Computational Approaches to Transcriptome Modeling and Applications to Plant Stress Regulation

华盛顿大学获得了为期三年的拨款，用于研究植物在非生物胁迫下的基因表达。由此产生的对胁迫反应机制的理解将有助于理解干旱、寒冷和盐碱造成的作物产量损失，并为其他胁迫因素的研究开发方法。这些发现将通过一套机器学习和数据挖掘工具来实现，这些工具将允许对以前产生的大量基因表达数据进行功能分析。这些计算工具将可以通过网络访问，也可以由其他研究人员作为独立应用程序安装，以便其他研究团队可以在新的表达数据可用时应用这些方法。该项目将确定顺应法规的要素和主题模块。它将创建转录调控模型以及共表达网络。该项目将与华盛顿大学S科学外展计划合作，为K-12教师提供专业发展机会。

Washington University has been awarded a three year grant to study the expression of genes in plants under abiotic stress. The resulting understanding of stress response mechanisms will lead to understanding and potential interventions in crop yield loss attributable to drought, cold and salinity and develop methodologies for research of other stressors. These discoveries will be enabled through a set of machine learning and data mining tools that will allow for functional analysis of a large collection of previously generated gene expression data. The computational tools will be web accessible or may be installed by other researchers as standalone applications so that other research teams can apply the methods to new expression data as it becomes available. This project will identify cis-regulatory elements and motif modules. It will create models of transcription regulation as well as co-expression networks. The project will cooperate with the Washington University?s Science Outreach Program to provide professional development opportunities for K-12 teachers.