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

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

基于生物信息学的Hi-C研究现状与发展趋势

吕红强, 郝乐乐, 刘二虎, 吴志芳, 韩九强, 刘源

西安交通大学电子与信息工程学院,西安 710049

收稿日期:2019-07-23 修回日期:2019-11-26 出版日期:2020-01-20 发布日期:2019-12-05
作者简介:吕红强,博士,副教授,研究方向：生物大数据分析与处理。E-mail: hongqianglv@mail.xjtu.edu.cn
基金资助:
国家自然科学基金青年科学基金项目资助编号(61602367)

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)

摘要/Abstract

摘要：

染色体的空间交互作用被视为影响基因表达调控的重要因素,高通量染色体构象捕获(high-throughput chromosome conformation capture, Hi-C)技术已成为3D基因组学中探索染色体空间交互作用的主要实验手段之一。随着Hi-C样本数据的持续累积以及分析处理流程复杂度的不断提升,基于生物信息学的Hi-C数据分析对探究基因表达的时空调控机制而言,是机遇也是挑战。本文从生物信息学角度,综合阐述了Hi-C的国内外研究现状及发展动态,包括数据标准化、多级结构分析、数据可视化以及三维建模,重点剖析了多级结构中的A/B区室(A/B compartments)、拓扑相关域(topological associated domains, TADs)和染色质环(chromain looping),在此基础上分析了该方向未来可能的研究热点及发展趋势,以期为将基因表达调控的探索从传统线性空间进一步拓展到三维结构空间提供支持。

关键词: 3D基因组学, Hi-C, 生物信息学

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

吕红强, 郝乐乐, 刘二虎, 吴志芳, 韩九强, 刘源. 基于生物信息学的Hi-C研究现状与发展趋势[J]. 遗传, 2020, 42(1): 87-99.

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.

