RFGB

Rice Functional Genomic and Breeding (RFGB) toolkit, version 2.0 includes five major modules, which are: Phenotype, Haplotype, SNP & InDel, Restore Sequence, and Germplasm. Their functions are described with the embedded 3K-RG data as example. Four tips of iceberg for user cases of RFGB v2.0 with corresponding technical routes were presented including: 1) exploring favorable donors for higher zinc concentration in milled grains, 2) shortlisting candidate genes for grain length with near isogenic lines, 3) mining favorable haplotypes for seedling vigor traits under paddy direct seeding system, and 4) variations and restore sequence seeking for a leaf rolling QTL region.水稻功能基因组与育种（Rice Functional Genomic and Breeding，简称RFGB）工具包v2.0包含五大模块，分别为表型组（Phenotype）、单倍型（Haplotype）、单核苷酸多态性与插入缺失多态性（SNP & InDel）、恢复序列（Restore Sequence）以及种质资源（Germplasm）。该工具包以内嵌的3K-RG数据作为示例，对各模块的功能进行了说明。本文展示了RFGB v2.0的四类典型应用场景（仅为其应用全貌的冰山一角）及对应的技术路线，具体包括：1）筛选可提升糙米锌含量的优良供体材料；2）借助近等基因系筛选粒长相关候选基因；3）挖掘水稻直播栽培体系下幼苗活力相关性状的优良单倍型；4）针对卷叶数量性状位点（Quantitative Trait Locus，QTL）区域开展变异与恢复序列挖掘。

搜集汇总

背景与挑战

背景概述

RFGB数据集是一个水稻基因组重测序资源，包含约3,000个来自全球89个国家的水稻种质资源，平均测序深度为14X，覆盖率和映射率均超过90%。该数据集旨在为水稻重要性状的大规模等位基因挖掘提供基础，并展示水稻种内多样性，以支持全球水稻育种技术的创新。