SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence

Hélène Lopez-Maestre; Lilia Brinza; Camille Marchet; Janice Kielbassa; Sylvère Bastien; Mathilde Boutigny; David Monnin; Adil El Filali; Claudia Marcia Carareto; Cristina Vieira; Franck Picard; Natacha Kremer; Fabrice Vavre; Marie-France Sagot; Vincent Lacroix

doi:10.1093/nar/gkw655

SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence

Hélène Lopez-Maestre ^{1, 2} Lilia Brinza ³ Camille Marchet ⁴ Janice Kielbassa ⁵ Sylvère Bastien ^{6, 2} Mathilde Boutigny ^{7, 2} David Monnin ⁸ Adil El Filali ⁹ Claudia Marcia Carareto ¹⁰ Cristina Vieira ^{1, 2, 11} Franck Picard ¹² Natacha Kremer ⁸ Fabrice Vavre ^{8, 2} Marie-France Sagot ^{7, 2} Vincent Lacroix ^{2, 7}

1 Eléments transposables, évolution, populations

GINSENG - Département génétique, interactions et évolution des génomes [LBBE]

2 ERABLE - Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale

Inria Grenoble - Rhône-Alpes

3 PT Génomique et Transcriptomique, BIOASTER, Lyon

4 GenScale - Scalable, Optimized and Parallel Algorithms for Genomics

Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE

5 Centre Léon Bérard [Lyon]

6 BPGE - Bioinformatique, phylogénie et génomique évolutive

PEGASE - Département PEGASE [LBBE]

7 Baobab

PEGASE - Département PEGASE [LBBE]

8 Génétique et évolution des interactions hôtes-parasites

GINSENG - Département génétique, interactions et évolution des génomes [LBBE]

9 LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558

10 UNESP - Universidade Estadual Paulista Júlio de Mesquita Filho [São José do Rio Preto]

11 IUF - Institut Universitaire de France

12 Statistique en grande dimension pour la génomique

PEGASE - Département PEGASE [LBBE]

Abstract : SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot be applied to non-model species. They are also mostly tailored for whole genome (re-)sequencing experiments , whereas in many cases, transcriptome se-quencing can be used as a cheaper alternative which already enables to identify SNPs located in transcribed regions. In this paper, we propose a method that identifies, quantifies and annotates SNPs without any reference genome, using RNA-seq data only. Individuals can be pooled prior to sequencing, if not enough material is available from one individual. Using pooled human RNA-seq data, we clarify the precision and recall of our method and discuss them with respect to other methods which use a reference genome or an assembled transcriptome. We then validate experimentally the predictions of our method using RNA-seq data from two non-model species. The method can be used for any species to annotate SNPs and predict their impact on the protein sequence. We further enable to test for the association of the identified SNPs with a phenotype of interest.

Document type :

Journal articles

Domain :

Computer Science [cs] / Bioinformatics [q-bio.QM]

Computer Science [cs] / Data Structures and Algorithms [cs.DS]

Life Sciences [q-bio]

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https://hal.inria.fr/hal-01352586
Contributor : Marie-France Sagot <>
Submitted on : Monday, August 8, 2016 - 3:35:24 PM
Last modification on : Thursday, December 19, 2019 - 1:36:15 AM

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SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence

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SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence

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