Statistical methods for the study of alternative splicing using deep sequencing

描述(申请人提供)：在理解一组外显子的剪接是如何共同调节的，一个外显子的剪接是如何被多个调控子组合控制的，以及“剪接密码”的一般规则是什么方面，存在着根本的差距。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理解协调和组合选择性剪接调控。然而，现有的统计和计算方法仍然落后于先进技术。长期的目标是开发统计和计算方法来发现多细胞真核生物中选择性剪接调控的原理，并探索受调控的剪接如何影响表型复杂性。这一特殊应用的目标是开发具有计算效率的算法的统计上可靠的方法，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控。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。这一建议的具体目标包括：(1)发展新的统计方法，以RNA-seq为基础准确量化和比较转录组复杂性。(2)开发新的统计工具，以确定替代剪接调控元件。(3)发展新的统计方法来重建剪接调控网络。(4)应用于大鼠干细胞和开发用户友好的软件。在第一个目标下，拟议的新的统计方法将明确地解决RNA-SEQ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地量化和比较转录本。在第二个目标下，将整合不同的证据来源，以区分用于选择性剪接的顺式调控元件与偶然匹配基序的虚假位点。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，以重构剪接调控网络。多种类型的基因组数据将被组合在一起，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本，并推断其自我更新和向神经元分化过程中的选择性剪接调节。所提出的方法具有创新性。它们可以应对高通量测序数据分析带来的挑战，并充分利用和集成多种类型的组学数据。这项拟议的研究意义重大，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.