The profiling of epigenetic marks like DNA methylation has become a central aspect of studies in evolution and ecology. Bisulphite 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, bisulphite 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 bisulphite sequencing (WGBS) data from four female great tits (Parus major) to evaluate the performance of seven tools for SNP calling from bisulphite 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.

中文翻译：

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从非模型物种中的亚硫酸氢盐测序数据中检测遗传变异的方法的性能

DNA甲基化等表观遗传标记的分析已成为进化和生态学研究的核心方面。亚硫酸氢盐测序通常用于以单核苷酸分辨率评估全基因组 DNA 甲基化，但这些数据还可以提供有关遗传变异的信息，如单核苷酸多态性 (SNP)。然而，亚硫酸氢盐转化会导致未甲基化的胞嘧啶以胸腺嘧啶的形式出现，从而使比对和随后的 SNP 调用复杂化。已经开发了几种工具来克服这一挑战，但是对于通常缺乏基因组参考的非模型物种，没有对此类工具的独立评估。在这里，我们使用了来自四只雌性大山雀（ Parus major）的全基因组亚硫酸氢盐测序 (WGBS) 数据) 评估从亚硫酸氢盐测序数据中调用 SNP 的七种工具的性能。我们使用来自相同样本的全基因组重测序数据的 SNP 作为基线 SNP 来评估常见的性能指标，例如灵敏度、精确度以及真阳性、假阳性和假阴性 SNP 的数量，用于全范围的变异和基因型质量值. 我们发现这些工具在优化精度 ( Bis-SNP )、灵敏度 ( biscuit ) 或两者之间的折衷（所有其他工具）方面存在明显差异。总体而言，SNP caller 的选择很大程度上取决于应最大化哪个性能参数以及是否应最小化确定偏差以优化下游分析，突出了评估此类差异的研究的必要性。