A de novo approach to disentangle partner identity and function in holobiont systems

Arnaud Meng (1, 2) , Camille Marchet (3) , Erwan Corre (4) , Pierre Peterlongo (3) , Adriana A. Alberti (5, 6) , Corinne da Silva (5, 6) , Patrick Wincker (5, 6) , Eric Pelletier (5, 6) , Ian Probert (4) , Johan Decelle (7) , Stéphane Le Crom (1, 2) , Fabrice Not (4) , Lucie Bittner (1, 2)

1 SU - Sorbonne Université
2 UPMC - Université Pierre et Marie Curie - Paris 6 - UFR de Médecine Pierre et Marie Curie
3 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics
4 SBR - Station biologique de Roscoff [Roscoff]
5 GENOSCOPE - Genoscope - Centre national de séquençage [Evry]
6 UMR 8030 - Génomique métabolique
7 UFZ - Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research

This work addresses the problem of grouping by genes long reads expressed in a whole transcriptome sequencing data set. Long read sequencing produces several thousands base- pair long sequences, although showing high error rate in comparison to short reads. Long reads can cover full-length RNA transcripts and thus are of high interest to complete refer- ences. However, the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads. As a consequence, we propose a novel algorithm based on community detection and its implementation. Since solution is meant to be reference-free (de novo), it is especially well-tailored for non model species. We demonstrate it performs well on a real mouse data set. When a reference is available, we show that it stands as an alternative to mapping. In addition, we show that quick assessment of gene's expression is a straightforward use case of our solution.

Domains: Symbiosis; Bioinformatics [q-bio.QM]

Arnaud Meng, Camille Marchet, Erwan Corre, Pierre Peterlongo, Adriana A. Alberti, et al.. A de novo approach to disentangle partner identity and function in holobiont systems. 2017. ⟨hal-01643153v1⟩