Fast and accurate variant identification tool for sequencing-based studies
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12915_2024_Article_1891.pdf
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1.56 MB
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Author(s)
Gaston, Jeffry M.
Alm, Eric J.
Zhang, An-Ni
Date Issued
April 22, 2024
Publisher
Springer Science and Business Media LLC
Citation
Gaston, J.M., Alm, E.J. & Zhang, AN. Fast and accurate variant identification tool for sequencing-based studies. BMC Biol 22, 90 (2024).
Version
Final published version
Abstract
Background
Accurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and disease diagnosis. Genetic studies into microbiomes often require processing numerous sequencing datasets, necessitating variant identifiers with high speed, accuracy, and robustness.
Results
We present QuickVariants, a bioinformatics tool that effectively summarizes variant information from read alignments and identifies variants. When tested on diverse bacterial sequencing data, QuickVariants demonstrates a ninefold higher median speed than bcftools, a widely used variant identifier, with higher accuracy in identifying both point mutations and indels. This accuracy extends to variant identification in virus samples, including SARS-CoV-2, particularly with significantly fewer false negative indels than bcftools. The high accuracy of QuickVariants is further demonstrated by its detection of a greater number of Omicron-specific indels (5 versus 0) and point mutations (61 versus 48–54) than bcftools in sewage metagenomes predominated by Omicron variants. Much of the reduced accuracy of bcftools was attributable to its misinterpretation of indels, often producing false negative indels and false positive point mutations at the same locations.
Conclusions
We introduce QuickVariants, a fast, accurate, and robust bioinformatics tool designed for identifying genetic variants for microbial studies. QuickVariants is available at
https://github.com/caozhichongchong/QuickVariants
.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s12915-024-01891-4
