary of the findings and allows the user to associate colours to the leaves of the tree according to any number of taxonomic partitions. Then, the colours are propagated to the branches of the tree. Conclusion PhyloView can be used at http://www.ogic.ca/projects/phyloview/. A tutorial, the software with documentation, and GPL licensed source code, can be accessed at the same web address.
Nitish K Mishra
Full Text Available Membrane transport proteins (transporters move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.Support vector machine (SVM-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM, were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC of 0.49 and a receiver operating characteristic area under the curve (AUC of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.Our analyses suggest that evolutionary information (i.e., the PSSM and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions
Lu, Hui-Meng; Yin, Da-Chuan; Ye, Ya-Jing; Luo, Hui-Min; Geng, Li-Qiang; Li, Hai-Sheng; Guo, Wei-Hong; Shang, Peng
As the most widely utilized technique to determine the 3-dimensional structure of protein molecules, X-ray crystallography can provide structure of the highest resolution among the developed techniques. The resolution obtained via X-ray crystallography is known to be influenced by many factors, such as the crystal quality, diffraction techniques, and X-ray sources, etc. In this paper, the authors found that the protein sequence could also be one of the factors. We extracted information of the resolution and the sequence of proteins from the Protein Data Bank (PDB), classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the best resolution obtained. The results showed that there was a pronounced correlation between the sequence similarity and the obtained resolution. These results indicate that protein structure itself is one variable that may affect resolution when X-ray crystallography is used.
Sridhar, Settu; Guruprasad, Kunchur
We have carried out a systematic computational analysis on a representative dataset of proteins of known three-dimensional structure, in order to evaluate whether it would possible to ‘swap’ certain short peptide sequences in naturally occurring proteins with their corresponding ‘inverted’ peptides and generate ‘artificial’ proteins that are predicted to retain native-like protein fold. The analysis of 3,967 representative proteins from the Protein Data Bank revealed 102,677 unique identical inverted peptide sequence pairs that vary in sequence length between 5–12 and 18 amino acid residues. Our analysis illustrates with examples that such ‘artificial’ proteins may be generated by identifying peptides with ‘similar structural environment’ and by using comparative protein modeling and validation studies. Our analysis suggests that natural proteins may be tolerant to accommodating such peptides. PMID:25210740
Lapedes, A.S. [Los Alamos National Lab., NM (United States). Theoretical Div.]|[Santa Fe Inst., NM (United States); Giraud, B.G. [C.E.N. Saclay, Gif/Yvette (France). Service Physique Theorique; Liu, L.C. [Los Alamos National Lab., NM (United States). Theoretical Div.; Stormo, G.D. [Univ. of Colorado, Boulder, CO (United States). Dept. of Molecular, Cellular and Developmental Biology
Covariation analysis of sets of aligned sequences for RNA molecules is relatively successful in elucidating RNA secondary structure, as well as some aspects of tertiary structure. Covariation analysis of sets of aligned sequences for protein molecules is successful in certain instances in elucidating certain structural and functional links, but in general, pairs of sites displaying highly covarying mutations in protein sequences do not necessarily correspond to sites that are spatially close in the protein structure. In this paper the authors identify two reasons why naive use of covariation analysis for protein sequences fails to reliably indicate sequence positions that are spatially proximate. The first reason involves the bias introduced in calculation of covariation measures due to the fact that biological sequences are generally related by a non-trivial phylogenetic tree. The authors present a null-model approach to solve this problem. The second reason involves linked chains of covariation which can result in pairs of sites displaying significant covariation even though they are not spatially proximate. They present a maximum entropy solution to this classic problem of causation versus correlation. The methodologies are validated in simulation.
Van Lent, Sarah; Creasy, Heather Huot; Myers, Garry S A; Vanrompay, Daisy
Variation is a central trait of the polymorphic membrane protein (Pmp) family. The number of pmp coding sequences differs between Chlamydia species, but it is unknown whether the number of pmp coding sequences is constant within a Chlamydia species. The level of conservation of the Pmp proteins has previously only been determined for Chlamydia trachomatis. As different Pmp proteins might be indispensible for the pathogenesis of different Chlamydia species, this study investigated the conservation of Pmp proteins both within and across C. trachomatis,C. pneumoniae,C. abortus, and C. psittaci. The pmp coding sequences were annotated in 16 C. trachomatis, 6 C. pneumoniae, 2 C. abortus, and 16 C. psittaci genomes. The number and organization of polymorphic membrane coding sequences differed within and across the analyzed Chlamydia species. The length of coding sequences of pmpA,pmpB, and pmpH was conserved among all analyzed genomes, while the length of pmpE/F and pmpG, and remarkably also of the subtype pmpD, differed among the analyzed genomes. PmpD, PmpA, PmpH, and PmpA were the most conserved Pmp in C. trachomatis,C. pneumoniae,C. abortus, and C. psittaci, respectively. PmpB was the most conserved Pmp across the 4 analyzed Chlamydia species. © 2016 S. Karger AG, Basel.
Yao, Yao; Docter, Margreet; van Ginkel, Jetty; de Ridder, Dick; Joo, Chirlmin
Proteins are vital in all biological systems as they constitute the main structural and functional components of cells. Recent advances in mass spectrometry have brought the promise of complete proteomics by helping draft the human proteome. Yet, this commonly used protein sequencing technique has fundamental limitations in sensitivity. Here we propose a method for single-molecule (SM) protein sequencing. A major challenge lies in the fact that proteins are composed of 20 different amino acids, which demands 20 molecular reporters. We computationally demonstrate that it suffices to measure only two types of amino acids to identify proteins and suggest an experimental scheme using SM fluorescence. When achieved, this highly sensitive approach will result in a paradigm shift in proteomics, with major impact in the biological and medical sciences.
Yao, Yao; Docter, Margreet; Van Ginkel, Jetty; Joo, Chirlmin; De Ridder, Dick
Proteins are vital in all biological systems as they constitute the main structural and functional components of cells. Recent advances in mass spectrometry have brought the promise of complete proteomics by helping draft the human proteome. Yet, this commonly used protein sequencing technique has fundamental limitations in sensitivity. Here we propose a method for single-molecule (SM) protein sequencing. A major challenge lies in the fact that proteins are composed of 20 different amino acids, which demands 20 molecular reporters. We computationally demonstrate that it suffices to measure only two types of amino acids to identify proteins and suggest an experimental scheme using SM fluorescence. When achieved, this highly sensitive approach will result in a paradigm shift in proteomics, with major impact in the biological and medical sciences. (paper)
Wrenbeck, Emily E; Faber, Matthew S; Whitehead, Timothy A
The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances. Copyright © 2016 Elsevier Ltd. All rights reserved.
Oliveira, L.; Paiva, P.B.; Paiva, A.C.; Vriend, G.
Sequence entropy-variability plots based on alignments of very large numbers of sequences-can indicate the location in proteins of the main active site and modulator sites. In the previous article in this issue, we applied this observation to a series of well-studied proteins and concluded that it
Mohr Stephanie E
Full Text Available Abstract Background Whereas the molecular assembly of protein expression clones is readily automated and routinely accomplished in high throughput, sequence verification of these clones is still largely performed manually, an arduous and time consuming process. The ultimate goal of validation is to determine if a given plasmid clone matches its reference sequence sufficiently to be "acceptable" for use in protein expression experiments. Given the accelerating increase in availability of tens of thousands of unverified clones, there is a strong demand for rapid, efficient and accurate software that automates clone validation. Results We have developed an Automated Clone Evaluation (ACE system – the first comprehensive, multi-platform, web-based plasmid sequence verification software package. ACE automates the clone verification process by defining each clone sequence as a list of multidimensional discrepancy objects, each describing a difference between the clone and its expected sequence including the resulting polypeptide consequences. To evaluate clones automatically, this list can be compared against user acceptance criteria that specify the allowable number of discrepancies of each type. This strategy allows users to re-evaluate the same set of clones against different acceptance criteria as needed for use in other experiments. ACE manages the entire sequence validation process including contig management, identifying and annotating discrepancies, determining if discrepancies correspond to polymorphisms and clone finishing. Designed to manage thousands of clones simultaneously, ACE maintains a relational database to store information about clones at various completion stages, project processing parameters and acceptance criteria. In a direct comparison, the automated analysis by ACE took less time and was more accurate than a manual analysis of a 93 gene clone set. Conclusion ACE was designed to facilitate high throughput clone sequence
Full Text Available Abstract Background Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to
Yu, Chenggang; Zavaljevski, Nela; Desai, Valmik; Johnson, Seth; Stevens, Fred J; Reifman, Jaques
.... With the existence of many programs and databases for inferring different protein functions, a pipeline that properly integrates these resources will benefit from the advantages of each method...
Sharpton Thomas J
Full Text Available Abstract Background New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences. Results We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as “Sifting Families,” or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology–based analyses. Conclusions We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/.
Xiao, Jiajie; Bonin, Keith; Guthold, Martin; Salsbury, Freddie
Interactions between proteins and deoxyribonucleic acid (DNA) play a significant role in the living systems, especially through gene regulation. However, short nucleic acids sequences (aptamers) with specific binding affinity to specific proteins exhibit clinical potential as therapeutics. Our capillary and gel electrophoresis selection experiments show that specific sequences of aptamers can be selected that bind specific proteins. Computationally, given the experimentally-determined structure and sequence of a thrombin-binding aptamer, we can successfully dock the aptamer onto thrombin in agreement with experimental structures of the complex. In order to further study the conformational flexibility of this thrombin-binding aptamer and to potentially develop a predictive computational model of aptamer-binding, we use GPU-enabled molecular dynamics simulations to both examine the conformational flexibility of the aptamer in the absence of binding to thrombin, and to determine our ability to fold an aptamer. This study should help further de-novo predictions of aptamer sequences by enabling the study of structural and sequence-dependent effects on aptamer-protein docking specificity.
Full Text Available Hotspot residues are important in the determination of protein-protein interactions, and they always perform specific functions in biological processes. The determination of hotspot residues is by the commonly-used method of alanine scanning mutagenesis experiments, which is always costly and time consuming. To address this issue, computational methods have been developed. Most of them are structure based, i.e., using the information of solved protein structures. However, the number of solved protein structures is extremely less than that of sequences. Moreover, almost all of the predictors identified hotspots from the interfaces of protein complexes, seldom from the whole protein sequences. Therefore, determining hotspots from whole protein sequences by sequence information alone is urgent. To address the issue of hotspot predictions from the whole sequences of proteins, we proposed an ensemble system with random projections using statistical physicochemical properties of amino acids. First, an encoding scheme involving sequence profiles of residues and physicochemical properties from the AAindex1 dataset is developed. Then, the random projection technique was adopted to project the encoding instances into a reduced space. Then, several better random projections were obtained by training an IBk classifier based on the training dataset, which were thus applied to the test dataset. The ensemble of random projection classifiers is therefore obtained. Experimental results showed that although the performance of our method is not good enough for real applications of hotspots, it is very promising in the determination of hotspot residues from whole sequences.
Nielsen-Marsh, Christina M; Richards, Michael P; Hauschka, Peter V; Thomas-Oates, Jane E; Trinkaus, Erik; Pettitt, Paul B; Karavanic, Ivor; Poinar, Hendrik; Collins, Matthew J
We report here protein sequences of fossil hominids, from two Neanderthals dating to approximately 75,000 years old from Shanidar Cave in Iraq. These sequences, the oldest reported fossil primate protein sequences, are of bone osteocalcin, which was extracted and sequenced by using MALDI-TOF/TOF mass spectrometry. Through a combination of direct sequencing and peptide mass mapping, we determined that Neanderthals have an osteocalcin amino acid sequence that is identical to that of modern humans. We also report complete osteocalcin sequences for chimpanzee (Pan troglodytes) and gorilla (Gorilla gorilla gorilla) and a partial sequence for orangutan (Pongo pygmaeus), all of which are previously unreported. We found that the osteocalcin sequences of Neanderthals, modern human, chimpanzee, and orangutan are unusual among mammals in that the ninth amino acid is proline (Pro-9), whereas most species have hydroxyproline (Hyp-9). Posttranslational hydroxylation of Pro-9 in osteocalcin by prolyl-4-hydroxylase requires adequate concentrations of vitamin C (l-ascorbic acid), molecular O(2), Fe(2+), and 2-oxoglutarate, and also depends on enzyme recognition of the target proline substrate consensus sequence Leu-Gly-Ala-Pro-9-Ala-Pro-Tyr occurring in most mammals. In five species with Pro-9-Val-10, hydroxylation is blocked, whereas in gorilla there is a mixture of Pro-9 and Hyp-9. We suggest that the absence of hydroxylation of Pro-9 in Pan, Pongo, and Homo may reflect response to a selective pressure related to a decline in vitamin C in the diet during omnivorous dietary adaptation, either independently or through the common ancestor of these species.
Törönen, Petri; Medlar, Alan; Holm, Liisa
The unprecedented growth of high-throughput sequencing has led to an ever-widening annotation gap in protein databases. While computational prediction methods are available to make up the shortfall, a majority of public web servers are hindered by practical limitations and poor performance. Here, we introduce PANNZER2 (Protein ANNotation with Z-scoRE), a fast functional annotation web server that provides both Gene Ontology (GO) annotations and free text description predictions. PANNZER2 uses SANSparallel to perform high-performance homology searches, making bulk annotation based on sequence similarity practical. PANNZER2 can output GO annotations from multiple scoring functions, enabling users to see which predictions are robust across predictors. Finally, PANNZER2 predictions scored within the top 10 methods for molecular function and biological process in the CAFA2 NK-full benchmark. The PANNZER2 web server is updated on a monthly schedule and is accessible at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. The source code is available under the GNU Public Licence v3.
HAYASHI, Yuuki; 相田, 拓洋; TOYOTA, Hitoshi; 伏見, 譲; URABE, Itaru; YOMO, Tetsuya
The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12-130 of the initial random polypeptide and selection for infectivity, the selected phag...
Lin David M
Full Text Available Abstract Background EST libraries are used in various biological studies, from microarray experiments to proteomic and genetic screens. These libraries usually contain many uncharacterized ESTs that are typically ignored since they cannot be mapped to known genes. Consequently, new discoveries are possibly overlooked. Results We describe a system (EST2Prot that uses multiple elements to map EST sequences to their corresponding protein products. EST2Prot uses UniGene clusters, substring analysis, information about protein coding regions in existing DNA sequences and protein database searches to detect protein products related to a query EST sequence. Gene Ontology terms, Swiss-Prot keywords, and protein similarity data are used to map the ESTs to functional descriptors. Conclusion EST2Prot extends and significantly enriches the popular UniGene mapping by utilizing multiple relations between known biological entities. It produces a mapping between ESTs and proteins in real-time through a simple web-interface. The system is part of the Biozon database and is accessible at http://biozon.org/tools/est/.
Full Text Available Abstract Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i NPS-HomPPI (Non partner-specific HomPPI, which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii PS-HomPPI (Partner-specific HomPPI, which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of
AB repeats; Mycobacterium tuberculosis genome; PE-PPE domain; PPE, PE proteins; sequence analysis; surface antigens. J. Biosci. | Vol. ... bacterium tuberculosis genomes resulted in the identification of a previously uncharacterized 225 amino acid- ...... Vega Lopez F, Brooks L A, Dockrell H M, De Smet K A,. Thompson ...
Andersen, C.A.F.; Brunak, Søren
-sequence information, using machine learning strategies, where the primary goal is the discovery of novel powerful representations for use in AI techniques. In the case of proteins and the 20 different amino acids they typically contain, it is also a secondary goal to discover how the current selection of amino acids...
Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert
Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.
Halperin Rebecca F
Full Text Available Abstract Background Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. Results GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. Conclusions GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.
Grahnen Johan A
Full Text Available Abstract Background Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field. Results Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS Conclusions Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model.
Sakai, Hiroaki; Naito, Ken; Takahashi, Yu; Sato, Toshiyuki; Yamamoto, Toshiya; Muto, Isamu; Itoh, Takeshi; Tomooka, Norihiko
The genus Vigna includes legume crops such as cowpea, mungbean and azuki bean, as well as >100 wild species. A number of the wild species are highly tolerant to severe environmental conditions including high-salinity, acid or alkaline soil; drought; flooding; and pests and diseases. These features of the genus Vigna make it a good target for investigation of genetic diversity in adaptation to stressful environments; however, a lack of genomic information has hindered such research in this genus. Here, we present a genome database of the genus Vigna, Vigna Genome Server ('VigGS', http://viggs.dna.affrc.go.jp), based on the recently sequenced azuki bean genome, which incorporates annotated exon-intron structures, along with evidence for transcripts and proteins, visualized in GBrowse. VigGS also facilitates user construction of multiple alignments between azuki bean genes and those of six related dicot species. In addition, the database displays sequence polymorphisms between azuki bean and its wild relatives and enables users to design primer sequences targeting any variant site. VigGS offers a simple keyword search in addition to sequence similarity searches using BLAST and BLAT. To incorporate up to date genomic information, VigGS automatically receives newly deposited mRNA sequences of pre-set species from the public database once a week. Users can refer to not only gene structures mapped on the azuki bean genome on GBrowse but also relevant literature of the genes. VigGS will contribute to genomic research into plant biotic and abiotic stresses and to the future development of new stress-tolerant crops. © The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: email@example.com.
U.S. Department of Health & Human Services — The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt databases are the UniProt Knowledgebase...
Chinappi, Mauro; Cecconi, Fabio
Proteins perform a huge number of central functions in living organisms, thus all the new techniques allowing their precise, fast and accurate characterization at single-molecule level certainly represent a burst in proteomics with important biomedical impact. In this review, we describe the recent progresses in the developing of nanopore based devices for protein sequencing. We start with a critical analysis of the main technical requirements for nanopore protein sequencing, summarizing some ideas and methodologies that have recently appeared in the literature. In the last sections, we focus on the physical modelling of the transport phenomena occurring in nanopore based devices. The multiscale nature of the problem is discussed and, in this respect, some of the main possible computational approaches are illustrated.
Shvets, Alexey A.; Kolomeisky, Anatoly B.
The process of protein search for specific binding sites on DNA is fundamentally important since it marks the beginning of all major biological processes. We present a theoretical investigation that probes the role of DNA sequence symmetry, heterogeneity, and chemical composition in the protein search dynamics. Using a discrete-state stochastic approach with a first-passage events analysis, which takes into account the most relevant physical-chemical processes, a full analytical description of the search dynamics is obtained. It is found that, contrary to existing views, the protein search is generally faster on DNA with more heterogeneous sequences. In addition, the search dynamics might be affected by the chemical composition near the target site. The physical origins of these phenomena are discussed. Our results suggest that biological processes might be effectively regulated by modifying chemical composition, symmetry, and heterogeneity of a genome
Shvets, Alexey A.; Kolomeisky, Anatoly B., E-mail: firstname.lastname@example.org [Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005 (United States)
The process of protein search for specific binding sites on DNA is fundamentally important since it marks the beginning of all major biological processes. We present a theoretical investigation that probes the role of DNA sequence symmetry, heterogeneity, and chemical composition in the protein search dynamics. Using a discrete-state stochastic approach with a first-passage events analysis, which takes into account the most relevant physical-chemical processes, a full analytical description of the search dynamics is obtained. It is found that, contrary to existing views, the protein search is generally faster on DNA with more heterogeneous sequences. In addition, the search dynamics might be affected by the chemical composition near the target site. The physical origins of these phenomena are discussed. Our results suggest that biological processes might be effectively regulated by modifying chemical composition, symmetry, and heterogeneity of a genome.
Liu, Jen-Wei; Cheng, Chih-Wen; Lin, Yu-Feng; Chen, Shao-Yu; Hwang, Jenn-Kang; Yen, Shih-Chung
Functional and biophysical constraints can cause different levels of sequence conservation in proteins. Previously, structural properties, e.g., relative solvent accessibility (RSA) and packing density of the weighted contact number (WCN), have been found to be related to protein sequence conservation (CS). The Voronoi volume has recently been recognized as a new structural property of the local protein structural environment reflecting CS. However, for surface residues, it is sensitive to water molecules surrounding the protein structure. Herein, we present a simple structural determinant termed the relative space of Voronoi volume (RSV); it uses the Voronoi volume and the van der Waals volume of particular residues to quantify the local structural environment. RSV (range, 0-1) is defined as (Voronoi volume-van der Waals volume)/Voronoi volume of the target residue. The concept of RSV describes the extent of available space for every protein residue. RSV and Voronoi profiles with and without water molecules (RSVw, RSV, VOw, and VO) were compared for 554 non-homologous proteins. RSV (without water) showed better Pearson's correlations with CS than did RSVw, VO, or VOw values. The mean correlation coefficient between RSV and CS was 0.51, which is comparable to the correlation between RSA and CS (0.49) and that between WCN and CS (0.56). RSV is a robust structural descriptor with and without water molecules and can quantitatively reflect evolutionary information in a single protein structure. Therefore, it may represent a practical structural determinant to study protein sequence, structure, and function relationships. Copyright © 2017 Elsevier B.V. All rights reserved.
Pujar, Shashikant; O'Leary, Nuala A; Farrell, Catherine M; Loveland, Jane E; Mudge, Jonathan M; Wallin, Craig; Girón, Carlos G; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; Martin, Fergal J; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Suner, Marie-Marthe; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bruford, Elspeth A; Bult, Carol J; Frankish, Adam; Murphy, Terence; Pruitt, Kim D
The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. Published by Oxford University Press on behalf of Nucleic Acids Research 2017.
Aalt D J van Dijk
Full Text Available Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and
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
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.
Full Text Available The structure, function, stability, and many other properties of a protein in a fixed environment are fully specified by its sequence, but in a manner that is difficult to discern. We present a general approach for rapidly mapping sequences directly to their energies on a pre-specified rigid backbone, an important sub-problem in computational protein design and in some methods for protein structure prediction. The cluster expansion (CE method that we employ can, in principle, be extended to model any computable or measurable protein property directly as a function of sequence. Here we show how CE can be applied to the problem of computational protein design, and use it to derive excellent approximations of physical potentials. The approach provides several attractive advantages. First, following a one-time derivation of a CE expansion, the amount of time necessary to evaluate the energy of a sequence adopting a specified backbone conformation is reduced by a factor of 10(7 compared to standard full-atom methods for the same task. Second, the agreement between two full-atom methods that we tested and their CE sequence-based expressions is very high (root mean square deviation 1.1-4.7 kcal/mol, R2 = 0.7-1.0. Third, the functional form of the CE energy expression is such that individual terms of the expansion have clear physical interpretations. We derived expressions for the energies of three classic protein design targets-a coiled coil, a zinc finger, and a WW domain-as functions of sequence, and examined the most significant terms. Single-residue and residue-pair interactions are sufficient to accurately capture the energetics of the dimeric coiled coil, whereas higher-order contributions are important for the two more globular folds. For the task of designing novel zinc-finger sequences, a CE-derived energy function provides significantly better solutions than a standard design protocol, in comparable computation time. Given these advantages
Full Text Available Abstract Background Biodiesel or ethanol derived from lipids or starch produced by microalgae may overcome many of the sustainability challenges previously ascribed to petroleum-based fuels and first generation plant-based biofuels. The paucity of microalgae genome sequences, however, limits gene-based biofuel feedstock optimization studies. Here we describe the sequencing and de novo transcriptome assembly for the non-model microalgae species, Dunaliella tertiolecta, and identify pathways and genes of importance related to biofuel production. Results Next generation DNA pyrosequencing technology applied to D. tertiolecta transcripts produced 1,363,336 high quality reads with an average length of 400 bases. Following quality and size trimming, ~ 45% of the high quality reads were assembled into 33,307 isotigs with a 31-fold coverage and 376,482 singletons. Assembled sequences and singletons were subjected to BLAST similarity searches and annotated with Gene Ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG orthology (KO identifiers. These analyses identified the majority of lipid and starch biosynthesis and catabolism pathways in D. tertiolecta. Conclusions The construction of metabolic pathways involved in the biosynthesis and catabolism of fatty acids, triacylglycrols, and starch in D. tertiolecta as well as the assembled transcriptome provide a foundation for the molecular genetics and functional genomics required to direct metabolic engineering efforts that seek to enhance the quantity and character of microalgae-based biofuel feedstock.
Colin A Smith
Full Text Available Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface, interactions between and within parts of the structure (e.g. domains can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.
Bharat Bhusan Patnaik
Full Text Available The freshwater mussel Cristaria plicata (Bivalvia: Eulamellibranchia: Unionidae, is an economically important species in molluscan aquaculture due to its use in pearl farming. The species have been listed as endangered in South Korea due to the loss of natural habitats caused by anthropogenic activities. The decreasing population and a lack of genomic information on the species is concerning for environmentalists and conservationists. In this study, we conducted a de novo transcriptome sequencing and annotation analysis of C. plicata using Illumina HiSeq 2500 next-generation sequencing (NGS technology, the Trinity assembler, and bioinformatics databases to prepare a sustainable resource for the identification of candidate genes involved in immunity, defense, and reproduction.The C. plicata transcriptome analysis included a total of 286,152,584 raw reads and 281,322,837 clean reads. The de novo assembly identified a total of 453,931 contigs and 374,794 non-redundant unigenes with average lengths of 731.2 and 737.1 bp, respectively. Furthermore, 100% coverage of C. plicata mitochondrial genes within two unigenes supported the quality of the assembler. In total, 84,274 unigenes showed homology to entries in at least one database, and 23,246 unigenes were allocated to one or more Gene Ontology (GO terms. The most prominent GO biological process, cellular component, and molecular function categories (level 2 were cellular process, membrane, and binding, respectively. A total of 4,776 unigenes were mapped to 123 biological pathways in the KEGG database. Based on the GO terms and KEGG annotation, the unigenes were suggested to be involved in immunity, stress responses, sex-determination, and reproduction. A total of 17,251 cDNA simple sequence repeats (cSSRs were identified from 61,141 unigenes (size of >1 kb with the most abundant being dinucleotide repeats.This dataset represents the first transcriptome analysis of the endangered mollusc, C. plicata
Zhou Yuan Wu
Full Text Available In statistical methods of predicting protein secondary structure, many researchers focus on single amino acid frequencies in α-helices, β-sheets, and so on, or the impact near amino acids on an amino acid forming a secondary structure. But the paper considers a short sequence of amino acids (3, 4, 5 or 6 amino acids as integer, and statistics short sequence's probability forming secondary structure. Also, many researchers select low homologous sequences as statistical database. But this paper select whole PDB database. In this paper we propose a strategy to predict protein secondary structure using simple statistical method. Numerical computation shows that, short amino acids sequence as integer to statistics, which can easy see trend of short sequence forming secondary structure, and it will work well to select large statistical database (whole PDB database without considering homologous, and Q3 accuracy is ca. 74% using this paper proposed simple statistical method, but accuracy of others statistical methods is less than 70%.
Feinstein, Wei P; Moreno, Juana; Jarrell, Mark; Brylinski, Michal
Intel Xeon Phi is a new addition to the family of powerful parallel accelerators. The range of its potential applications in computationally driven research is broad; however, at present, the repository of scientific codes is still relatively limited. In this study, we describe the development and benchmarking of a parallel version of eFindSite, a structural bioinformatics algorithm for the prediction of ligand-binding sites in proteins. Implemented for the Intel Xeon Phi platform, the parallelization of the structure alignment portion of eFindSite using pragma-based OpenMP brings about the desired performance improvements, which scale well with the number of computing cores. Compared to a serial version, the parallel code runs 11.8 and 10.1 times faster on the CPU and the coprocessor, respectively; when both resources are utilized simultaneously, the speedup is 17.6. For example, ligand-binding predictions for 501 benchmarking proteins are completed in 2.1 hours on a single Stampede node equipped with the Intel Xeon Phi card compared to 3.1 hours without the accelerator and 36.8 hours required by a serial version. In addition to the satisfactory parallel performance, porting existing scientific codes to the Intel Xeon Phi architecture is relatively straightforward with a short development time due to the support of common parallel programming models by the coprocessor. The parallel version of eFindSite is freely available to the academic community at www.brylinski.org/efindsite.
Fenwick, Matthew; Hoch, Jeffrey C. [UConn Health, Department of Molecular Biology and Biophysics (United States); Ulrich, Eldon [University of Wisconsin-Madison, Department of Biochemistry (United States); Gryk, Michael R., E-mail: email@example.com [UConn Health, Department of Molecular Biology and Biophysics (United States)
Reproducibility is a cornerstone of the scientific method, essential for validation of results by independent laboratories and the sine qua non of scientific progress. A key step toward reproducibility of biomolecular NMR studies was the establishment of public data repositories (PDB and BMRB). Nevertheless, bio-NMR studies routinely fall short of the requirement for reproducibility that all the data needed to reproduce the results are published. A key limitation is that considerable metadata goes unpublished, notably manual interventions that are typically applied during the assignment of multidimensional NMR spectra. A general solution to this problem has been elusive, in part because of the wide range of approaches and software packages employed in the analysis of protein NMR spectra. Here we describe an approach for capturing missing metadata during the assignment of protein NMR spectra that can be generalized to arbitrary workflows, different software packages, other biomolecules, or other stages of data analysis in bio-NMR. We also present extensions to the NMR-STAR data dictionary that enable machine archival and retrieval of the “missing” metadata.
Fenwick, Matthew; Hoch, Jeffrey C.; Ulrich, Eldon; Gryk, Michael R.
