pSONIC: Ploidy-aware Syntenic Orthologous Networks Identified via Collinearity

With the rapid rise in availability of high-quality genomes for closely related species, methods for orthology inference that incorporate synteny are increasingly useful. Polyploidy perturbs the 1:1 expected frequencies of orthologs between two species, complicating the identification of orthologs....

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Bibliographic Details
Published in:G3 : genes - genomes - genetics 2021-08, Vol.11 (8)
Main Authors: Conover, Justin L, Sharbrough, Joel, Wendel, Jonathan F
Format: Article
Language:English
Subjects:
Algorithms
Genome
Humans
Phylogeny
Ploidies
Polyploidy
Software and Data Resources
Synteny
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Summary:With the rapid rise in availability of high-quality genomes for closely related species, methods for orthology inference that incorporate synteny are increasingly useful. Polyploidy perturbs the 1:1 expected frequencies of orthologs between two species, complicating the identification of orthologs. Here we present a method of ortholog inference, Ploidy-aware Syntenic Orthologous Networks Identified via Collinearity (pSONIC). We demonstrate the utility of pSONIC using four species in the cotton tribe (Gossypieae), including one allopolyploid, and place between 75% and 90% of genes from each species into nearly 32,000 orthologous groups, 97% of which consist of at most singletons or tandemly duplicated genes—58.8% more than comparable methods that do not incorporate synteny. We show that 99% of singleton gene groups follow the expected tree topology and that our ploidy-aware algorithm recovers 97.5% identical groups when compared to splitting the allopolyploid into its two respective subgenomes, treating each as separate “species.”
ISSN:2160-1836
2160-1836
DOI:10.1093/g3journal/jkab170