High sensitivity discovery of cis-regulatory modules

描述(申请人提供)：尽管包括人类基因组在内的许多基因组已经完全测序，但大多数DNA的具体功能仍不清楚。确定基因组的所有功能成分已成为美国国立卫生研究院的一个重要目标(例如，通过ENCODE和modENCODE倡议)。这种DNA的很大一部分被认为参与了基因表达的调控，这是一个在正常发育和疾病中发挥关键作用的基本过程。基因调控的一个基本单位是顺式调控模块(CRM；通常被称为“增强子”)，但在基因组规模上识别这些模块已被证明是困难的。在大多数情况下，只有在已经有大量关于与CRM结合的转录因子及其结合的序列(基序)的知识的情况下，用于CRM发现的计算方法才有效。在这个提议中，我们开发了用于CRM发现的新的计算工具。特别是，我们通过开发不依赖于转录因子结合基序的先验知识的算法，脱离了目前发现CRM的方法。通过这样做，我们能够识别CRM，即使在研究较少的生物学环境中，重要的先验知识很少或缺乏。然后，我们通过额外开发利用已知参与特定生物过程的CRM的部分先验知识的方法来扩展这一方法。我们将把我们的新方法与有前景的现有方法结合起来，生成一个计算管道，使用补充策略来发现敏感和特定的CRM，并对在许多组织和细胞类型中起作用的CRM进行广泛的预测。我们将利用模式生物黑腹果蝇可用的强大基因组和实验资源，分别使用我们汇编的大量现有CRM数据和在转基因动物中的广泛经验测试，在电子计算机和体内对我们的所有方法进行验证。我们在这里开发的方法将有助于在任何后生动物基因组中识别一类重要的基因组功能元件，即顺式调节模块。 顺式调节模块(CRM)是正常表型变异的关键介质，是进化变化的驱动因素，是导致出生缺陷以及慢性和急性疾病的原因。在全基因组范围内识别CRM是理解基因调节和基因功能的正常和病理方面的重要的第一步，对于理解疾病、预测疾病风险以及预防和治疗疾病具有广泛的意义。

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.