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参考文献 76

[1]	Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES, Dekker J . Comprehensive mapping of long- range interactions reveals folding principles of the human genome. Science, 2009,326(5950):289-293. doi: 10.1126/science.1181369 pmid: 19815776
[2]	Schmitt AD, Hu M, Ren B . Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol, 2016,17(12):743-755. doi: 10.1038/nrm.2016.104 pmid: 27580841
[3]	Li GL, Ruan YJ, Gu RS, Du SM . Emergence of 3D genomics. Chin Sci Bull, 2014,59(13):1165-1172. doi: 10.1360/N972014-00163
	李国亮, 阮一骏, 谷瑞升, 杜生明 . 起航三维基因组学研究. 科学通报, 2014,59(13):1165-1172. doi: 10.1360/N972014-00163
[4]	Dekker J, Rippe K, Dekker M, Kleckner N . Capturing chromosome conformation. Science, 2002,295(5558):1306-1311. doi: 10.1126/science.1067799 pmid: 11847345
[5]	Zhao ZH, Tavoosidana G, Sjölinder M, Göndör A, Mariano P, Wang S, Kanduri C, Lezcano M, Sandhu KS, Singh U, Pant V, Tiwari V, Kurukuti S, Ohlsson R . Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat Genet, 2006,38(11):1341-1347. doi: 10.1038/ng1891 pmid: 17033624
[6]	Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A, Lamb J, Nusbaum C, Green RD, Dekker J . Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res, 2006,16(10):1299-1309. doi: 10.1101/gr.5571506 pmid: 16954542
[7]	Zhang XY, He C, Ye BY, Xie DJ, Shi ML, Zhang Y, Shen WL, Li P, Zhao ZH . Optimization and quality control of genome-wide Hi-C library preparation. Hereditas(Beijing), 2017,39(9):847-855. doi: 10.16288/j.yczz.17-152 pmid: 28936982
	张香媛, 何超, 叶丙雨, 谢德健, 师明磊, 张彦, 沈文龙, 李平, 赵志虎 . 全基因组染色质相互作用Hi-C文库制备的优化及其质量控制. 遗传, 2017,39(9):847-855. doi: 10.16288/j.yczz.17-152 pmid: 28936982
[8]	de Wit E, de Laat W . A decade of 3C technologies: insights into nuclear organization. Genes Dev, 2012,26(1):11-24. doi: 10.1101/gad.179804.111 pmid: 22215806
[9]	Schoenfelder S, Furlan-Magaril M, Mifsud B, Tavares- Cadete F, Sugar R, Javierre BM, Nagano T, Katsman Y, Sakthidevi M, Wingett SW, Dimitrova E, Dimond A, Edelman LB, Elderkin S, Tabbada K, Darbo E, Andrews S, Herman B, Higgs A, LeProust E, Osborne CS, Mitchell JA, Luscombe NM, Fraser P,. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res, 2015,25(4):582-597. doi: 10.1101/gr.185272.114 pmid: 25752748
[10]	Takashi Nagano, Yaniv Lubling, Tim J. Stevens, Stefan Schoenfelder, Eitan Yaffe, Wendy Dean, Ernest D,. Laue, Amos Tanay, Peter Fraser. Single-cell Hi-C reveals cell-to- cell variability in chromosome structure. Nature, 2013,502(7469):59-64. doi: 10.1038/nature12593
[11]	Liang ZY, Li GP, Wang ZJ, Djekidel MN, Li YJ, Qian MP, Zhang MQ, Chen Y . BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions. Nat Commun, 2017,8(1):1622. doi: 10.1038/s41467-017-01754-3 pmid: 29158486
[12]	Lin D, Hong P, Zhang SH, Xu WZ, Jamal M, Yan KJ, Lei YY, Li L, Ruan YJ, Fu Z, Li GL, Cao G . Digestion- ligation-only Hi-C is an efficient and cost-effective method for chromosome conformation capture. Nat Genet, 2018,50(5):754-763. doi: 10.1038/s41588-018-0111-2 pmid: 29700467
[13]	Barrett T, Edgar R . Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Method Enzymol, 2006,411:352-369. doi: 10.1016/S0076-6879(06)11019-8 pmid: 16939800
[14]	Qu HZ, Fang XD . A brief review on the human encyclopedia of DNA elements (encode) project. Genomics Proteomics Bioinformatics, 2013,11(3):135-141. doi: 10.1016/j.gpb.2013.05.001 pmid: 23722115
[15]	Moore D, Dines J, Doss MM, Vepa J, Cheng O, Hain T . Juicer: A weighted finite-state transducer speech decoder. International Workshop on Machine Learning for Multimodal Interaction, 2006,4299:285-296.
[16]	de Wit E, de Laat W . A decade of 3C technologies: insights into nuclear organization. Genes Dev, 2012,26(1):11-24. doi: 10.1101/gad.179804.111 pmid: 22215806
[17]	Shavit Y, Merelli I, Milanesi L, Lio’ P . How computer science can help in understanding the 3D genome architecture. Brief Bioinform, 2016,17(5):733-744. doi: 10.1093/bib/bbv085 pmid: 26433013
