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• W3003861457 abstract "Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online." @default.
• W3003861457 created "2020-02-07" @default.
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• W3003861457 date "2020-01-27" @default.
• W3003861457 modified "2023-10-10" @default.
• W3003861457 title "Bivartect: accurate and memory-saving breakpoint detection by direct read comparison" @default.
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• W3003861457 doi "https://doi.org/10.1093/bioinformatics/btaa059" @default.
• W3003861457 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7203739" @default.
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