Bioinformatics-based hypothesis generation with biological validation for plant stress biology

2000年模式生物拟南芥基因组序列的发表是植物生物学中的一件大事，已被证明是植物研究许多不同领域的研究人员的宝贵资源。然而，在预测的25498个基因产物中，只有~10%的功能得到了实验验证。基因表达谱用于识别在特定条件下在基因表达水平上共调控的基因，是功能表征的一种方法。AtGenExpress的国际努力提供了一组1400个Affymetrix微阵列数据集，这些数据来自在不同条件和处理下收获的野生型和突变植物材料，以指导研究人员了解他们感兴趣的基因的潜在基因功能。至少13个拟南芥反向遗传学收集的可用性将允许对使用AtGenExpress或其他数据库资源产生的电子假说进行快速实验测试。为了充分利用这些植物生物学资源，并将其进一步开发用于植物功能基因组学，我在这次拨款申请中提出了一种双管齐下的生物信息学和基于生物学的方法。首先，以实验室为基础的植物研究人员，包括我们自己，必须拥有可供他们使用的工具，以利用正在积累的大量信息。查询数据集和显示结果的用户友好界面是必须的。我的实验室已经在植物学阵列资源中提供了几个这样的工具，包括一个电子或e-Northern工具，用于在所有数据集中识别共调控基因的表达角度，以及用于识别共调控基因中顺式元件的Proomer。其他工具正在开发中。我提出的研究的另一个方面是专注于功能预测的平面测试。为此，将使用机器学习算法挖掘来自遭受非生物和其他胁迫的植物的表达数据集。使用反向遗传学和细胞生物学程序相结合的方法，将研究带有“表达蛋白”等注释的压力相关基因的功能。

The publication of the genomic sequence of the model organism Arabidopsis thaliana in 2000 was a significant event in plant biology, and has proved to be a valuable resource to investigators in many different areas of plant research. However, of the 25498 predicted gene products, only ~10% have had their function identified experimentally. Gene expression profiling for the identification of genes that are coregulated at the level of gene expression under a given condition or conditions is one method for functional characterization. The international AtGenExpress effort has provided a reference set of 1400 Affymetrix microarray datasets from both wild-type and mutant plant material harvested under diverse conditions and treatments to guide researchers towards potential gene functions for their genes of interest. The availability of at least 13 collections for Arabidopsis reverse genetics will allow rapid experimental testing of in silico hypotheses generated using AtGenExpress or other database resources. To fully take advantage of these plant biology resources and further their development for use in plant functional genomics, I am proposing a two-pronged bioinformatic and biology-based approach in this grant application. First, it is imperative that lab-based plant researchers, including ourselves, have the tools at their disposal to make use of the enormous amount of information that is being accumulated. User-friendly interfaces for querying the datasets and displaying the results are a must. My lab has made available several such tools at the Botany Array Resource, including an electronic or e-Northern tool, Expression Angler for identifying coregulated genes across all datasets, and Promomer for identifying cis-elements in coregulated genes. Other tools are in development. The other aspect of my proposed research is to focus on in planta testing of functional predictions. To this end, expression data sets from plants subject to abiotic and other stresses will be mined using machine learning algorithms. The function of stress-associated genes with annotations such as 'expressed protein' will be examined using a combination of reverse genetics and cell biological procedures.