Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods

By Adrian M. Altenhoff, Christophe Dessimoz. PLoS Comput. Biol., 2009

420 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9c290f5c8da3c95d0486baaf2c5094916ef85b58

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Citations in the corpus

EDGAR: A software framework for the comparative analysis of prokaryotic genomes (2009, neutral): “Recently, Altenhoff et al. confirmed the good performance of BBHs in a comparison of 11 orthology estimation methods and concluded that BBHs give comparable results to the more sophisticated methods for both the "phylogenetic" orthology definition by Fitch and the widespread "functional" definition ”
OMA standalone: orthology inference among public and custom genomes and transcriptomes (2018, background): “inference pipeline (Altenhoff and Dessimoz 2009; Boeckmann et al. 2011; Linard et al. 2011; Afrasiabi et al. 2013), the featurerich web and programmatic interfaces, large size and taxonomic breadth of its precomputed data (currently 2167 genomes), its regular update schedule of two releases per year”
InParanoid 7: new algorithms and tools for eukaryotic orthology analysis (2009, background): “Altenhoff and Dessimoz (9) also did a phylogenetic test, in which InParanoid performed near the top.”
OMA 2011: orthology inference among 1000 complete genomes (2010, methodology): “In an extensive assessment of 11 databases and methods, we showed that the orthologs inferred by OMA show high accuracy in terms of Fitch’s phylogeny-based deﬁnition and high conservation in terms of functional annotations (18).”
eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges (2011, background): “So far, the majority of quality assessment tests are based on the functional conservation of predicted orthologs (41–44); however, it has been acknowledged that a phylogeny-based benchmarking approach would be more appropriate (44,45).”
A Phylogeny-Based Benchmarking Test for Orthology Inference Reveals the Limitations of Function-Based Validation (2014, methodology): “Initially, Altenhoff and Dessimoz [26] used manually curated trees from literature to validate 12 different orthology projects.”
Standardised Benchmarking in the Quest for Orthologs (2016, methodology): “The species tree discordance test exploits this relationship by assessing the accuracy of orthologs in terms of the accuracy of the species tree that can be reconstructed from them 15 .”
An integrative approach to ortholog prediction for disease-focused and other functional studies (2011, background): “Their results also highlight that different tools have different advantages [16].”