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是如何共同调节的、外显子的剪接是如何由多个调节因子组合控制的以及"剪接密码"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的选择性剪接调控。然而，现有的统计和计算方法仍然落后于先进的技术。长期目标是开发统计和计算方法，以发现多细胞真核生物中选择性剪接调控的原理，并探索调控剪接如何影响表型复杂性。本申请的目的是开发具有计算效率高的算法的统计学上合理的方法，以基于深度测序数据在个体和网络水平上研究选择性剪接及其调节。我们将应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。该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，以确定可变剪接调控元件。(3)开发新的统计方法来重建剪接调控网络。(4)大鼠干细胞的应用和用户友好软件的开发。在第一个目标下，提出的新的统计方法将明确解决RNA-seq中固有的位置偏差问题，以在基因水平和转录异构体水平上准确定量和比较转录组。在第二个目标下，将整合不同的证据来源，以区分顺式调节元件的选择性剪接与错误的网站匹配的模体的机会。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，以重建剪接调控网络。多种类型的基因组数据将被组合以推断调控关系。对于应用，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期间的选择性剪接调控。 所提出的方法是创新的。它们满足了高通量测序数据分析所带来的挑战，并且它们充分利用和整合了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选择性剪接调控的理解，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措施的发展提供信息，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.