In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome.

在全基因组关联研究（GWAS）和个性化医学的时代，预测单核苷酸多态性（SNP）对调控元件的影响是一个重要的目标。当前确定调节性SNPs潜力的方法取决于对细胞特异性DNA结合基序的了解不足。在这里，我们介绍Sasquatch，这是一种新的计算方法，它使用DNase足迹数据来估计和可视化非编码变体对转录因子结合的影响。Sasquatch对DNase足迹进行全面的基于k -mer的分析以确定任何k-mer在特定细胞类型中结合蛋白质的潜力，以及如何通过序列变体改变这种结合的可能性。因此，Sasquatch使用无偏方法，与已知的转录因子结合位点和基序无关。Sasquatch仅需要每种基因型每种细胞类型的单个DNase-seq数据集，并根据不同实验程序和不同序列深度产生的数据产生一致的预测。在这里，我们演示了Sasquatch使用先前验证的功能性SNP的有效性，并对照现有方法对它的性能进行了基准测试。Sasquatch是一种多功能Web工具，其中包含公开可用的数据，包括人类ENCODE集合。因此，Sasquatch提供了一个功能强大的工具和存储库，用于对非编码基因组中可能的调节性SNP进行优先级排序。