Using deep learning to identify recent positive selection in malaria parasite sequence data.

ABSTRACT:

Background

Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated.

Methods

A deep learning-based approach (called "DeepSweep") was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep .

Results

Using simulated

SUBMITTER: Deelder W 

PROVIDER: S-EPMC8201710 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature