CAREER: Building an informative machine learning model of plant gene regulatory control

尽管植物研究在可持续食品和能源生产、对环境负责的材料生物合成和安全有效的药物方面具有巨大的变革性进步潜力，但对植物基因如何“开启”或“关闭”以生产这些有用产品的基本了解仍然是主要的科学瓶颈。为了理解这些遗传途径是如何起作用的，人们必须识别和理解最终控制这些基因的DNA元素的模式。为了解决生物信息学中的这一基本科学挑战，该项目将使用模式识别技术来详细了解植物基因受dna编码信息控制的方式。为了做到这一点，我们将开发特定的计算技术来构建和解释模式识别模型。这项研究最重要的方面是创建能够识别不同DNA元件模式何时为基因控制提供生物学相关解决方案的算法。该项目开发的算法、模型和生物学结果将提供给科学界，包括植物科学界。其基础计算方法有望应用于许多其他科学和工程领域。该项目开发的教学和培训模块将包括拓展到我们服务不足的俄勒冈州农村社区，包括为高中生提供动手科学体验，以及教师培训。这些模块将特别关注对计算训练有素的植物科学研究人员的需求，以及对植物基因调控网络的急剧增加的理解的需求，作为新药物发现，农业和材料生物合成挑战的基础。转录因子（tf）对基因表达的组合控制是真核生物生命之树最基本的调控机制之一，从植物到人类。基因起始部分周围的DNA区域称为启动子区域，包含称为转录因子结合位点（TFBSs）的短调控序列元件。在植物中，尽管许多令人兴奋的潜在进展取决于对植物基因调控的详细了解，但对导致基因表达的特定TFBS模式知之甚少。该项目使用并扩展了Megraw实验室开发的机器学习模型，该模型能够以高精度和高分辨率预测转录起始位点（tss）的存在。该模型的一个主要优势是它提出了特定的TFBS集合：启动子相互作用，有可能“打开”特定基因。该模型新颖地利用了目前可用的高通量TSS数据来检查模式植物拟南芥中启动子的全局结构，并在初步研究中表明植物和动物DNA调控代码之间存在意想不到的差异。该项目的目标是严格测试和扩展这种有前途的新计算方法，以解剖哪些TFBS集是生物样本中基因上调的最佳预测因子。拟南芥基因组的相对简单性使这一挑战易于处理，但能够为多细胞生物的基因调控提供革命性的见解。该项目的结果可以在http://megraw.cgrb.oregonstate.edu/projects/PlantRegThis上找到，该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Despite the enormous potential of plant research for transformative advances in sustainable food and energy production, environmentally responsible materials biosynthesis, and safe effective medicines, a basic understanding of how plant genes are 'turned on' or 'turned off' in order to produce these useful products remains a primary scientific bottleneck. In order to understand how these genetic pathways function, one must identify and understand the patterns of DNA elements that ultimately control these genes. To address this fundamental scientific challenge in biological informatics, this project will use pattern recognition techniques in order to develop a detailed understanding of the way in which plant genes are controlled by DNA-encoded information. In order to do this, we will develop specific computational techniques for building and interpreting pattern recognition models. The most important aspect of this research is creating algorithms with the ability to recognize when a number of different DNA element patterns provide biologically relevant solutions for gene control. The algorithms, models, and biological outcomes developed by this project will be made available to the scientific community, including the plant science community. The underlying computational methods are expected to be applicable in many other fields of science and engineering. Teaching and training modules developed by this project will include outreach to our underserved communities of rural Oregon, including hands-on science experiences for high school students, as well as teacher training. The modules will draw special attention to the need for computationally trained researchers in the plant sciences, and to the need for a dramatically increased understanding of plant gene regulatory networks as a foundation for new drug discovery, agricultural, and materials biosynthesis challenges. The combinatorial control of gene expression by Transcription Factors (TFs) is one of the most fundamental regulatory mechanisms across the eukaryotic tree of life, from plants to humans. The DNA region surrounding the start of a gene, called the promoter region, contains short regulatory sequence elements known as Transcription Factor Binding Sites (TFBSs). In plants, despite the many exciting potential advances that hinge on a detailed understanding of plant gene regulation, relatively little is known about the specific TFBS patterns that lead to gene expression. This project uses and extends a machine learning model developed by the Megraw lab that is able to predict the presence of Transcription Start Sites (TSSs) with high accuracy and resolution. A primary advantage of this model is that it suggests specific sets of TFBS:promoter interactions which have the potential to 'turn on' a particular gene. This model makes novel use of currently available high-throughput TSS data to examine the global structure of promoters in the model plant Arabidopsis, and in preliminary research suggests striking unanticipated differences between plant and animal DNA regulatory codes. The goal of this project is to rigorously test and expand this promising new computational approach for dissecting which TFBS sets are optimal predictors of gene up-regulation in a biological sample. The relative simplicity of the Arabidopsis genome makes this challenge tractable, yet capable of providing transformative insight into gene regulation in a multicellular organism. The results of this project can be found at http://megraw.cgrb.oregonstate.edu/projects/PlantRegThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.