RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Anish M S Shrestha, Mark Edward M Gonzales, Phoebe Clare L Ong, Pierre Larmande, Hyun-Sook Lee, Ji-Ung Jeung, Ajay Kohli, Dmytro Chebotarov, Ramil P Mauleon, Jae-Sung Lee, Kenneth L McNally
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引用次数: 0

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

RicePilaf:GWAS/QTL 后仪表板,用于整合泛基因组学、共表达、调控、表观基因组学、本体论、通路和文本挖掘信息,为水稻 QTL 和 GWAS 基因座提供功能性见解。
背景:随着水稻全基因组关联研究(GWAS)和数量性状位点(QTL)图谱的数量不断增加,与重要农艺性状相关的基因组位点清单也越来越长。通常情况下,GWAS/QTL 分析所涉及的位点包含几十个、几百个到几千个单核苷酸多态性(SNPs)/基因,其中并非所有基因都是因果关系,而且许多基因都位于非编码区。揭示将 GWAS 区域和 QTL 与相关性状联系起来的生物学机制具有挑战性,特别是因为这需要整理来自多个不同数据源的有关基因座的功能基因组学信息:我们介绍了一款用于 GWAS/QTL 后分析的网络应用程序 RicePilaf,它能执行一系列新颖的生物信息学分析,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引用。特别是,它整合了(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)来自基因组规模共表达网络的共表达信息;(iii)本体和通路信息;(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学实验的表观基因组信息;以及(vi)从连接基因和性状的科学摘要中提取的文本挖掘信息。我们应用 RicePilaf 分析了收获前发芽的 GWAS 峰值和干旱下产量 QTLs 的潜在基因,从而证明了 RicePilaf 的实用性:RicePilaf使水稻科学家和育种家能够对他们的GWAS区域和QTLs进行功能阐释,并为他们提供了一种方法来优先选择SNPs/基因进行进一步的实验。RicePilaf 的源代码、Docker 镜像和演示版可在 https://github.com/bioinfodlsu/rice-pilaf 上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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