@article{fdi:010091456,
  title = {{R}ice{P}ilaf : a post-{GWAS}/{QTL} dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice {QTL}s and {GWAS} loci},
  author = {{S}hrestha, {A}.{M}.{S} and {G}onzales, {M}.{E}.{M}. and {O}ng, {P}.{C}.{L}. and {L}armande, {P}ierre and {L}ee, {H}.{S}. and {J}eung, {J}.{U}. and {K}ohli, {A}. and {C}hebotarov, {D}. and {M}auleon, {R}.{P}. and {L}ee, {J}.{S}. and {M}c{N}ally, {K}.{L}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground : {A}s the number of genome-wide association study ({GWAS}) and quantitative trait locus ({QTL}) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. {T}ypically, loci implicated by {GWAS}/{QTL} analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms



({SNP}s)/genes, not all of which are causal and many of which are in noncoding regions. {U}nraveling the biological mechanisms that tie the {GWAS} regions and {QTL}s to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.



{R}esults : {W}e present {R}ice{P}ilaf, a web app for post-{GWAS}/{QTL} analysis, that performs a slew of novel bioinformatics analyses to cross-reference {GWAS} results and {QTL} mappings with a host of publicly available rice databases. {I}n particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. {W}e demonstrate the utility of {R}ice{P}ilaf by applying it to analyze {GWAS} peaks of preharvest sprouting and genes underlying yield-under-drought {QTL}s.



{C}onclusions : {R}ice{P}ilaf enables rice scientists and breeders to shed functional light on their {GWAS} regions and {QTL}s, and it provides them with a means to prioritize {SNP}s/genes for further experiments. {T}he source code, a {D}ocker image, and a demo version of {R}ice{P}ilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.},
  keywords = {},
  booktitle = {},
  journal = {{G}iga{S}cience},
  volume = {13},
  numero = {},
  pages = {giae013 [12 ]},
  ISSN = {2047-217{X}},
  year = {2024},
  DOI = {10.1093/gigascience/giae013},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091456},
}