ArticleGenome biology2019
A comparative evaluation of hybrid error correction methods for error-prone long reads.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Review
- Density-reducing Jaccard estimators for sketch-based long read applications.BMC bioinformatics · 2025Article
- Strain-level characterization of bacterial pathogens using metagenomic sequencing for patients with pneumonia.Journal of translational medicine · 2025Article
- Long-Read Sequencing and Structural Variant Detection: Unlocking the Hidden Genome in Rare Genetic Disorders.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- TrAnnoScope: A Modular Snakemake Pipeline for Full-Length Transcriptome Analysis and Functional Annotation.Genes · 2024Article
- Advances in long-read single-cell transcriptomics.Human genetics · 2024Review
- High-sensitivity in situ capture of endogenous RNA-protein interactions in fixed cells and primary tissues.Nature communications · 2024Article
- Maptcha: an efficient parallel workflow for hybrid genome scaffolding.BMC bioinformatics · 2024Article
- An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification.RNA biology · 2024Article
- Adaptable and comprehensive approaches for long-read nanopore sequencing of polyadenylated and non-polyadenylated RNAs.Frontiers in genetics · 2024Article
- Hybrid-hybrid correction of errors in long reads with HERO.Genome biology · 2023Article
- Application of Nanopore Sequencing in the Diagnosis and Treatment of Pulmonary Infections.Molecular diagnosis & therapy · 2023Review
- Next-generation fungal identification using target enrichment and Nanopore sequencing.BMC genomics · 2023Article
- Manual correction of genome annotation improved alternative splicing identification of Artemisia annua.Planta · 2023Article
- Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim.GigaScience · 2023Article
- Benchmarking machine learning robustness in Covid-19 genome sequence classification.Scientific reports · 2023Article
- Polishing De Novo Nanopore Assemblies of Bacteria and Eukaryotes With FMLRC2.Molecular biology and evolution · 2023Article
- Coverage-preserving sparsification of overlap graphs for long-read assembly.Bioinformatics (Oxford, England) · 2023Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
backgroundThird-generation sequencing technologies have advanced the progress of the biological research by generating reads that are substantially longer than second-generation sequencing technologies. However, their notorious high error rate impedes straightforward data analysis and limits their application. A handful of error correction methods for these error-prone long reads have been developed to date. The output data quality is very important for downstream analysis, whereas computing resources could limit the utility of some computing-intense tools. There is a lack of standardized assessments for these long-read error-correction methods.
resultsHere, we present a comparative performance assessment of ten state-of-the-art error-correction methods for long reads. We established a common set of benchmarks for performance assessment, including sensitivity, accuracy, output rate, alignment rate, output read length, run time, and memory usage, as well as the effects of error correction on two downstream applications of long reads: de novo assembly and resolving haplotype sequences.
conclusionsTaking into account all of these metrics, we provide a suggestive guideline for method choice based on available data size, computing resources, and individual research goals.
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