CAREER: Statistical Models and Parallel-computing Methods for Analyzing Sparse and Large Single-cell Chromatin Interaction Datasets

核染色质的3D组织在维持正常细胞功能中起着关键作用。单细胞Hi-C（scHi-C）技术的最新发展使研究人员能够描绘单个细胞中的全基因组染色质相互作用，并回答基本的生物学问题。然而，用于分析scHi-C数据的计算方法由于数据稀疏、高度多样性和染色体组织的复杂层次而在很大程度上滞后。该项目将通过为实验和计算生物学家构建一套模型和并行工具来解决这些挑战，以分析稀疏和大型scHi-C数据集，这将有助于他们了解3D染色质相互作用并深入了解功能结果。该项目产生的饱和度模型将导致实验质量的重大改进，增强数据分析，并降低实验成本。综合研究和教育活动包括不同的学生培训和跨学科学科的本科生和研究生课程开发，涵盖计算机科学，统计学，生物信息学和生物学。该项目的目标是利用随机理论、小波变换和并行计算技术研究不同尺度下细胞特异性染色质的组织。首先，将建立随机模型以通过评估主要方案步骤和测序深度来了解scHi-C数据的饱和状态。其次，将设计基于多尺度的方法来检测拓扑相关的结构域和蛋白质介导的环，细胞聚类和比较分析。第三，所有模型和工具将完全并行化，以处理大型数据集。这些方法的高性能和运行速度将帮助研究人员在一站式服务中实现生物学发现，并节省实验和计算分析的时间和金钱。该软件管道将通过Python/R语言实现，并通过GitHub存储库https://github.com/chenyongrowan和罗文大学的网络服务器公开访问。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The 3D organization of nuclear chromatin plays a critical role in maintaining normal cellular functions. Recent development of single-cell Hi-C (scHi-C) technologies allows researchers to delineate the genome-wide chromatin interactions in individual cells and answer fundamental biological questions. However, computational methods for analyzing scHi-C data are largely lagging behind because of data sparsity, high diversity and complicated hierarchy of chromosomal organization. This project will address these challenges by building a suite of models and parallel tools for both experimental and computational biologists to analyze sparse and large scHi-C datasets, which will help them understand 3D chromatin interactions and gain deep insights into functional outcomes. The saturation model produced by this project will lead to a major improvement in experimental quality, enhanced data analysis, and reduced experimental costs. The integrated research and educational activities include diverse students training and curriculum development for both undergraduate and graduate courses in interdisciplinary subjects spanning computer science, statistics, bioinformatics and biology. The objectives of this project are to study the cell-specific chromatin organization at different scales by using stochastic theory, wavelet transform, and parallel-computing techniques. First, a stochastic model will be established to understand the saturation status of scHi-C data by evaluating the major protocol steps and sequencing depth. Second, multiscale-based methods will be designed for detecting topologically associated domains and protein-mediated loops, cell clustering, and comparative analysis. Third, all models and tools will be fully parallelized for processing large datasets. The high performance and running speed of these methods will help researchers achieve biological discoveries in a one-stop service, and save time and money for experiments and computational analysis. The software pipelines will be implemented by Python/R languages and publicly accessible through the GitHub repository https://github.com/chenyongrowan and a webserver at Rowan University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.