Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, College of Computer Studies, De La Salle University, Manila 1004, Philippines.
International Rice Research Institute (IRRI), Metro Manila 1301, Philippines.
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giae013.
As 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. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.
We present RicePilaf, 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. In 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. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.
RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.
随着全基因组关联研究（GWAS）和数量性状位点（QTL）在水稻中的数量不断增加，与重要农艺性状相关的基因组位点数量已经很长。通常，GWAS/QTL 分析中涉及的基因座包含数十到数百到数千个单核苷酸多态性（SNP）/基因，并非所有这些基因座都具有因果关系，其中许多位于非编码区域。解析将 GWAS 区域和 QTL 与感兴趣的性状联系起来的生物学机制具有挑战性，特别是因为它需要从多个不同的数据源整理有关基因座的功能基因组学信息。
我们提出了 RicePilaf，这是一个用于 GWAS/QTL 分析后的网络应用程序，它执行了一系列新颖的生物信息学分析，以将 GWAS 结果和 QTL 映射与大量公开可用的水稻数据库进行交叉参考。特别是，它集成了（i）来自多个水稻品种高质量基因组构建的泛基因组信息，（ii）来自全基因组共表达网络的共表达信息，（iii）本体论和途径信息，（iv）来自水稻转录因子数据库的调控信息，（v）来自多个高通量表观遗传实验的表观遗传信息，以及（vi）从将基因和性状联系起来的科学摘要中提取的文本挖掘信息。我们通过将其应用于分析收获前发芽的 GWAS 峰和耐旱性 QTL 下的基因来展示 RicePilaf 的实用性。
RicePilaf 使水稻科学家和育种家能够深入了解他们的 GWAS 区域和 QTL，并为他们提供了一种优先考虑进一步实验的 SNP/基因的方法。RicePilaf 的源代码、Docker 镜像和演示版本可在 https://github.com/bioinfodlsu/rice-pilaf 上公开获取。
