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

• 批准号: 10382360
• 负责人: Junichi Iwata
• 金额: $ 55.19万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382360
• 关键词: Address; Algorithms; Atlases; Automobile Driving; Biological; Biology; CRISPR/Cas technology; Cell Proliferation; Cell physiology; Cells; Communities; Complex; Data; Data Analyses; Data Set; Dental; Development; Developmental Biology; Developmental Process; Disease; Embryo; Enhancers; FaceBase; Funding; Funding Mechanisms; Gene Expression; Gene Expression Regulation; Gene Mutation; 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; Regulation; Research; Research Personnel; Research Project Grants; Resolution; Role; Series; Time; Time Series Analysis; Tissues; Untranslated RNA; Validation; Variant; analytical tool; base; 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

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.

项目摘要 对颅面发育过程中基因调节和功能的深入了解不仅很重要 我们的生物学知识，但对于确定许多牙齿，口服和 颅面（DOC）疾病。基因组，表观基因组，（单细胞）转录组的许多 - 词素数据集 已经为颅面发育和DOC疾病产生了水平。这些数据集高度 异质（例如平台，物种，组织，发育阶段）和跨物种（例如人类和人类 小鼠），需要新颖的分析方法来解码遗传调节，分子功能和细胞 颅面发育中的地图。至关重要的是，由于人类胚胎颅面的实际不可用 组织，在鼠颅颅发育中丰富的 - 组学和功能研究之间存在很大的差距 和大规模的DOC疾病遗传研究。在此建议中，我们将机器学习结合在一起， 基因组学，单细胞RNA测序（SCRNA-SEQ），复杂的疾病遗传学，发育生物学到 设计新颖的方法，旨在解码颅面期间复杂的遗传调节和细胞图 发展。我们提出了三个具体目标。目的1。开发一种深度学习方法，深面 在颅面发育过程中表征和优先考虑遗传变异和调节。深面是 旨在破译非编码变体的功能影响，并将成为第一个深入学习方法 在颅面发育中整合跨物种的功能特征。我们将使用数据验证深面 来自全基因组关联研究（15个数据集）和基于病例的三重奏整个基因组测序（3个 （OFCS）的数据集）。该验证将确定常见和DE的潜在因果变异 Novo突变，在OFC中。目标2。为时间序列开发深度学习方法SCRNA-SEQ数据分析 颅面发展。我们将开发新的算法，包括用于整合时间序列scrna-的TTNNET- SEQ数据和用于追踪发展轨迹并识别驱动转录因子的Drivaer 颅面发展。我们将使用facebase的scrna-seq数据集验证这些方法 用于小鼠pa形成的财团和生成的数据。目标3。实验验证和 表征排名最高的新型突变（AIM 1）和调节剂（AIM 2）。在我们以前的研究的基础上， 强大的初步数据和经验丰富的团队，该建议是及时开发机器学习的 有效解决鼠颅颅发育中基因组学研究之间当前差距的方法 和人类裂纹的人类遗传研究。成功完成将提供1）NIDCR研究 社区一套针对基因组/表观基因组/scrna-seq数据的新型方法和分析工具，以及2） 对突变/基因和转录调节因子的机械评估，这些调节剂可能参与 OFC和相关的颅面疾病。