ArticleNature communications2022
VeChat: correcting errors in long reads using variation graphs.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Strain-level metagenomic profiling using pangenome graphs with PanTax.Genome research · 2026Article
- Three reference genomes for freshwater diatom ecology and evolution.Journal of phycology · 2025Article
- BonoboFlow: viral genome assembly and haplotype reconstruction from nanopore reads.Bioinformatics advances · 2025Article
- Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat.Communications biology · 2024Article
- Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing.Nature methods · 2024Article
- When less is more: sketching with minimizers in genomics.Genome biology · 2024Review
- High-quality metagenome assembly from long accurate reads with metaMDBG.Nature biotechnology · 2024Article
- Unveiling microbial diversity: harnessing long-read sequencing technology.Nature methods · 2024Review
- Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies.Nucleic acids research · 2024Article
- Co-linear chaining on pangenome graphs.Algorithms for molecular biology : AMB · 2024Article
- Article
- DeepCorr: a novel error correction method for 3GS long reads based on deep learning.PeerJ. Computer science · 2024Article
- Hybrid-hybrid correction of errors in long reads with HERO.Genome biology · 2023Article
- Applications of long-read sequencing to Mendelian genetics.Genome medicine · 2023Review
- Enhancing Long-Read-Based Strain-Aware Metagenome Assembly.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.