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

该培训和研究应用程序的目的是使用深度学习技术的新发展来研究移动的元素插入（MEI）对神经系统疾病（ND）的功能影响。MEI是能够插入整个人类基因组的可转座DNA片段。至少有124个独立的MEI与人类疾病有关。大约20%的这些疾病代表了一系列ND，但MEI对ND病因学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MEI的综合功能图谱，以确定组织特异性和脑特异性影响;（3）将转录变化归因于将生成全基因组测序（WGS）数据的各种ND。拟议的申请还将为计算生物学家和统计遗传学家Dadi Gao博士开发一个广泛的研究计划，他曾接受过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的培训。他开发了新的方法来研究转录组的调控，并促进药物开发中的分析。他现在试图通过将统计和深度学习模型应用于来自对照组和尸检组织ND病例的大型测序数据队列来扩展自己的专业知识，然后在大规模疾病队列中估算来自WGS的MEI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。该研究计划旨在通过分解MEI的转录组变化来全面探索基因组中的功能变异。马萨诸塞州总医院、哈佛和布罗德研究所的Michael Talkowski博士将担任主要导师，而麻省理工学院、麻省理工学院计算生物学小组和布罗德研究所的Manolis Kellis博士将担任共同导师和密切合作者。这些导师是公认的基因组结构变异，功能基因组学，神经系统疾病的遗传学和计算建模，以建立人类基因组中的功能元件的专家。此外，来自基础和转化研究的独立研究人员团队将为高博士提供全面的反馈，以保持他的科学和职业发展走上正轨。CGM、MGH、哈佛医学院、布罗德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。这个优秀的导师团队和培训计划将促进高博士的职业发展，因为他试图重新定义MEI在人类基因组中的功能图谱，并将其影响归因于大规模神经系统疾病。

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.