目前微阵列数据分析方法都基于具有相似表达模式的基因可能具有相近的生物学功能这一假设, 而实际上参与同一生物学功能的基因, 在表达时间和空间上是有关联的, 而并非表现为相似模式。利用水稻cDNA微阵列, 对水稻在ABA及干旱、寒冷和高盐胁迫条件下的基因表达进行了研究。选取环境胁迫和ABA应答的相关基因, 采用最短路径法(shortest path), 利用自行编制的计算软件, 在表达模式不直接相关的基因之间构建最短路径。研究表明, 通过分析这些基因的表达数据, 可以发现它们在功能上的关联性, 并对未知基因的功能预测进行了探索, 为构建水稻在ABA和环境胁迫条件下的分子应答网络奠定了基础。

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions. However genes involved in the same biological pathway could be associated because of their temporal and spatial relationship but not always because of the high expression similarity. Here we explored the possible application of shortest-path analysis on expression of rice genes under abscisic-acid, drought, cold and salinity stress by rice cDNA microarray. We constructed the shortest path among the selected genes with low expression similarity using software we developed and found functional relationships among rice genes and predicted the function of unknown genes. The data were explored to understand the network of gene expression and response to environmental stress in rice.