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

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，但也是至关重要的，以确定因果变异和基因背后的许多牙齿，口腔和 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 已经产生了头面部发育和DOC疾病的水平。这些数据集具有高度的 异质性(如平台、物种、组织、发育阶段)和跨物种(如人类和 小鼠)，需要新的分析方法来解码遗传调节、分子功能和细胞 头面部发育的地图。关键是，由于实际无法获得人类胚胎颅面 组织，在小鼠颅面发育的丰富组学和功能研究之间有很大的差距 以及对DOC疾病的大规模人类基因研究。在这个方案中，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 设计新的方法，旨在破译复杂的遗传规律和颅面过程中的细胞图谱 发展。我们提出了三个具体目标。目标1.开发深度学习方法DeepFace，用于 确定颅面发育过程中的遗传变异和调控的特征和优先顺序。DeepFace是 旨在破译非编码变体对功能的影响，并将成为第一个深入学习的方法 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-亲本三重序列的全基因组测序(3 数据集)。这一验证将确定潜在的因果变量，包括常见和非常见的 OFCs中的新突变。目的2.发展用于时间序列scRNA-seq数据分析的深度学习方法 头面部发育。我们将开发包括TTNNet在内的新算法来整合时间序列scRNA- SEQ数据和DrivAER用于追踪发育轨迹和识别驱动转录因子 头面部发育。我们将使用来自FaceBase的scRNA-seq数据集来验证这些方法 联盟和将要生成的小鼠上颚形成的数据。目标3.通过实验验证和 描述排名最靠前的新突变(目标1)和调节因子(目标2)。在我们之前研究的基础上， 强大的前期数据和经验丰富的团队，这一建议对发展机器学习是及时的 有效解决目前小鼠颅面发育基因组研究之间的差距的方法 以及人类对口面部裂隙的基因研究。该项目的成功完成将提供1)NIDCR研究 社区提供了一套用于基因组/表观基因组/scRNA-seq数据的新方法和分析工具，以及2) 对可能涉及到的突变/基因和转录调控因子的机械性评估 OFC和相关的头面部疾病。

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.