High sensitivity discovery of cis-regulatory modules

描述（由申请人提供）：尽管包括人类基因组在内的许多基因组已被完全测序，但大多数DNA的特定功能仍然未知。确定基因组的所有功能组件已成为NIH的一个重要目标（例如，通过ENCODE和modENCODE倡议）。据信，这种DNA的一个重要部分参与调节基因表达，这是一个在正常发育和疾病中起关键作用的基本过程。基因调控的基本单元是顺式调控模块（CRM;通常称为“增强子”），但在基因组规模上鉴定这些模块已被证明是困难的。在大多数情况下，用于CRM发现的计算方法仅在已经有关于与CRM结合的转录因子以及它们结合的序列（基序）的广泛知识的那些情况下有效。在这个建议中，我们开发新的计算工具CRM发现。特别是，我们离开目前的方法CRM发现开发算法，不依赖于转录因子结合基序的先验知识。通过这样做，我们能够识别标准物质，即使在研究较少的生物背景下，显着的先验知识是最小的或缺乏。然后，我们扩展这种方法，另外开发的方法，利用部分已知参与特定的生物过程中的CRM的先验知识。我们将联合收割机结合我们的新方法与有前途的现有方法，以产生一个计算管道，使用互补的策略，敏感和特定的CRM发现，并进行广泛的预测CRM的功能在许多组织和细胞类型。我们将利用强大的基因组和实验资源，可用于模式生物黑腹果蝇，我们所有的方法进行验证，在硅片和体内，使用大量的现有CRM数据，我们已经编译和广泛的经验测试转基因动物，分别。我们在这里开发的方法将有助于帮助确定一类重要的基因组功能元件，顺式调控模块，在任何后生动物基因组。 顺式调节模块（CRM）是正常表型变异的关键介质，进化变化的驱动因素，出生缺陷以及慢性和急性疾病的原因。识别CRMs全基因组是理解基因调控和基因功能的正常和病理方面的重要第一步，对理解疾病，预测疾病风险以及预防和治疗疾病具有广泛意义。

DESCRIPTION (provided by applicant): Although numerous genomes, including the human genome, have been completely sequenced, the specific function of the most of the DNA remains unknown. Identifying all the functional components of genomes has become an important goal of the NIH (e.g., via the ENCODE and modENCODE initiatives). A significant fraction of this DNA is believed to be involved in regulating gene expression, a fundamental process that plays key roles in both normal development and in disease. A basic unit for gene regulation is the cis-regulatory module (CRM; often referred to as an "enhancer"), but identification of these modules on a genomic scale has proven difficult. For the most part, computational methods for CRM discovery have been effective only in those situations where there is already an extensive body of knowledge about the transcription factors that bind to the CRMs, and the sequences (motifs) to which they bind. In this proposal, we develop novel computational tools for CRM discovery. In particular, we depart from current approaches to CRM discovery by developing algorithms that do not rely on prior knowledge of transcription factor binding motifs. By doing so, we are able to identify CRMs even in less well-studied biological contexts where significant prior knowledge is minimal or lacking. We then expand upon this approach by additionally developing methods that utilize partial prior knowledge of CRMs known to be involved in a particular biological process. We will combine our new methods with promising existing approaches to generate a computational pipeline that uses complementary strategies for sensitive and specific CRM discovery, and conduct extensive prediction of CRMs that function in many tissues and cell types. We will take advantage of the powerful genomic and experimental resources available for the model organism Drosophila melanogaster to subject all of our methods to validation both in silico and in vivo, using a large body of existing CRM data that we have compiled and extensive empirical testing in transgenic animals, respectively. The methods we develop here will be instrumental in helping to identify an important class of genomic functional element, the cis-regulatory module, in any metazoan genome. cis-Regulatory modules (CRMs) are key mediators of normal phenotypic variation, drivers of evolutionary change, and causes of birth defects as well as chronic and acute disease. Identifying CRMs genome-wide is an important first step on the way to comprehending both normal and pathological aspects of gene regulation and gene function with broad implications for understanding disease, predicting disease risk, and preventing and curing disease.