VeChat: correcting errors in long reads using variation graphs

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.

Automated summary

This article introduces a method based on variation graphs for error correction in long-read sequencing data. This method reduces error correction by using a graph-based reference system, addressing the bias issue caused by consensus sequences in current methods. Extensive benchmarking experiments show that the corrected long reads using this method have 4 to 15 times fewer errors compared to state-of-the-art approaches. Using VeChat prior to long-read assembly also significantly improves haplotype awareness.