Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks.

ABSTRACT: Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs cod

SUBMITTER: Blischak PD 

PROVIDER: S-EPMC8675098 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature