De novo clustering of long reads by gene from transcriptomics data

Nucleic Acids Research
Camille MarchetPierre Peterlongo

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 alter...Continue Reading

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Aug 21, 2019·G3 : Genes - Genomes - Genetics·Dario I OjedaTanja Pyhäjärvi
Mar 18, 2020·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Kristoffer Sahlin, Paul Medvedev
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Feb 20, 2021·G3 : Genes - Genomes - Genetics·James G Baldwin-BrownMichael D Shapiro

Datasets Mentioned

BETA
ERP107503

Methods Mentioned

BETA
RNA-seq

Related Concepts

Metazoa
RNA
Genome
Sequence Determinations, DNA
MRNA Differential Display
Genomics
Mouse, Swiss
High-Throughput Nucleotide Sequencing
Gene Expression Profiles
Gene Clusters

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