Machine learning approaches to identify core and dispensable genes in pangenomes

Abstract

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 and Oryza sativa) 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.