Reproducibility is a cornerstone of the scientific method, essential for validation of results by independent laboratories and the sine qua non of scientific progress. A key step toward reproducibility of biomolecular NMR studies was the establishment of public data repositories (PDB and BMRB). Nevertheless, bio-NMR studies routinely fall short of the requirement for reproducibility that all the data needed to reproduce the results are published. A key limitation is that considerable metadata goes unpublished, notably manual interventions that are typically applied during the assignment of multidimensional NMR spectra. A general solution to this problem has been elusive, in part because of the wide range of approaches and software packages employed in the analysis of protein NMR spectra. Here we describe an approach for capturing missing metadata during the assignment of protein NMR spectra that can be generalized to arbitrary workflows, different software packages, other biomolecules, or other stages of data analysis in bio-NMR. We also present extensions to the NMR-STAR data dictionary that enable machine archival and retrieval of the “missing” metadata
da Fonseca, Néli José; Lima Afonso, Marcelo Querino; Pedersolli, Natan Gonçalves; de Oliveira, Lucas Carrijo; Andrade, Dhiego Souto; Bleicher, Lucas
Flaviviruses are responsible for serious diseases such as dengue, yellow fever, and zika fever. Their genomes encode a polyprotein which, after cleavage, results in three structural and seven non-structural proteins. Homologous proteins can be studied by conservation and coevolution analysis as detected in multiple sequence alignments, usually reporting positions which are strictly necessary for the structure and/or function of all members in a protein family or which are involved in a specific sub-class feature requiring the coevolution of residue sets. This study provides a complete conservation and coevolution analysis on all flaviviruses non-structural proteins, with results mapped on all well-annotated available sequences. A literature review on the residues found in the analysis enabled us to compile available information on their roles and distribution among different flaviviruses. Also, we provide the mapping of conserved and coevolved residues for all sequences currently in SwissProt as a supplementary material, so that particularities in different viruses can be easily analyzed. Copyright © 2017 Elsevier Inc. All rights reserved.
Demarchi, Beatrice; Hall, Shaun; Roncal-Herrero, Teresa
of Laetoli (3.8 Ma) and Olduvai Gorge (1.3 Ma) in Tanzania. By tracking protein diagenesis back in time we find consistent patterns of preservation, demonstrating authenticity of the surviving sequences. Molecular dynamics simulations of struthiocalcin-1 and -2, the dominant proteins within the eggshell......, reveal that distinct domains bind to the mineral surface. It is the domain with the strongest calculated binding energy to the calcite surface that is selectively preserved. Thermal age calculations demonstrate that the Laetoli and Olduvai peptides are 50 times older than any previously authenticated...
Malhis, Nawar; Gsponer, Jörg
Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge. In this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set. We evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. http://www.chibi.ubc.ca/morf/. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org.
Kö ser, Claudio U.; Niemann, Stefan; Summers, David K.; Archer, John A.C.
Since its publication in 1998, the genome sequence of the Mycobacterium tuberculosis H37Rv laboratory strain has acted as the cornerstone for the study of tuberculosis. In this review we address some of the practical aspects that have come to light
Lacatus, Gabriela; Sunter, Garry
We demonstrate that the AL2 gene of Cabbage leaf curl virus (CaLCuV) activates the CP promoter in mesophyll and acts to derepress the promoter in vascular tissue, similar to that observed for Tomato golden mosaic virus (TGMV). Binding studies indicate that sequences mediating repression and activation of the TGMV and CaLCuV CP promoter specifically bind different nuclear factors common to Nicotiana benthamiana, spinach and tomato. However, chromatin immunoprecipitation demonstrates that TGMV AL2 can interact with both sequences independently. Binding of nuclear protein(s) from different crop species to viral sequences conserved in both bipartite and monopartite begomoviruses, including TGMV, CaLCuV, Pepper golden mosaic virus and Tomato yellow leaf curl virus suggests that bipartite begomoviruses bind common host factors to regulate the CP promoter. This is consistent with a model in which AL2 interacts with different components of the cellular transcription machinery that bind viral sequences important for repression and activation of begomovirus CP promoters
Parton, Angela; Bayne, Christopher J; Barnes, David W
Elasmobranchs are the most commonly used experimental models among the jawed, cartilaginous fish (Chondrichthyes). Previously we developed cell lines from embryos of two elasmobranchs, Squalus acanthias the spiny dogfish shark (SAE line), and Leucoraja erinacea the little skate (LEE-1 line). From these lines cDNA libraries were derived and expressed sequence tags (ESTs) generated. From the SAE cell line 4303 unique transcripts were identified, with 1848 of these representing unknown sequences (showing no BLASTX identification). From the LEE-1 cell line, 3660 unique transcripts were identified, and unknown, unique sequences totaled 1333. Gene Ontology (GO) annotation showed that GO assignments for the two cell lines were in general similar. These results suggest that the procedures used to derive the cell lines led to isolation of cell types of the same general embryonic origin from both species. The LEE-1 transcripts included GO categories "envelope" and "oxidoreductase activity" but the SAE transcripts did not. GO analysis of SAE transcripts identified the category "anatomical structure formation" that was not present in LEE-1 cells. Increased organelle compartments may exist within LEE-1 cells compared to SAE cells, and the higher oxidoreductase activity in LEE-1 cells may indicate a role for these cells in responses associated with innate immunity or in steroidogenesis. These EST libraries from elasmobranch cell lines provide information for assembly of genomic sequences and are useful in revealing gene diversity, new genes and molecular markers, as well as in providing means for elucidation of full-length cDNAs and probes for gene array analyses. This is the first study of this type with members of the Chondrichthyes. Copyright 2010 Elsevier Inc. All rights reserved.
Hayat, Maqsood; Khan, Asifullah
Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://18.104.22.168/Mem-Predictor. Copyright Â© 2010 Elsevier Ltd. All rights reserved.
Holliday, Gemma L; Davidson, Rebecca; Akiva, Eyal; Babbitt, Patricia C
The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.
Full Text Available Abstract Background In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm. Results This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins. Conclusion A new Stochastic Context Free
Richardson, Dale N.; Wiehe, Thomas
Whole genome duplication (WGD) has catalyzed the formation of new species, genes with novel functions, altered expression patterns, complexified signaling pathways and has provided organisms a level of genetic robustness. We studied the long-term evolution and interrelationships of 5’ upstream regulatory sequences (URSs), protein coding sequences (CDSs) and expression correlations (EC) of duplicated gene pairs in Arabidopsis. Three distinct methods revealed significant evolutionary conservation between paralogous URSs and were highly correlated with microarray-based expression correlation of the respective gene pairs. Positional information on exact matches between sequences unveiled the contribution of micro-chromosomal rearrangements on expression divergence. A three-way rank analysis of URS similarity, CDS divergence and EC uncovered specific gene functional biases. Transcription factor activity was associated with gene pairs exhibiting conserved URSs and divergent CDSs, whereas a broad array of metabolic enzymes was found to be associated with gene pairs showing diverged URSs but conserved CDSs.
Full Text Available Complex informational spectrum analysis for protein sequences (CISAPS and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.
Brister, James Rodney; Bao, Yiming; Kuiken, Carla; Lefkowitz, Elliot J; Le Mercier, Philippe; Leplae, Raphael; Madupu, Ramana; Scheuermann, Richard H; Schobel, Seth; Seto, Donald; Shrivastava, Susmita; Sterk, Peter; Zeng, Qiandong; Klimke, William; Tatusova, Tatiana
Improvements in DNA sequencing technologies portend a new era in virology and could possibly lead to a giant leap in our understanding of viral evolution and ecology. Yet, as viral genome sequences begin to fill the world's biological databases, it is critically important to recognize that the scientific promise of this era is dependent on consistent and comprehensive genome annotation. With this in mind, the NCBI Genome Annotation Workshop recently hosted a study group tasked with developing sequence, function, and metadata annotation standards for viral genomes. This report describes the issues involved in viral genome annotation and reviews policy recommendations presented at the NCBI Annotation Workshop.
Full Text Available Improvements in DNA sequencing technologies portend a new era in virology and could possibly lead to a giant leap in our understanding of viral evolution and ecology. Yet, as viral genome sequences begin to fill the world’s biological databases, it is critically important to recognize that the scientific promise of this era is dependent on consistent and comprehensive genome annotation. With this in mind, the NCBI Genome Annotation Workshop recently hosted a study group tasked with developing sequence, function, and metadata annotation standards for viral genomes. This report describes the issues involved in viral genome annotation and reviews policy recommendations presented at the NCBI Annotation Workshop.
Hanna Isa Anderberg
Full Text Available Major intrinsic proteins (MIPs also called aquaporins form pores in membranes to facilitate the permeation of water and certain small polar solutes across membranes. MIPs are present in virtually every organism but are uniquely abundant in land plants. To elucidate the evolution and function of MIPs in terrestrial plants, the MIPs encoded in the genome of the spikemoss Selaginella moellendorffii were identified and analyzed. In total 19 MIPs were found in S. moellendorffii belonging to six of the seven MIP subfamilies previously identified in the moss Physcomitrella patens. Only three of the MIPs were classified as members of the conserved water specific plasma membrane intrinsic protein (PIP subfamily whereas almost half were found to belong to the diverse NOD26-like intrinsic protein (NIP subfamily permeating various solutes. The small number of PIPs in S. moellendorffii is striking compared to all other land plants and no other species has more NIPs than PIPs. Similar to moss, S. moellendorffii only has one type of tonoplast intrinsic protein (TIP. Based on ESTs from non-angiosperms we conclude that the specialized groups of TIPs present in higher plants are not found in primitive vascular plants but evolved later in a common ancestor of seed plants. We also note that the silicic acid permeable NIP2 group that has been reported from angiosperms appears at the same time. We suggest that the expansion of the number MIP isoforms in higher plants is primarily associated with an increase in the different types of specialized tissues rather than the emergence of vascular tissue per se and that the loss of subfamilies has been possible due to a functional overlap between some subfamilies.
Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.
Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita
The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional ap...
Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita; Deegan, Jennifer; Clutterbuck, John; Andersen, Mikael R; Archer, David; Bencina, Mojca; Braus, Gerhard; Coutinho, Pedro; von Döhren, Hans; Doonan, John; Driessen, Arnold J M; Durek, Pawel; Espeso, Eduardo; Fekete, Erzsébet; Flipphi, Michel; Estrada, Carlos Garcia; Geysens, Steven; Goldman, Gustavo; de Groot, Piet W J; Hansen, Kim; Harris, Steven D; Heinekamp, Thorsten; Helmstaedt, Kerstin; Henrissat, Bernard; Hofmann, Gerald; Homan, Tim; Horio, Tetsuya; Horiuchi, Hiroyuki; James, Steve; Jones, Meriel; Karaffa, Levente; Karányi, Zsolt; Kato, Masashi; Keller, Nancy; Kelly, Diane E; Kiel, Jan A K W; Kim, Jung-Mi; van der Klei, Ida J; Klis, Frans M; Kovalchuk, Andriy; Krasevec, Nada; Kubicek, Christian P; Liu, Bo; Maccabe, Andrew; Meyer, Vera; Mirabito, Pete; Miskei, Márton; Mos, Magdalena; Mullins, Jonathan; Nelson, David R; Nielsen, Jens; Oakley, Berl R; Osmani, Stephen A; Pakula, Tiina; Paszewski, Andrzej; Paulsen, Ian; Pilsyk, Sebastian; Pócsi, István; Punt, Peter J; Ram, Arthur F J; Ren, Qinghu; Robellet, Xavier; Robson, Geoff; Seiboth, Bernhard; van Solingen, Piet; Specht, Thomas; Sun, Jibin; Taheri-Talesh, Naimeh; Takeshita, Norio; Ussery, Dave; vanKuyk, Patricia A; Visser, Hans; van de Vondervoort, Peter J I; de Vries, Ronald P; Walton, Jonathan; Xiang, Xin; Xiong, Yi; Zeng, An Ping; Brandt, Bernd W; Cornell, Michael J; van den Hondel, Cees A M J J; Visser, Jacob; Oliver, Stephen G; Turner, Geoffrey
The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional
Wortman, Jennifer Russo; Gilsenan, Jane Mabey; Joardar, Vinita; Deegan, Jennifer; Clutterbuck, John; Andersen, Mikael R.; Archer, David; Bencina, Mojca; Braus, Gerhard; Coutinho, Pedro; von Doehren, Hans; Doonan, John; Driessen, Arnold J. M.; Durek, Pawel; Espeso, Eduardo; Fekete, Erzsebet; Flipphi, Michel; Garcia Estrada, Carlos; Geysens, Steven; Goldman, Gustavo; de Groot, Piet W. J.; Hansen, Kim; Harris, Steven D.; Heinekamp, Thorsten; Helmstaedt, Kerstin; Henrissat, Bernard; Hofmann, Gerald; Homan, Tim; Horio, Tetsuya; Horiuchi, Hiroyuki; James, Steve; Jones, Meriel; Karaffa, Levente; Karanyi, Zsolt; Kato, Masashi; Keller, Nancy; Kelly, Diane E.; Kiel, Jan A. K. W.; Kim, Jung-Mi; van der Klei, Ida J.; Klis, Frans M.; Kovalchuk, Andriy; Krasevec, Nada; Kubicek, Christian P.; Liu, Bo; MacCabe, Andrew; Meyer, Vera; Mirabito, Pete; Miskei, Marton; Mos, Magdalena; Mullins, Jonathan; Nelson, David R.; Nielsen, Jens; Oakley, Berl R.; Osmani, Stephen A.; Pakula, Tiina; Paszewski, Andrzej; Paulsen, Ian; Pilsyk, Sebastian; Pocsi, Istvan; Punt, Peter J.; Ram, Arthur F. J.; Ren, Qinghu; Robellet, Xavier; Robson, Geoff; Seiboth, Bernhard; van Solingen, Piet; Specht, Thomas; Sun, Jibin; Taheri-Talesh, Naimeh; Takeshita, Norio; Ussery, Dave; Vankuyk, Patricia A.; Visser, Hans; de Vondervoort, Peter J. I. van; Walton, Jonathan; Xiang, Xin; Xiong, Yi; Zeng, An Ping; Brandt, Bernd W.; Cornell, Michael J.; van den Hondel, Cees A. M. J. J.; Visser, Jacob; Oliver, Stephen G.; Turner, Geoffrey; Kraševec, Nada; Kuyk, Patricia A. van; Döhren, D.H.; van Seilboth, B; de Vries, R.
The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional
Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin
Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.
Giraud, B.G.; Lapedes, A.; Liu, L.C.
A criterion based on conditional probabilities, related to the concept of algorithmic distance, is used to detect correlated mutations at noncontiguous sites on sequences. We apply this criterion to the problem of analyzing correlations between sites in protein sequences; however, the analysis applies generally to networks of interacting sites with discrete states at each site. Elementary models, where explicit results can be derived easily, are introduced. The number of states per site considered ranges from 2, illustrating the relation to familiar classical spin systems, to 20 states, suitable for representing amino acids. Numerical simulations show that the criterion remains valid even when the genetic history of the data samples (e.g., protein sequences), as represented by a phylogenetic tree, introduces nonindependence between samples. Statistical fluctuations due to finite sampling are also investigated and do not invalidate the criterion. A subsidiary result is found: The more homogeneous a population, the more easily its average properties can drift from the properties of its ancestor. copyright 1998 The American Physical Society
Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan
A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.
You, Ronghui; Zhang, Zihan; Xiong, Yi; Sun, Fengzhu; Mamitsuka, Hiroshi; Zhu, Shanfeng
Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only advantage over state-of-the-art AFP methods. http://datamining-iip.fudan.edu.cn/golabeler. email@example.com. Supplementary data are available at Bioinformatics online.
