ArticleNucleic acids research2019
De novo clustering of long reads by gene from transcriptomics data.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Improving gene isoform quantification with miniQuant.Nature biotechnology · 2026Article
- A comprehensive evaluation of long-read de novo transcriptome assembly.Genome biology · 2026Article
- De novo clustering of large long-read transcriptome datasets with isONclust3.Bioinformatics (Oxford, England) · 2025Article
- cONcat: Computational reconstruction of concatenated fragments from long Oxford Nanopore reads.PloS one · 2025Article
- Highly efficient clustering of long-read transcriptomic data with GeLuster.Bioinformatics (Oxford, England) · 2024Article
- Riboflavin Targets the Cellular Metabolic and Ribosomal Pathways of Candida albicansMicrobiology spectrum · 2023Article
- RATTLE: reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing.Genome biology · 2022Article
- Clustering biological sequences with dynamic sequence similarity threshold.BMC bioinformatics · 2022Article
- kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on theFrontiers in genetics · 2022Article
- Nanopore sequencing technology, bioinformatics and applications.Nature biotechnology · 2021Review
- The assembled and annotated genome of the pigeon louse Columbicola columbae, a model ectoparasite.G3 (Bethesda, Md.) · 2021Article
- Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis.Nature communications · 2021Article
- Investigating the mitochondrial genomic landscape of Arabidopsis thaliana by long-read sequencing.PLoS computational biology · 2021Article
- De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality Value-Based Algorithm.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Direct full-length RNA sequencing reveals unexpected transcriptome complexity duringGenome research · 2020Article
- Methodologies for Transcript Profiling Using Long-Read Technologies.Frontiers in genetics · 2020Review
- Computational methods for 16S metabarcoding studies using Nanopore sequencing data.Computational and structural biotechnology journal · 2020Review
- ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning.Genome biology · 2019Article
- Utilization of Tissue Ploidy Level Variation inG3 (Bethesda, Md.) · 2019Article
- Getting the Entire Message: Progress in Isoform Sequencing.Frontiers in genetics · 2019Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long-read sequencing currently provides sequences of several thousand base pairs. It is therefore possible to obtain complete transcripts, offering an unprecedented vision of the cellular transcriptome. However the literature lacks tools for de novo clustering of such data, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads. Our goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution both proposes a new algorithm adapted to clustering of reads by gene and a practical and free access tool that allows to scale the complete processing of eukaryotic transcriptomes. We sequenced a mouse RNA sample using the MinION device. This dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate that it is the best approach for transcriptomics long reads. When a reference is available to enable mapping, we show that it stands as an alternative method that predicts complementary clusters.
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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.