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

项目摘要 顺式调控元件（克雷斯）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学背景下注释克雷斯。然而，尽管这些集合的全面性，了解规则决定如何克雷斯调控基因仍然是一个重大的基因组学悬而未决的问题。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，其中CRE-gene相互作用被建模为图。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，这个试点项目的目的是评估的可行性和普遍性的CRE相互作用图预测基因表达的方法。我们将使用公共数据集，包括共同基金项目生成的数据集，通过整合基因组相互作用数据（如CRISPR扰动和Hi-C环）与注释的克雷斯，在三种生物学背景下构建CRE-interaction graphs。然后，为了证明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，以更好地理解调节转录的机制。该项目不仅将为预测基因表达提供更好的模型，还将为未来的基因调控研究建立一个灵活的框架。该项目还将为计算和机器学习社区提供资源，并提高现有资源的效用。

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.