ArticleBriefings in bioinformatics2016
Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction.
Article in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
153 citing papers in PubMed.
- HyDRA: A pipeline for integrating long- and short-read RNAseq data for custom transcriptome assembly.iScience · 2026Article
- PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data.bioRxiv : the preprint server for biology · 2026Article
- Article
- 3AIM-seq: Quality assessment of mRNA therapeutics using sequencing for 3' poly(A) tails of in vitro-transcribed mRNA.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- UMI-nea: a fast, robust tool for reference-free UMI deduplication and accurate quantification.Bioinformatics (Oxford, England) · 2025Article
- Short-Read and Long-Read Whole Genome Sequencing for SARS-CoV-2 Variants Identification.Viruses · 2025Article
- Germline mosaicism in TCF20-associated neurodevelopmental disorders: a case study and literature review.Journal of human genetics · 2025Review
- A detailed analysis of second and third-generation sequencing approaches for accurate length determination of short tandem repeats and homopolymers.Nucleic acids research · 2025Article
- Sex disparities in the association between rare earth elements exposure and genetic mutation frequencies in lung cancer patients.Scientific reports · 2025Article
- Reflective Evaluation of Next-Generation Sequencing Data during Early Phase Detection of the Delta Variant.OBM genetics · 2025Article
- Nutritional niches of potentially endemic, facultatively anaerobic heterotrophs from an isolated Antarctic terrestrial hydrothermal refugium elucidated through metagenomics.Environmental microbiome · 2024Article
- Matching excellence: Oxford Nanopore Technologies' rise to parity with Pacific Biosciences in genome reconstruction of non-model bacterium with high G+C content.Microbial genomics · 2024Article
- A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses.Molecular ecology resources · 2024Review
- Detection of rare variants among nuclei populating the arbuscular mycorrhizal fungal model species Rhizophagus irregularis DAOM197198.G3 (Bethesda, Md.) · 2024Article
- Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.Nature communications · 2024Article
- Storage-D: A user-friendly platform that enables practical and personalized DNA data storage.iMeta · 2024Article
- Characterization and mitigation of artifacts derived from NGS library preparation due to structure-specific sequences in the human genome.BMC genomics · 2024Article
- MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.BMC bioinformatics · 2024Article
- Diversification of gene content in theMicrobiology spectrum · 2024Article
- Illumina reads correction: evaluation and improvements.Scientific reports · 2024Article
93 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.
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Registered trials
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.