基于生物信息学的Hi-C研究现状与发展趋势

doi:10.16288/j.yczz.19-163

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Hereditas(Beijing) ›› 2020, Vol. 42 ›› Issue (1): 87-99.doi: 10.16288/j.yczz.19-163

• Special Section: 3D Genome • Previous Articles Next Articles

Current status and future perspectives in bioinformatical analysis of Hi-C data

Hongqiang Lyu, Lele Hao, Erhu Liu, Zhifang Wu, Jiuqiang Han, Yuan Liu

School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Received:2019-07-23 Revised:2019-11-26 Online:2020-01-20 Published:2019-12-05
Supported by:
Supported by the National Natural Science Foundation of China No(61602367)

Abstract

Abstract:

The spatial interaction of chromosomes is regarded as an important issue affecting the regulation of gene expression, and the high-throughput chromosome conformation capture (Hi-C) technology has become the primary tool to explore the temporal and spatial interactions of chromosomes in three-dimensional genomics. With the continuous accumulation of Hi-C samples and the increasing complexity of pipelines, the bioinformatic analysis of Hi-C data has been considered an opportunity and a challenge for understanding the spatial regulation mechanism of gene expression. In this paper, the current status and development outline of bioinformatic methods for Hi-C data are introduced, including data normalization, multi-level structure analysis, data visualization and 3D modeling, especially of multi-level structure at A/B compartments, topological associated domains (TADs) and chromain looping levels. Based on this, we provide the outlook of future hotspots and trends in this area. Hopefully our insight will be beneficial for the exploration of gene expression regulation from the traditional linear model to the 3D mode.

Key words: 3D genomics, Hi-C, bioinformatics

Hongqiang Lyu, Lele Hao, Erhu Liu, Zhifang Wu, Jiuqiang Han, Yuan Liu. Current status and future perspectives in bioinformatical analysis of Hi-C data[J]. Hereditas(Beijing), 2020, 42(1): 87-99.

Figures/Tables 7

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方法	分类	特点	实现语言	典型程序
SCN	隐式,单样本	行列归一化的矩阵平衡	MATLAB	SCN_sumV2.m
HiCNorm	显式,单样本	泊松回归估计系统偏差	R	HiCNorm.R/HiTC
ICE	隐式,单样本	迭代修正的矩阵平衡	R,C,Python	HiTC/Hi-Corrector
KR	隐式,单样本	内外迭代的快速矩阵平衡	MATLAB	BNEWT.m
caICB	显式,单样本	移除拷贝数偏差的改进ICE	R,perl	HiCapp
HiCcompare	隐式,跨样本	双样本,局部加权线性回归	R	HiCcompare
MultiHiCcompare	隐式,跨样本	多样本,局部加权线性回归	R	multiHiCcompare
Binless	隐式,跨样本	配对末端序列片段的统计显著性分析	R	Binless

方法	分类	特点	实现语言	典型程序
DI	边界点,非差异	隐马尔科夫模型	R,Python	HiTC/TADtool
HiCseg	边界点,非差异	二维分割矩阵	R	HiCseg
TopDom	边界点,非差异	钻石形滑窗法	R	TopDom.R
TADtree	层级式,非差异	交互频率经验分布	Python	TADtree
TADbit	边界点,非差异	基于BIC惩罚的概率模型	Python	TADbit
HiTAD	层级式,非差异	隐马尔科夫模型	Python	TADLib
IC-Finder	层级式,非差异	层次聚类	MATLAB	IC-Finder.m
GMAP	层级式,非差异	高斯混合模型	R	GMAP
3DNetMod	层级式,非差异	基于图理论	Python	3DNetMod
deDoc	层级式,非差异	基于图结构墒理论	R	deDoc
HiCDB	边界点,差异性	局部相对隔绝指数和多尺度聚类	R,MATLAB	RHiCDB/HiCDB.m

方法	分类	特点	实现语言	典型程序
Fit-HiC	显著交互	基于二项分布	R,Python	Fit-HiC
HiCCUPS	显著交互	基于泊松分布	Java	Juicebox
HIPPIE	显著交互	基于负二项分布	R,Perl	HIPPIE
dffHiC	差异交互	基于负二项分布	R	diffiHic
CISD_loop	显著交互	基于支持向量机模型	R,Python	CISD_loop
FIND	差异交互	基于泊松分布	R	FIND

方法	交互	网址
WashU Epigenome Browser	浏览器	http://epigenomegateway.wustl.edu/
HiCPlotter	Python软件工具	https://github.com/kcakdemir/HiCPlotter
3Disease Browser	浏览器	http://3dgb.cbi.pku.edu.cn/disease/
Juicebox	浏览器,Java软件工具	http://aidenlab.org/juicebox
HiC-3Dviewer	浏览器	http://bioinfo.au.tsinghua.edu.cn/member/nadhir/HiC3DViewer/
Delta	Java软件工具	http://delta.big.ac.cn
GITAR	Python软件工具	http://genomegitar.org
3D Genome browser	浏览器	http://3dgenome.org
Galaxy HiCExplorer	浏览器	https://hicexplorer.usegalaxy.eu

Current status and future perspectives in bioinformatical analysis of Hi-C data

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