作物学报 ›› 2021, Vol. 47 ›› Issue (12): 2423-2439.doi: 10.3724/SP.J.1006.2021.02084
LIU Ya-Wen1(
), ZHANG Hong-Yan1,2,*(
), CAO Dan2, LI Lan-Zhi2
摘要:
基于多平台基因表达数据挖掘水稻胁迫相关基因, 可增加关键基因预测的可靠性, 获得更具普适意义的结果。本研究从NCBI数据库中收集了与水稻非生物胁迫相关的94份affymetrix基因芯片数据和42份RNA-seq转录组数据。首先对同一类型同一胁迫相关的多个数据集以数据转换法融合, 得到干旱胁迫相关的affymetrix数据集D_affy和RNA-seq数据集D_rnaseq, 盐胁迫相关的affymetrix数据集S_affy和RNA-seq数据集S_rnaseq; 接着对4个数据集分别基于Pearson线性相关系数的经典WGCNA法和基于MIC非线性相关系数的改进WGCNA法进行基因共表达网络分析, 共获取胁迫相关的8个Hub基因集; 进一步, 对同一胁迫相关的Hub基因进行整合分析, 得到最终的水稻干旱胁迫相关Hub基因1936个、盐胁迫相关的Hub基因1504个。最后, 从预测性能、富集分析、文献报道、STRING在线互作分析和Cytoscape可视化分析等多角度解析Hub基因的生物学意义。结果显示: Hub基因整体预测性能较优, 且大多富集到了与干旱/盐胁迫相关的通路上, 其中有文献已报道的干旱胁迫响应基因31个和盐胁迫响应基因22个。此外, 通过对Hub基因的互作分析, 预测得到11个干旱胁迫候选基因和5个盐胁迫候选基因。本研究为“高维度、小样本”的农作物基因测序数据的有效分析提供了新思路, 实验结果为抗逆水稻品种研究提供了参考。
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