Travasso, Rui D M; FaIsca, Patricia F N; Gama, Margarida M Telo da
For the vast majority of naturally occurring, small, single-domain proteins, folding is often described as a two-state process that lacks detectable intermediates. This observation has often been rationalized on the basis of a nucleation mechanism for protein folding whose basic premise is the idea that, after completion of a specific set of contacts forming the so-called folding nucleus, the native state is achieved promptly. Here we propose a methodology to identify folding nuclei in small lattice polymers and apply it to the study of protein molecules with a chain length of N = 48. To investigate the extent to which protein topology is a robust determinant of the nucleation mechanism, we compare the nucleation scenario of a native-centric model with that of a sequence-specific model sharing the same native fold. To evaluate the impact of the sequence's finer details in the nucleation mechanism, we consider the folding of two non-homologous sequences. We conclude that, in a sequence-specific model, the folding nucleus is, to some extent, formed by the most stable contacts in the protein and that the less stable linkages in the folding nucleus are solely determined by the fold's topology. We have also found that, independently of the protein sequence, the folding nucleus performs the same 'topological' function. This unifying feature of the nucleation mechanism results from the residues forming the folding nucleus being distributed along the protein chain in a similar and well-defined manner that is determined by the fold's topological features
Casey R Richardson
Full Text Available MicroRNAs (miRNAs and trans-acting small-interfering RNAs (tasi-RNAs are small (20-22 nt long RNAs (smRNAs generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.We explored rice (Oryza sativa sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other
Full Text Available Abstract Background In the last decade, sequencing projects have led to the development of a number of annotation systems dedicated to the structural and functional annotation of protein-coding genes. These annotation systems manage the annotation of the non-protein coding genes (ncRNAs in a very crude way, allowing neither the edition of the secondary structures nor the clustering of ncRNA genes into families which are crucial for appropriate annotation of these molecules. Results LeARN is a flexible software package which handles the complete process of ncRNA annotation by integrating the layers of automatic detection and human curation. Conclusion This software provides the infrastructure to deal properly with ncRNAs in the framework of any annotation project. It fills the gap between existing prediction software, that detect independent ncRNA occurrences, and public ncRNA repositories, that do not offer the flexibility and interactivity required for annotation projects. The software is freely available from the download section of the website http://bioinfo.genopole-toulouse.prd.fr/LeARN
Full Text Available Abstract Background The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level. Results Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins. Conclusion By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism.
Background Birds have a ZZ male: ZW female sex chromosome system and while the Z-linked DMRT1 gene is necessary for testis development, the exact mechanism of sex determination in birds remains unsolved. This is partly due to the poor annotation of the W chromosome, which is speculated to carry a female determinant. Few genes have been mapped to the W and little is known of their expression. Results We used RNA-seq to produce a comprehensive profile of gene expression in chicken blastoderms and embryonic gonads prior to sexual differentiation. We found robust sexually dimorphic gene expression in both tissues pre-dating gonadogenesis, including sex-linked and autosomal genes. This supports the hypothesis that sexual differentiation at the molecular level is at least partly cell autonomous in birds. Different sets of genes were sexually dimorphic in the two tissues, indicating that molecular sexual differentiation is tissue specific. Further analyses allowed the assembly of full-length transcripts for 26 W chromosome genes, providing a view of the W transcriptome in embryonic tissues. This is the first extensive analysis of W-linked genes and their expression profiles in early avian embryos. Conclusion Sexual differentiation at the molecular level is established in chicken early in embryogenesis, before gonadal sex differentiation. We find that the W chromosome is more transcriptionally active than previously thought, expand the number of known genes to 26 and present complete coding sequences for these W genes. This includes two novel W-linked sequences and three small RNAs reassigned to the W from the Un_Random chromosome. PMID:23531366
A graphical method is presented for displaying how binding proteins and other macromolecules interact with individual bases of nucleotide sequences. Characters representing the sequence are either oriented normally and placed above a line indicating favorable contact, or upside-down and placed below the line indicating unfavorable contact. The positive or negative height of each letter shows the contribution of that base to the average sequence conservation of the binding site, as represented by a sequence logo.
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.
Hayashi, Yuuki; Aita, Takuyo; Toyota, Hitoshi; Husimi, Yuzuru; Urabe, Itaru; Yomo, Tetsuya
The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12–130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7×104-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1) the dependence of stationary fitness on library size, which increased gradually, and (2) the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18–24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region. PMID:17183728
Hayashi, Yuuki; Aita, Takuyo; Toyota, Hitoshi; Husimi, Yuzuru; Urabe, Itaru; Yomo, Tetsuya
The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12-130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7x10(4)-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1) the dependence of stationary fitness on library size, which increased gradually, and (2) the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18-24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region.
Full Text Available The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12-130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7x10(4-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1 the dependence of stationary fitness on library size, which increased gradually, and (2 the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18-24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region.
Full Text Available The regulation of gene transcription is fundamental to the existence of complex multicellular organisms such as humans. Although it is widely recognized that much of gene regulation is controlled by gene-specific protein-DNA interactions, there presently exists little in the way of tools to identify proteins that interact with the genome at locations of interest. We have developed a novel strategy to address this problem, which we refer to as GENECAPP, for Global ExoNuclease-based Enrichment of Chromatin-Associated Proteins for Proteomics. In this approach, formaldehyde cross-linking is employed to covalently link DNA to its associated proteins; subsequent fragmentation of the DNA, followed by exonuclease digestion, produces a single-stranded region of the DNA that enables sequence-specific hybridization capture of the protein-DNA complex on a solid support. Mass spectrometric (MS analysis of the captured proteins is then used for their identification and/or quantification. We show here the development and optimization of GENECAPP for an in vitro model system, comprised of the murine insulin-like growth factor-binding protein 1 (IGFBP1 promoter region and FoxO1, a member of the forkhead rhabdomyosarcoma (FoxO subfamily of transcription factors, which binds specifically to the IGFBP1 promoter. This novel strategy provides a powerful tool for studies of protein-DNA and protein-protein interactions.
Radivojac, P.; Clark, W.T.; Oron, T.R.; Schnoes, A.M.; Wittkop, T.; Kourmpetis, Y.A.I.; Dijk, van A.D.J.; Friedberg, I.
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be
Alexander M Sevy
Full Text Available Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD paradigm. Multi-specificity design (MSD, on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON. The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.
Höps, Wolfram; Jeffryes, Matt; Bateman, Alex
We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation. Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases. We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes. Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.
Full Text Available Recent molecular studies have identified substantial fungal diversity in indoor environments. Fungi and fungal particles have been linked to a range of potentially unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. The study of the built mycobiome is hampered by a number of constraints, one of which is the poor state of the metadata annotation of fungal DNA sequences from the built environment in public databases. In order to enable precise interrogation of such data – for example, “retrieve all fungal sequences recovered from bathrooms” – a workshop was organized at the University of Gothenburg (May 23-24, 2016 to annotate public fungal barcode (ITS sequences according to the MIxS-Built Environment annotation standard (http://gensc.org/mixs/. The 36 participants assembled a total of 45,488 data points from the published literature, including the addition of 8,430 instances of countries of collection from a total of 83 countries, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results were implemented in the UNITE database for molecular identification of fungi (http://unite.ut.ee and were shared with other online resources. Data obtained from human/animal pathogenic fungi will furthermore be verified on culture based metadata for subsequent inclusion in the ISHAM-ITS database (http://its.mycologylab.org.
von Kohn, Christopher; Kiełkowska, Agnieszka; Havey, Michael J
Male-sterile (S) cytoplasm of onion is an alien cytoplasm introgressed into onion in antiquity and is widely used for hybrid seed production. Owing to the biennial generation time of onion, classical crossing takes at least 4 years to classify cytoplasms as S or normal (N) male-fertile. Molecular markers in the organellar DNAs that distinguish N and S cytoplasms are useful to reduce the time required to classify onion cytoplasms. In this research, we completed next-generation sequencing of the chloroplast DNAs of N- and S-cytoplasmic onions; we assembled and annotated the genomes in addition to identifying polymorphisms that distinguish these cytoplasms. The sizes (153 538 and 153 355 base pairs) and GC contents (36.8%) were very similar for the chloroplast DNAs of N and S cytoplasms, respectively, as expected given their close phylogenetic relationship. The size difference was primarily due to small indels in intergenic regions and a deletion in the accD gene of N-cytoplasmic onion. The structures of the onion chloroplast DNAs were similar to those of most land plants with large and small single copy regions separated by inverted repeats. Twenty-eight single nucleotide polymorphisms, two polymorphic restriction-enzyme sites, and one indel distributed across 20 chloroplast genes in the large and small single copy regions were selected and validated using diverse onion populations previously classified as N or S cytoplasmic using restriction fragment length polymorphisms. Although cytoplasmic male sterility is likely associated with the mitochondrial DNA, maternal transmission of the mitochondrial and chloroplast DNAs allows for polymorphisms in either genome to be useful for classifying onion cytoplasms to aid the development of hybrid onion cultivars.
Full Text Available The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller's organ, located in the tick's forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues. The two forelegs from adult R. australis were excised, RNA extracted, and pyrosequenced with 454 technology. Reads were assembled into unigenes and annotated by sequence similarity. Python scripts were written to find open reading frames (ORFs from each unigene. These ORFs were analyzed by different GPCR prediction approaches based on sequence alignments, support vector machines, hidden Markov models, and principal component analysis. GPCRs consistently predicted by multiple methods were further studied by phylogenetic analysis and 3D homology modeling. From 4,782 assembled unigenes, 40,907 possible ORFs were predicted. Using Blastp, Pfam, GPCRpred, TMHMM, and PCA-GPCR, a basic set of 46 GPCR candidates were compiled and a phylogenetic tree was constructed. With further screening of tertiary structures predicted by RaptorX, 6 likely GPCRs emerged and the strongest candidate was classified by PCA-GPCR to be a GABAB receptor.
Al-Shahib, A.; Breitling, R.; Gilbert, D.
Abstract: When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence
Bunick, Christopher G; Nelson, Melanie R; Mangahas, Sheryll; Hunter, Michael J; Sheehan, Jonathan H; Mizoue, Laura S; Bunick, Gerard J; Chazin, Walter J
The extent of conformational change that calcium binding induces in EF-hand proteins is a key biochemical property specifying Ca(2+) sensor versus signal modulator function. To understand how differences in amino acid sequence lead to differences in the response to Ca(2+) binding, comparative analyses of sequence and structures, combined with model building, were used to develop hypotheses about which amino acid residues control Ca(2+)-induced conformational changes. These results were used to generate a first design of calbindomodulin (CBM-1), a calbindin D(9k) re-engineered with 15 mutations to respond to Ca(2+) binding with a conformational change similar to that of calmodulin. The gene for CBM-1 was synthesized, and the protein was expressed and purified. Remarkably, this protein did not exhibit any non-native-like molten globule properties despite the large number of mutations and the nonconservative nature of some of them. Ca(2+)-induced changes in CD intensity and in the binding of the hydrophobic probe, ANS, implied that CBM-1 does undergo Ca(2+) sensorlike conformational changes. The X-ray crystal structure of Ca(2+)-CBM-1 determined at 1.44 A resolution reveals the anticipated increase in hydrophobic surface area relative to the wild-type protein. A nascent calmodulin-like hydrophobic docking surface was also found, though it is occluded by the inter-EF-hand loop. The results from this first calbindomodulin design are discussed in terms of progress toward understanding the relationships between amino acid sequence, protein structure, and protein function for EF-hand CaBPs, as well as the additional mutations for the next CBM design.
Tian, Pengfei; Best, Robert B
Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance. Published by Elsevier Inc.
Cui, Xuefeng; Lu, Zhiwu; Wang, Sheng; Jing-Yan Wang, Jim; Gao, Xin
Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment
DiGuistini, Scott; Ralph, Steven G; Lim, Young W; Holt, Robert; Jones, Steven; Bohlmann, Jörg; Breuil, Colette
Ophiostoma clavigerum is a destructive pathogen of lodgepole pine (Pinus contorta) forests in western North America. It is therefore a relevant system for a genomics analysis of fungi vectored by bark beetles. To begin characterizing molecular interactions between the pathogen and its conifer host, we created an expressed sequence tag (EST) collection for O. clavigerum. Lodgepole pine sawdust and oleoresin media were selected to stimulate gene expression that would be specific to this host interaction. Over 6500 cDNA clones, derived from four normalized cDNA libraries, were single-pass sequenced from the 3' end. After quality screening, we identified 5975 high-quality reads with an average PHRED 20 of greater than 750 bp. Clustering and assembly of this high-quality EST set resulted in the identification of 2620 unique putative transcripts. BLASTX analysis revealed that only 67% of these unique transcripts could be matched to known or predicted protein sequences in public databases. Functional classification of these sequences provided initial insights into the transcriptome of O. clavigerum. Of particular interest, our ESTs represent an extensive collection of cytochrome P450 s, ATP-binding-cassette-type transporters and genes involved in 1,8-dihydroxynaphthalene-melanin biosynthesis. These results are discussed in the context of detoxification of conifer oleoresins and fungal pathogenesis.
Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F
Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.
Abdel Messih, Mario A.; Chitale, Meghana; Bajic, Vladimir B.; Kihara, Daisuke; Gao, Xin
Motivation: Burgeoning sequencing technologies have generated massive amounts of genomic and proteomic data. Annotating the functions of proteins identified in this data has become a big and crucial problem. Various computational methods have been
Under a cooperative agreement with the U.S. Department of Energy's Office of Science and Technology, Waste Policy Institute (WPI) is conducting a five-year research project to develop a research-based approach for integrating communication products in stakeholder involvement related to innovative technology. As part of the research, WPI developed this annotated bibliography which contains almost 100 citations of articles/books/resources involving topics related to communication and public involvement aspects of deploying innovative cleanup technology. To compile the bibliography, WPI performed on-line literature searches (e.g., Dialog, International Association of Business Communicators Public Relations Society of America, Chemical Manufacturers Association, etc.), consulted past years proceedings of major environmental waste cleanup conferences (e.g., Waste Management), networked with professional colleagues and DOE sites to gather reports or case studies, and received input during the August 1996 Research Design Team meeting held to discuss the project's research methodology. Articles were selected for annotation based upon their perceived usefulness to the broad range of public involvement and communication practitioners
Van den Berg, B.A.; Reinders, M.J.; Roubos, J.A.; De Ridder, D.
Background Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction,
Pascale, Raffaella; Grossi, Gerarda; Cruciani, Gabriele; Mecca, Giansalvatore; Santoro, Donatello; Sarli Calace, Renzo; Falabella, Patrizia; Bianco, Giuliana
Sequence protein identification by a randomized sequence database and transcriptome mass spectrometry software package has been developed at the University of Basilicata in Potenza (Italy) and designed to facilitate the determination of the amino acid sequence of a peptide as well as an unequivocal identification of proteins in a high-throughput manner with enormous advantages of time, economical resource and expertise. The software package is a valid tool for the automation of a de novo sequencing approach, overcoming the main limits and a versatile platform useful in the proteomic field for an unequivocal identification of proteins, starting from tandem mass spectrometry data. The strength of this software is that it is a user-friendly and non-statistical approach, so protein identification can be considered unambiguous.