[18]	Schmitt AD, Hu M, Ren B . Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol, 2016,17(12):743-755. doi: 10.1038/nrm.2016.104 pmid: 27580841
[19]	Eagen KP . Principles of chromosome architecture revealed by Hi-C. Trends Biochem Sci, 2018,43(6):469-478. doi: 10.1016/j.tibs.2018.03.006 pmid: 29685368
[20]	Zhang XL, Fang H, Wang XW . The progress of methods for analysing 3D genome data. Prog Biochem Biophys, 2018,45(11):1093-1105.
	张祥林, 方欢, 汪小我 . 三维基因组数据分析方法进展. 生物化学与生物物理进展, 2018,45(11):1093-1105.
[21]	Yaffe E, Tanay A . Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat Genet, 2011,43(11):1059-1065. doi: 10.1038/ng.947 pmid: 22001755
[22]	Cournac A, Marie-Nelly H, Marbouty M, Koszul R, Mozziconacci J . Normalization of a chromosomal contact map. BMC Genomics, 2012,13(1):436. doi: 10.1093/nar/gkx644 pmid: 28973466
[23]	Hu M, Deng K, Selvaraj S, Qin ZH, Ren B, Liu JS . HiCNorm: removing biases in Hi-C data via poisson regression. Bioinformatics, 2012,28(23):3131-3133. doi: 10.1093/bioinformatics/bts570
[24]	Imakaev M, Fudenberg F, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA,. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods, 2012,9(10):999-1003. doi: 10.1038/NMETH.2148
[25]	Knight PA, Ruiz D . A fast algorithm for matrix balancing. IMA J Numer Anal, 2013,33(3):1029-1047. doi: 10.1093/imanum/drs019
[26]	Wu HJ, Michor F . A computational strategy to adjust for copy number in tumor Hi-C data. Bioinformatics, 2016,32(24):3695-3701. doi: 10.1093/bioinformatics/btw540 pmid: 27531101
[27]	Stansfield JC, Cresswell KG, Vladimirov VI, Dozmorov MG . HiCcompare: an R-package for joint normalization and comparison of Hi-C datasets. BMC Bioinformatics, 2018,19(1):279. doi: 10.1186/s12859-018-2288-x pmid: 30064362
[28]	Stansfield JC, Cresswell KG, Dozmorov MG,. multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments. Bioinformatics, 2019,35(17):2916-2923. doi: 10.1093/bioinformatics/btz048 pmid: 30668639
[29]	Spill YG, Castillo D, Vidal E, Marti-Renom MA . Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution. Nat Commun, 2019,10(1):1938. doi: 10.1038/s41467-019-09907-2 pmid: 31028255
[30]	Ning CY, He MN, Tang QZ, Zhu Q, Li MZ, Li DY . Advances in mammalian three-dimensional genome by using Hi-C technology approach. Hereditas(Beijing), 2019,41(3):215-233.
	宁椿游, 何梦楠, 唐茜子, 朱庆, 李明洲, 李地艳 . 基于Hi-C技术哺乳动物三维基因组研究进展. 遗传, 2019,41(3):215-233.
[31]	Fortin JP, Hansen KD . Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. Genome Biol, 2015,16(1):180. doi: 10.1186/s13059-015-0741-y pmid: 26316348
[32]	Dong PF, Tu XY, Chu PY, Lu P, Zhu N, Grierson D, Du BJ, Li PH, Zhong SL . 3D chromatin architecture of large plant genomes determined by local A/B compartments. Mol Plant, 2017,10(12):1497-1509. doi: 10.1016/j.molp.2017.11.005 pmid: 29175436
[33]	Miura H, Poonperm R, Takahashi S, Hiratani I . Practical analysis of Hi-C data: generating A/B compartment profiles. Methods Mol Biol, 2018: 221-245. doi: 10.1007/978-1-0716-0247-8_19 pmid: 31939184
[34]	Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, Hu M, Liu JS, Ren B . Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 2012,485(7398):376-380. doi: 10.1038/nature11082
[35]	Phillips-Cremins JE, Sauria MEG, Sanyal A, Gerasimova TI, Lajoie BR, Bell JSK, Ong CT, Hookway TA, Guo CY, Sun YH, Bland NJ, Wagstaff W, Dalton S, McDevitt TC, Sen R, Dekker J, Taylor J, Corces VG,. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell, 2013,153(6):1281-1295. doi: 10.1016/j.cell.2013.04.053
[36]	Pope BD, Ryba T, Dileep V, Yue F, Wu WS, Denas O, Vera DL, Wang YL, Hansen RS, Canfield TK, Thurman RE, Cheng Y, Gülsoy G, Dennis JH, Snyder MP, Stamatoyannopoulos JA, Taylor J, Hardison RC, Kahveci T, Ren B, Gilbert DM . Topologically associating domains are stable units of replication-timing regulation. Nature, 2014,515(7527):402-405. doi: 10.1038/nature13986
[37]	Narendra V, Bulajić M, Dekker J, Mazzoni EO, Reinberg D . Corrigendum: CTCF-mediated topological boundaries during development foster appropriate gene regulation. Genes Dev, 2016,30(24):2657-2662. doi: 10.1101/gad.288324.116 pmid: 28087711
