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PMID: 11743721 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't

Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

Journal of molecular biology ·Vol. 314 ·No. 5 ·2001-12-14 ·Pages 1041-52

Remm M, Storm CE, Sonnhammer EL

Abstract

Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location.

MeSH Terms
Algorithms Animals Automation/methods Caenorhabditis elegans/genetics Caenorhabditis elegans Proteins/genetics Cluster Analysis Computational Biology/methods Databases, Genetic Drosophila Proteins/genetics Drosophila melanogaster/genetics Eukaryotic Cells/metabolism Evolution, Molecular Genome Genomics/methods Humans Phylogeny Sequence Homology Software Species Specificity
Chemicals
Caenorhabditis elegans Proteins Drosophila Proteins
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Remm M
Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.
Storm C E
Sonnhammer E L
Article Info
Journal
Journal of molecular biology
Abbr.
J Mol Biol
ISSN
0022-2836
Published
2001-12-14
Pages
1041-52
Language
English
Region
England
NLM ID
2985088R
Subset
IM
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