Motivation: Inference of the evolutionary relation between proteins, in particular the identification of orthologs, is a central problem in comparative genomics. Several large-scale efforts with various methodologies and scope tackle this problem, including OMA (the Orthologous MAtrix project). Results: Based on the results of the OMA project, we introduce here the OMA Browser, a web-based tool allowing the exploration of orthologous relations over 352 complete genomes. Orthologs can be viewed as groups across species, but also at the level of sequence pairs, allowing the distinction among one-to-one, one-to-many and many-to-many orthologs. Availability: http://omabrowser.org Contact: schneadr@inf.ethz.c

Dessimoz, Christophe

Gonnet, Gaston H.

Schneider, Adrian

RERO DOC Digital Library

Vol. 23 no. 16 2007, pages 2180–2182BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm295Genome analysisOMA Browser—Exploring orthologous relations across 352complete genomesAdrian Schneider*, Christophe Dessimoz and Gaston H. GonnetInstitute of Computational Science, ETH Zurich, SwitzerlandReceived on February 8, 2007; revised and accepted on May 25, 2007Advance Access publication June 1, 2007Associate Editor: Alfonso ValenciaABSTRACTMotivation: Inference of the evolutionary relation between proteins,in particular the identification of orthologs, is a central problem incomparative genomics. Several large-scale efforts with variousmethodologies and scope tackle this problem, including OMA(the Orthologous MAtrix project).Results: Based on the results of the OMA project, we introduce herethe OMA Browser, a web-based tool allowing the exploration oforthologous relations over 352 complete genomes. Orthologs can beviewed as groups across species, but also at the level of sequencepairs, allowing the distinction among one-to-one, one-to-many andmany-to-many orthologs.Availability: http://omabrowser.orgContact: schneadr@inf.ethz.ch1 BACKGROUNDAccurate orthology assignment is a prerequisite for numerousbioinformatics analyses, including the construction ofphylogenetic trees, function inference of novel proteins,identification of conserved regions, detection of horizontalgene transfer and transcription binding site prediction.Evidence for the importance of these tasks can be found inthe growing number of orthology assignment projectsdeveloped in recent years.The COG method (Tatusov et al., 1997) was the first oneto extend the systematic orthology search beyond the relativelysimple reciprocally best matches approach. This algorithm,originally applied mostly on bacterial genomes, waslater extended and applied to eukaryotic genomes: the KOGdatabase (Tatusov et al., 2003) and the EGO/TOGA project(Lee et al., 2002)’. Following these approaches, more sophis-ticated orthology determination algorithms were proposedin recent years, most notably InParanoid/MultiParanoid(Alexeyenko et al., 2006; Remm et al., 2001), OrthoMCL(Li et al., 2003), KEGG Orthology (Kanehisa et al., 2004) andRoundup (DeLuca et al., 2006). Other projects, such as IMG(Markowitz et al., 2006) and MicrobesOnline (Alm et al., 2005)employ more basic algorithms, but distinguish themselves by alarge number of analyzed sequences.The OMA project (Dessimoz et al., 2005) is a massivecross-comparison of complete genomes to identifythe evolutionary relation between any pair of proteins.The main features of OMA are the large number of genomesfrom all kingdoms of life, the strict verification of orthologyassignments and the determination of the phylogeneticrelationship between any two proteins. These major differencesto other orthologs projects will be explained in the followingsubsections. All improvements made to the algorithm sincethe introductionary paper (Dessimoz et al., 2005) are describedon the OMA Browser web page. Most notable are the use ofdistances instead of scores and statistically sounder measuresfor establishing the set of potential orthologs in the early stagesof the algorithm.1.1 Scope and automationThe analysis so far has been performed on 352 genomes from allkingdoms of life (44 eukaryotes, 282 bacteria and 26 archaea).Since summer 2004, over 300 CPUs have performed more than375 years of computation to align 1.6 million proteins, resultingin 1.3 trillion (1012) alignments. In the OMA release fromJanuary 2007, 206,326 groups of orthologs are identified, thusmaking OMA one of the largest orthology projects.Dealing with such large amounts of data requires a highdegree of automation and integrated quality checks. Unlikecomparable projects such as the COGs database or KEGGOrthology, OMA does not rely on human intervention. Once agenome database is integrated into the local databases,the process is fully automated.1.2 StrictnessOrthology inference can be a difficult problem, for instancewhen gene families went through intense expansionand reduction or when duplication and speciation eventsoccurred at close time intervals. In OMA, we have a strictapproach across the entire procedure, such as full dynamicprogramming alignments instead of Blast or the systematic useof evolutionary distances with confidence intervals. Whenlacking discriminating information, we favor false negative(missing orthologous relations) over false positive (erroneousorthology assignment). A notable and in this extent uniquefeature is the systematic verification of every putative pair*To whom correspondence should be addressed. 