[38]	Lévy-Leduc C, Delattre M, Mary-Huard T, Robin S . Two- dimensional segmentation for analyzing Hi-C data. Bioinformatics, 2014,30(17):i386-i392. doi: 10.1093/bioinformatics/btu443
[39]	Shin HJ, Shi Y, Dai C, Tjong H, Gong K, Alber F, Zhou XJ . TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res, 2015,44(7):e70. doi: 10.1093/nar/gkv1505 pmid: 26704975
[40]	Weinreb C, Raphael BJ . Identification of hierarchical chromatin domains. Bioinformatics, 2016,32(11):1601-1609. doi: 10.1093/bioinformatics/btv485 pmid: 26315910
[41]	Serra F, Baù D, Goodstadt M, Castillo D, Filion GJ, Marti-Renom MA . Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors. PLoS Comput Biol, 2017,13(7):e1005665. doi: 10.1371/journal.pcbi.1005665 pmid: 28723903
[42]	Wang XT, Cui W, Peng C . HiTAD: detecting the structural and functional hierarchies of topologically associating domains from chromatin interactions. Nucleic Acids Res, 2017,45(19):e163. doi: 10.1093/nar/gkx735 pmid: 28977529
[43]	Haddad N, Vaillant C, Jost D . IC-Finder: inferring robustly the hierarchical organization of chromatin folding. Nucleic Acids Res, 2017,45(10):e81. doi: 10.1093/nar/gkx036 pmid: 28130423
[44]	Yu WB, He B, Tan K . Identifying topologically associating domains and subdomains by gaussian mixture model and proportion test. Nat Commun, 2017,8(1):535. doi: 10.1038/s41467-017-00478-8 pmid: 28912419
[45]	Norton HK, Emerson DJ, Huang H, Kim J, Titus KR, Gu S, Bassett DS, Phillips-Cremins JE . Detecting hierarchical genome folding with network modularity. Nat Methods, 2018,15(2):119-122. doi: 10.1038/nmeth.4560 pmid: 29334377
[46]	Li AS, Yin XC, Xu BX, Wang DY, Han JM, Wei Y, Deng Y, Xiong Y, Zhang ZH . Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy. Nat Commun, 2018,9(1):3265. doi: 10.1038/s41467-018-05691-7 pmid: 30111883
[47]	Chen FL, Li GP, Zhang MQ, Chen Y . HiCDB: a sensitive and robust method for detecting contact domain boundaries. Nucleic Acids Res, 2018,46(21):11239-11250. doi: 10.1093/nar/gky789 pmid: 30184171
[48]	Sexton T, Bantignies F, Cavalli G . Genomic interactions: chromatin loops and gene meeting points in transcriptional regulation. Semin Cell Dev Biol, 2009,20(7):849-855. doi: 10.1016/j.semcdb.2009.06.004
[49]	Lu YL, Zhou YP, Tian WD . Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome. Nucleic Acids Res, 2013,41(22):10391-10402. doi: 10.1093/nar/gkt785 pmid: 24003029
[50]	Ay F, Bailey TL, Noble WS . Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res, 2014,24(6):999-1011. doi: 10.1101/gr.160374.113
[51]	Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL . A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 2014,159(7):1665-1680. doi: 10.1016/j.cell.2014.11.021
[52]	Hwang YC, Lin CF, Valladares O, Malamon J, Kuksa PP, Zheng Q, Gregory BD, Wang LS . HIPPIE: a high-throughput identification pipeline for promoter interacting enhancer elements. Bioinformatics, 2014,31(8):1290-1292. doi: 10.1093/bioinformatics/btu801 pmid: 25480377
[53]	Lun ATL, Smyth GK,. diffHic: a bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics, 2015,16(1):258. doi: 10.1186/s12870-016-0945-7 pmid: 27905870
[54]	Zhang H, Li FF, Jia Y, Xu BX, Zhang YQ, Li XL, Zhang ZH . Characteristic arrangement of nucleosomes is predictive of chromatin interactions at kilobase resolution. Nucleic Acids Res, 2017,45(22):12739-12751. doi: 10.1093/nar/gkx885 pmid: 29036650
[55]	Djekidel MN, Chen Y, Zhang MQ . FIND: difFerential chromatin interactions detection using a spatial poisson process. Genome Res, 2018,28(3):412-422. doi: 10.1101/gr.212241.116 pmid: 29440282
[56]	Manduchi E, Chesi A, Hall MA, Grant SFA, Moore JH . Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in type 2 diabetes GWAS. Pac Symp Biocomput, 2018,23:548-558. pmid: 29218913
[57]	Zhou X, Lowdon RF, Li DF, Lawson HA, Madden PAF, Costello JF, Wang T . Exploring long-range genome interactions using the WashU epigenome browser. Nat Methods, 2013,10(5):375-376. doi: 10.1038/nmeth.2440 pmid: 23629413
