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Quantitative Biology

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Quant. Biol.    2017, Vol. 5 Issue (3) : 215-225    https://doi.org/10.1007/s40484-017-0113-6
REVIEW
Computational tools for Hi-C data analysis
Zhijun Han1,2, Gang Wei1()
1. CAS Key Laboratory of Computational Biology, Collaborative Innovation Center for Genetics and Developmental Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

Background: In eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher-order structures. Emerging evidence indicates that these higher-order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication. With the advancement in 3C (chromosome conformation capture) based technologies, Hi-C has been widely used to investigate genome-wide long-range chromatin interactions during cellular differentiation and oncogenesis. Since the first publication of Hi-C assay in 2009, lots of bioinformatic tools have been implemented for processing Hi-C data from mapping raw reads to normalizing contact matrix and high interpretation, either providing a whole workflow pipeline or focusing on a particular process.

Results: This article reviews the general Hi-C data processing workflow and the currently popular Hi-C data processing tools. We highlight on how these tools are used for a full interpretation of Hi-C results.

Conclusions: Hi-C assay is a powerful tool to investigate the higher-order chromatin structure. Continued development of novel methods for Hi-C data analysis will be necessary for better understanding the regulatory function of genome organization.
Author Summary  Hi-C, the derivative of the chromosome conformation capture (3C) technology, has been widely used to dissect chromatin architecture and greatly contributed to our understanding of the relationship between genome organization and genome function. The computational methods for data analysis are essential for a full interpretation of Hi-C data. In this article, we review the general Hi-C data processing workflow and popular Hi-C data processing tools. We also discuss the challenges and future perspective regarding the improvement of Hi-C data analysis.
Keywords 3D genome structure      Hi-C data processing tool      chromatin interactions     
Corresponding Author(s): Gang Wei   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.
Online First Date: 01 August 2017    Issue Date: 24 August 2017
 Cite this article:   
Zhijun Han,Gang Wei. Computational tools for Hi-C data analysis[J]. Quant. Biol., 2017, 5(3): 215-225.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-017-0113-6
https://academic.hep.com.cn/qb/EN/Y2017/V5/I3/215
  General Hi-C data processing workflow.
  Filtering mapped PETs using restriction enzyme (RE) fragments.
Tool Aligner Mapping strategy PETs filtering Normalization Descriptions
Hiclib [15] Bowtie2 Iterative RE fragments ICE No standalone pipeline provided. runHiC is based on hiclib and is command-line based
HIPPIE [14] BWA - - Explicit model Designed for high performance computing cluster with Oracle Grid Engine. Can integrate with epigenetic datasets and GWAS data
HiC-inspector [11] Bowtie - RE fragments Coverage correction RE filtering only keeps PETs with 3'-end facing the restriction site. Command-line based and provides simple interactive browser
HiC-Box [12] Bowtie2 - Not detailed Not detailed GUI based, compatible with Genome Re-Assembly Assessing Likelihood (GRAAL). No published paper with details
HiC-Pro [18] Bowtie2 Trimming RE fragments Optimized ICE Command-line based and easy to use. Provides complete workflow from mapping to normalized matrix, can handle SNP information
HiCUP [17] Bowtie, Bowtie2 Pre-truncation RE fragments - Command-line based with incomplete workflow, needs other tools such as HiCpipe [11] to finish normalization and other processes
HiCdat [13] Subread, Bowtie2 - RE fragments Three options GUI and R based with mapping command provided but not piped. Provides comprehensive functions for high-order analysis and integrating with epigenetic datasets
TADbit [19] GEM Iterative /Trimming RE fragments ICE No standalone pipeline provided. Can call and compare TADs between samples. No published paper with details
Juicer [15] BWA - RE fragments Matrix balancing Command-line based. Provides many high-order functions such as calling TADs, loops, compartments and displaying with Juicebox
Tab.1  Tools for Hi-C data processing pipeline.
Tool name Model assumption Description
Normalization HiCNorm [22] Three systematic biases Generalized linear regression-based
method, much faster than Yaffe’s method [24]
Hi-Corrector [42] Matrix balancing Parallelized and memory-controllable ICE, very fast
HiFive [43] Three options GUI based and integrated into Galaxy
HiCNormCis [10] Three systematic biases Poisson-regression-based method for local regions, result can be used to call FIREs. Not publicly available
Calling TADs DI-HMM [9] Directional indexes bias with HMM Insensitive to parameters and hence it is hard to identify sub-TADs
Arrowhead [15] Dynamic programming Can call sub-TADs, integrated in Juicer
Armatus [27] Multi-scale approach Can call TADs with different scales, but not easy to choose fine scale ranges
HiCseg [28] Linear segmentation Turn 2D into 1D, can model the uncertainty
CHDF [29] Dynamic programming Robust to different resolution but need users to control the total number of TADs for each chr
Tab.2  Tools for post-processing Hi-C data (tools provide complete workflow are listed in Table 1).
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