ArticleGenome biology2020
Benchmarking of computational error-correction methods for next-generation sequencing data.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Advance in the assembly of the plant mitochondrial genomes using high-throughput DNA sequencing data of total cellular DNAs.Plant biotechnology journal · 2025Review
- How error correction affects polymerase chain reaction deduplication: A survey based on unique molecular identifier datasets of short reads.Quantitative biology (Beijing, China) · 2025Review
- Characterization of red pigmented yeasts and genes associated with astaxanthin synthesis inF1000Research · 2025Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
- Measurable Residual Disease Detection in Acute Myeloid Leukemia: Current Challenges and Future Directions.Biomedicines · 2024Review
- MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.BMC bioinformatics · 2024Article
- Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.Microbial biotechnology · 2024Review
- Benefits of applying molecular barcoding systems are not uniform across different genomic applications.Journal of translational medicine · 2023Article
- Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.PLoS computational biology · 2023Article
- Viral oncogenes, viruses, and cancer: a third-generation sequencing perspective on viral integration into the human genome.Frontiers in oncology · 2023Review
- RNA-seq data science: From raw data to effective interpretation.Frontiers in genetics · 2023Review
- A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.Scientific reports · 2022Article
- P-smoother: efficient PBWT smoothing of large haplotype panels.Bioinformatics advances · 2022Article
- Article
- Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.Nucleic acids research · 2021Article
- ngsComposer: an automated pipeline for empirically based NGS data quality filtering.Briefings in bioinformatics · 2021Article
- Article
- On the optimistic performance evaluation of newly introduced bioinformatic methods.Genome biology · 2021Article
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Authors and funding
26 authors.
Funding
Abstract
backgroundRecent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown.
resultsIn this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods.
conclusionsIn terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.
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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.