Tyler, Ryan E.; Ewing, Sean G.; Imperiale, Michael J.
During adenovirus virion assembly, the packaging sequence mediates the encapsidation of the viral genome. This sequence is composed of seven functional units, termed A repeats. Recent evidence suggests that the adenovirus IVa2 protein binds the packaging sequence and is involved in packaging of the genome. Study of the IVa2-packaging sequence interaction has been hindered by difficulty in purifying the protein produced in virus-infected cells or by recombinant techniques. We report the first ...
Malik, Uzma; Javed, Aneela; Ali, Amjad; Asghar, Kashif
Human immune system is a complex amalgam of a greatly diverse ensemble comprising of various cellular and non-cellular components, including proteins. FAM26F (family with sequence similarity 26, member F) is a relatively recently identified gene reported to play important role in diverse immune responses. Numerous studies have reported FAM26F to be differentially expressed in several viral, bacterial and parasitic infections, in certain pathophysiological conditions such as heart and liver transplantation, and in several cancers. FAM26F has also been found to be upregulated by various stimulants such as polyI:C, LPS, INF gamma and TNF alpha, and via various anticipated pathways including TLR3, TLR4 IFN-β and Dectin-1. Moreover, the synergistic expression of FAM26F on both NK-cells and myeloid dendritic cells is required to activate NK-cells against tumors via its cytoplasmic tail, thus emphasizing the therapeutic potential of FAM26F for NK sensitive tumors. Although a considerable amount of evidence is present regarding the potential role of FAM26F in immune modulation, the exact function and modulatory pathways of this gene are yet to be elucidated. We aimed to completely characterize FAM26F in order to apprehend its function and role in the immune responses. The results revealed human FAM26F to be located at chromosomal position 6q22.1. FAM26F mRNA contains 1141bp coding region encoding a 315 amino acid long, stable protein that has been well-conserved throughout evolution. It is a signal peptide deprived transmembrane protein that is secreted through non-classical pathway. The presence of a single well-conserved Ca_hom_mod domain indicated FAM26F to be a cation channel involved in the transport of molecules. A potential N-glycosylation and 14 phosphorylation sites were also predicted, along with four interacting partners of FAM26F. The secondary and tertiary structures of FAM26F were determined. Moreover, the presence of an immunoglobulin-like fold in FAM26F
Full Text Available The presence of common epitopes among tropomyosins of invertebrates, including arthropods, e.g. edible ones, may help to explain the molecular basis of cross-reactivity between allergens. The work presented is the first survey concerning global distribution of epitopes from Pen a 1.0102 in universal proteome. In the group of known tropomyosin epitopes, the fragment with the sequence ESKIVELEEEL was found in the sequence of channel catfish (Ictalurus punctatus tropomyosin. To date, this is the first result suggesting the presence of a complete sequential epitope interacting with gE in vertebrate tropomyosin. Another fragment with the sequence VAALNRRIQL, a major part of the epitope, was found in 11 fish, 8 amphibians, 3 birds, 19 mammalians and 4 human tropomyosin sequences. Identical epitopes are common in sequences of invertebrate tropomyosins, including food and non-food allergens annotated in the Allergome database. The rare pentapeptide with the DEERM sequence occurs in proteins not sharing homology with tropomyosins. Pathogenic microorganisms are the most abundant category of organisms synthesizing such proteins.
Bromilow, Sophie; Gethings, Lee A; Buckley, Mike; Bromley, Mike; Shewry, Peter R; Langridge, James I; Clare Mills, E N
The unique physiochemical properties of wheat gluten enable a diverse range of food products to be manufactured. However, gluten triggers coeliac disease, a condition which is treated using a gluten-free diet. Analytical methods are required to confirm if foods are gluten-free, but current immunoassay-based methods can unreliable and proteomic methods offer an alternative but require comprehensive and well annotated sequence databases which are lacking for gluten. A manually a curated database (GluPro V1.0) of gluten proteins, comprising 630 discrete unique full length protein sequences has been compiled. It is representative of the different types of gliadin and glutenin components found in gluten. An in silico comparison of their coeliac toxicity was undertaken by analysing the distribution of coeliac toxic motifs. This demonstrated that whilst the α-gliadin proteins contained more toxic motifs, these were distributed across all gluten protein sub-types. Comparison of annotations observed using a discovery proteomics dataset acquired using ion mobility MS/MS showed that more reliable identifications were obtained using the GluPro V1.0 database compared to the complete reviewed Viridiplantae database. This highlights the value of a curated sequence database specifically designed to support the proteomic workflows and the development of methods to detect and quantify gluten. We have constructed the first manually curated open-source wheat gluten protein sequence database (GluPro V1.0) in a FASTA format to support the application of proteomic methods for gluten protein detection and quantification. We have also analysed the manually verified sequences to give the first comprehensive overview of the distribution of sequences able to elicit a reaction in coeliac disease, the prevalent form of gluten intolerance. Provision of this database will improve the reliability of gluten protein identification by proteomic analysis, and aid the development of targeted mass
Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony
To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.
Mishra, Pooja; Nath Pandey, Paras
In the present work we have employed the method of estimating residue correlation within the protein sequences, by using the mutual information (MI) of adjacent residues, based on structural and solvent accessibility properties of amino acids. The long range correlation between nonadjacent residues is improved by constructing a mutual information vector (MIV) for a single protein sequence, like this each protein sequence is associated with its corresponding MIVs. These MIVs are given to Elman RNN to obtain the classification of protein sequences. The modeling power of MIV was shown to be significantly better, giving a new approach towards alignment free classification of protein sequences. We also conclude that sequence structural and solvent accessible property based MIVs are better predictor. Copyright © 2012 Elsevier Ltd. All rights reserved.
Ansong, Charles; Tolic, Nikola; Purvine, Samuel O.; Porwollik, Steffen; Jones, Marcus B.; Yoon, Hyunjin; Payne, Samuel H.; Martin, Jessica L.; Burnet, Meagan C.; Monroe, Matthew E.; Venepally, Pratap; Smith, Richard D.; Peterson, Scott; Heffron, Fred; Mcclelland, Michael; Adkins, Joshua N.
Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify coding regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.
Full Text Available Abstract Background Whole-genome sequencing projects are rapidly producing an enormous number of new sequences. Consequently almost every family of proteins now contains hundreds of members. It has thus become necessary to develop tools, which classify protein sequences automatically and also quickly and reliably. The difficulty of this task is intimately linked to the mechanism by which protein sequences diverge, i.e. by simultaneous residue substitutions, insertions and/or deletions and whole domain reorganisations (duplications/swapping/fusion. Results Here we present a novel approach, which is based on random sampling of sub-sequences (probes out of a set of input sequences. The probes are compared to the input sequences, after a normalisation step; the results are used to partition the input sequences into homogeneous groups of proteins. In addition, this method provides information on diagnostic parts of the proteins. The performance of this method is challenged by two data sets. The first one contains the sequences of prokaryotic lyases that could be arranged as a multiple sequence alignment. The second one contains all proteins from Swiss-Prot Release 36 with at least one Src homology 2 (SH2 domain – a classical example for proteins with modular architecture. Conclusion The outcome of our method is robust, highly reproducible as shown using bootstrap and resampling validation procedures. The results are essentially coherent with the biology. This method depends solely on well-established publicly available software and algorithms.
ADRIANA ISVORAN; LAURA UNIPAN; DANA CRACIUN; VASILE MORARIU
The results presented here suggest the existence of correlations in the sequence data of proteins. 32 proteins, both globular and fibrous, both monomeric and polymeric, were analyzed. The primary structures of these proteins were treated as time series. Three spatial series of data for each sequence of a protein were generated from numerical correspondences between each amino acid and a physical property associated with it, i.e., its electric charge, its polar character and its dipole moment....
Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng
Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.
Full Text Available Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM. Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS, segmented PsePSSM, and segmented autocovariance transformation (ACT based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640 are adopted in this paper. Then a 700-dimensional (700D feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA. To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.
Merchant Sabeeha S
Full Text Available Abstract Background Progress in genome sequencing is proceeding at an exponential pace, and several new algal genomes are becoming available every year. One of the challenges facing the community is the association of protein sequences encoded in the genomes with biological function. While most genome assembly projects generate annotations for predicted protein sequences, they are usually limited and integrate functional terms from a limited number of databases. Another challenge is the use of annotations to interpret large lists of 'interesting' genes generated by genome-scale datasets. Previously, these gene lists had to be analyzed across several independent biological databases, often on a gene-by-gene basis. In contrast, several annotation databases, such as DAVID, integrate data from multiple functional databases and reveal underlying biological themes of large gene lists. While several such databases have been constructed for animals, none is currently available for the study of algae. Due to renewed interest in algae as potential sources of biofuels and the emergence of multiple algal genome sequences, a significant need has arisen for such a database to process the growing compendiums of algal genomic data. Description The Algal Functional Annotation Tool is a web-based comprehensive analysis suite integrating annotation data from several pathway, ontology, and protein family databases. The current version provides annotation for the model alga Chlamydomonas reinhardtii, and in the future will include additional genomes. The site allows users to interpret large gene lists by identifying associated functional terms, and their enrichment. Additionally, expression data for several experimental conditions were compiled and analyzed to provide an expression-based enrichment search. A tool to search for functionally-related genes based on gene expression across these conditions is also provided. Other features include dynamic visualization of
Van den Berg, B.A.; Reinders, M.J.T.; Hulsman, M.; Wu, L.; Pel, H.J.; Roubos, J.A.; De Ridder, D.
Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large
Full Text Available Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.
Itoh, Takeshi; Tanaka, Tsuyoshi; Barrero, Roberto A.; Yamasaki, Chisato; Fujii, Yasuyuki; Hilton, Phillip B.; Antonio, Baltazar A.; Aono, Hideo; Apweiler, Rolf; Bruskiewich, Richard; Bureau, Thomas; Burr, Frances; Costa de Oliveira, Antonio; Fuks, Galina; Habara, Takuya; Haberer, Georg; Han, Bin; Harada, Erimi; Hiraki, Aiko T.; Hirochika, Hirohiko; Hoen, Douglas; Hokari, Hiroki; Hosokawa, Satomi; Hsing, Yue; Ikawa, Hiroshi; Ikeo, Kazuho; Imanishi, Tadashi; Ito, Yukiyo; Jaiswal, Pankaj; Kanno, Masako; Kawahara, Yoshihiro; Kawamura, Toshiyuki; Kawashima, Hiroaki; Khurana, Jitendra P.; Kikuchi, Shoshi; Komatsu, Setsuko; Koyanagi, Kanako O.; Kubooka, Hiromi; Lieberherr, Damien; Lin, Yao-Cheng; Lonsdale, David; Matsumoto, Takashi; Matsuya, Akihiro; McCombie, W. Richard; Messing, Joachim; Miyao, Akio; Mulder, Nicola; Nagamura, Yoshiaki; Nam, Jongmin; Namiki, Nobukazu; Numa, Hisataka; Nurimoto, Shin; O’Donovan, Claire; Ohyanagi, Hajime; Okido, Toshihisa; OOta, Satoshi; Osato, Naoki; Palmer, Lance E.; Quetier, Francis; Raghuvanshi, Saurabh; Saichi, Naomi; Sakai, Hiroaki; Sakai, Yasumichi; Sakata, Katsumi; Sakurai, Tetsuya; Sato, Fumihiko; Sato, Yoshiharu; Schoof, Heiko; Seki, Motoaki; Shibata, Michie; Shimizu, Yuji; Shinozaki, Kazuo; Shinso, Yuji; Singh, Nagendra K.; Smith-White, Brian; Takeda, Jun-ichi; Tanino, Motohiko; Tatusova, Tatiana; Thongjuea, Supat; Todokoro, Fusano; Tsugane, Mika; Tyagi, Akhilesh K.; Vanavichit, Apichart; Wang, Aihui; Wing, Rod A.; Yamaguchi, Kaori; Yamamoto, Mayu; Yamamoto, Naoyuki; Yu, Yeisoo; Zhang, Hao; Zhao, Qiang; Higo, Kenichi; Burr, Benjamin; Gojobori, Takashi; Sasaki, Takuji
We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is ∼32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene. PMID:17210932
Ashish V Tendulkar
Full Text Available BACKGROUND: FragKB (Fragment Knowledgebase is a repository of clusters of structurally similar fragments from proteins. Fragments are annotated with information at the level of sequence, structure and function, integrating biological descriptions derived from multiple existing resources and text mining. METHODOLOGY: FragKB contains approximately 400,000 conserved fragments from 4,800 representative proteins from PDB. Literature annotations are extracted from more than 1,700 articles and are available for over 12,000 fragments. The underlying systematic annotation workflow of FragKB ensures efficient update and maintenance of this database. The information in FragKB can be accessed through a web interface that facilitates sequence and structural visualization of fragments together with known literature information on the consequences of specific residue mutations and functional annotations of proteins and fragment clusters. FragKB is accessible online at http://ubio.bioinfo.cnio.es/biotools/fragkb/. SIGNIFICANCE: The information presented in FragKB can be used for modeling protein structures, for designing novel proteins and for functional characterization of related fragments. The current release is focused on functional characterization of proteins through inspection of conservation of the fragments.
Higgins, Sean A; Savage, David F
A fundamental goal of protein biochemistry is to determine the sequence-function relationship, but the vastness of sequence space makes comprehensive evaluation of this landscape difficult. However, advances in DNA synthesis and sequencing now allow researchers to assess the functional impact of every single mutation in many proteins, but challenges remain in library construction and the development of general assays applicable to a diverse range of protein functions. This Perspective briefly outlines the technical innovations in DNA manipulation that allow massively parallel protein biochemistry and then summarizes the methods currently available for library construction and the functional assays of protein variants. Areas in need of future innovation are highlighted with a particular focus on assay development and the use of computational analysis with machine learning to effectively traverse the sequence-function landscape. Finally, applications in the fundamentals of protein biochemistry, disease prediction, and protein engineering are presented.
Reiz, Bela; Li, Liang
Controlled hydrolysis of proteins to generate peptide ladders combined with mass spectrometric analysis of the resultant peptides can be used for protein sequencing. In this paper, two methods of improving the microwave-assisted protein hydrolysis process are described to enable rapid sequencing of proteins containing disulfide bonds and increase sequence coverage, respectively. It was demonstrated that proteins containing disulfide bonds could be sequenced by MS analysis by first performing hydrolysis for less than 2 min, followed by 1 h of reduction to release the peptides originally linked by disulfide bonds. It was shown that a strong base could be used as a catalyst for microwave-assisted protein hydrolysis, producing complementary sequence information to that generated by microwave-assisted acid hydrolysis. However, using either acid or base hydrolysis, amide bond breakages in small regions of the polypeptide chains of the model proteins (e.g., cytochrome c and lysozyme) were not detected. Dynamic light scattering measurement of the proteins solubilized in an acid or base indicated that protein-protein interaction or aggregation was not the cause of the failure to hydrolyze certain amide bonds. It was speculated that there were some unknown local structures that might play a role in preventing an acid or base from reacting with the peptide bonds therein. 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.
Konstantin B Zeldovich
Full Text Available There have been considerable attempts in the past to relate phenotypic trait--habitat temperature of organisms--to their genotypes, most importantly compositions of their genomes and proteomes. However, despite accumulation of anecdotal evidence, an exact and conclusive relationship between the former and the latter has been elusive. We present an exhaustive study of the relationship between amino acid composition of proteomes, nucleotide composition of DNA, and optimal growth temperature (OGT of prokaryotes. Based on 204 complete proteomes of archaea and bacteria spanning the temperature range from -10 degrees C to 110 degrees C, we performed an exhaustive enumeration of all possible sets of amino acids and found a set of amino acids whose total fraction in a proteome is correlated, to a remarkable extent, with the OGT. The universal set is Ile, Val, Tyr, Trp, Arg, Glu, Leu (IVYWREL, and the correlation coefficient is as high as 0.93. We also found that the G + C content in 204 complete genomes does not exhibit a significant correlation with OGT (R = -0.10. On the other hand, the fraction of A + G in coding DNA is correlated with temperature, to a considerable extent, due to codon patterns of IVYWREL amino acids. Further, we found strong and independent correlation between OGT and the frequency with which pairs of A and G nucleotides appear as nearest neighbors in genome sequences. This adaptation is achieved via codon bias. These findings present a direct link between principles of proteins structure and stability and evolutionary mechanisms of thermophylic adaptation. On the nucleotide level, the analysis provides an example of how nature utilizes codon bias for evolutionary adaptation to extreme conditions. Together these results provide a complete picture of how compositions of proteomes and genomes in prokaryotes adjust to the extreme conditions of the environment.
Gaston K Mazandu
Full Text Available UNLABELLED: The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. AVAILABILITY: Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.
Wang, Qinghua; Arighi, Cecilia N; King, Benjamin L; Polson, Shawn W; Vincent, James; Chen, Chuming; Huang, Hongzhan; Kingham, Brewster F; Page, Shallee T; Rendino, Marc Farnum; Thomas, William Kelley; Udwary, Daniel W; Wu, Cathy H
Recent advances in high-throughput DNA sequencing technologies have equipped biologists with a powerful new set of tools for advancing research goals. The resulting flood of sequence data has made it critically important to train the next generation of scientists to handle the inherent bioinformatic challenges. The North East Bioinformatics Collaborative (NEBC) is undertaking the genome sequencing and annotation of the little skate (Leucoraja erinacea) to promote advancement of bioinformatics infrastructure in our region, with an emphasis on practical education to create a critical mass of informatically savvy life scientists. In support of the Little Skate Genome Project, the NEBC members have developed several annotation workshops and jamborees to provide training in genome sequencing, annotation and analysis. Acting as a nexus for both curation activities and dissemination of project data, a project web portal, SkateBase (http://skatebase.org) has been developed. As a case study to illustrate effective coupling of community annotation with workforce development, we report the results of the Mitochondrial Genome Annotation Jamborees organized to annotate the first completely assembled element of the Little Skate Genome Project, as a culminating experience for participants from our three prior annotation workshops. We are applying the physical/virtual infrastructure and lessons learned from these activities to enhance and streamline the genome annotation workflow, as we look toward our continuing efforts for larger-scale functional and structural community annotation of the L. erinacea genome.
Wang, Qinghua; Arighi, Cecilia N.; King, Benjamin L.; Polson, Shawn W.; Vincent, James; Chen, Chuming; Huang, Hongzhan; Kingham, Brewster F.; Page, Shallee T.; Farnum Rendino, Marc; Thomas, William Kelley; Udwary, Daniel W.; Wu, Cathy H.
Recent advances in high-throughput DNA sequencing technologies have equipped biologists with a powerful new set of tools for advancing research goals. The resulting flood of sequence data has made it critically important to train the next generation of scientists to handle the inherent bioinformatic challenges. The North East Bioinformatics Collaborative (NEBC) is undertaking the genome sequencing and annotation of the little skate (Leucoraja erinacea) to promote advancement of bioinformatics infrastructure in our region, with an emphasis on practical education to create a critical mass of informatically savvy life scientists. In support of the Little Skate Genome Project, the NEBC members have developed several annotation workshops and jamborees to provide training in genome sequencing, annotation and analysis. Acting as a nexus for both curation activities and dissemination of project data, a project web portal, SkateBase (http://skatebase.org) has been developed. As a case study to illustrate effective coupling of community annotation with workforce development, we report the results of the Mitochondrial Genome Annotation Jamborees organized to annotate the first completely assembled element of the Little Skate Genome Project, as a culminating experience for participants from our three prior annotation workshops. We are applying the physical/virtual infrastructure and lessons learned from these activities to enhance and streamline the genome annotation workflow, as we look toward our continuing efforts for larger-scale functional and structural community annotation of the L. erinacea genome. PMID:22434832
Li Mingfeng [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China); Huang Yanzhao [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China); Xu Ruizhen [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China); Xiao Yi [Biomolecular Physics and Modeling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China)]. E-mail: firstname.lastname@example.org
The tertiary structures of proteins of beta-trefoil family have three-fold quasi-symmetry while their amino acid sequences appear almost at random. In the present paper we show that these amino acid sequences have hidden symmetries in fact and furthermore the degrees of these hidden symmetries are the same as those of their tertiary structures. We shall present a modified recurrence plot to reveal hidden symmetries in protein sequences. Our results can explain the contradiction in sequence-structure relations of proteins of beta-trefoil family.
Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.
Asara, John M; Schweitzer, Mary H; Freimark, Lisa M; Phillips, Matthew; Cantley, Lewis C
Fossilized bones from extinct taxa harbor the potential for obtaining protein or DNA sequences that could reveal evolutionary links to extant species. We used mass spectrometry to obtain protein sequences from bones of a 160,000- to 600,000-year-old extinct mastodon (Mammut americanum) and a 68-million-year-old dinosaur (Tyrannosaurus rex). The presence of T. rex sequences indicates that their peptide bonds were remarkably stable. Mass spectrometry can thus be used to determine unique sequences from ancient organisms from peptide fragmentation patterns, a valuable tool to study the evolution and adaptation of ancient taxa from which genomic sequences are unlikely to be obtained.
Wilbrandt, Jeanne; Misof, Bernhard; Niehuis, Oliver
The comparison of gene and genome structures across species has the potential to reveal major trends of genome evolution. However, such a comparative approach is currently hampered by a lack of standardization (e.g., Elliott TA, Gregory TR, Philos Trans Royal Soc B: Biol Sci 370:20140331, 2015). For example, testing the hypothesis that the total amount of coding sequences is a reliable measure of potential proteome diversity (Wang M, Kurland CG, Caetano-Anollés G, PNAS 108:11954, 2011) requires the application of standardized definitions of coding sequence and genes to create both comparable and comprehensive data sets and corresponding summary statistics. However, such standard definitions either do not exist or are not consistently applied. These circumstances call for a standard at the descriptive level using a minimum of parameters as well as an undeviating use of standardized terms, and for software that infers the required data under these strict definitions. The acquisition of a comprehensive, descriptive, and standardized set of parameters and summary statistics for genome publications and further analyses can thus greatly benefit from the availability of an easy to use standard tool. We developed a new open-source command-line tool, COGNATE (Comparative Gene Annotation Characterizer), which uses a given genome assembly and its annotation of protein-coding genes for a detailed description of the respective gene and genome structure parameters. Additionally, we revised the standard definitions of gene and genome structures and provide the definitions used by COGNATE as a working draft suggestion for further reference. Complete parameter lists and summary statistics are inferred using this set of definitions to allow down-stream analyses and to provide an overview of the genome and gene repertoire characteristics. COGNATE is written in Perl and freely available at the ZFMK homepage ( https://www.zfmk.de/en/COGNATE ) and on github ( https
Howe, Kevin; Davis, Paul; Paulini, Michael; Tuli, Mary Ann; Williams, Gary; Yook, Karen; Durbin, Richard; Kersey, Paul; Sternberg, Paul W
WormBase (www.wormbase.org) has been serving the scientific community for over 11 years as the central repository for genomic and genetic information for the soil nematode Caenorhabditis elegans. The resource has evolved from its beginnings as a database housing the genomic sequence and genetic and physical maps of a single species, and now represents the breadth and diversity of nematode research, currently serving genome sequence and annotation for around 20 nematodes. In this article, we focus on WormBase's role of genome sequence annotation, describing how we annotate and integrate data from a growing collection of nematode species and strains. We also review our approaches to sequence curation, and discuss the impact on annotation quality of large functional genomics projects such as modENCODE.
Wright, James C.; Sugden, Deana; Francis-McIntyre, Sue; Riba Garcia, Isabel; Gaskell, Simon J.; Grigoriev, Igor V.; Baker, Scott E.; Beynon, Robert J.; Hubbard, Simon J.
Abstract Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were ac...
Full Text Available Protein-protein interactions (PPIs play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs and a novel local conjoint triad description (LCTD feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.
Recent advances in DNA sequencing, synthesis and genetic engineering have enabled the introduction of choice DNA sequences into living cells. This is an exciting prospect for the field of industrial biotechnology, which aims at using microorganisms to produce foods, beverages, pharmaceuticals and
... analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the signiﬁcance of sequence alignments, the use of hidden Markov models as the basis for proﬁle searches to identify distant members of sequence families, and the inference...
Wu, Chang-De; Pang, Wan-Yong; Zhao, De-Ming
The prion protein gene of African lion (Panthera Leo) was first cloned and polymorphisms screened. The results suggest that the prion protein gene of eight African lions is highly homogenous. The amino acid sequences of the prion protein (PrP) of all samples tested were identical. Four single nucleotide polymorphisms (C42T, C81A, C420T, T600C) in the prion protein gene (Prnp) of African lion were found, but no amino acid substitutions. Sequence analysis showed that the higher homology is observed to felis catus AF003087 (96.7%) and to sheep number M31313.1 (96.2%) Genbank accessed. With respect to all the mammalian prion protein sequences compared, the African lion prion protein sequence has three amino acid substitutions. The homology might in turn affect the potential intermolecular interactions critical for cross species transmission of prion disease.
Zhu, Yunye; Huang, Shengxiong; Miao, Min; Tang, Xiaofeng; Yue, Junyang; Wang, Wenjie; Liu, Yongsheng
One hundred DDB1 (damaged DNA binding protein-1)-binding WD40-repeat domain (DWD) family genes were identified in the S. lycopersicum genome. The DWD genes encode proteins presumably functioning as the substrate recognition subunits of the cullin4-ring ubiquitin E3 ligase complex. These findings provide candidate genes and a research platform for further gene functionality and molecular breeding study. A subclass of DDB1 (damaged DNA binding protein-1)-binding WD40-repeat domain (DWD) family proteins has been demonstrated to function as the substrate recognition subunits of the cullin4-ring ubiquitin E3 ligase complex. However, little information is available about the cognate subfamily genes in tomato (S. lycopersicum). In this study, based on the recently released tomato genome sequences, 100 tomato genes encoding DWD proteins that potentially interact with DDB1 were identified and characterized, including analyses of the detailed annotations, chromosome locations and compositions of conserved amino acid domains. In addition, a phylogenetic tree, which comprises of three main groups, of the subfamily genes was constructed. The physical interaction between tomato DDB1 and 14 representative DWD proteins was determined by yeast two-hybrid and co-immunoprecipitation assays. The subcellular localization of these 14 representative DWD proteins was determined. Six of them were localized in both nucleus and cytoplasm, seven proteins exclusively in cytoplasm, and one protein either in nucleus and cytoplasm, or exclusively in cytoplasm. Comparative genomic analysis demonstrated that the expansion of these subfamily members in tomato predominantly resulted from two whole-genome triplication events in the evolution history.
Bárcena José A
Full Text Available Abstract Background Annotation of protein-coding genes is a key step in sequencing projects. Protein functions are mainly assigned on the basis of the amino acid sequence alone by searching of homologous proteins. However, fully automated annotation processes often lead to wrong prediction of protein functions, and therefore time-intensive manual curation is often essential. Here we describe a fast and reliable way to correct function annotation in sequencing projects, focusing on surface proteomes. We use a proteomics approach, previously proven to be very powerful for identifying new vaccine candidates against Gram-positive pathogens. It consists of shaving the surface of intact cells with two proteases, the specific cleavage-site trypsin and the unspecific proteinase K, followed by LC/MS/MS analysis of the resulting peptides. The identified proteins are contrasted by computational analysis and their sequences are inspected to correct possible errors in function prediction. Results When applied to the zoonotic pathogen Streptococcus suis, of which two strains have been recently sequenced and annotated, we identified a set of surface proteins without cytoplasmic contamination: all the proteins identified had exporting or retention signals towards the outside and/or the cell surface, and viability of protease-treated cells was not affected. The combination of both experimental evidences and computational methods allowed us to determine that two of these proteins are putative extracellular new adhesins that had been previously attributed a wrong cytoplasmic function. One of them is a putative component of the pilus of this bacterium. Conclusion We illustrate the complementary nature of laboratory-based and computational methods to examine in concert the localization of a set of proteins in the cell, and demonstrate the utility of this proteomics-based strategy to experimentally correct function annotation errors in sequencing projects. This
Full Text Available Abstract Background Odorant binding proteins (OBPs are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC in conjunction with multiple physicochemical properties of amino acids to predict odorant-binding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively. Conclusion Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information.
Zu-Guo, Yu; Qian-Jun, Xiao; Long, Shi; Jun-Wu, Yu; Anh, Vo
Investigating the biological function of proteins is a key aspect of protein studies. Bioinformatic methods become important for studying the biological function of proteins. In this paper, we first give the chaos game representation (CGR) of randomly-linked functional protein sequences, then propose the use of the recurrent iterated function systems (RIFS) in fractal theory to simulate the measure based on their chaos game representations. This method helps to extract some features of functional protein sequences, and furthermore the biological functions of these proteins. Then multifractal analysis of the measures based on the CGRs of randomly-linked functional protein sequences are performed. We find that the CGRs have clear fractal patterns. The numerical results show that the RIFS can simulate the measure based on the CGR very well. The relative standard error and the estimated probability matrix in the RIFS do not depend on the order to link the functional protein sequences. The estimated probability matrices in the RIFS with different biological functions are evidently different. Hence the estimated probability matrices in the RIFS can be used to characterise the difference among linked functional protein sequences with different biological functions. From the values of the D q curves, one sees that these functional protein sequences are not completely random. The D q of all linked functional proteins studied are multifractal-like and sufficiently smooth for the C q (analogous to specific heat) curves to be meaningful. Furthermore, the D q curves of the measure μ based on their CGRs for different orders to link the functional protein sequences are almost identical if q ≥ 0. Finally, the C q curves of all linked functional proteins resemble a classical phase transition at a critical point. (cross-disciplinary physics and related areas of science and technology)
Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim
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...
