Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods

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.

Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes

“…First, we classified all 2,129,548 proteins encoded within chromosomes in our genome sample into orthologous protein families. The common protein families reconstruction methods COGs (Tatusov et al 2003) and MCL (Enright et al 2002) are inappropriate for our purpose since they sometimes yield protein families that include paralogs in addition to orthologs, and a reciprocal best BLAST hit (rBBH) procedure outperforms many more complicated clustering algorithms (Altenhoff and Dessimoz 2009). We therefore used a greedy algorithm similar to the bits-score algorithm used in COG database (Tatusov et al 2003), which groups all rBBHs into one orthogroup.…”

Deubiquitylases From Genes to Organism

“…DUBs FROM GENES TO ORGANISM Table 1. Proposed orthologs of human DUBs assigned in silico as bidirectional best hits (9,280) …”

Uncertain Groupings: Probabilistic Combination of Grouping Data

“…There are various computational methods for determining orthology between genes from different species [7,1]. These methods result in databases that contain groups of proteins or genes that are likely to be orthologous.…”

“…Two groups collide iff they overlap but are not equal. 1 Figure 3(c) shows the collisions between groups in our example. The idea behind the COLL-view on trust is that if two sources disagree on a group, i.e., the groups collide, only one can be correct.…”