Direct and tissue-specific assessment of the damaging potential of regulatory SNPs
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ABSTRACT: In the era of GWAS and personalized medicine systematically deciphering the impact of non-coding sequence variation on the regulatory genome is a critical bottleneck. Here we present Sasquatch a novel computational approach to estimate and visualize the effects of noncoding-variants on transcription factor (TF) binding using DNase I footprint data. Developed to provide an unbiased approach to prioritise non-coding variants for functional analysis, Sasquatch performs exhaustive, k-mer based analysis of average footprints to determine any k-mers potential for TF binding and quantifies how this is changed by sequence variants. Importantly the approach only requires one DNase-seq dataset per cell-type, from any genotype and is resilient across differing experimental procedures and sequence depths. We have made Sasquatch available as a versatile, high-throughput, webtool incorporating pre-processed human data from the Encode project and we demonstrate its effectiveness using validated causal GWAS SNPs and known polymorphic TF binding sites in erythroid tissues.
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE86393 | GEO | 2017/06/15
SECONDARY ACCESSION(S): PRJNA341669
REPOSITORIES: GEO
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