Amit Kumar Dubey
Full Text Available A total of 121 protein sequences of pectate lyases were subjected to homology search, multiple sequence alignment, phylogenetic tree construction, and motif analysis. The phylogenetic tree constructed revealed different clusters based on different source organisms representing bacterial, fungal, plant, and nematode pectate lyases. The multiple accessions of bacterial, fungal, nematode, and plant pectate lyase protein sequences were placed closely revealing a sequence level similarity. The multiple sequence alignment of these pectate lyase protein sequences from different source organisms showed conserved regions at different stretches with maximum homology from amino acid residues 439–467, 715–816, and 829–910 which could be used for designing degenerate primers or probes specific for pectate lyases. The motif analysis revealed a conserved Pec_Lyase_C domain uniformly observed in all pectate lyases irrespective of variable sources suggesting its possible role in structural and enzymatic functions.
Busk, Peter Kamp
Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited...... number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than...... the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition...
Liu, Jen-Wei; Lin, Jau-Ji; Cheng, Chih-Wen; Lin, Yu-Feng; Hwang, Jenn-Kang; Huang, Tsun-Tsao
Residues that are crucial to protein function or structure are usually evolutionarily conserved. To identify the important residues in protein, sequence conservation is estimated, and current methods rely upon the unbiased collection of homologous sequences. Surprisingly, our previous studies have shown that the sequence conservation is closely correlated with the weighted contact number (WCN), a measure of packing density for residue's structural environment, calculated only based on the C α positions of a protein structure. Moreover, studies have shown that sequence conservation is correlated with environment-related structural properties calculated based on different protein substructures, such as a protein's all atoms, backbone atoms, side-chain atoms, or side-chain centroid. To know whether the C α atomic positions are adequate to show the relationship between residue environment and sequence conservation or not, here we compared C α atoms with other substructures in their contributions to the sequence conservation. Our results show that C α positions are substantially equivalent to the other substructures in calculations of various measures of residue environment. As a result, the overlapping contributions between C α atoms and the other substructures are high, yielding similar structure-conservation relationship. Take the WCN as an example, the average overlapping contribution to sequence conservation is 87% between C α and all-atom substructures. These results indicate that only C α atoms of a protein structure could reflect sequence conservation at the residue level. © 2017 Wiley Periodicals, Inc.
Lua, Rhonald C; Wilson, Stephen J; Konecki, Daniel M; Wilkins, Angela D; Venner, Eric; Morgan, Daniel H; Lichtarge, Olivier
The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Pérez, Antonio J; Perez-Iratxeta, Carolina; Bork, Peer; Thode, Guillermo; Andrade, Miguel A
The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat
Schneider, Michel; Lane, Lydie; Boutet, Emmanuel; Lieberherr, Damien; Tognolli, Michael; Bougueleret, Lydie; Bairoch, Amos
The UniProt knowledgebase, UniProtKB, is the main product of the UniProt consortium. It consists of two sections, UniProtKB/Swiss-Prot, the manually curated section, and UniProtKB/TrEMBL, the computer translation of the EMBL/GenBank/DDBJ nucleotide sequence database. Taken together, these two sections cover all the proteins characterized or inferred from all publicly available nucleotide sequences. The Plant Proteome Annotation Program (PPAP) of UniProtKB/Swiss-Prot focuses on the manual annotation of plant-specific proteins and protein families. Our major effort is currently directed towards the two model plants Arabidopsis thaliana and Oryza sativa. In UniProtKB/Swiss-Prot, redundancy is minimized by merging all data from different sources in a single entry. The proposed protein sequence is frequently modified after comparison with ESTs, full length transcripts or homologous proteins from other species. The information present in manually curated entries allows the reconstruction of all described isoforms. The annotation also includes proteomics data such as PTM and protein identification MS experimental results. UniProtKB and the other products of the UniProt consortium are accessible online at www.uniprot.org.
Agarwal, Pratul Kumar [Knoxville, TN
The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.
Coruh, Ceyda; Shahid, Saima; Axtell, Michael J
A key goal in genomics is the complete annotation of the expressed regions of the genome. In plants, substantial portions of the genome make regulatory small RNAs produced by Dicer-Like (DCL) proteins and utilized by Argonaute (AGO) proteins. These include miRNAs and various types of endogenous siRNAs. Small RNA-seq, enabled by cheap and fast DNA sequencing, has produced an enormous volume of data on plant miRNA and siRNA expression in recent years. In this review, we discuss recent progress in using small RNA-seq data to produce stable and reliable annotations of miRNA and siRNA genes in plants. In addition, we highlight key goals for the future of small RNA gene annotation in plants. Copyright © 2014 Elsevier Ltd. All rights reserved.
Stefanie D. Hueber
Full Text Available Phylogenetic methods are key to providing models for how a given protein family evolved. However, these methods run into difficulties when sequence divergence is either too low or too high. Here, we provide a case study of Hox and ParaHox proteins so that additional insights can be gained using a new computational approach to help solve old classification problems. For two (Gsx and Cdx out of three ParaHox proteins the assignments differ between the currently most established view and four alternative scenarios. We use a non-phylogenetic, pairwise-sequence-similarity-based method to assess which of the previous predictions, if any, are best supported by the sequence-similarity relationships between Hox and ParaHox proteins. The overall sequence-similarities show Gsx to be most similar to Hox2–3, and Cdx to be most similar to Hox4–8. The results indicate that a purely pairwise-sequence-similarity-based approach can provide additional information not only when phylogenetic inference methods have insufficient information to provide reliable classifications (as was shown previously for central Hox proteins, but also when the sequence variation is so high that the resulting phylogenetic reconstructions are likely plagued by long-branch-attraction artifacts.
Abriata, L.A.; Salverda, M.L.M.; Tomatis, P.E.
A dataset of TEM lactamase variants with different substrate and inhibition profiles was compiled and analyzed. Trends show that loops are the main evolvable regions in these enzymes, gradually accumulating mutations to generate increasingly complex functions. Notably, many mutations present in
Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert
A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often
Skala, Wolfgang; Wohlschlager, Therese; Senn, Stefan; Huber, Gabriel E; Huber, Christian G
Hybrid mass spectrometry (MS) is an emerging technique for characterizing glycoproteins, which typically display pronounced microheterogeneity. Since hybrid MS combines information from different experimental levels, it crucially depends on computational methods. Here, we describe a novel software tool, MoFi, which integrates hybrid MS data to assign glycans and other post-translational modifications (PTMs) in deconvoluted mass spectra of intact proteins. Its two-stage search algorithm first assigns monosaccharide/PTM compositions to each peak and then compiles a hierarchical list of glycan combinations compatible with these compositions. Importantly, the program only includes those combinations which are supported by a glycan library as derived from glycopeptide or released glycan analysis. By applying MoFi to mass spectra of rituximab, ado-trastuzumab emtansine, and recombinant human erythropoietin, we demonstrate how integration of bottom-up data may be used to refine information collected at the intact protein level. Accordingly, our software reveals that a single mass frequently can be explained by a considerable number of glycoforms. Yet, it simultaneously ranks proteoforms according to their probability, based on a score which is calculated from relative glycan abundances. Notably, glycoforms that comprise identical glycans may nevertheless differ in score if those glycans occupy different sites. Hence, MoFi exposes different layers of complexity that are present in the annotation of a glycoprotein mass spectrum.
Full Text Available We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM model and Local Phase Quantization (LPQ to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.
Milacic, Marija; Haw, Robin, E-mail: email@example.com; Rothfels, Karen; Wu, Guanming [Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, ON, M5G0A3 (Canada); Croft, David; Hermjakob, Henning [European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD (United Kingdom); D’Eustachio, Peter [Department of Biochemistry, NYU School of Medicine, New York, NY 10016 (United States); Stein, Lincoln [Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, ON, M5G0A3 (Canada)
Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.
Milacic, Marija; Haw, Robin; Rothfels, Karen; Wu, Guanming; Croft, David; Hermjakob, Henning; D'Eustachio, Peter; Stein, Lincoln
Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of "cancer" pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.
Milacic, Marija; Haw, Robin; Rothfels, Karen; Wu, Guanming; Croft, David; Hermjakob, Henning; D’Eustachio, Peter; Stein, Lincoln
Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics
Full Text Available The results presented here suggest the existence of correlations in the sequence data of proteins. 32 proteins, both globular and fibrous, both monomeric and polymeric, were analyzed. The primary structures of these proteins were treated as time series. Three spatial series of data for each sequence of a protein were generated from numerical correspondences between each amino acid and a physical property associated with it, i.e., its electric charge, its polar character and its dipole moment. For each series, the spectral coefficient, the scaling exponent and the Hurst coefficient were determined. The values obtained for these coefficients revealed non-randomness in the series of data.
Marx, Harald; Hahne, Hannes; Ulbrich, Susanne E; Schnieke, Angelika; Rottmann, Oswald; Frishman, Dmitrij; Kuster, Bernhard
The pig is one of the earliest domesticated animals in the history of human civilization and represents one of the most important livestock animals. The recent sequencing of the Sus scrofa genome was a major step toward the comprehensive understanding of porcine biology, evolution, and its utility as a promising large animal model for biomedical and xenotransplantation research. However, the functional and structural annotation of the Sus scrofa genome is far from complete. Here, we present mass spectrometry-based quantitative proteomics data of nine juvenile organs and six embryonic stages between 18 and 39 days after gestation. We found that the data provide evidence for and improve the annotation of 8176 protein-coding genes including 588 novel and 321 refined gene models. The analysis of tissue-specific proteins and the temporal expression profiles of embryonic proteins provides an initial functional characterization of expressed protein interaction networks and modules including as yet uncharacterized proteins. Comparative transcript and protein expression analysis to human organs reveal a moderate conservation of protein translation across species. We anticipate that this resource will facilitate basic and applied research on Sus scrofa as well as its porcine relatives.
Possenti, Andrea; Vendruscolo, Michele; Camilloni, Carlo; Tiana, Guido
Proteins employ the information stored in the genetic code and translated into their sequences to carry out well-defined functions in the cellular environment. The possibility to encode for such functions is controlled by the balance between the amount of information supplied by the sequence and that left after that the protein has folded into its structure. We study the amount of information necessary to specify the protein structure, providing an estimate that keeps into account the thermodynamic properties of protein folding. We thus show that the information remaining in the protein sequence after encoding for its structure (the 'information gap') is very close to what needed to encode for its function and interactions. Then, by predicting the information gap directly from the protein sequence, we show that it may be possible to use these insights from information theory to discriminate between ordered and disordered proteins, to identify unknown functions, and to optimize artificially-designed protein sequences. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.
Wu, Cathy H; Apweiler, Rolf; Bairoch, Amos; Natale, Darren A; Barker, Winona C; Boeckmann, Brigitte; Ferro, Serenella; Gasteiger, Elisabeth; Huang, Hongzhan; Lopez, Rodrigo; Magrane, Michele; Martin, Maria J; Mazumder, Raja; O'Donovan, Claire; Redaschi, Nicole; Suzek, Baris
The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at http://www.uniprot.org or downloaded at ftp://ftp.uniprot.org/pub/databases/.
Dea-Ayuela, María Auxiliadora; Pérez-Castillo, Yunierkis; Meneses-Marcel, Alfredo; Ubeira, Florencio M; Bolas-Fernández, Francisco; Chou, Kuo-Chen; González-Díaz, Humberto
The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region Mdata base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we
Mi, Huaiyu; Guo, Nan; Kejariwal, Anish; Thomas, Paul D.
PANTHER is a freely available, comprehensive software system for relating protein sequence evolution to the evolution of specific protein functions and biological roles. Since 2005, there have been three main improvements to PANTHER. First, the sequences used to create evolutionary trees are carefully selected to provide coverage of phylogenetic as well as functional information. Second, PANTHER is now a member of the InterPro Consortium, and the PANTHER hidden markov Models (HMMs) are distri...
Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures
Chen, Peng; Hu, ShanShan; Zhang, Jun; Gao, Xin; Li, Jinyan; Xia, Junfeng; Wang, Bing
Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures
Full Text Available The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the representation of such a protein sequence facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D space derived from amino acid substitution matrices provides an adequate representation that can be used for direct comparison of protein sequences based on geometry. We measure the performance of such a representation in the context of the protein structural fold prediction problem. We compare the results of classifying different sets of proteins belonging to distinct structural folds against classifications of the same proteins obtained from sequence alone or directly from structural information. We find that sequence alone performs poorly as a structure classifier. We show in contrast that the use of the three dimensional representation of the sequences significantly improves the classification accuracy. We conclude with a discussion of the current limitations of such a representation and with a description of potential improvements.
Shu, Shengqiang; Rokhsar, Dan; Goodstein, David; Hayes, David; Mitros, Therese
Plant genomes vary in size and are highly complex with a high amount of repeats, genome duplication and tandem duplication. Gene encodes a wealth of information useful in studying organism and it is critical to have high quality and stable gene annotation. Thanks to advancement of sequencing technology, many plant species genomes have been sequenced and transcriptomes are also sequenced. To use these vastly large amounts of sequence data to make gene annotation or re-annotation in a timely fashion, an automatic pipeline is needed. JGI plant genomics gene annotation pipeline, called integrated gene call (IGC), is our effort toward this aim with aid of a RNA-seq transcriptome assembly pipeline. It utilizes several gene predictors based on homolog peptides and transcript ORFs. See Methods for detail. Here we present genome annotation of JGI flagship green plants produced by this pipeline plus Arabidopsis and rice except for chlamy which is done by a third party. The genome annotations of these species and others are used in our gene family build pipeline and accessible via JGI Phytozome portal whose URL and front page snapshot are shown below.
Elias, Mikael; Liebschner, Dorothee; Gotthard, Guillaume; Chabriere, Eric
DING proteins constitute a recently discovered protein family that is ubiquitous in eukaryotes. The structural insights and the physiological involvements of these intriguing proteins are hereby deciphered. DING proteins constitute an intriguing family of phosphate-binding proteins that was identified in a wide range of organisms, from prokaryotes and archae to eukaryotes. Despite their seemingly ubiquitous occurrence in eukaryotes, their encoding genes are missing from sequenced genomes. Such a lack has considerably hampered functional studies. In humans, these proteins have been related to several diseases, like atherosclerosis, kidney stones, inflammation processes and HIV inhibition. The human phosphate binding protein is a human representative of the DING family that was serendipitously discovered from human plasma. An original approach was developed to determine ab initio the complete and exact sequence of this 38 kDa protein by utilizing mass spectrometry and X-ray data in tandem. Taking advantage of this first complete eukaryotic DING sequence, a immunohistochemistry study was undertaken to check the presence of DING proteins in various mice tissues, revealing that these proteins are widely expressed. Finally, the structure of a bacterial representative from Pseudomonas fluorescens was solved at sub-angstrom resolution, allowing the molecular mechanism of the phosphate binding in these high-affinity proteins to be elucidated
Elias, Mikael, E-mail: firstname.lastname@example.org [Weizmann Institute of Science, Rehovot (Israel); Liebschner, Dorothee [CRM2, Nancy Université (France); Gotthard, Guillaume; Chabriere, Eric [AFMB, Université Aix-Marseille II (France)
DING proteins constitute a recently discovered protein family that is ubiquitous in eukaryotes. The structural insights and the physiological involvements of these intriguing proteins are hereby deciphered. DING proteins constitute an intriguing family of phosphate-binding proteins that was identified in a wide range of organisms, from prokaryotes and archae to eukaryotes. Despite their seemingly ubiquitous occurrence in eukaryotes, their encoding genes are missing from sequenced genomes. Such a lack has considerably hampered functional studies. In humans, these proteins have been related to several diseases, like atherosclerosis, kidney stones, inflammation processes and HIV inhibition. The human phosphate binding protein is a human representative of the DING family that was serendipitously discovered from human plasma. An original approach was developed to determine ab initio the complete and exact sequence of this 38 kDa protein by utilizing mass spectrometry and X-ray data in tandem. Taking advantage of this first complete eukaryotic DING sequence, a immunohistochemistry study was undertaken to check the presence of DING proteins in various mice tissues, revealing that these proteins are widely expressed. Finally, the structure of a bacterial representative from Pseudomonas fluorescens was solved at sub-angstrom resolution, allowing the molecular mechanism of the phosphate binding in these high-affinity proteins to be elucidated.
Full Text Available Abstract Background The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand. Results The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes". Conclusion RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from http://tools.camera.calit2.net/camera/rammcap/.
Elias, Dwayne A.; Mukhopadhyay, Aindrila; Joachimiak, Marcin P.; Drury, Elliott C.; Redding, Alyssa M.; Yen, Huei-Che B.; Fields, Matthew W.; Hazen, Terry C.; Arkin, Adam P.; Keasling, Jay D.; Wall, Judy D.
Hypothetical and conserved hypothetical genes account for>30percent of sequenced bacterial genomes. For the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough, 347 of the 3634 genes were annotated as conserved hypothetical (9.5percent) along with 887 hypothetical genes (24.4percent). Given the large fraction of the genome, it is plausible that some of these genes serve critical cellular roles. The study goals were to determine which genes were expressed and provide a more functionally based annotation. To accomplish this, expression profiles of 1234 hypothetical and conserved genes were used from transcriptomic datasets of 11 environmental stresses, complemented with shotgun LC-MS/MS and AMT tag proteomic data. Genes were divided into putatively polycistronic operons and those predicted to be monocistronic, then classified by basal expression levels and grouped according to changes in expression for one or multiple stresses. 1212 of these genes were transcribed with 786 producing detectable proteins. There was no evidence for expression of 17 predicted genes. Except for the latter, monocistronic gene annotation was expanded using the above criteria along with matching Clusters of Orthologous Groups. Polycistronic genes were annotated in the same manner with inferences from their proximity to more confidently annotated genes. Two targeted deletion mutants were used as test cases to determine the relevance of the inferred functional annotations.
Widespread adoption of quality protein maize (QPM), especially among tropical farming systems has been slow mainly due to the slow process of generating varieties with acceptable kernel quality and adaptability to different agroecological contexts. A molecular based foreground selection system for opaque 2 (o2), the ...
Windig, J.J.; Hoving, R.A.H.; Priem, J.; Bossers, A.; Keulen, van L.J.M.; Langeveld, J.P.M.
Scrapie is a neurodegenerative disease occurring in goats and sheep. Several haplotypes of the prion protein increase resistance to scrapie infection and may be used in selective breeding to help eradicate scrapie. In this study, frequencies of the allelic variants of the PrP gene are determined
Holly J Atkinson
Full Text Available The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.
Atkinson, Holly J; Morris, John H; Ferrin, Thomas E; Babbitt, Patricia C
The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.
Full Text Available Abstract Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results In this paper, we further refine and extend the hidden semi-Markov model (HSMM initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. Conclusions We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable
Neuwald, Andrew F
The patterns of sequence similarity and divergence present within functionally diverse, evolutionarily related proteins contain implicit information about corresponding biochemical similarities and differences. A first step toward accessing such information is to statistically analyze these patterns, which, in turn, requires that one first identify and accurately align a very large set of protein sequences. Ideally, the set should include many distantly related, functionally divergent subgroups. Because it is extremely difficult, if not impossible for fully automated methods to align such sequences correctly, researchers often resort to manual curation based on detailed structural and biochemical information. However, multiply-aligning vast numbers of sequences in this way is clearly impractical. This problem is addressed using Multiply-Aligned Profiles for Global Alignment of Protein Sequences (MAPGAPS). The MAPGAPS program uses a set of multiply-aligned profiles both as a query to detect and classify related sequences and as a template to multiply-align the sequences. It relies on Karlin-Altschul statistics for sensitivity and on PSI-BLAST (and other) heuristics for speed. Using as input a carefully curated multiple-profile alignment for P-loop GTPases, MAPGAPS correctly aligned weakly conserved sequence motifs within 33 distantly related GTPases of known structure. By comparison, the sequence- and structurally based alignment methods hmmalign and PROMALS3D misaligned at least 11 and 23 of these regions, respectively. When applied to a dataset of 65 million protein sequences, MAPGAPS identified, classified and aligned (with comparable accuracy) nearly half a million putative P-loop GTPase sequences. A C++ implementation of MAPGAPS is available at http://mapgaps.igs.umaryland.edu. Supplementary data are available at Bioinformatics online.
Full Text Available Barnacle adhesion underwater is an important phenomenon to understand for the prevention of biofouling and potential biotechnological innovations, yet so far, identifying what makes barnacle glue proteins 'sticky' has proved elusive. Examination of a broad range of species within the barnacles may be instructive to identify conserved adhesive domains. We add to extensive information from the acorn barnacles (order Sessilia by providing the first protein analysis of a stalked barnacle adhesive, Lepas anatifera (order Lepadiformes. It was possible to separate the L. anatifera adhesive into at least 10 protein bands using SDS-PAGE. Intense bands were present at approximately 30, 70, 90 and 110 kilodaltons (kDa. Mass spectrometry for protein identification was followed by de novo sequencing which detected 52 peptides of 7-16 amino acids in length. None of the peptides matched published or unpublished transcriptome sequences, but some amino acid sequence similarity was apparent between L. anatifera and closely-related Dosima fascicularis. Antibodies against two acorn barnacle proteins (ab-cp-52k and ab-cp-68k showed cross-reactivity in the adhesive glands of L. anatifera. We also analysed the similarity of adhesive proteins across several barnacle taxa, including Pollicipes pollicipes (a stalked barnacle in the order Scalpelliformes. Sequence alignment of published expressed sequence tags clearly indicated that P. pollicipes possesses homologues for the 19 kDa and 100 kDa proteins in acorn barnacles. Homology aside, sequence similarity in amino acid and gene sequences tended to decline as taxonomic distance increased, with minimum similarities of 18-26%, depending on the gene. The results indicate that some adhesive proteins (e.g. 100 kDa are more conserved within barnacles than others (20 kDa.
We have previously published extensive genomic surveys [1-3], reporting NAT-homologous sequences in hundreds of sequenced bacterial, fungal and vertebrate genomes. We present here the results of our latest search of 2445 genomes, representing 1532 (70 archaeal, 1210 bacterial, 43 protist, 97 fungal,...
Ghaffari, S.H.; Olson, M.O.J.
Using synthetic oligonucleotides as primers and probes, the authors have isolated and sequenced cDNA clones encoding protein C23, a putative nucleolus organizer protein. Poly(A + ) RNA was isolated from rat Novikoff hepatoma cells and enriched in C23 mRNA by sucrose density gradient ultracentrifugation. Two deoxyoligonuleotides, a 48- and a 27-mer, were synthesized on the basis of amino acid sequence from the C-terminal half of protein C23 and cDNA sequence data from CHO cell protein. The 48-mer was used a primer for synthesis of cDNA which was then inserted into plasmid pUC9. Transformed bacterial colonies were screened by hybridization with 32 P labeled 27-mer. Two clones among 5000 gave a strong positive signal. Plasmid DNAs from these clones were purified and characterized by blotting and nucleotide sequence analysis. The length of C23 mRNA was estimated to be 3200 bases in a northern blot analysis. The sequence of a 267 b.p. insert shows high homology with the CHO cDNA with only 9 nucleotide differences and an identical amino acid sequence. These studies indicate that this region of the protein is highly conserved
Borodina, Irina; Do, Duy Duc; Eriksen, Jens C.
Background Methylotrophic yeast Pichia pastoris is widely used for recombinant protein production, largely due to its ability to secrete correctly folded heterologous proteins to the fermentation medium. Secretion is usually achieved by cloning the recombinant gene after a leader sequence, where...... alpha‐mating factor (MF) prepropeptide from Saccharomyces cerevisiae is most commonly used. Our aim was to test whether signal peptides from P. pastoris native secreted proteins could be used to direct secretion of recombinant proteins. Results Eleven native signal peptides from P. pastoris were tested...... by optimization of expression of three different proteins in P. pastoris. Conclusions Native signal peptides from P. pastoris can be used to direct secretion of recombinant proteins. A novel USER‐based P. pastoris system allows easy cloning of protein‐coding gene with the promoter and leader sequence of choice....
Garrido-Martín, Diego; Pazos, Florencio
The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely. In this work we evaluate how the growth and change with time in the content of sequence databases affect five sequence-based approaches for detecting functional sites and subfamilies. We do that by recreating historical versions of the multiple sequence alignments that would have been obtained in the past based on the database contents at different time points, covering a period of 20 years. Applying the methods to these historical alignments allows quantifying the temporal variation in their performance. Our results show that the number of families to which these methods can be applied sharply increases with time, while their ability to detect potentially functional residues remains almost constant. These results are informative for the methods' developers and final users, and may have implications in the design of new sequencing initiatives.
Rogolinski, J.; Widlak, P.; Rzeszowska-Wolny, J.
Using the Southwestern blot analysis we have studied the interactions between rat repetitive sequence MspI8 and the nuclear matrix proteins of rats testis cells. Starting from 2 weeks the young to adult animal showed differences in type of testis nuclear matrix proteins recognizing the MspI8 sequence. The same sets of nuclear matrix proteins were detected in some enriched in spermatocytes and spermatids and obtained after fractionation of cells of adult animal by the velocity sedimentation technique. (author). 21 refs, 5 figs
Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J
Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.
Konc, Janez; Skrlj, Blaz; Erzen, Nika; Kunej, Tanja; Janezic, Dusanka
Discovery of potentially deleterious sequence variants is important and has wide implications for research and generation of new hypotheses in human and veterinary medicine, and drug discovery. The GenProBiS web server maps sequence variants to protein structures from the Protein Data Bank (PDB), and further to protein-protein, protein-nucleic acid, protein-compound, and protein-metal ion binding sites. The concept of a protein-compound binding site is understood in the broadest sense, which includes glycosylation and other post-translational modification sites. Binding sites were defined by local structural comparisons of whole protein structures using the Protein Binding Sites (ProBiS) algorithm and transposition of ligands from the similar binding sites found to the query protein using the ProBiS-ligands approach with new improvements introduced in GenProBiS. Binding site surfaces were generated as three-dimensional grids encompassing the space occupied by predicted ligands. The server allows intuitive visual exploration of comprehensively mapped variants, such as human somatic mis-sense mutations related to cancer and non-synonymous single nucleotide polymorphisms from 21 species, within the predicted binding sites regions for about 80 000 PDB protein structures using fast WebGL graphics. The GenProBiS web server is open and free to all users at http://genprobis.insilab.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Gutiérrez Acosta, Olga B; Schleheck, David; Schink, Bernhard
The sulfate-reducing bacterium Desulfococcus biacutus is able to utilize acetone for growth by an inducible degradation pathway that involves a novel activation reaction for acetone with CO as a co-substrate. The mechanism, enzyme(s) and gene(s) involved in this acetone activation reaction are of great interest because they represent a novel and yet undefined type of activation reaction under strictly anoxic conditions. In this study, a draft genome sequence of D. biacutus was established. Sequencing, assembly and annotation resulted in 159 contigs with 5,242,029 base pairs and 4773 predicted genes; 4708 were predicted protein-encoding genes, and 3520 of these had a functional prediction. Proteins and genes were identified that are specifically induced during growth with acetone. A thiamine diphosphate-requiring enzyme appeared to be highly induced during growth with acetone and is probably involved in the activation reaction. Moreover, a coenzyme B12- dependent enzyme and proteins that are involved in redox reactions were also induced during growth with acetone. We present for the first time the genome of a sulfate reducer that is able to grow with acetone. The genome information of this organism represents an important tool for the elucidation of a novel reaction mechanism that is employed by a sulfate reducer in acetone activation.
Full Text Available Two Component Systems and Phosphorelays (TCS/PR are environmental signal transduction cascades in prokaryotes and, less frequently, in eukaryotes. The internal domain organization of proteins and the topology of TCS/PR cascades play an important role in shaping the responses of the circuits. It is thus important to maintain updated censuses of TCS/PR proteins in order to identify the various topologies used by nature and enable a systematic study of the dynamics associated with those topologies. To create such a census, we analyzed the proteomes of 7,609 organisms from all domains of life with fully sequenced and annotated genomes. To begin, we survey each proteome searching for proteins containing domains that are associated with internal signal transmission within TCS/PR: Histidine Kinase (HK, Response Regulator (RR and Histidine Phosphotranfer (HPt domains, and analyze how these domains are arranged in the individual proteins. Then, we find all types of operon organization and calculate how much more likely are proteins that contain TCS/PR domains to be coded by neighboring genes than one would expect from the genome background of each organism. Finally, we analyze if the fusion of domains into single TCS/PR proteins is more frequently observed than one might expect from the background of each proteome. We find 50 alternative ways in which the HK, HPt, and RR domains are observed to organize into single proteins. In prokaryotes, TCS/PR coding genes tend to be clustered in operons. 90% of all proteins identified in this study contain just one of the three domains, while 8% of the remaining proteins combine one copy of an HK, a RR, and/or an HPt domain. In eukaryotes, 25% of all TCS/PR proteins have more than one domain. These results might have implications for how signals are internally transmitted within TCS/PR cascades. These implications could explain the selection of the various designs in alternative circumstances.
Castillo Luis F.
Full Text Available Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.
Ehrlich, L.; Reczko, M.; Bohr, Henrik
The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two...... separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are teined with protein crystal structures. The prediction of hydration is improved by adding information on secondary...... structure and solvent accessibility and, using actual values of these properties, redidue hydration can be predicted to 77% accuracy with a Metthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed...
PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.
Full Text Available X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM. Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I. Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II, which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization
Bastiaan A van den Berg
Full Text Available Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy.
Bustamam, A.; Siswantining, T.; Febriyani, N. L.; Novitasari, I. D.; Cahyaningrum, R. D.
The herpes virus can be found anywhere and one of the important characteristics is its ability to cause acute and chronic infection at certain times so as a result of the infection allows severe complications occurred. The herpes virus is composed of DNA containing protein and wrapped by glycoproteins. In this work, the Herpes viruses family is classified and analyzed by clustering their protein-sequence using Tribe Markov Clustering (Tribe-MCL) algorithm. Tribe-MCL is an efficient clustering method based on the theory of Markov chains, to classify protein families from protein