Your browser doesn't support javascript.
loading
Machine learning approaches to identify core and dispensable genes in pangenomes.
Yocca, Alan E; Edger, Patrick P.
Afiliación
  • Yocca AE; Dep. of Plant Biology, Michigan State Univ., East Lansing, MI, 48824, USA.
  • Edger PP; Dep. of Horticulture, Michigan State Univ., East Lansing, MI, 48824, USA.
Plant Genome ; 15(1): e20135, 2022 03.
Article en En | MEDLINE | ID: mdl-34533282
A gene in a given taxonomic group is either present in every individual (core) or absent in at least a single individual (dispensable). Previous pangenomic studies have identified certain functional differences between core and dispensable genes. However, identifying if a gene belongs to the core or dispensable portion of the genome requires the construction of a pangenome, which involves sequencing the genomes of many individuals. Here we aim to leverage the previously characterized core and dispensable gene content for two grass species [Brachypodium distachyon (L.) P. Beauv. and Oryza sativa L.] to construct a machine learning model capable of accurately classifying genes as core or dispensable using only a single annotated reference genome. Such a model may mitigate the need for pangenome construction, an expensive hurdle especially in orphan crops, which often lack the adequate genomic resources.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Genómica Idioma: En Revista: Plant Genome Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Genómica Idioma: En Revista: Plant Genome Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos