Language
English
Publication Date
12-28-2022
Journal
Gigascience
DOI
10.1093/gigascience/giac125
PMID
36644891
PMCID
PMC9841152
PubMedCentral® Posted Date
1-16-2023
PubMedCentral® Full Text Version
Post-print
Abstract
Background: The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets.
Findings: We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities. xAtlas reports SNVs with 99.11% recall and 98.43% precision across a reference HG002 sample at 60× whole-genome coverage in less than 2 CPU hours. Applying xAtlas to 3,202 samples at 30× whole-genome coverage from the 1000 Genomes Project achieves an average runtime of 1.7 hours per sample and a clear separation of the individual populations in principal component analysis across called SNVs.
Conclusions: xAtlas is a fast, lightweight, and accurate SNV and small indel calling method. Source code for xAtlas is available under a BSD 3-clause license at https://github.com/jfarek/xatlas.
Keywords
Algorithms, Software, Genome, INDEL Mutation, High-Throughput Nucleotide Sequencing, Polymorphism, Single Nucleotide
Published Open-Access
yes
Recommended Citation
Farek, Jesse; Hughes, Daniel; Salerno, William; et al., "xAtlas: Scalable Small Variant Calling Across Heterogeneous Next-Generation Sequencing Experiments" (2022). Faculty and Staff Publications. 4262.
https://digitalcommons.library.tmc.edu/baylor_docs/4262