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Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost ‘capture’ method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels within ATAC-seq peak regions with at least 10 reads. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost 'capture' method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.

10Most known cancer driver mutations are within protein coding regions of the genome, however, there are 11 several important examples of oncogenic non-coding regulatory mutations. We developed a method to 12 identify insertions and deletions (indels) in regulatory regions using aligned reads from chromatin 13 immunoprecipitation followed by sequencing (ChIP-seq) or the assay for transposase-accessible 14 chromatin (ATAC-seq). Our method, which we call BreakCA for Breaks in Chromatin Accessible 15 regions, allows non-coding indels to be discovered in the absence of whole genome sequencing data, out-16 performs popular variant callers such as the GATK-HaplotypeCaller and VarScan2, and detects known 17 oncogenic regulatory mutations in T-cell acute lymphoblastic leukemia cell lines. We apply BreakCA to 18 identify indels in H3K27ac ChIP-seq peaks in 23 neuroblastoma cell lines and, after removing common 19 germline variants, we identify 23 rare germline or somatic indels that occur in multiple neuroblastoma 20 cell lines. Among them, 4 indels are candidate oncogenic drivers that are present in 4 or 5 cell lines, 21 absent from the genome aggregation database of over 15,000 whole genome sequences, and within the 22 promoters or first introns of known genes (PHF21A, ADAMTS19, GPR85 and RALGDS). In addition, we 23