Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Details

Serval ID
serval:BIB_06AA547BD763
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes
Journal
PLOS Computational Biology
Author(s)
Moi David, Kilchoer Laurent, Aguilar Pablo S., Dessimoz Christophe
ISSN
1553-7358
ISSN-L
1553-734X
Publication state
Published
Issued date
22/07/2020
Editor
Ouzounis Christos A.
Volume
16
Number
7
Pages
e1007553
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf.
Keywords
Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation
Create date
23/01/2020 16:29
Last modification date
22/01/2021 7:24
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