Statistical methods for the study of alternative splicing using deep sequencing

描述（由申请人提供）：在理解一组外显子的剪接如何被共同调节，外显子的剪接如何由多个调节器组合控制，以及“剪接代码”的一般规则是什么方面存在根本性的差距。高通量测序技术的出现为我们提供了一个前所未有的机会来了解协调和组合选择性剪接调节。然而，现有的统计和计算方法仍然落后于先进的技术。长期目标是发展统计和计算方法，以发现多细胞真核生物中选择性剪接调节的原理，并探索调节剪接如何促进表型复杂性。在这个特殊的应用程序的目标是开发统计健全的方法与计算高效的算法来研究选择性剪接及其调控在个体和网络水平基于深度测序数据。我们将运用我们提出的方法来研究大鼠胚胎干细胞的分化和自我更新。本提案的具体目标包括：(1)开发新的统计方法，以准确量化和比较基于RNA-seq的转录组复杂性。(2)开发新的统计工具来识别可选择的剪接调控元件。(3)开发新的统计方法重构拼接调控网络。(4)大鼠干细胞的应用及人性化软件的开发。在第一个目标下，提出的新的统计方法将明确解决RNA-seq固有的位置偏差问题，以便在基因水平和转录异构体水平上准确量化和比较转录组。在第二个目标下，将整合不同的证据来源，以区分选择性剪接的顺式调控元件和偶然匹配基序的错误位点。在第三个目标中，将开发一种有效的算法来减少模型搜索空间以重建拼接调节网络。多种类型的基因组数据将被结合起来推断调控关系。在应用方面，这将是第一次表征大鼠胚胎干细胞转录组，并推断其自我更新和向神经元分化过程中的替代剪接调节。所提出的方法具有创新性。他们面对高通量测序数据分析带来的挑战，充分利用和整合多种类型的组学数据。这项研究具有重要意义，因为它有望促进我们对剪接调节的理解，特别是在大鼠胚胎干细胞中，并有助于破译剪接密码。最终，这些知识有可能为剪接相关疾病的预防和治疗干预的发展提供信息，并为再生医学铺平道路。

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding how the splicing of a group of exons is co-regulated, how the splicing of an exon is combinatorially controlled by multiple regulators, and what are the general rules of "splicing code." The advent of high-throughput sequencing technologies provides us an unprecedented opportunity to understand the coordinate and combinatorial alternative splicing regulation. However, existing statistical and computational methods are still lagging behind the advanced technologies. The long-term goal is to develop statistical and computational methods to discover principles of alternative splicing regulation in multicellular eukaryotes and explore how regulated splicing contributes to phenotypic complexity. The objective in this particular application is to develop statistically sound methods with computationally efficient algorithms to study alternative splicing and its regulation at both individual and network levels based on deep sequencing data. We will apply our proposed methods to study rat embryonic stem cell differentiation and self-renewal. The specific aims of this proposal include: (1) Develop novel statistical methods to accurately quantify and compare transcriptome complexity based on RNA-seq. (2) Develop novel statistical tools to identify alternative splicing regulatory elements. (3) Develop novel statistical methods to reconstruct splicing regulatory networks. (4) Applications to rat stem cells and development of user-friendly software. Under the first aim, the proposed novel statistical methods will explicitly address the issue of positional bias inherent in RNA-seq to accurately quantify and compare transcriptomes at both the gene level and the transcript isoform level. Under the second aim, different evidence sources will be integrated to distinguish cis regulatory elements for alternative splicing from the false sites matching the motifs by chance. In the third aim, an efficient algorithm will be developed to reduce the model search space to reconstruct splicing regulatory networks. Multiple types of genomic data will be combined to infer regulation relationships. For the applications, this will be the first time to characterize rat embryonic stem cell transcriptomes and infer alternative splicing regulation during their self-renewal and differentiation toward neurons. The proposed methods are innovative. They meet the challenges arisen from the analysis of high- throughput sequencing data, and they fully utilize and integrate multiple types of omics data. The proposed research is significant, because it is expected to advance our understanding of alternative splicing regulation especially in rat embryonic stem cells, and contribute to deciphering the splicing code. Ultimately, such knowledge has the potential to inform the development of preventive and therapeutic interventions for splicing- related diseases, and pave the way for regenerative medicine. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because the proposed statistical and computational methods will lead to the discovery of alternative splicing regulation especially in rat embryonic stem cells, which is ultimately expected to increase the understanding of cell fate determination and the pathogenesis of splicing-related diseases. The resultant discoveries will shed light on regenerative medicine and therapeutic treatment of human diseases. Thus, the proposed research is relevant to the part of NIH's mission in pursuit of fundamental knowledge that will help to prevent and cure of human diseases.