VeChat: correcting errors in long reads using variation graphs

Xiao Luo, Xiongbin Kang and Alexander Schönhuth ()
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Xiao Luo: Bielefeld University
Xiongbin Kang: Bielefeld University
Alexander Schönhuth: Bielefeld University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Error correction is the canonical first step in long-read sequencing data analysis. Current self-correction methods, however, are affected by consensus sequence induced biases that mask true variants in haplotypes of lower frequency showing in mixed samples. Unlike consensus sequence templates, graph-based reference systems are not affected by such biases, so do not mistakenly mask true variants as errors. We present VeChat, as an approach to implement this idea: VeChat is based on variation graphs, as a popular type of data structure for pangenome reference systems. Extensive benchmarking experiments demonstrate that long reads corrected by VeChat contain 4 to 15 (Pacific Biosciences) and 1 to 10 times (Oxford Nanopore Technologies) less errors than when being corrected by state of the art approaches. Further, using VeChat prior to long-read assembly significantly improves the haplotype awareness of the assemblies. VeChat is an easy-to-use open-source tool and publicly available at https://github.com/HaploKit/vechat .

Date: 2022
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DOI: 10.1038/s41467-022-34381-8

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