Phylogenetic Annotation Research Articles

Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes.

We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This ‘GO Phylogenetic Annotation’ approach integrates GO annotations from evolutionarily related genes across ∼100 different organisms in the context of a gene family tree, in which curators build an explicit model of the evolution of gene functions. GO Phylogenetic Annotation models the gain and loss of functions in a gene family tree, which is used to infer the functions of uncharacterized (or incompletely characterized) gene products, even for human proteins that are relatively well studied. Here, we report our results from applying this paradigm to two well-characterized cellular processes, apoptosis and autophagy. This revealed several important observations with respect to GO annotations and how they can be used for function inference. Notably, we applied only a small fraction of the experimentally supported GO annotations to infer function in other family members. The majority of other annotations describe indirect effects, phenotypes or results from high throughput experiments. In addition, we show here how feedback from phylogenetic annotation leads to significant improvements in the PANTHER trees, the GO annotations and GO itself. Thus GO phylogenetic annotation both increases the quantity and improves the accuracy of the GO annotations provided to the research community. We expect these phylogenetically based annotations to be of broad use in gene enrichment analysis as well as other applications of GO annotations.Database URL: http://amigo.geneontology.org/amigo

Data for constructing insect genome content matrices for phylogenetic analysis and functional annotation.

Twenty one fully sequenced and well annotated insect genomes were used to construct genome content matrices for phylogenetic analysis and functional annotation of insect genomes. To examine the role of e-value cutoff in ortholog determination we used scaled e-value cutoffs and a single linkage clustering approach.. The present communication includes (1) a list of the genomes used to construct the genome content phylogenetic matrices, (2) a nexus file with the data matrices used in phylogenetic analysis, (3) a nexus file with the Newick trees generated by phylogenetic analysis, (4) an excel file listing the Core (CORE) genes and Unique (UNI) genes found in five insect groups, and (5) a figure showing a plot of consistency index (CI) versus percent of unannotated genes that are apomorphies in the data set for gene losses and gains and bar plots of gains and losses for four consistency index (CI) cutoffs.

PANTHER version 10: expanded protein families and functions, and analysis tools.

PANTHER (Protein Analysis THrough Evolutionary Relationships, http://pantherdb.org) is a widely used online resource for comprehensive protein evolutionary and functional classification, and includes tools for large-scale biological data analysis. Recent development has been focused in three main areas: genome coverage, functional information (‘annotation’) coverage and accuracy, and improved genomic data analysis tools. The latest version of PANTHER, 10.0, includes almost 5000 new protein families (for a total of over 12 000 families), each with a reference phylogenetic tree including protein-coding genes from 104 fully sequenced genomes spanning all kingdoms of life. Phylogenetic trees now include inference of horizontal transfer events in addition to speciation and gene duplication events. Functional annotations are regularly updated using the models generated by the Gene Ontology Phylogenetic Annotation Project. For the data analysis tools, PANTHER has expanded the number of different ‘functional annotation sets’ available for functional enrichment testing, allowing analyses to access all Gene Ontology annotations—updated monthly from the Gene Ontology database—in addition to the annotations that have been inferred through evolutionary relationships. The Prowler (data browser) has been updated to enable users to more efficiently browse the entire database, and to create custom gene lists using the multiple axes of classification in PANTHER.

A conserved arbuscular mycorrhizal fungal core-species community colonizes potato roots in the Andes

