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Clustering de Novo by Gene of Long Reads from Transcriptomics Data

By Camille Marchet, Lolita Lecompte, Corinne Da Silva, Corinne Cruaud, Jean-Marc Aury, Jacques Nicolas, Pierre Peterlongo

Posted 30 Jul 2017
bioRxiv DOI: 10.1101/170035 (published DOI: 10.1093/nar/gky834)

Long-read sequencing currently provides sequences of several thousand base pairs. This allows to obtain complete transcripts, which offers an unprecedented vision of the cellular transcriptome. However the literature is lacking tools to cluster such data de novo, 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 is both to propose a new algorithm adapted to clustering of reads by gene and a practical and free access tool that permits 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 its is better-suited for transcriptomics long reads. When a reference is available thus mapping possible, we show that it stands as an alternative method that predicts complementary clusters.

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