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 深入了解颅面发育过程中的基因调控和功能不仅对 我们的生物学知识，但也至关重要，以确定因果变异和基因的基础上，许多牙科，口腔， 颅面（DOC）疾病。基因组学、表观基因组学、（单细胞）转录组学的大量组学数据集 已经产生了颅面发育和DOC疾病的水平。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 颅面发育的地图关键的是，由于人类胚胎颅面的实际可用性 组织，在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。在这个提议中，我们将联合收割机机器学习， 基因组学，单细胞RNA测序（scRNA-seq），复杂疾病遗传学，发育生物学， 设计新的方法，旨在解码复杂的遗传调控和细胞图，在颅面 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， 表征和优先考虑颅面发育过程中的遗传变异和调节。DeepFace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， 在颅面发育中整合跨物种功能特征。我们将通过使用数据来验证DeepFace 来自全基因组关联研究（15个数据集）和基于病例亲本trio的全基因组测序（3个 口面裂（orofacial clefts，OFC）。这种验证将识别潜在的因果变异，包括常见的和不常见的。 OFC中的新生突变。目标二。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，用于整合时间序列scRNA- seq数据和DrivAER用于追踪发育轨迹和识别驱动转录因子， 颅面发育我们将使用FaceBase中的scRNA-seq数据集来验证这些方法。 联合体和将要生成的小鼠腭形成的数据。目标3。通过实验验证和 表征排名靠前的新突变（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，这一建议是及时发展机器学习 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 以及人类对口面裂的遗传学研究。成功完成将提供1）NIDCR研究 社区的一套新的方法和分析工具的基因组/表观基因组/scRNA-seq数据，和2） 对潜在参与的突变/基因和转录调节因子的机制评估 OFC和相关颅面疾病。