De Novo Clustering of Long Reads by Gene from Transcriptomics Data

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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Submitted on : Friday, September 14, 2018 - 10:35:37 AM
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Camille Marchet, Lolita Lecompte, Corinne da Silva, Corinne Cruaud, Jean-Marc Aury, et al.. De Novo Clustering of Long Reads by Gene from Transcriptomics Data. Nucleic Acids Research, Oxford University Press, In press, pp.1-12. ⟨10.1093/nar/gky834⟩. ⟨hal-01643156v2⟩

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