GrapHiC: An integrative graph based approach for imputing missing Hi-C reads.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh
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引用次数: 0

Abstract

Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell types. Existing frameworks rely on a sparse Hi-C dataset or cheaper-to-acquire ChIP-seq data to predict Hi-C contact maps with high read coverage. However, these methods fail to generalize to sparse or cross-cell-type inputs because they do not account for the contributions of epigenomic features or the impact of the structural neighborhood in predicting Hi-C reads. We propose GrapHiC, which combines Hi-C and ChIP-seq in a graph representation, allowing more accurate embedding of structural and epigenomic features. Each node represents a binned genomic region, and we assign edge weights using the observed Hi-C reads. Additionally, we embed ChIP-seq and relative positional information as node attributes, allowing our representation to capture structural neighborhoods and the contributions of proteins and their modifications for predicting Hi-C reads. We show that GrapHiC generalizes better than the current state-of-the-art on cross-cell-type settings and sparse Hi-C inputs. Moreover, we can utilize our framework to impute Hi-C reads even when no Hi-C contact map is available, thus making high-quality Hi-C data accessible for many cell types. Availability: https://github.com/rsinghlab/GrapHiC.

GrapHiC:一种基于图的综合方法,用于估算缺失的 Hi-C 读数。
Hi-C 实验使研究人员能够研究和了解三维基因组的组织及其调控功能。遗憾的是,测序成本和技术限制严重制约了对许多细胞类型的高质量 Hi-C 数据的获取。现有的框架依赖于稀疏的 Hi-C 数据集或获取成本更低的 ChIP-seq 数据来预测高读数覆盖率的 Hi-C 接触图。然而,这些方法无法推广到稀疏或跨细胞类型的输入,因为它们没有考虑表观基因组特征的贡献或结构邻域对预测 Hi-C 读数的影响。我们提出的 GrapHiC 方法将 Hi-C 和 ChIP-seq 结合到图表示法中,可以更准确地嵌入结构和表观基因组特征。每个节点代表一个二进制基因组区域,我们使用观察到的 Hi-C 读数分配边缘权重。此外,我们还将 ChIP-seq 和相对位置信息嵌入节点属性,从而使我们的表征能够捕捉结构邻域和蛋白质及其修饰对预测 Hi-C 读数的贡献。我们的研究表明,在交叉细胞类型设置和稀疏 Hi-C 输入上,GrapHiC 的通用性优于目前最先进的技术。此外,即使没有 Hi-C 接触图,我们也能利用我们的框架来推算 Hi-C 读数,从而使许多细胞类型都能获得高质量的 Hi-C 数据。可用性:https://github.com/rsinghlab/GrapHiC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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