Joining forces in the quest for orthologs

Building momentum to coordinate and leverage community orthology prediction resources

The Microbe browser for comparative genomics

The Microbe browser is a web server providing comparative microbial genomics data. It offers comprehensive, integrated data from GenBank, RefSeq, UniProt, InterPro, Gene Ontology and the Orthologs Matrix Project (OMA) database, displayed along with gene predictions from five software packages. The Microbe browser is daily updated from the source databases and includes all completely sequenced bacterial and archaeal genomes. The data are displayed in an easy-to-use, interactive website based on Ensembl software. The Microbe browser is available at http://microbe.vital-it.ch/. Programmatic access is available through the OMA application programming interface (API) at http://microbe.vital-it.ch/api

A MOSAIC of methods: Improving ortholog detection through integration of algorithmic diversity

Ortholog detection (OD) is a critical step for comparative genomic analysis of protein-coding sequences. In this paper, we begin with a comprehensive comparison of four popular, methodologically diverse OD methods: MultiParanoid, Blat, Multiz, and OMA. In head-to-head comparisons, these methods are shown to significantly outperform one another 12-30% of the time. This high complementarity motivates the presentation of the first tool for integrating methodologically diverse OD methods. We term this program MOSAIC, or Multiple Orthologous Sequence Analysis and Integration by Cluster optimization. Relative to component and competing methods, we demonstrate that MOSAIC more than quintuples the number of alignments for which all species are present, while simultaneously maintaining or improving functional-, phylogenetic-, and sequence identity-based measures of ortholog quality. Further, we demonstrate that this improvement in alignment quality yields 40-280% more confidently aligned sites. Combined, these factors translate to higher estimated levels of overall conservation, while at the same time allowing for the detection of up to 180% more positively selected sites. MOSAIC is available as python package. MOSAIC alignments, source code, and full documentation are available at http://pythonhosted.org/bio-MOSAIC

eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges

Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%)

OrthoDB: the hierarchical catalog of eukaryotic orthologs in 2011

The concept of homology drives speculation on a gene’s function in any given species when its biological roles in other species are characterized. With reference to a specific species radiation homologous relations define orthologs, i.e. descendants from a single gene of the ancestor. The large-scale delineation of gene genealogies is a challenging task, and the numerous approaches to the problem reflect the importance of the concept of orthology as a cornerstone for comparative studies. Here, we present the updated OrthoDB catalog of eukaryotic orthologs delineated at each radiation of the species phylogeny in an explicitly hierarchical manner of over 100 species of vertebrates, arthropods and fungi (including the metazoa level). New database features include functional annotations, and quantification of evolutionary divergence and relations among orthologous groups. The interface features extended phyletic profile querying and enhanced text-based searches. The ever-increasing sampling of sequenced eukaryotic genomes brings a clearer account of the majority of gene genealogies that will facilitate informed hypotheses of gene function in newly sequenced genomes. Furthermore, uniform analysis across lineages as different as vertebrates, arthropods and fungi with divergence levels varying from several to hundreds of millions of years will provide essential data for uncovering and quantifying long-term trends of gene evolution. OrthoDB is freely accessible from http://cegg.unige.ch/orthodb

OMA 2011: orthology inference among 1000 complete genomes

OMA (Orthologous MAtrix) is a database that identifies orthologs among publicly available, complete genomes. Initiated in 2004, the project is at its 11th release. It now includes 1000 genomes, making it one of the largest resources of its kind. Here, we describe recent developments in terms of species covered; the algorithmic pipeline—in particular regarding the treatment of alternative splicing, and new features of the web (OMA Browser) and programming interface (SOAP API). In the second part, we review the various representations provided by OMA and their typical applications. The database is publicly accessible at http://omabrowser.org

Cost-Effective Cloud Computing: A Case Study Using the Comparative Genomics Tool, Roundup

Background Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource—Roundup—using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. Methods Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. Results We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure

Refining orthologue groups at the transcript level

<p>Abstract</p> <p>Background</p> <p>Orthologues are genes in different species that are related through divergent evolution from a common ancestor and are expected to have similar functions. Many databases have been created to describe orthologous genes based on existing sequence data. However, alternative splicing (in eukaryotes) is usually disregarded in the determination of orthologue groups and the functional consequences of alternative splicing have not been considered. Most multi-exon genes can encode multiple protein isoforms which often have different functions and can be disease-related. Extending the definition of orthologue groups to take account of alternate splicing and the functional differences it causes requires further examination.</p> <p>Results</p> <p>A subset of the orthologous gene groups between human and mouse was selected from the InParanoid database for this study. Each orthologue group was divided into sub-clusters, at the transcript level, using a method based on the sequence similarity of the isoforms. Transcript based sub-clusters were verified by functional signatures of the cluster members in the InterPro database. Functional similarity was higher within than between transcript-based sub-clusters of a defined orthologous group. In certain cases, cancer-related isoforms of a gene could be distinguished from other isoforms of the gene. Predictions of intrinsic disorder in protein regions were also correlated with the isoform sub-clusters within an orthologue group.</p> <p>Conclusions</p> <p>Sub-clustering of orthologue groups at the transcript level is an important step to more accurately define functionally equivalent orthologue groups. This work appears to be the first effort to refine orthologous groupings of genes based on the consequences of alternative splicing on function. Further investigation and refinement of the methodology to classify and verify isoform sub-clusters is needed, particularly to extend the technique to more distantly related species.</p