InParanoid 6: eukaryotic ortholog clusters with inparalogs

The InParanoid eukaryotic ortholog database (http://InParanoid.sbc.su.se/) has been updated to version 6 and is now based on 35 species. We collected all available ‘complete’ eukaryotic proteomes and Escherichia coli, and calculated ortholog groups for all 595 species pairs using the InParanoid program. This resulted in 2 642 187 pairwise ortholog groups in total. The orthology-based species relations are presented in an orthophylogram. InParanoid clusters contain one or more orthologs from each of the two species. Multiple orthologs in the same species, i.e. inparalogs, result from gene duplications after the species divergence. A new InParanoid website has been developed which is optimized for speed both for users and for updating the system. The XML output format has been improved for efficient processing of the InParanoid ortholog clusters

Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations

The identification of orthologous relationships forms the basis for most comparative genomics studies. Here, we present the second version of the eggNOG database, which contains orthologous groups (OGs) constructed through identification of reciprocal best BLAST matches and triangular linkage clustering. We applied this procedure to 630 complete genomes (529 bacteria, 46 archaea and 55 eukaryotes), which is a 2-fold increase relative to the previous version. The pipeline yielded 224 847 OGs, including 9724 extended versions of the original COG and KOG. We computed OGs for different levels of the tree of life; in addition to the species groups included in our first release (i.e. fungi, metazoa, insects, vertebrates and mammals), we have now constructed OGs for archaea, fishes, rodents and primates. We automatically annotate the non-supervised orthologous groups (NOGs) with functional descriptions, protein domains, and functional categories as defined initially for the COG/KOG database. In-depth analysis is facilitated by precomputed high-quality multiple sequence alignments and maximum-likelihood trees for each of the available OGs. Altogether, eggNOG covers 2 242 035 proteins (built from 2 590 259 proteins) and provides a broad functional description for at least 1 966 709 (88%) of them. Users can access the complete set of orthologous groups via a web interface at: http://eggnog.embl.de

Ortho2ExpressMatrix—a web server that interprets cross-species gene expression data by gene family information

<p>Abstract</p> <p>Background</p> <p>The study of gene families is pivotal for the understanding of gene evolution across different organisms and such phylogenetic background is often used to infer biochemical functions of genes. Modern high-throughput experiments offer the possibility to analyze the entire transcriptome of an organism; however, it is often difficult to deduct functional information from that data.</p> <p>Results</p> <p>To improve functional interpretation of gene expression we introduce Ortho2ExpressMatrix, a novel tool that integrates complex gene family information, computed from sequence similarity, with comparative gene expression profiles of two pre-selected biological objects: gene families are displayed with two-dimensional matrices. Parameters of the tool are object type (two organisms, two individuals, two tissues, etc.), type of computational gene family inference, experimental meta-data, microarray platform, gene annotation level and genome build. Family information in Ortho2ExpressMatrix bases on computationally different protein family approaches such as EnsemblCompara, InParanoid, SYSTERS and Ensembl Family. Currently, respective all-against-all associations are available for five species: human, mouse, worm, fruit fly and yeast. Additionally, microRNA expression can be examined with respect to miRBase or TargetScan families. The visualization, which is typical for Ortho2ExpressMatrix, is performed as matrix view that displays functional traits of genes (differential expression) as well as sequence similarity of protein family members (BLAST e-values) in colour codes. Such translations are intended to facilitate the user's perception of the research object.</p> <p>Conclusions</p> <p>Ortho2ExpressMatrix integrates gene family information with genome-wide expression data in order to enhance functional interpretation of high-throughput analyses on diseases, environmental factors, or genetic modification or compound treatment experiments. The tool explores differential gene expression in the light of orthology, paralogy and structure of gene families up to the point of ambiguity analyses. Results can be used for filtering and prioritization in functional genomic, biomedical and systems biology applications. The web server is freely accessible at <url>http://bioinf-data.charite.de/o2em/cgi-bin/o2em.pl</url>.</p

A Computational Study of Elongation Factor G (EFG) Duplicated Genes: Diverged Nature Underlying the Innovation on the Same Structural Template

BACKGROUND: Elongation factor G (EFG) is a core translational protein that catalyzes the elongation and recycling phases of translation. A more complex picture of EFG's evolution and function than previously accepted is emerging from analyzes of heterogeneous EFG family members. Whereas the gene duplication is postulated to be a prominent factor creating functional novelty, the striking divergence between EFG paralogs can be interpreted in terms of innovation in gene function. METHODOLOGY/PRINCIPAL FINDINGS: We present a computational study of the EFG protein family to cover the role of gene duplication in the evolution of protein function. Using phylogenetic methods, genome context conservation and insertion/deletion (indel) analysis we demonstrate that the EFG gene copies form four subfamilies: EFG I, spdEFG1, spdEFG2, and EFG II. These ancient gene families differ by their indispensability, degree of divergence and number of indels. We show the distribution of EFG subfamilies and describe evidences for lateral gene transfer and recent duplications. Extended studies of the EFG II subfamily concern its diverged nature. Remarkably, EFG II appears to be a widely distributed and a much-diversified subfamily whose subdivisions correlate with phylum or class borders. The EFG II subfamily specific characteristics are low conservation of the GTPase domain, domains II and III; absence of the trGTPase specific G2 consensus motif "RGITI"; and twelve conserved positions common to the whole subfamily. The EFG II specific functional changes could be related to changes in the properties of nucleotide binding and hydrolysis and strengthened ionic interactions between EFG II and the ribosome, particularly between parts of the decoding site and loop I of domain IV. CONCLUSIONS/SIGNIFICANCE: Our work, for the first time, comprehensively identifies and describes EFG subfamilies and improves our understanding of the function and evolution of EFG duplicated genes

A quality metric for homology modeling: the H-factor

<p>Abstract</p> <p>Background</p> <p>The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, <it>in silico </it>protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and <it>in silico </it>methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.</p> <p>Results</p> <p>In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.</p> <p>Conclusions</p> <p>We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at <url>http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor</url>.</p

The human phylome

The human phylome, which includes evolutionary relationships of all human proteins and their homologs among thirty-nine fully sequenced eukaryotes, is reconstructed