Plant-symbiotic arbuscular mycorrhizal fungi (AMF) are of high global ecological and economic importance, but describing environmental communities of AMF at the species level remains a challenge, despite the need to understand AMF-plant preferences and to apply AMF in sustainable agriculture. Here, the potato-associated AMF species community composition was assessed for three Andean countries along an altitudinal gradient and at different plant stages, by using 454 GS-FLX+ sequencing of a 760 bp LSU rRNA gene PCR amplicon. Two methods were compared: defining OTUs based on a simple sequence similarity threshold, or affiliating reference sequences to species based on a high throughput phylogenetic annotation approach using an evolutionary placement algorithm (EPA). The EPA-based approach was not only more precise, but also fundamental to robustly unveil the AMF species community composition. The principal advantage of this approach was also demonstrated by using artificially constructed datasets based on validated public database sequences. The affiliation of sequence reads to species using phylogenetic annotation revealed a surprisingly conserved AMF core-species community structure in Andean potatoes, regardless of different plant stages and environmental factors. In total, 41 species were detected and in some cases more than 25 species were found colonizing an individual root system. Acaulospora species were identified as dominant colonizers, co-occurring with Cetraspora nodosa and certain Claroideoglomus and Rhizophagus species in most potato root samples.

The complexity and diversity of the Pathogenicity Locus in Clostridium difficile clade 5.

The symptoms of Clostridium difficile infection are caused by two closely related toxins, TcdA and TcdB, which are encoded by the 19.6 kb Pathogenicity Locus (PaLoc). The PaLoc is variably present among strains, and in this respect it resembles a mobile genetic element. The C. difficile population structure consists mainly of five phylogenetic clades designated 1–5. Certain genotypes of clade 5 are associated with recently emergent highly pathogenic strains causing human disease and animal infections. The aim of this study was to explore the evolutionary history of the PaLoc in C. difficile clade 5. Phylogenetic analyses and annotation of clade 5 PaLoc variants and adjoining genomic regions were undertaken using a representative collection of toxigenic and nontoxigenic strains. Comparison of the core genome and PaLoc phylogenies obtained for clade 5 and representatives of the other clades identified two distinct PaLoc acquisition events, one involving a toxin A+B+ PaLoc variant and the other an A−B+ variant. Although the exact mechanism of each PaLoc acquisition is unclear, evidence of possible homologous recombination with other clades and between clade 5 lineages was found within the PaLoc and adjacent regions. The generation of nontoxigenic variants by PaLoc loss via homologous recombination with PaLoc-negative members of other clades was suggested by analysis of cdu2, although none is likely to have occurred recently. A variant of the putative holin gene present in the clade 5 A−B+ PaLoc was likely acquired via allelic exchange with an unknown element. Fine-scale phylogenetic analysis of C. difficile clade 5 revealed the extent of its genetic diversity, consistent with ancient evolutionary origins and a complex evolutionary history for the PaLoc.

Visual analysis of the quantitative composition of metagenomic communities: the AmphoraVizu webserver.

Low-cost DNA sequencing methods have given rise to an enormous development of metagenomics in the past few years. One basic--and difficult--task is the phylogenetic annotation of the metagenomic samples studied. The difficulty comes from the fact that the typical environmental sample contains hundreds of unknown and still uncharacterized microorganisms. There are several possible methods to assign at least partial phylogenetic information to these uncharacterized data. Originally, the 16S ribosomal RNA was used as phylogenetic marker, then genome sequence alignments and similarity measures between the unknown genome and the reference genomes were applied (e.g., in the MEGAN software), and more recently, phylogeny-based methods applying suitable sets of marker genes were suggested (AMPHORA, AMPHORA2, and the webserver implementation AmphoraNet). Here, we present a visual analysis tool that is capable of demonstrating the quantitative relations gained from the output of the AMPHORA2 program or the easy-to-use AmphoraNet webserver. Our web-based tool, the AmphoraVizu webserver, makes the phylogenetic distribution of the metagenomic sample clearly visible by using the native output format of AMPHORA2 or AmphoraNet. The user may set the phylogenetic resolution (i.e., superkingdom, phylum, class, order, family, genus, and species) along with the chart type and will receive the distribution data detailed for all relevant marker genes in the sample. For publication quality results, the chart labels can be customized by the user. The visualization webserver is available at the address http://amphoravizu.pitgroup.org. The AmphoraNet webserver is available at http://amphoranet.pitgroup.org. The open-source version of the AmphoraVizu program is available for download at http://pitgroup.org/apps/amphoravizu/AmphoraVizu.pl.

Evolutionary history of the Clostridium difficile pathogenicity locus.

The symptoms of Clostridium difficile infection are caused by toxins expressed from its 19 kb pathogenicity locus (PaLoc). Stable integration of the PaLoc is suggested by its single chromosomal location and the clade specificity of its different genetic variants. However, the PaLoc is variably present, even among closely related strains, and thus resembles a mobile genetic element. Our aim was to explain these apparently conflicting observations by reconstructing the evolutionary history of the PaLoc. Phylogenetic analyses and annotation of the regions spanning the PaLoc were performed using C. difficile population-representative genomes chosen from a collection of 1,693 toxigenic (PaLoc present) and nontoxigenic (PaLoc absent) isolates. Comparison of the core genome and PaLoc phylogenies demonstrated an eventful evolutionary history, with distinct PaLoc variants acquired clade specifically after divergence. In particular, our data suggest a relatively recent PaLoc acquisition in clade 4. Exchanges and losses of the PaLoc DNA have also occurred, via long homologous recombination events involving flanking chromosomal sequences. The most recent loss event occurred ∼30 years ago within a clade 1 genotype. The genetic organization of the clade 3 PaLoc was unique in containing a stably integrated novel transposon (designated Tn6218), variants of which were found at multiple chromosomal locations. Tn6218 elements were Tn916-related but nonconjugative and occasionally contained genes conferring resistance to clinically relevant antibiotics. The evolutionary histories of two contrasting but clinically important genetic elements were thus characterized: the PaLoc, mobilized rarely via homologous recombination, and Tn6218, mobilized frequently through transposition.

Gene Ontology Annotations and Resources

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new ‘phylogenetic annotation’ process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.

Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium

The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortium's Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.

Genome-scale phylogenetic function annotation of large and diverse protein families

The Statistical Inference of Function Through Evolutionary Relationships (SIFTER) framework uses a statistical graphical model that applies phylogenetic principles to automate precise protein function prediction. Here we present a revised approach (SIFTER version 2.0) that enables annotations on a genomic scale. SIFTER 2.0 produces equivalently precise predictions compared to the earlier version on a carefully studied family and on a collection of 100 protein families. We have added an approximation method to SIFTER 2.0 and show a 500-fold improvement in speed with minimal impact on prediction results in the functionally diverse sulfotransferase protein family. On the Nudix protein family, previously inaccessible to the SIFTER framework because of the 66 possible molecular functions, SIFTER achieved 47.4% accuracy on experimental data (where BLAST achieved 34.0%). Finally, we used SIFTER to annotate all of the Schizosaccharomyces pombe proteins with experimental functional characterizations, based on annotations from proteins in 46 fungal genomes. SIFTER precisely predicted molecular function for 45.5% of the characterized proteins in this genome, as compared with four current function prediction methods that precisely predicted function for 62.6%, 30.6%, 6.0%, and 5.7% of these proteins. We use both precision-recall curves and ROC analyses to compare these genome-scale predictions across the different methods and to assess performance on different types of applications. SIFTER 2.0 is capable of predicting protein molecular function for large and functionally diverse protein families using an approximate statistical model, enabling phylogenetics-based protein function prediction for genome-wide analyses. The code for SIFTER and protein family data are available at http://sifter.berkeley.edu.

A complete analysis of HA and NA genes of influenza A viruses.

BackgroundMore and more nucleotide sequences of type A influenza virus are available in public databases. Although these sequences have been the focus of many molecular epidemiological and phylogenetic analyses, most studies only deal with a few representative sequences. In this paper, we present a complete analysis of all Haemagglutinin (HA) and Neuraminidase (NA) gene sequences available to allow large scale analyses of the evolution and epidemiology of type A influenza.Methodology/Principal FindingsThis paper describes an analysis and complete classification of all HA and NA gene sequences available in public databases using multivariate and phylogenetic methods.Conclusions/SignificanceWe analyzed 18975 HA sequences and divided them into 280 subgroups according to multivariate and phylogenetic analyses. Similarly, we divided 11362 NA sequences into 202 subgroups. Compared to previous analyses, this work is more detailed and comprehensive, especially for the bigger datasets. Therefore, it can be used to show the full and complex phylogenetic diversity and provides a framework for studying the molecular evolution and epidemiology of type A influenza virus. For more than 85% of type A influenza HA and NA sequences into GenBank, they are categorized in one unambiguous and unique group. Therefore, our results are a kind of genetic and phylogenetic annotation for influenza HA and NA sequences. In addition, sequences of swine influenza viruses come from 56 HA and 45 NA subgroups. Most of these subgroups also include viruses from other hosts indicating cross species transmission of the viruses between pigs and other hosts. Furthermore, the phylogenetic diversity of swine influenza viruses from Eurasia is greater than that of North American strains and both of them are becoming more diverse. Apart from viruses from human, pigs, birds and horses, viruses from other species show very low phylogenetic diversity. This might indicate that viruses have not become established in these species. Based on current evidence, there is no simple pattern of inter-hemisphere transmission of avian influenza viruses and it appears to happen sporadically. However, for H6 subtype avian influenza viruses, such transmissions might have happened very frequently and multiple and bidirectional transmission events might exist.

The Gene Ontology's Reference Genome Project: *A Unified Framework for Functional Annotation across Species

AbstractComplete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).

The Gene Ontology’s Reference Genome Project: *A Unified Framework for Functional Annotation across Species

Complete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).

PhyloGena—a user-friendly system for automated phylogenetic annotation of unknown sequences

Phylogenomic approaches towards functional and evolutionary annotation of unknown sequences have been suggested to be superior to those based only on pairwise local alignments. User-friendly software tools making the advantages of phylogenetic annotation available for the ever widening range of bioinformatically uninitiated biologists involved in genome/EST annotation projects are, however, not available. We were particularly confronted with this issue in the annotation of sequences from different groups of complex algae originating from secondary endosymbioses, where the identification of the phylogenetic origin of genes is often more problematic than in taxa well represented in the databases (e.g. animals, plants or fungi). We present a flexible pipeline with a user-friendly, interactive graphical user interface running on desktop computers that automatically performs a basic local alignment search tool (BLAST) search of query sequences, selects a representative subset of them, then creates a multiple alignment from the selected sequences, and finally computes a phylogenetic tree. The pipeline, named PhyloGena, uses public domain software for all standard bioinformatics tasks (similarity search, multiple alignment, and phylogenetic reconstruction). As the major technological innovation, selection of a meaningful subset of BLAST hits was implemented using logic programming, mimicing the selection procedure (BLAST tables, multiple alignments and phylogenetic trees) are displayed graphically, allowing the user to interact with the pipeline and deduce the function and phylogenetic origin of the query. PhyloGena thus makes phylogenomic annotation available also for those biologists without access to large computing facilities and with little informatics background. Although phylogenetic annotation is particularly useful when working with composite genomes (e.g. from complex algae), PhyloGena can be helpful in expressed sequence tag and genome annotation also in other organisms. PhyloGena (executables for LINUX and Windows 2000/XP as well as source code) is available by anonymous ftp from http://www.awi.de/en/phylogena.

Phylogenetic Analysis of General Bacterial Porins: A Phylogenomic Case Study

Bacterial porin proteins allow for the selective movement of hydrophilic solutes through the outer membrane of Gram-negative bacteria. The purpose of this study was to clarify the evolutionary relationships among the Type 1 general bacterial porins (GBPs), a porin protein subfamily that includes outer membrane proteins ompC and ompF among others. Specifically, we investigated the potential utility of phylogenetic analysis for refining poorly annotated or mis-annotated protein sequences in databases, and for characterizing new functionally distinct groups of porin proteins. Preliminary phylogenetic analysis of sequences obtained from GenBank indicated that many of these sequences were incompletely or even incorrectly annotated. Using a well-curated set of porins classified via comparative genomics, we applied recently developed bayesian phylogenetic methods for protein sequence analysis to determine the relationships among the Type 1 GBPs. Our analysis found that the major GBP classes (ompC, phoE, nmpC and ompN) formed strongly supported monophyletic groups, with the exception of ompF, which split into two distinct clades. The relationships of the GBP groups to one another had less statistical support, except for the relationships of ompC and ompN sequences, which were strongly supported as sister groups. A phylogenetic analysis comparing the relationships of the GenBank GBP sequences to the correctly annotated set of GBPs identified a large number of previously unclassified and mis-annotated GBPs. Given these promising results, we developed a tree-parsing algorithm for automated phylogenetic annotation and tested it with GenBank sequences. Our algorithm was able to automatically classify 30 unidentified and 15 mis-annotated GBPs out of 78 sequences. Altogether, our results support the potential for phylogenomics to increase the accuracy of sequence annotations.

A rigorous method for multigenic families' functional annotation: the peptidyl arginine deiminase (PADs) proteins family example

Backgroundlarge scale and reliable proteins' functional annotation is a major challenge in modern biology. Phylogenetic analyses have been shown to be important for such tasks. However, up to now, phylogenetic annotation did not take into account expression data (i.e. ESTs, Microarrays, SAGE, ...). Therefore, integrating such data, like ESTs in phylogenetic annotation could be a major advance in post genomic analyses. We developed an approach enabling the combination of expression data and phylogenetic analysis. To illustrate our method, we used an example protein family, the peptidyl arginine deiminases (PADs), probably implied in Rheumatoid Arthritis.Resultsthe analysis was performed as follows: we built a phylogeny of PAD proteins from the NCBI's NR protein database. We completed the phylogenetic reconstruction of PADs using an enlarged sequence database containing translations of ESTs contigs. We then extracted all corresponding expression data contained in EST database This analysis allowed us 1/To extend the spectrum of homologs-containing species and to improve the reconstruction of genes' evolutionary history. 2/To deduce an accurate gene expression pattern for each member of this protein family. 3/To show a correlation between paralogous sequences' evolution rate and pattern of tissular expression.Conclusioncoupling phylogenetic reconstruction and expression data is a promising way of analysis that could be applied to all multigenic families to investigate the relationship between molecular and transcriptional evolution and to improve functional annotation.

A Web-based classification system of DNA-binding protein families.

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The family of DNA-binding proteins is one of the most populated and studied amongst the various genomes of bacteria, archaea and eukaryotes and the Web-based system presented here is an approach to their classification. The DnaProt resource is an annotated and searchable collection of protein sequences for the families of DNA-binding proteins. The database contains 3238 full-length sequences (retrieved from the SWISS-PROT database, release 38) that include, at least, a DNA-binding domain. Sequence entries are organized into families defined by PROSITE patterns, PRINTS motifs and de novo excised signatures. Combining global similarities and functional motifs into a single classification scheme, DNA-binding proteins are classified into 33 unique classes, which helps to reveal comprehensive family relationships. To maximize family information retrieval, DnaProt contains a collection of multiple alignments for each DNA-binding family while the recognized motifs can be used as diagnostically functional fingerprints. All available structural class representatives have been referenced. The resource was developed as a Web-based management system for online free access of customized data sets. Entries are fully hyperlinked to facilitate easy retrieval of the original records from the source databases while functional and phylogenetic annotation will be applied to newly sequenced genomes. The database is freely available for online search of a library containing specific patterns of the identified DNA-binding protein classes and retrieval of individual entries from our WWW server (http://kronos.biol.uoa.gr/~mariak/dbDNA.html).

The COG database: a tool for genome-scale analysis of protein functions and evolution.

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.