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.

该培训和研究应用程序的目的是研究移动元素插入（MEI）在神经系统疾病（NDS）中的功能影响，使用深度学习技术的新发展。 Meis是能够在整个人类基因组中插入的可旋转DNA片段。至少有124个与人类疾病相关的独立MEI。这些疾病中约有20％代表了NDS的光谱，但是尚未系统地估算MEI对NDS病因的总体贡献。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）构建一个全面的Meis功能图，以确定组织特异性和大脑特异性影响；（3）将生成全基因组测序（WGS）数据的各种NDS上的转录变化。拟议的应用还将为Dadi Gao博士开发一项广泛的研究计划。他开发了新的方法来研究转录组的调节并促进药物开发中的分析。现在，他试图通过将统计和深度学习模型应用于来自验尸后的NDS的大量测序数据，然后在大规模疾病队列中引起MEI的功能后果，从而扩大他的专业知识。该培训计划包括两年的指导研究，以学习基因组分析，MEI表征和先进的深度学习技巧的新技能，然后塑造独立实验室的三年。该研究计划的制定是通过分解针对MEI的转录组变化来全面探索基因组的功能变化。马萨诸塞州哈佛大学和广泛学院的迈克尔·坦科夫斯基（Michael Talkowski）博士将担任主要导师，而麻省理工学院和麻省理工学院计算生物学小组的Manolis Kellis博士和Broad Institute将担任合作者和密切合作者。这些指导者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的专家，以在人类基因组中建立功能元素。此外，来自基础和转化研究的独立调查员团队将为Gao博士提供全面的反馈，以保持其科学和职业发展。在CGM，MGH，哈佛医学院，Broad Institute和Michigan医学院的高度协作环境将为GAO博士的过渡到独立调查员的准备。这个杰出的指导团队和培训计划将促进GAO博士的职业发展，因为他试图重新定义人类基因组中MEI的功能地图，并将其在大规模神经系统疾病中产生影响。