Benchmarking strategies for CNV calling from whole genome bisulfite data in humans

It's important to dissect the relationship between copy number variations (CNVs) and DNA methylation, because both greatly change the dosages of genes and are responsible for diverse human cancers. Although whole genome bisulfite sequencing (WGBS) informs CNVs and DNA methylation, no study has provided a systematic benchmark for detecting CNVs from WGBS data. Herein, based on simulated and real WGBS datasets of 84.62 billion reads, we undertook 714 CNV detections to comprehensively benchmark the performance of 35 strategies, 5 alignment algorithms (bismarkbt2, bsbolt, bsmap, bwameth, and walt) wrapping with 7 CNV detection applications (BreakDancer, cn.mops, CNVkit, CNVnator, DELLY, GASV and Pindel). The results highlighted a subset of strategies that accurately called CNVs depending on numbers, lengths, precision, recall, and F1 scores of CNV detections. We found that bwameth-DELLY and bwameth-BreakDancer were the best strategies for calling deletions, and walt-CNVnator and bismarkbt2-CNVnator were the best strategies for calling duplications. These works provided investigators with useful information to accurately explore CNVs from WGBS data in humans.