The Quest for Orthologs benchmark service and consensus calls in 2020

Altenhoff, Adrian M.; Garrayo-Ventas, Javier; Cosentino, Salvatore; Emms, David; Glover, Natasha; Hernández-Plaza, Ana; Nevers, Yannis; Sundesha, Vicky; Szklarczyk, Damian; Fernández, José Marı́a; Codó, Laia; Gelpí, Josep Lluís; Huerta-Cepas, Jaime; Kelly, Steven L.; Lecompte, Odile; Muffato, Matthieu; Martin, Maria Jesus; Capella-Gutiérrez, Salvador; Thomas, Paul D.; Sonnhammer, Erik L. L.; Dessimoz, Christophe

doi:10.1093/nar/gkaa308

Cited by 49 publications

(50 citation statements)

References 41 publications

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“…Published methods that had been submitted to the latest Quest for Orthologs benchmarks server ( Altenhoff et al. 2016 , 2020 ) and which inferred orthogroups were selected for benchmarking. Where the methods presented multiple options, both the fastest and the most accurate options were tested.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking Orthogroup Inference Accuracy: Revisiting Orthobench

Emms

Kelly

2020

Genome Biology and Evolution

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Orthobench is the standard benchmark to assess the accuracy of orthogroup inference methods. It contains 70 expert curated reference orthogroups (RefOGs) that span the Bilateria and cover a range of different challenges for orthogroup inference. Here we leveraged improvements in tree inference algorithms and computational resources to re-interrogate these RefOGs and carry out an extensive phylogenetic delineation of their composition. This phylogenetic revision altered the membership of 31 of the 70 RefOGs, with 24 subject to extensive revision and a further 7 that required minor changes. We further used these revised and updated RefOGs to provide an assessment of the orthogroup inference accuracy of widely used orthogroup inference methods. Finally, we provide an open-source benchmarking suite to support the future development and use of the Orthobench benchmark.

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Section: Methodsmentioning

confidence: 99%

Benchmarking Orthogroup Inference Accuracy: Revisiting Orthobench

Emms

Kelly

2020

Genome Biology and Evolution

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“…Our coexpression conservation analysis is made possible by the use of OrthoDB to define orthologous genes. However, orthology prediction is an active area of research in genomics 23,43 , and relying on a single algorithm has known limitations [44][45][46] . How would our results hold up if we had used a different reference?…”

Section: Coexpression Conservation Is Associated With Ortholog Confidmentioning

confidence: 99%

“…We breathe new life into this area by taking advantage of high-powered coexpression networks from animals, plants and yeast RNA-sequencing (RNA-seq) data 21 , as well as modern orthology prediction algorithms [22][23][24] , and measuring the conservation of coexpression relationships. As expected, we find that coexpression conservation tracks with evolutionary distances 25 , and is significantly higher for genes expressed in all cell types.…”

Section: Introductionmentioning

confidence: 99%

Coexpression reveals conserved mechanisms of transcriptional cell identity

Crow

Suresh

et al. 2020

Preprint

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What makes a mouse a mouse, and not a hamster? The answer lies in the genome, and more specifically, in differences in gene regulation between the two organisms: where and when each gene is expressed. To quantify differences, a typical study will either compare functional genomics data from homologous tissues, limiting the approach to closely related species; or compare gene repertoires, limiting the resolution of the analysis to gross correlations between phenotypes and gene family size. As an alternative, gene coexpression networks provide a basis for studying the evolution of gene regulation without these constraints. By incorporating data from hundreds of independent experiments, meta-analytic coexpression networks reflect the convergent output of species-specific transcriptional regulation.In this work, we develop a measure of regulatory evolution based on gene coexpression. Comparing data from 14 species, we quantify the conservation of coexpression patterns 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. Strikingly, we uncover deeply conserved patterns of gradient-like expression across cell types from both the animal and plant kingdoms. These results suggest that ancient genes contribute to transcriptional cell identity through mechanisms that are independent of duplication and divergence.

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“…Third, two main gene-dating methods, proteinfamily based pipeline (FBP) and synteny-based pipeline (SBP), are optimal for either old genes or young genes (11). Also, the dating results rely on the ortholog datasets and the phylogenetic tree (17), making it impractical for the integration of gene age estimates from different resources.…”

Section: Introductionmentioning

confidence: 99%

GenOrigin: A Comprehensive Protein-coding Gene Origination Database on the Evolutionary Timescale of Life

Tong

Shi

Qian

et al. 2020

Preprint

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The origination of new genes contributes to the biological diversity of life. New genes may quickly build their own network in the genomes, exert important functions, and generate novel phenotypes. Dating gene age and inferring the origination mechanisms of new genes, like primate-specific gene, is the basis for the functional study of the genes. However, no comprehensive resource of gene age estimates across species is available. Here, we systematically dated the age of 9,102,113 protein-coding genes from 565 species in the Ensembl and Ensembl Genomes databases, including 82 bacteria, 57 protists, 134 fungi, 58 plants, 56 metazoa, and 178 vertebrates, using protein-family-based pipeline with Wagner parsimony algorithm. We also collected gene age estimate data from other studies and uniformed the gene age estimates to time ranges in million years for comparison across studies. All the data were cataloged into GenOrigin (http://genorigin.chenzxlab.cn/), a user-friendly new database of gene age estimates, where users can browse gene age estimates by species, age and gene ontology. In GenOrigin, the information such as gene age estimates, annotation, gene ontology, ortholog and paralog, as well as detailed gene presence/absence views for gene age inference based on the species tree with evolutionary timescale, was provided to researchers for exploring gene functions.

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The Quest for Orthologs benchmark service and consensus calls in 2020

Cited by 49 publications

References 41 publications

Benchmarking Orthogroup Inference Accuracy: Revisiting Orthobench

Benchmarking Orthogroup Inference Accuracy: Revisiting Orthobench

Coexpression reveals conserved mechanisms of transcriptional cell identity

GenOrigin: A Comprehensive Protein-coding Gene Origination Database on the Evolutionary Timescale of Life

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