The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulfite sequencing is commonly used for assessing genome-wide DNA methylation at single nucleotide resolution but these data can also provide information on genetic variants like single nucleotide polymorphisms (SNPs). However, bisulfite conversion causes unmethylated cytosines to appear as thymines, complicating the alignment and subsequent SNP calling. Several tools have been developed to overcome this challenge, but there is no independent evaluation of such tools for non-model species, which often lack genomic references. Here, we used whole-genome bisulfite sequencing (WGBS) data from four female great tits (Parus major) to evaluate the performance of seven tools for SNP calling from bisulfite sequencing data. We used SNPs from whole-genome resequencing data of the same samples as baseline SNPs to assess common performance metrics like sensitivity, precision, and the number of true positive, false positive, and false negative SNPs for the full range of variant and genotype quality values. We found clear differences between the tools in either optimizing precision (Bis-SNP), sensitivity (biscuit), or a compromise between both (all other tools). Overall, the choice of SNP caller strongly depends on which performance parameter should be maximized and whether ascertainment bias should be minimized to optimize downstream analysis, highlighting the need for studies that assess such differences.

A total of 2015 female great tits were genotyped using a custom made Affymetrix great tit 650K SNP chip (Kim et al. 2018) at Edinburgh Genomics (Edinburgh, United Kingdom). Axiom Analysis Suite 1.1 was used for SNP calling following the Affymetrix best practices workflow.

Kim, J. M., A. W. Santure, H. J. Barton, J. L. Quinn, E. F. Cole, Great Tit HapMap Consortium, M. E. Visser, et al. 2018. “A High-Density SNP Chip for Genotyping Great Tit ( Parus Major ) Populations and Its Application to Studying the Genetic Architecture of Exploration Behaviour.” Molecular Ecology Resources 18 (4): 877–91. https://doi.org/10.1111/1755-0998.12778.