Computational Approaches to Transcriptome Modeling and Applications to Plant Stress Regulation

华盛顿大学获得了一项为期三年的资助，用于研究植物在非生物胁迫下的基因表达。对压力反应机制的理解将导致对干旱、寒冷和盐碱造成的作物产量损失的理解和潜在的干预措施，并制定研究其他压力因素的方法。这些发现将通过一套机器学习和数据挖掘工具来实现，这些工具将允许对大量先前生成的基因表达数据进行功能分析。 这些计算工具可以通过网络访问，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将这些方法应用于新的表达数据。本项目将鉴定顺式调控元件和基序模块。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合作？的科学外展计划，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.