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

摘要 染色体构象捕获技术，特别是Hi-C，已经有益于空间构象的研究。 邻近性、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。许多观察变得更加 在3D中很明显，因为一些关系-例如，进化约束或细胞间的变异性， 哺乳动物染色体结构-不能仅通过基因组序列推测。虽然成员 包括PI在内的生物信息学社区已经开发了许多用于重建3D基因组的算法 基于人口Hi-C数据的结构，我们缺乏计算有效的方法来精确建模， 高分辨率（<= 5 kb）。其中一个困难是，在这种情况下，碎片的数量呈指数级增长。 分辨率我在过去五年的工作为目前的建议提供了前提和独特的立场 我的跨学科研究计划，以开展拟议的研究。PI建议进行领导 研究以克服这一挑战，并解决关于如何（以及为什么）3D的重要问题 跨细胞的基因组结构是有组织的，关于3D结构和遗传之间的关系， 基因表达的表观遗传机制。在未来五年内，PI的目标是发展 基于计算和机器学习的模型，以进一步突出层次组织， 基因组内部的精细结构。PI建议探索3D创新模型的开发 使用一种新的基于非实例的可推广模型的染色体和基因组重建 图卷积神经网络，以概括分辨率，染色体，限制性内切酶， 细胞群鉴于PI在基因组研究领域的背景、记录和生产力， 这里定义的计算目标不仅是可行的，而且在计算上和生物学上都是有益的 对整个生物信息学社区来说。在计算上，我们的方法将类似于一个强大的一刀切- 所有模型都可以在不太复杂的数据上以较低的计算成本进行充分训练， 用于3D结构建模的多种更高分辨率。从生物学上讲，我们提出的重建算法将 帮助疾病的诊断、预防或治疗， 相互作用和人类细胞中的基因表达，以及基因和 增强子或沉默子可以异常地改变基因表达。因此，这项研究表明， 了解基因组结构对理解生物过程的潜在影响， 人类疾病一旦完成了提议的目标，PI最终将得到很好的建立 作为一个独立的调查员，并将提出领导强大的，高性能的，和有效的 计算算法，这将提供新的垂直的染色质基因组学研究领域的进步。

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.