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Deep learning for decoding genetic regulation and cellular maps in craniofacial development

深度学习解码颅面发育中的遗传调控和细胞图谱

基本信息

批准号：
10600857
负责人：
Junichi Iwata
金额：
$ 55.74万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10600857
关键词：
Address Algorithms Atlases Automobile Driving Biological Biology CRISPR/Cas technology Cell Proliferation Cell physiology Cells Child Communities Complex Data Data Analyses Data Set Dedications Dental Development Developmental Biology Developmental Process Dimensions Disease Embryo Enhancers FaceBase Funding Funding Mechanisms Gene Expression Gene Expression Regulation Genes Genetic Genetic Diseases Genetic Transcription Genetic study Genomics Genotype-Tissue Expression Project Human Human Genetics Knock-in Knock-out Knowledge Learning Machine Learning Maps Messenger RNA Methods MicroRNAs Molecular Morphology Mus Mutation National Institute of Dental and Craniofacial Research Oral Palate Parents Phenotype Play Process Proliferating Regulation Research Research Personnel Research Project Grants Resolution Role Series Time Time Series Analysis Tissues Untranslated RNA Validation Variant analytical tool causal variant cell motility cell type cleft lip and palate craniofacial craniofacial development craniofacial tissue data integration de novo mutation deep learning deep learning algorithm design epigenomics experience gene function genetic analysis genetic variant genome sequencing genome wide association study genomic data genomic tools heterogenous data learning strategy machine learning method novel orofacial cleft programs secondary analysis single-cell RNA sequencing success transcription factor transcriptomics whole genome

项目摘要

Project Summary A deep understanding of gene regulation and function during craniofacial development is not only important for our biological knowledge, but also critical to identify causal variants and genes underlying many dental, oral, and craniofacial (DOC) diseases. Numerous -omics datasets at the genomic, epigenomic, (single-cell) transcriptomic levels have been generated for craniofacial development and DOC diseases. These datasets are highly heterogeneous (e.g. platforms, species, tissues, developmental stages) and cross-species (e.g. human and mouse), requiring novel analytical approaches for decoding genetic regulation, molecular function, and cellular maps in craniofacial development. Critically, because of practical unavailability of human embryonic craniofacial tissue, there is a big gap between the abundant -omics and functional studies in murine craniofacial development and large-scale human genetic studies of DOC diseases. In this proposal, we combine machine learning, genomics, single-cell RNA sequencing (scRNA-seq), complex disease genetics, developmental biology to design novel methods aiming to decode complex genetic regulation and cellular maps during craniofacial development. We propose three specific aims. Aim 1. To develop a deep learning method, DeepFace, for characterizing and prioritizing genetic variants and regulation during craniofacial development. DeepFace is designed to decipher functional impact of noncoding variants and will be the first deep learning method to integrate cross-species functional features in craniofacial development. We will validate DeepFace by using data from genome-wide association studies (15 datasets) and case-parent trio-based whole genome sequencing (3 datasets) of orofacial clefts (OFCs). This validation will identify potential causal variants, both common and de novo mutations, in OFCs. Aim 2. To develop deep learning methods for time-series scRNA-seq data analysis in craniofacial development. We will develop novel algorithms including TTNNet for integrating time-series scRNA- seq data and DrivAER for tracing developmental trajectories and identifying driving transcription factors in craniofacial development. We will validate the methods using scRNA-seq datasets from the FaceBase consortium and to-be-generated data for mouse palate formation. Aim 3. To experimentally validate and characterize the top ranked novel mutations (Aim 1) and regulators (Aim 2). Building on our previous studies, strong preliminary data and highly experienced team, this proposal is timely to develop machine learning methods to effectively address the current gap between the genomics studies in murine craniofacial development and human genetic studies of orofacial clefts. The successful completion will provide 1) the NIDCR research community a suite of novel methods and analytical tools for genomic/epigenomic/scRNA-seq data, and 2) the mechanistic assessment on the mutations/genes and transcriptional regulators that are potentially involved in OFCs and related craniofacial diseases.

项目概要深入了解颅面发育过程中的基因调控和功能不仅对于我们的生物学知识，而且对于识别许多牙科、口腔和口腔疾病背后的因果变异和基因也至关重要颅面（DOC）疾病。基因组、表观基因组、（单细胞）转录组的大量组学数据集已针对颅面发育和 DOC 疾病产生了水平。这些数据集高度异质性（例如平台、物种、组织、发育阶段）和跨物种（例如人类和小鼠），需要新的分析方法来解码遗传调控、分子功能和细胞颅面发育图。至关重要的是，由于人类胚胎颅面实际上无法获得组织方面，小鼠颅面发育的丰富组学和功能研究之间存在很大差距以及 DOC 疾病的大规模人类遗传学研究。在这个提案中，我们结合了机器学习，基因组学、单细胞 RNA 测序 (scRNA-seq)、复杂疾病遗传学、发育生物学设计旨在解码颅面过程中复杂的遗传调控和细胞图谱的新方法发展。我们提出三个具体目标。目标 1. 开发一种深度学习方法 DeepFace，用于描述颅面发育过程中遗传变异和调控的特征和优先顺序。深脸是旨在破译非编码变体的功能影响，并将成为第一个深度学习方法整合颅面发育中的跨物种功能特征。我们将使用数据来验证 DeepFace 来自全基因组关联研究（15 个数据集）和基于病例亲本三重奏的全基因组测序（3 口面裂（OFC）的数据集）。该验证将识别潜在的因果变异，包括常见的和德 OFC 中的新生突变。目标 2. 开发用于时间序列 scRNA-seq 数据分析的深度学习方法颅面发育。我们将开发新的算法，包括 TTNNet，用于集成时间序列 scRNA- seq 数据和 DrivAER 用于追踪发育轨迹并识别驱动转录因子颅面发育。我们将使用 FaceBase 的 scRNA-seq 数据集验证这些方法联盟和待生成的小鼠上颚形成数据。目标 3. 通过实验验证和描述排名最高的新突变（目标 1）和调节因子（目标 2）。基于我们之前的研究，强大的初步数据和经验丰富的团队，这个建议对于发展机器学习来说是及时的有效解决目前小鼠颅面发育基因组学研究之间差距的方法以及口颌裂的人类遗传学研究。成功完成将提供 1) NIDCR 研究社区提供一套用于基因组/表观基因组/scRNA-seq 数据的新方法和分析工具，以及 2) 对可能涉及的突变/基因和转录调节因子的机制评估 OFC 和相关颅面疾病。