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 genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). Conclusion The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data

“…A revised set of SNPs and insertions/deletions (indels) was called with GATK’s HaplotypeCaller (version 4.1.4.1) using the option -ERC GVCF 5 , 22 . Variants were then assigned a quality score using GATK’s Variant Quality Score Recalibration (VQSR), and those with a VQSLOD score < 0, representing variants more likely to be false than true, were filtered out 7 , 22 . Additionally, SNPs were removed if they had more than 10% missing alleles 7 , 22 .The resulting dataset comprised of parasite genomes of P. falciparum (5,957 isolates, 750 k SNPs) and of P. vivax (659 isolates, 588 k SNPs).…”

“…Variants were then assigned a quality score using GATK’s Variant Quality Score Recalibration (VQSR), and those with a VQSLOD score < 0, representing variants more likely to be false than true, were filtered out 7 , 22 . Additionally, SNPs were removed if they had more than 10% missing alleles 7 , 22 .The resulting dataset comprised of parasite genomes of P. falciparum (5,957 isolates, 750 k SNPs) and of P. vivax (659 isolates, 588 k SNPs). The population structure was assessed using a principal component analysis (PCA) of between isolate SNP differences.…”

Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification

“…In [ 16 ], a DL-based approach (named DeepSweep) is conceived to train on the haplotypic image in a genetic region with identified sweeps for identifying loci under positive selection. The DL method detects positive selective signatures from malaria parasite WGS data.…”

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

“…Furthermore, the best features are selected using the whale optimization method for malaria cell classification with 99.67% accuracy ( 33 ). Malaria cells are classified using deep-sweep software with >0.95 ROC ( 34 ). Features are extracted from transfer learning models which are dense-net-201, dense-net-121, Resnet-101, Resnet-50, VGG-16, and VGG-19 for features extraction and input to SVM, NB, and KNN classifiers for malaria cell classification ( 35 ).…”