Assessing Performance of Orthology Detection Strategies Applied to Eukaryotic Genomes

By Feng Chen, Aaron J. Mackey, Jeroen K. Vermunt, David S. Roos. PLoS ONE, 2007

407 citations recorded by the source corpus.

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale ‘gold standard’ orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2376e947bd8facdb36af32add6e7041eae1a4346

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Citations in the corpus

Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods (2009, background): “few comparative studies of the quality of the different prediction algorithms [9,10].”
Assessing the root of bilaterian animals with scalable phylogenomic methods (2009, background): “The last several years have seen a proliferation of tools for identifying homologous sequences and evaluating orthology (Chen et al. 2007), but fully automated phylogenomic matrix construction based on explicit criteria is still in its infancy.”
A Phylogeny-Based Benchmarking Test for Orthology Inference Reveals the Limitations of Function-Based Validation (2014, methodology): “Alternatively, there are statistical approaches, such as metaPhors [28] or latent class analysis [25], which use multiple projects to achieve statistical power and model the error for orthology inference.”
Genome sequence and analysis of the tuber crop potato (2011, methodology): “Comparative genome analyses Paralogous and orthologous clusters were identified using OrthoMC”
Using MCL to extract clusters from networks. (2012, background): “No real alternatives exist for Protocol 1, but a more specialised version is available as Orthomcl [14].”
Utility of RNA Sequencing for Analysis of Maize Reproductive Transcriptomes (2011, methodology): “Identifi cation and Analysis of Paralogous Gene Families Paralogous gene families were identifi ed by OrthoMCL (Chen et al., 2007; Li et al., 2003) with default parameters.”
BLAST-EXPLORER helps you building datasets for phylogenetic analysis (2010, background): “Tools have been specifically designed to distinguish between orthologous and paralogous in genome/proteome datasets [1] and expressed sequence tag datasets [2,3], but they not always the most convenient for punctual analyses.”
Genomic-scale prioritization of drug targets: the TDR Targets database (2008, methodology): “The sequences of approximately 1,400 proteins with known drug-like ligands were mapped onto pathogen genomes using a high-confidence orthology method 10,11 and a lower-confidence sequence homology (basic local alignment tool; BLAST) method to identify sets of orthologous and homologous druggable…”
Ortholog identification in the presence of domain architecture rearrangement (2011, background): “As noted in numerous studies, phylogenetic methods have been shown to have greater precision than graph-based methods [43], but the combined dependency on human expertise and the computational cost of phylogenomic analyses has limited their large-scale application [44].”