Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Abstract

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.

A Novel NCSTN Mutation in a Three-Generation Chinese Family with Hidradenitis Suppurative

“…After the PCR duplicates were removed by the Picard software, the SNPs were first identified using the SOAPsnp program ( https://soap.genomics.org.cn/soapsnp.html ) [ 28 ]. Subsequently, we realigned the reads to the reference genome using BWA ( https://bio-bwa.sourceforge.net/ ) and identified the insertions or deletions (InDels) using the GATK program ( https://www.broadinstitute.org/gsa/wiki/index.php/Home_Page ) [ 29 ] to filter variants. The filtered standard as follows: (a) variants with mapping qualities <30; (b) the total mapping quality zero reads <4; (c) approximate read depth <5; (d) QUAL < 50.0; (e) phred-scaled p value using Fisher's exact test to detect strand bias >10.0.…”

Deletion mapping of regulatory elements for GATA3 reveals a distal T helper 2 cell enhancer involved in allergic diseases

“…Lead SNPs belonging in three categories (autoimmune or allergic diseases, leukemia or lymphoma, and blood cells) are indicated below. d , Chromatin accessibility surrounding GATA3 in immune cells and Jurkat T cells from published ATAC-seq datasets 16,61 . e , Chromatin contact map of the region surrounding GATA3 from published Hi-C data from CD4 + T cells that were activated for 48 hours 15 .…”

Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors

“…Beyond scDNA-seq data, accurate SNV variant calling from scRNA-seq and ATAC-seq datasets is also challenging [ 41 , 42 ]. But new methods, such as Monopogen [ 43 ], SComatic [ 44 ], VarCA [ 45 ], scAllele [ 46 ], reference-based methods [ 47 ] and the use of patient derived cell lines [ 48 ] are rapidly improving our ability to accurately conduct single-cell somatic mutational profiling from diverse technologies beyond scDNA-seq. By accounting for different error profiles in variant calling, Phertilizer could be extended to directly model the evolution of somatic variants from scRNA-seq and ATAC-seq datasets.…”