Can one size fit all? - High-Resolution 3D Genome Spatial Organization Inference with Generalizable Models

一种尺寸可以适合所有人吗？

• 批准号: 10707587
• 负责人: Oluwatosin Oluwadare
• 金额: $ 32.22万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-22 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707587
• 关键词: 3-Dimensional; Address; Algorithms; Architecture; Bioinformatics; Biological Process; Cells; Chromatin; Chromosome Structures; Chromosomes; Communities; Computational algorithm; Cytogenetics; Data; Development; Enhancers; Epigenetic Process; Gene Expression; Genes; Genetic; Genome; Genomics; Graph; Hi-C; High-Throughput Nucleotide Sequencing; Human; Interdisciplinary Study; Light; Mammalian Chromosomes; Methodology; Methods; Modeling; Molecular Conformation; Population; Positioning Attribute; Prevention; Productivity; Research; Research Personnel; Resolution; Rewards; Structural Models; Structure; Techniques; Technology; Training; Work; chromosome conformation capture; complex data; convolutional neural network; cost; disease diagnosis; human disease; innovation; machine learning model; member; next generation; novel; programs; reconstruction; restriction enzyme; three dimensional structure; three-dimensional modeling

ABSTRACT Chromosome conformation capture techniques, particularly Hi-C, have benefitted the study of the spatial proximity, interaction, genome conformation of cells, and genome architecture leading to the development of several three-dimensional (3D) chromosome structure modeling methods. Many observations become more apparent in 3D because some relationships—for example, evolutionary constraints or cell-to-cell variability of mammalian chromosome structures—cannot be surmised by genomic sequences alone. Although members of the bioinformatics community, including the PI, have developed many algorithms for reconstructing 3D genome structures based on population Hi-C data, we lack computationally effective methods to precisely model at a high-resolution (<=5 kilobase (kb)). One difficulty is the exponentially increasing number of fragments at this resolution. My work in the last five years provides the premise for the current proposal and uniquely positions my interdisciplinary research program to carry out the proposed studies. The PI proposes to conduct leading research to overcome this challenge and address important questions that remain about how (and why) 3D genome structures across cells are organized and about the relationship between 3D structure and genetic and epigenetic mechanisms for gene expression. During the next five years, the PI’s objective is to develop computational and machine learning-based models to further highlight the hierarchical organization of, and the refined structures within, the genome. The PI proposes to explore the development of innovative models for 3D chromosome and genome reconstruction using a novel noninstance-based generalizable model based on a graph convolutional neural network to generalize across resolutions, chromosomes, restriction enzymes, and cell populations. Given the PI’s background, track record, and productivity in the genomic research field, the computational objectives defined here are not only feasible but also computationally and biologically rewarding to the bioinformatics community at large. Computationally, our methodology will resemble a robust one-size-fits- all model that can be sufficiently trained at a lower computational cost on less complex data and be used across multiple higher resolutions for 3D structural modeling. Biologically, our proposed reconstruction algorithms will aid diseases diagnosis, prevention or treatment by shedding light on the relationship between long-range interaction and gene expression in human cells and how disruptions in physical interactions between genes and the enhancers or silencers could aberrantly alter gene expression. Thus, this research demonstrates the potential impact of knowing the architecture of the genome to the understanding of biological processes and human disease. Once the proposed objectives are completed, the PI will ultimately have been well established as an independent investigator, and will have proposed leading robust, high-performing, and efficient computational algorithms that will provide new vertical advancement in the chromatin genomics research field.

摘要 染色体构象捕捉技术，特别是Hi-C技术，为空间构象的研究提供了有利条件 邻近、相互作用、细胞的基因组构象和基因组结构导致了 几种三维(3D)染色体结构建模方法。许多观察结果变得更多 在3D中很明显，因为一些关系-例如，进化限制或细胞到细胞的可变性 哺乳动物的染色体结构--不能仅靠基因组序列来推测。虽然成员们 生物信息学社区，包括PI，已经开发了许多重建3D基因组的算法 基于人口Hi-C数据的结构，我们缺乏在计算上有效的方法来精确地模拟 高分辨率(&lt；=5kb)。其中一个困难是碎片的数量呈指数级增长 决议。我过去五年的工作为现在的建议提供了前提和独特的立场 我的跨学科研究计划拟开展的研究。PI建议进行领导 旨在克服这一挑战并解决有关3D方式(以及为什么)的重要问题的研究 跨细胞的基因组结构被组织起来，并关于三维结构与遗传和 基因表达的表观遗传机制。未来五年，PI的目标是发展 基于计算和机器学习的模型，以进一步突出 基因组内部的精细结构。PI建议探索3D创新模型的开发 一种新的基于非实例的泛化模型用于染色体和基因组重建 图形卷积神经网络，用于跨分辨率、染色体、限制酶和 细胞群。考虑到PI的背景、往绩和在基因组研究领域的生产力， 这里定义的计算目标不仅是可行的，而且在计算上和生物上都是有回报的 给整个生物信息学社区。在计算上，我们的方法将类似于强大的一刀切- 所有模型都可以以较低的计算成本对不太复杂的数据进行充分的训练，并可在 多个更高分辨率的3D结构建模。从生物学上讲，我们提出的重建算法将 辅助疾病的诊断、预防或治疗与远期的关系 人类细胞中的相互作用和基因表达，以及基因和基因之间的物理相互作用如何中断 增强剂或抑制剂可能会异常地改变基因表达。因此，这项研究证明了 了解基因组的结构对理解生物过程和 人类疾病。一旦拟议的目标完成，PI最终将得到很好的确立 作为一名独立的调查员，并将建议领导强大、高效和高效的 将在染色质基因组研究领域提供新的纵向进展的计算算法。