PeerJBioinformaticsPeer-reviewed journal article
DiscoSnp-RAD: de novo detection of small variants for population genomics
Jérémy GauthieriD, Charlotte Mouden, Tomasz SuchaniD, Nadir ÁlvareziD, Nils ArrigoiD, Chloé Riou, Claire LemaitreiD, Pierre PeterlongoiD
Abstract
We present an original method to de novo call variants for Restriction site associated DNA Sequencing (RAD-Seq). RAD-Seq is a technique characterized by the sequencing of specific loci along the genome, that is widely employed in the field of evolutionary biology since it allows to exploit variants (mainly SNPs) information from entire populations at a reduced cost. Common RAD dedicated tools, as STACKS or IPyRAD, are based on all-versus-all read comparisons, which require consequent time and computing resources. Based on the variant caller DiscoSnp, initially designed for shotgun sequencing, DiscoSnp-RAD avoids this pitfall as variants are detected by exploring the De Bruijn Graph built from all the read datasets. We tested the implementation on RAD data from 259 specimens of Chiastocheta flies, morphologically assigned to 7 species. All individuals were successfully assigned to their species using both STRUCTURE and Maximum Likelihood phylogenetic reconstruction. Moreover, identified variants succeeded to reveal a within species structuration and the existence of two populations linked to their geographic distributions. Furthermore, our results show that DiscoSnp-RAD is at least one order of magnitude faster than state-of-the-art tools. The overall results show that DiscoSnp-RAD is suitable to identify variants from RAD data, and stands out from other tools due to his completely different principle, making it significantly faster, in particular on large datasets.\n\nLicenseGNU Affero general public license\n\nAvailabilityhttps://github.com/GATB/DiscoSnp\n\nContactjeremy.gauthier@inria.fr
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: benchmarking
- research focus: phylogenetics / evolution
- approach: statistical model
- Citations
- 23
- FWCI
- 0.89
- Field percentile
- top 30%
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