HiC-DC plus enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

被引:25
|
作者
Sahin, Merve [1 ,2 ]
Wong, Wilfred [1 ,2 ]
Zhan, Yingqian [3 ]
Van Deynze, Kinsey [4 ]
Koche, Richard [3 ]
Leslie, Christina S. [1 ]
机构
[1] Mem Sloan Kettering Canc Ctr, Computat & Syst Biol Program, New York, NY 10021 USA
[2] Triinst Training Program Computat Biol & Med, New York, NY USA
[3] Mem Sloan Kettering Canc Ctr, Ctr Epigenet Res, New York, NY USA
[4] Univ Calif San Diego, Bioinformat Program, La Jolla, CA USA
关键词
DNA LOOPS; RESOLUTION; GENOME; PROVIDES;
D O I
10.1038/s41467-021-23749-x
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Recent genome-wide chromosome conformation capture assays such as Hi-C and HiChIP have vastly expanded the resolution and throughput with which we can study 3D genomic architecture and function. Here, we present HiC-DC+, a software tool for Hi-C/HiChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools and includes topologically associating domain (TAD) and A/B compartment callers. We found that HiC-DC+ can more accurately identify enhancer-promoter interactions in H3K27ac HiChIP, as validated by CRISPRi-FlowFISH experiments, compared to existing methods. Differential HiC-DC+ analyses of published HiChIP and Hi-C data sets in settings of cellular differentiation and cohesin perturbation systematically and quantitatively recovers biological findings, including enhancer hubs, TAD aggregation, and the relationship between promoter-enhancer loop dynamics and gene expression changes. HiC-DC+ therefore provides a principled statistical analysis tool to empower genome-wide studies of 3D chromatin architecture and function. The genome-wide investigation of chromatin organization enables insights into global gene expression control. Here, the authors present a computationally efficient method for the analysis of chromatin organization data and use it to recover principles of 3D organization across conditions.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Microrheology for Hi-C Data Reveals the Spectrum of the Dynamic 3D Genome Organization
    Shinkai, Soya
    Sugawara, Takeshi
    Miura, Hisashi
    Hiratani, Ichiro
    Onami, Shuichi
    BIOPHYSICAL JOURNAL, 2020, 118 (09) : 2220 - 2228
  • [22] 3D Chromosome Rendering from Hi-C Data using Virtual Reality
    Zhu, Yixin
    Selvaraj, Siddarth
    Weber, Philip
    Fang, Jennifer
    Schulze, Juergen P.
    Ren, Bing
    VISUALIZATION AND DATA ANALYSIS 2015, 2015, 9397
  • [23] Reconstruction of the chromatin 3D conformation from single cell Hi-C data
    Kos, Pavel I.
    Galitsyna, Aleksandra A.
    Ulianov, Sergey V.
    Gelfand, Mikhail S.
    Razin, Sergey V.
    Chertovich, Alexander V.
    PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, : 2476 - 2476
  • [24] 3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data
    Zhang, Zhizhuo
    Li, Guoliang
    Toh, Kim-Chuan
    Sung, Wing-Kin
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2013, 20 (11) : 831 - 846
  • [25] HiCube: interactive visualization of multiscale and multimodal Hi-C and 3D genome data
    Ye, Tiantian
    Hu, Yangyang
    Pun, Sydney
    Ma, Wenxiu
    BIOINFORMATICS, 2023, 39 (04)
  • [26] Heterogeneous Loop Model to Infer 3D Chromosome Structures from Hi-C
    Liu, Lei
    Kim, Min Hyeok
    Hyeon, Changbong
    BIOPHYSICAL JOURNAL, 2019, 117 (03) : 613 - 625
  • [27] PHi-C2: interpreting Hi-C data as the dynamic 3D genome state
    Shinkai, Soya
    Itoga, Hiroya
    Kyoda, Koji
    Onami, Shuichi
    BIOINFORMATICS, 2022, 38 (21) : 4984 - 4986
  • [28] Can 3D diploid genome reconstruction from unphased Hi-C data be salvaged?
    Segal, Mark R.
    NAR GENOMICS AND BIOINFORMATICS, 2022, 4 (02)
  • [29] Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
    Hauth, Antonia
    Galupa, Rafael
    Servant, Nicolas
    Villacorta, Laura
    Hauschulz, Kai
    van Bemmel, Joke Gerarda
    Loda, Agnese
    Heard, Edith
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2022, (188):
  • [30] miniMDS: 3D structural inference from high-resolution Hi-C data
    Rieber, Lila
    Mahony, Shaun
    BIOINFORMATICS, 2017, 33 (14) : I261 - I266