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Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods

Overview of attention for article published in PLoS Computational Biology, January 2009
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Title
Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods
Published in
PLoS Computational Biology, January 2009
DOI 10.1371/journal.pcbi.1000262
Pubmed ID
Authors

Adrian M. Altenhoff, Christophe Dessimoz

Abstract

Accurate genome-wide identification of orthologs is a central problem in comparative genomics, a fact reflected by the numerous orthology identification projects developed in recent years. However, only a few reports have compared their accuracy, and indeed, several recent efforts have not yet been systematically evaluated. Furthermore, orthology is typically only assessed in terms of function conservation, despite the phylogeny-based original definition of Fitch. We collected and mapped the results of nine leading orthology projects and methods (COG, KOG, Inparanoid, OrthoMCL, Ensembl Compara, Homologene, RoundUp, EggNOG, and OMA) and two standard methods (bidirectional best-hit and reciprocal smallest distance). We systematically compared their predictions with respect to both phylogeny and function, using six different tests. This required the mapping of millions of sequences, the handling of hundreds of millions of predicted pairs of orthologs, and the computation of tens of thousands of trees. In phylogenetic analysis or in functional analysis where high specificity is required, we find that OMA and Homologene perform best. At lower functional specificity but higher coverage level, OrthoMCL outperforms Ensembl Compara, and to a lesser extent Inparanoid. Lastly, the large coverage of the recent EggNOG can be of interest to build broad functional grouping, but the method is not specific enough for phylogenetic or detailed function analyses. In terms of general methodology, we observe that the more sophisticated tree reconstruction/reconciliation approach of Ensembl Compara was at times outperformed by pairwise comparison approaches, even in phylogenetic tests. Furthermore, we show that standard bidirectional best-hit often outperforms projects with more complex algorithms. First, the present study provides guidance for the broad community of orthology data users as to which database best suits their needs. Second, it introduces new methodology to verify orthology. And third, it sets performance standards for current and future approaches.

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Geographical breakdown

Country Count As %
United States 14 3%
United Kingdom 11 2%
Germany 8 2%
Brazil 6 1%
Australia 4 <1%
Spain 4 <1%
France 3 <1%
Sweden 3 <1%
Argentina 2 <1%
Other 19 4%
Unknown 394 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 137 29%
Researcher 107 23%
Student > Master 62 13%
Student > Bachelor 33 7%
Professor > Associate Professor 28 6%
Other 69 15%
Unknown 32 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 302 65%
Biochemistry, Genetics and Molecular Biology 71 15%
Computer Science 25 5%
Environmental Science 8 2%
Medicine and Dentistry 3 <1%
Other 17 4%
Unknown 42 9%