Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0
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Wang, Chun‐Chao; Yu, Hong; Huang, Ji; Wang, Wen‐Sheng; Faruquee, Muhiuddin; Zhang, Fan; Zhao, Xiu‐Qin; Fu, Bin‐Ying; Chen, Kai; Zhang, Hong‐Liang; Tai, Shuai‐Shuai; Wei, Chaochun; McNally, Kenneth L.; Alexandrov, Nickolai; Gao, Xiu‐Ying; Li, Jiayang; Li, Zhi‐Kang; Xu, Jian‐Long and Zheng, Tian‐Qing. 2020. Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0. Plant Biotechnology Journal, Volume 18 no. 1 p. 14-16
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Abstract/Description
Rice (Oryza sativa L.) not only provides insurance covering global food security but also works as a model for plant research. Currently, with overwhelmingly accumulated sequencing data, various databases were constructed for different target users, including the Genome Variation Map (Song et al., 2018), RiceVarMap (Zhao et al., 2015), SNP-Seek database (Alexandrov et al., 2015), RPAN (Sun et al., 2017) and MBK V1 (Institute of Genetics and Developmental Biology, C.A.S., 2018). However, none of them is designed to meet increasing demands of correlation mining between huge sets of phenotypic and genotypic data of re-sequenced genome. Online tool for deeper mining of favourable allele/haplotypes is in urgent need.