Deep learning approaches to decipher the impact of mobile element insertion on alternative splicing in neurological disorders

这项培训和研究应用的目的是利用深度学习技术的新发展来研究移动元素插入（MEIs）对神经系统疾病（nd）的功能影响。MEIs是可转座的DNA片段，能够插入整个人类基因组。至少有124个与人类疾病相关的独立环境影响因子。这些疾病中约有20%代表一系列非传染性疾病，但MEIs对非传染性疾病病因的总体贡献尚未得到系统估计。为了解决这个问题，我们将(1)在健康个体的GTEx队列中表征功能性mei；(2)构建MEIs的综合功能图谱，以确定组织特异性和脑特异性影响；(3)推测将产生全基因组测序（WGS）数据的各种NDs的转录变化。该申请还将为高Dadi博士开发一个广泛的研究项目，他是一名计算生物学家和统计遗传学家，曾在神经退行性疾病中选择性剪接的功能基因组研究和导致严重神经发育障碍的剪接缺陷的治疗靶向方面接受过培训。他开发了新的方法来研究转录组的调节和促进药物开发的分析。他现在寻求通过将统计和深度学习模型应用于来自死后组织的对照和NDs病例的大型队列测序数据来扩展他的专业知识，然后在大规模疾病队列中推断来自WGS的MEIs的功能后果。培训计划包括两年的指导研究，以学习基因组分析，MEI表征和高级深度学习技术的新技能，然后是三年的独立实验室建设。本研究计划通过分解MEIs对转录组变化的影响，全面探索基因组的功能变异。哈佛大学麻省总医院的Michael Talkowski博士和Broad研究所将担任主要导师，而麻省理工学院的Manolis Kellis博士和麻省理工学院计算生物学小组以及Broad研究所将担任共同导师和密切合作者。这些导师是基因组结构变异、功能基因组学、神经疾病遗传学和建立人类基因组功能元素的计算建模方面公认的专家。此外，一组来自基础研究和转化研究的独立研究人员将为高博士提供全面的反馈，以确保他的科学和职业发展步入正轨。CGM、MGH、哈佛医学院、Broad研究所和密歇根大学医学院的高度协作环境将为高博士过渡到独立研究者做好准备。这个杰出的指导团队和培训计划将促进高博士的职业发展，因为他寻求重新定义人类基因组中MEIs的功能图谱，并将其在大规模神经系统疾病中的影响归咎于高博士。

The purpose of this training and research application is to study the functional impact of mobile element insertions (MEIs) in neurological disorders (NDs) using new developments in deep learning techniques. MEIs are transposable DNA fragments that are able to insert throughout the human genome. There are at least 124 independent MEIs associated with human diseases. Approximately 20% of these diseases represent a spectrum of NDs, yet the overall contribute of MEIs to the etiology of NDs has not been systematically estimated. To address this, we will (1) characterize functional MEIs in GTEx cohorts in healthy individuals; (2) build a comprehensive functional map of MEIs to determine tissue-specific and brain-specific impact; and (3) impute transcriptional changes on various NDs where whole-genome sequencing (WGS) data will be generated. The proposed application will also develop an extensive research program for Dr. Dadi Gao, a computational biologist and statistical geneticist who has trained in functional genomic studies of alternative splicing in neurodegenerative disorders and therapeutic targeting of a splicing defect that causes a severe neurodevelopmental disorder. He has developed novel methods to investigate regulation of the transcriptome and to facilitate analyses in drug development. He now seeks to expand his expertise by applying statistical and deep learning models on large cohorts of sequencing data from controls and cases with NDs from post-mortem tissues, then impute functional consequences of MEIs from WGS in large-scale disease cohorts. The training plan consists of two years of mentored research to learn new skills in genome analysis, MEI characterization, and advanced deep learning techniques, followed by three years of shaping an independent laboratory. The research plan is developed to comprehensively explore functional variation in the genome by decomposing transcriptomic changes against MEIs. Dr. Michael Talkowski at Massachusetts General Hospital, Harvard, and the Broad Institute will serve as the primary mentor, while Dr. Manolis Kellis at MIT and the MIT Computational Biology Group, and the Broad Institute will serve as a co-mentor and close collaborator. These mentors are recognized experts in genomic structural variants, functional genomics, the genetics of neurological disorders, and computational modeling to establish functional elements in the human genome. In addition, a team of independent investigators from basic and translational research will provide Dr. Gao with comprehensive feedback to keep both his science and career development on track. The highly collaborative environment in CGM, MGH, Harvard Medical School, the Broad Institute and the University of Michigan Medical School will prepare Dr. Gao for his transition to an independent investigator. This outstanding mentorship team and training program will facilitate the career development of Dr. Gao as he seeks to redefine the functional maps of MEIs in the human genome and to impute their impact in large-scale neurological disorders.