Genome-wide scans for selective sweeps using convolutional neural networks.

ABSTRACT:

Motivation

Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.

Results

We present ASDEC (https://github.com/pephco/ASDEC), a neural-network-based framework that can scan whole genomes for selective sweeps. ASDEC achieves similar classification performance to other convolutional neural network-based classifiers that rely on summary statistics

SUBMITTER: Zhao H 

PROVIDER: S-EPMC10311404 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature