Density-based binning of gene clusters to infer function or evolutionary history using GeneGrouper

Abstract

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 04, 2021
Source ID
10.1093/bioinformatics/btab752

Entities

People

  • Alexander McFarland
  • Carolyn E Mills
  • Curtis Huttenhower
  • Danielle Tullman-Ercek
  • Erica M. Hartmann
  • Nolan W Kennedy

Organizations

  • Army Research Office
  • Broad Institute
  • Harvard University
  • Istituto Superiore di Sanità
  • National Science Foundation
  • Northwestern University

Tags

Fields of Study

  • Biology

Readers

  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • Biotechnology