Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq.
Arya R Massarat, Arko Sen, Jeff Jaureguy, Sélène T Tyndale, Yi Fu, Galina Erikson, Graham McVicker
Author Information
Arya R Massarat: Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA. ORCID
Arko Sen: Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA.
Jeff Jaureguy: Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Sélène T Tyndale: Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA.
Yi Fu: Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Galina Erikson: Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA.
Graham McVicker: Integrative Biology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Road, La Jolla, CA 92037, USA. ORCID
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.
