Evaluating the utility of cis-regulatory element graphs for modeling gene regulation

评估顺式调控元件图在基因调控建模中的效用

• 批准号: 10776793
• 负责人: Jill Elizabeth Moore
• 金额: $ 33.47万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10776793
• 关键词: 3-Dimensional; ATAC-seq; Algorithms; Area; Benchmarking; Binding Sites; Biological; Biological Assay; Cell Nucleus; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Complex; Computing Methodologies; DNA Binding; Data; Data Set; Enhancers; Funding; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Genomics; Graph; Hi-C; Individual; Intestines; K562 Cells; Lead; Link; Machine Learning; Measurement; Methods; Modeling; Multiomic Data; Neural Network Simulation; Performance; Pilot Projects; Play; Post-Transcriptional Regulation; Qualifying; Quantitative Trait Loci; Registries; Regulator Genes; Regulatory Element; Research; Research Personnel; Resources; Signal Transduction; Site; Source; Structure; Testing; Tissues; Training; Transcription Initiation Site; Transcriptional Regulation; Work; cell type; computer framework; computing resources; flexibility; gene interaction; genetic regulatory protein; genomic data; genomic locus; graph neural network; human disease; improved; learning community; lymphoblastoid cell line; neural network algorithm; predictive modeling; rapid growth

Project Summary Cis-regulatory elements (CREs) are crucial components of transcriptional regulation and the rapid growth of genomic data has enabled researchers to annotate CREs across many biological contexts. However, despite the comprehensiveness of these collections, understanding the rules dictating how CREs regulate genes remains a major unresolved problem in genomics. Therefore, to better understand gene regulation, we are proposing to develop a new framework where CRE-gene interactions are modeled as graphs. This will enable researchers to accomplish a wide range of computational tasks such as comparisons between cell types, predictions of new interactions, and predictions of gene expression. Specifically, this pilot project aims to evaluate the feasibility and generalizability of a CRE-interaction graph approach for predicting gene expression. We will build CRE-interaction graphs in three biological contexts using public datasets, including those generated by Common Fund projects, by integrating genomic interaction data, such as CRISPR perturbations and Hi-C loops, with annotated CREs. Then to demonstrate the utility of these graph models, we will use graph neural networks to predict gene expression, testing different algorithms and gene expression qualifications to maximize model performance. Finally, we will use feature attribution methods and prediction explainer algorithms to interpret our models to gain a better understanding of the mechanisms regulating transcription. The project will not only lead to a better model for predicting gene expression, but also establish a flexible framework for future research on gene regulation. The project will also produce a resource for the computational and machine learning community and improve the utility of existing resources.

项目概要 顺式调控元件 (CRE) 是转录调控的重要组成部分，基因组数据的快速增长使研究人员能够在许多生物背景下注释 CRE。然而，尽管这些集合非常全面，但了解 CRE 如何调节基因的规则仍然是基因组学中尚未解决的主要问题。因此，为了更好地理解基因调控，我们建议开发一个新的框架，将 CRE-基因相互作用建模为图表。这将使研究人员能够完成广泛的计算任务，例如细胞类型之间的比较、新相互作用的预测以及基因表达的预测。具体来说，该试点项目旨在评估用于预测基因表达的 CRE 交互图方法的可行性和普遍性。我们将使用公共数据集（包括共同基金项目生成的数据集），通过将基因组相互作用数据（例如 CRISPR 扰动和 Hi-C 环）与带注释的 CRE 集成，在三种生物环境中构建 CRE 相互作用图。然后，为了演示这些图模型的实用性，我们将使用图神经网络来预测基因表达，测试不同的算法和基因表达资格，以最大限度地提高模型性能。最后，我们将使用特征归因方法和预测解释器算法来解释我们的模型，以更好地理解转录调节机制。该项目不仅将带来更好的基因表达预测模型，还将为未来的基因调控研究建立灵活的框架。该项目还将为计算和机器学习社区提供资源，并提高现有资源的利用率。