| Literature DB >> 27461508 |
Emmanuel Paradis1, Thierry Gosselin2, Jérôme Goudet3, Thibaut Jombart4, Klaus Schliep5.
Abstract
Population genetics and genomics have developed and been treated as independent fields of study despite having common roots. The continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. We review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. We then expose how r, as a computing language and development environment, proposes some solutions to meet these challenges. We focus on some specific issues that are often encountered in practice: handling and analysing single-nucleotide polymorphism data, handling and reading variant call format files, analysing haplotypes and linkage disequilibrium and performing multivariate analyses. We illustrate these implementations with some analyses of three recently published data sets that contain between 60 000 and 1 000 000 loci. We conclude with some perspectives on future developments of r software for population genomics.Entities:
Keywords: zzm321990rzzm321990; multivariate analysis; next-generation sequencing; single-nucleotide polymorphism; variant call format
Mesh:
Year: 2016 PMID: 27461508 DOI: 10.1111/1755-0998.12577
Source DB: PubMed Journal: Mol Ecol Resour ISSN: 1755-098X Impact factor: 7.090