ArticleScientific reports2021
Scalable long read self-correction and assembly polishing with multiple sequence alignment.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Review
- QoALa: A comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data.PLoS computational biology · 2026Article
- Highly accurate assembly polishing with DeepPolisher.Genome research · 2025Article
- CloseRead: a tool for assessing assembly errors in immunoglobulin loci applied to vertebrate long-read genome assemblies.Genome biology · 2025Article
- Skeletal Muscle mRNA Splicing Variants Association With Four Different Fitness and Energetic Measures in the GESTALT Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Benchmarking of bioinformatics tools for the hybridComputational and structural biotechnology journal · 2025Article
- Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat.Communications biology · 2024Article
- A stepwise guide for pangenome development in crop plants: an alfalfa (Medicago sativa) case study.BMC genomics · 2024Review
- Genome assembly in the telomere-to-telomere era.Nature reviews. Genetics · 2024Review
- CAREx: context-aware read extension of paired-end sequencing data.BMC bioinformatics · 2024Article
- NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads.Genome biology · 2024Article
- First whole-genome sequence and assembly of the Ecuadorian brown-headed spider monkey (Ateles fusciceps fusciceps), a critically endangered species, using Oxford Nanopore Technologies.G3 (Bethesda, Md.) · 2024Article
- Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors.PeerJ · 2024Article
- DeepCorr: a novel error correction method for 3GS long reads based on deep learning.PeerJ. Computer science · 2024Article
- DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer.Nature biotechnology · 2023Article
- Strategies and tools in illumina and nanopore-integrated metagenomic analysis of microbiome data.iMeta · 2023Review
- Next-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) for the Diagnosis of Thalassemia.Diagnostics (Basel, Switzerland) · 2023Review
- Application of third-generation sequencing to herbal genomics.Frontiers in plant science · 2023Review
- SAKE: Strobemer-assisted k-mer extraction.PloS one · 2023Article
- VeChat: correcting errors in long reads using variation graphs.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Third-generation sequencing technologies allow to sequence long reads of tens of kbp, that are expected to solve various problems. However, they display high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads, and allows to process a full human dataset, containing reads reaching up to 1.5 Mbp, in 10 days. Moreover, our experiments show that error correction with CONSENT improves the quality of Flye assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools, while providing comparable results. Furthermore, we show that, on a human dataset, assembling the raw data and polishing the assembly is less resource consuming than correcting and then assembling the reads, while providing better results. CONSENT is available at https://github.com/morispi/CONSENT .
Indexed as
Identifiers
What OpenQuestion holds
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.