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Density-based binning of gene clusters to infer function or evolutionary history using GeneGrouper
Author(s) -
Alexander McFarland,
Nolan W. Kennedy,
Carolyn E. Mills,
Danielle TullmanErcek,
Curtis Huttenhower,
Erica M. Hartmann
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab752
Subject(s) - pseudogene , biology , genome , gene , gene cluster , computational biology , genetics , gene prediction , cluster analysis , orthologous gene , homology (biology) , evolutionary biology , computer science , machine learning
Identifying variant forms of gene clusters of interest in phylogenetically proximate and distant taxa can help to infer their evolutionary histories and functions. Conserved gene clusters may differ by only a few genes, but these small differences can in turn induce substantial phenotypes, such as by the formation of pseudogenes or insertions interrupting regulation. Particularly as microbial genomes and metagenomic assemblies become increasingly abundant, unsupervised grouping of similar, but not necessarily identical, gene clusters into consistent bins can provide a population-level understanding of their gene content variation and functional homology.

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