Scientific article
OA Policy
English

DiscoSnp-RAD: de novo detection of small variants for RAD-Seq population genomics

Published inPeerJ, vol. 8, e9291
Publication date2020-06-10
First online date2020-06-10
Abstract

Restriction site Associated DNA Sequencing (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 Single Nucleotide Polymorphism—SNPs) information from entire populations at a reduced cost. Common RAD dedicated tools, such as STACKS or IPyRAD , are based on all-vs-all read alignments, which require consequent time and computing resources. We present an original method, DiscoSnp-RAD, that avoids this pitfall since variants are detected by exploiting specific parts of the assembly graph built from the reads, hence preventing all-vs-all read alignments. We tested the implementation on simulated datasets of increasing size, up to 1,000 samples, and on real RAD-Seq data from 259 specimens of Chiastocheta flies, morphologically assigned to seven 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 genetic structure linked to the geographic distribution. Furthermore, our results show that DiscoSnp-RAD is significantly faster than state-of-the-art tools. The overall results show that DiscoSnp-RAD is suitable to identify variants from RAD-Seq data, it does not require time-consuming parameterization steps and it stands out from other tools due to its completely different principle, making it substantially faster, in particular on large datasets.

Research groups
Citation (ISO format)
GAUTHIER, Jérémy et al. DiscoSnp-RAD: de novo detection of small variants for RAD-Seq population genomics. In: PeerJ, 2020, vol. 8, p. e9291. doi: 10.7717/peerj.9291
Main files (1)
Article (Published version)
Identifiers
Additional URL for this publicationhttps://peerj.com/articles/9291
Journal ISSN2167-8359
2views
7downloads

Technical informations

Creation31/05/2023 16:13:09
First validation08/06/2026 10:16:58
Update08/06/2026 10:16:58
Status update08/06/2026 10:16:58
Last indexation08/06/2026 10:16:59
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack