@article{fdi:010068788,
  title = {{L}inking genomics and population genetics with {R}},
  author = {{P}aradis, {E}mmanuel and {G}osselin, {T}. and {G}oudet, {J}. and {J}ombart, {T}. and {S}chliep, {K}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}opulation genetics and genomics have developed and been treated as independent fields of study despite having common roots. {T}he continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. {W}e review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. {W}e then expose how {R}, as a computing language and development environment, proposes some solutions to meet these challenges. {W}e 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. {W}e illustrate these implementations with some analyses of three recently published data sets that contain between 60 000 and 1 000 000 loci. {W}e conclude with some perspectives on future developments of {R} software for population genomics.},
  keywords = {multivariate analysis ; next-generation sequencing ; {R} ; single-nucleotide ; polymorphism ; variant call format},
  booktitle = {},
  journal = {{M}olecular {E}cology {R}esources},
  volume = {17},
  numero = {1},
  pages = {54--66},
  ISSN = {1755-098{X}},
  year = {2017},
  DOI = {10.1111/1755-0998.12577},
  URL = {https://www.documentation.ird.fr/hor/fdi:010068788},
}