2007 The Author(s)This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.of orthologs by an exhaustive search for ‘witnesses ofnon-orthology’ in third-party genomes (Dessimoz et al., 2006).1.3 Orthology inference at the level of pairsOrthology is not necessarily a one-to-one relation and also notalways transitive, therefore any clustering approach willhave its limits. Although OMA initially focused on groups oforthologs, the OMA Browser also displays informationabout pairwise orthologous relations, the ‘verified stablepairs’ in Dessimoz et al., (2005), which can be categorized asfollows: one-to-one orthologs: in both species, there is only onecorresponding ortholog. one-to-many or many-to-many orthologs: in at least one ofthe two species, the gene duplicated after speciation. paralogs: the two proteins arose through gene duplication,not speciation.As far as we know, the only other project providingorthology inference at the level of pairs is Ensembl (Hubbardet al., 2005).Nevertheless, for many analyses (particularly for phyloge-netics), it is convenient to have groups of orthologs with atmost one protein from each species and every pair insidethe group being a pair of orthologs. Therefore, theOMA Browser also provides a group-centric view. OMAgroups are cliques of orthologs chosen in a way such thatthe alignment scores are maximized. The clique requirementensures that the above stated properties of an orthologousgroup are fulfilled.2 OMA BROWSER2.1 ImplementationThe OMA browser is a web application using as basis Darwin(Gonnet et al., 2000), a software package for bioinformaticsdeveloped within our group. Benefits include efficient data-structures for biological sequences, and a large libraryof functions for bioinformatics analyses. While most data ispre-computed, some computations are performed in real timeor on user request.2.2 Protein-centric viewProteins of interest can be accessed through a search interface.Searches can be conducted on identifiers, accession numbers ordescriptions. Furthermore, we provide a fast sequence searchon the 1.6 million protein sequences that takes as inputany sequence substring.The protein view provides cross-references, mainly toGenBank, Ensembl or Swiss-Prot/UniProt (for more than92% of the proteins a link to another databases canbe provided), annotations as found in the sourcedatabase, chromosome/locus information as well as links tothe different types of orthologs and the corresponding OMAgroup.2.3 OMA group-centric viewThe OMA group detail view contains several ‘tabs’.The main view is a list of all proteins in the group withan identifier and a description, providing the completeinformation of the protein family. Since the OMA projectitself is automated, no additional annotation by hand isperformed. A short description of the OMA group is inferredfrom the available sequence descriptions.A multiple sequence alignment of all proteins of a givengroup can be requested and will be displayed after computationis completed.Related groups can be explored by two options: ‘Closegroups’ are OMA groups in which at least one protein isorthologous to a protein in the current data-set, while ‘Phyleticprofile’ lists groups having similar patterns of presence/absence across species. This is a possible way of identifyinginteracting protein families, where either all members must bepresent in a genome or none of them are required. Wheneveravailable, Gene Ontology (Harris et al., 2004) annotations ofthe different proteins of the group can also be compared andprovide additional indication about the functionality of theproteins.2.4 Data export and integrationAll the data can also be downloaded from the browserweb page in numerous formats: FASTA, text (list of IDs),Darwin database (SGML format) or in a COG-compatibleformat. These files are available for all OMA groups in one fileor for each group individually.The OMA Browser offers also a SOAP-based applicationprogramming interface (API), allowing for the integration ofthe OMA data into applications or web services. Funding topay the open access charges was provided by ETH Zurich.Conflict of Interest: none declared.REFERENCESAlexeyenko,A. et al. (2006) Automatic clustering of orthologs an inparalogsshared by multiple genomes. Bioinformatics, 22, e9–e15.Alm,E.J. et al. (2005) The MicrobesOnline web site for comparative genomics.Genome Res., 15, 1015–1022.DeLuca,T.F. et al. (2006) Roundup: a multi-genome repository of orthologsand evolutionary distances. Bioinformatics, 22 (16), 2044–2046.Dessimoz,C. et al. (2006) Detecting non-orthology in the COG database andother approaches grouping orthologs using genome-specific best hits. NucleicAcids Res., 34, 3309–3316.Dessimoz,C. et al. (2005) OMA, a comprehensive, automated project for theidentification of orthologs from complete genome data: introduction and firstachievements. In: McLysath,A. and Huson,D.H. (eds) In RECOMB 2005Workshop on Comparative Genomics. Vol. LNBI 3678, of Lecture Notes inBioinformatics. Berlin/Heidelberg: Springer-Verlag, pp. 61–72.Gonnet,G.H. et al. (2000) Darwin v. 2.0: an interpreted computer languagefor the biosciences. Bioinformatics, 16, 101–103.Harris,M.A. et al. (2004) The Gene Ontology (GO) database and informaticsresource. Nucleic Acids Res., 32 (Database issue), 258–261.Hubbard,T. et al. (2005) Ensembl 2005. Nucleic Acids Res., 33 (Suppl.1),D447–D453.Kanehisa,M. et al. (2004) The KEGG resource for deciphering the genome.Nucleic Acids Res., 32 (Database issue), 277–280.OMA Browser2181Lee,Y. et al. (2002) Cross-referencing eukaryotic genomes: TIGR orthologousgene alignments (TOGA). Genome Res., 12, 493–502.Li,L. et al. (2003) OrthoMCL: identification of ortholog groups for eukaryoticgenomes. Genome Res., 13, 2178–2189.Markowitz,V.M. et al. (2006) The integrated microbial genomes (IMG) system.Nucl. Acids Res., 34 (Database issue), D334–338.Remm,M. et al. (2001) Automatic clustering of orthologs andin-paralogs from pairwise species comparisons. J Mol. Biol., 314, 1041–1052.Tatusov,R.L. et al. (2003) The COGdatabase: an updated version includes eukary-otes. BMC Bioinformatics, 4, http://www.biomedcentral.com/1471–2105/4/41.Tatusov,R.L. et al. (1997) A genomic perspective on protein families. Science,278, 631–637.A.Schneider et al.2182

OMA Browser—Exploring orthologous relations across 352 complete genomes

Abstract

Similar works

Full text

Available Versions

RERO DOC Digital Library