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

深度学习方法破译移动元件插入对神经系统疾病选择性剪接的影响

基本信息

批准号：
10041366
负责人：
DADI GAO
金额：
$ 12.69万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10041366
关键词：
Address Algorithms Alternative Splicing Alzheimer&apos s Disease Autopsy Basic Science Biology Blood Brain Brain Diseases CRISPR/Cas technology Cells Chromosome Pairing Cohort Studies Computational Biology Computer Analysis Computer Models DNA DNA Insertion Elements Data Data Set Defect Detection Development Disease Disease model Dorsal Dystonia Elements Etiology Event Evolution Excision Repair Familial Dysautonomia Feedback Fellowship Filipino General Hospitals Generations Genes Genetic Genetic Transcription Genome Genomics Genotype-Tissue Expression Project Haplotypes Human Human Genome Individual Institutes International Introns Laboratories Lateral Lead Learning Linear Regressions Link Machine Learning Maps Massachusetts Measures Mentors Mentorship Methods Michigan Mind Minisatellite Repeats Modeling Molecular Mosaicism Neurodegenerative Disorders Neurodevelopmental Disorder Neuromuscular Diseases Neurons Outcome Parkinsonian Disorders Pathogenicity Pattern Peripheral Pharmaceutical Preparations Phase Population Prefrontal Cortex Process Property RNA Splicing Regulation Research Research Personnel Retroelements Role Sampling Schizophrenia Science Shapes Short Interspersed Nucleotide Elements Source Specificity Structure TAF1 gene Techniques Therapeutic Trials Tissue-Specific Splicing Tissues Training Training Programs Transcription Alteration Translational Research Universities Untranslated RNA Variant Work brain tissue career development cohort collaborative environment convolutional neural network deep learning drug development functional genomics functional outcomes gene function genetic architecture genome analysis genome editing genome sequencing genome-wide human disease in silico insight medical schools mind control nervous system disorder neuron development novel programs response skills statistical learning structural genomics therapeutic target transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

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.

此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件插入 (MEI) 对神经系统疾病 (ND) 的功能影响。 MEI 是可转座 DNA 片段，能够插入整个人类基因组。至少有 124 个独立的 MEIs 与人类疾病相关。这些疾病中大约 20% 代表一系列 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) 表征健康个体 GTEx 队列中的功能性 MEI； (2) 构建 MEIs 的综合功能图谱，以确定组织特异性和大脑特异性影响； (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。拟议的应用程序还将为计算生物学家和统计遗传学家 Dadi Gau 博士开发一个广泛的研究计划，他接受过神经退行性疾病中选择性剪接的功能基因组研究以及针对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的分析。他现在寻求通过对来自死后组织的对照和 ND 病例的大量测序数据应用统计和深度学习模型来扩展自己的专业知识，然后将来自 WGS 的 MEI 的功能后果归因于大规模疾病队列。培训计划包括两年的指导研究，以学习基因组分析、MEI 表征和先进深度学习技术方面的新技能，随后三年打造独立实验室。该研究计划旨在通过分解 MEIs 的转录组变化来全面探索基因组的功能变异。麻省总医院、哈佛大学和布罗德研究所的 Michael Talkowski 博士将担任主要导师，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布罗德研究所将担任联合导师和密切合作者。这些导师是基因组结构变异、功能基因组学、神经系统疾病遗传学以及建立人类基因组功能元件的计算模型方面的公认专家。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，以确保他的科学和职业发展步入正轨。 CGM、麻省总医院、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向独立研究者的转变做好准备。这个出色的导师团队和培训计划将促进高博士的职业发展，因为他致力于重新定义人类基因组中 MEI 的功能图谱，并估算其对大规模神经系统疾病的影响。