[58]	Akdemir KC, Chin L . HiCPlotter integrates genomic data with interaction matrices. Genome Biol, 2015,16(1):198. doi: 10.1186/s13059-015-0767-1 pmid: 26392354
[59]	Li RF, Liu YY, Li TT, Li C . 3Disease Browser: a web server for integrating 3D genome and disease-associated chromosome rearrangement data. Sci Rep, 2016,6:34651. doi: 10.1038/srep34651 pmid: 27734896
[60]	Durand NC, Robinson JT, Shamim MS, Machol I, Mesirov JP, Lander ES, Aiden EL . Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst, 2016,3(1):99-101. doi: 10.1016/j.cels.2015.07.012 pmid: 27467250
[61]	Djekidel MN, Wang MJ, Zhang MQ, Gao JT . HiC- 3DViewer: a new tool to visualize Hi-C data in 3D space. Quant Biol, 2017,5(2):183-190. doi: 10.1007/s40484-017-0091-8
[62]	Tang BX, Li FF, Li J, Zhao WM, Zhang ZH . Delta: a new web-based 3D genome visualization and analysis platform. Bioinformatics, 2017,34(8):1409-1410. doi: 10.1093/bioinformatics/btx805 pmid: 29253110
[63]	Calandrelli R, Wu QY, Guan JH, Zhong S . GITAR: an open source tool for analysis and visualization of Hi-C data. Genomics, Proteomics & Bioinformatics, 2018,16(5):365-372. doi: 10.1016/j.cbd.2020.100654 pmid: 31954363
[64]	Wang YL, Song F, Zhang B, Zhang LJ, Xu J, Kuang D, Li DF, Choudhary MNK, Li Y, Hu M, Hardison R, Wang T, Yue F . The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol, 2018,19(1):151. doi: 10.1186/s13059-018-1519-9 pmid: 30286773
[65]	Wolff J, Bhardwaj V, Nothjunge S, Richard G, Renschler G, Gilsbach R, Manke T, Backofen R, Ramírez F, Grüning BA . Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization. Nucleic Acids Res, 2018,46(W1):W11-W16. doi: 10.1093/nar/gky504 pmid: 29901812
[66]	Rousseau M, Fraser J, Ferraiuolo MA, Dostie J, Blanchette M . Three-dimensional modeling of chromatin structure from interaction frequency data using markov chain monte carlo sampling. BMC Bioinformatics, 2011,12(1):414. doi: 10.1186/1471-2105-12-414 pmid: 22026390
[67]	Zhang ZZ, Li GL, Toh KC, Sung WK . Inference of spatial organizations of chromosomes using semi-definite embedding approach and Hi-C data. Annual International Conference on Research in Computational Molecular Biology, 2013: 317-332.
[68]	Peng C, Fu LY, Dong PF, Deng ZL, Li JX, Wang XT, Zhang HY . The sequencing bias relaxed characteristics of Hi-C derived data and implications for chromatin 3D modeling. Nucleic Acids Res, 2013,41(19):e183. doi: 10.1093/nar/gkt745 pmid: 23965308
[69]	Trieu T, Cheng JL . MOGEN: a tool for reconstructing 3D models of genomes from chromosomal conformation capturing data. Bioinformatics, 2015,32(9):1286-1292. doi: 10.1093/bioinformatics/btv754 pmid: 26722115
[70]	Paulsen J, Sekelja M, Oldenburg AR, Barateau A, Briand N, Delbarre E, Shah A, Sørensen AL, Vigouroux C, Buendia B, Collas P . Chrom3D: three-dimensional genome modeling from Hi-C and nuclear lamin-genome contacts. Genome Biol, 2017,18(1):21. doi: 10.1186/s13059-016-1146-2 pmid: 28137286
[71]	Segal MR, Bengtsson HL . Improved accuracy assessment for 3D genome reconstructions. BMC Bioinformatics, 2018,19(1):196. doi: 10.1186/s12859-018-2214-2 pmid: 29848293
[72]	Zhu GX, Deng WX, Hu HL, Ma R, Zhang S, Yang JL, Peng J, Kaplan T, Zeng JY . Reconstructing spatial organizations of chromosomes through manifold learning. Nucleic Acids Res, 2018,46(8):e50. doi: 10.1093/nar/gky065 pmid: 29408992
[73]	Fraser J, Ferrai C, Chiariello AM, Schueler M, Rito T, Laudanno G, Barbieri M, Moore BL, Kraemer DCA, Aitken S, Xie SQ, Morris KJ, Itoh M, Kawaji H, Jaeger I, Hayashizaki Y, Carninci P, Forrest ARR, Semple CA, Dostie J, Pombo A, Nicodemi N . Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol Syst Biol, 2015,11(12):852. doi: 10.15252/msb.20156492 pmid: 26700852
[74]	Liu T, Wang Z. scHiCNorm: a software package to eliminate systematic biases in single-cell Hi-C data. Bioinformatics, 2017,34(6):1046-1047. doi: 10.1093/bioinformatics/btx747 pmid: 29186290
[75]	Liu ST, Stroncek DF, Zhao YD, Chen V, Shi RY, Chen JG, Ren JQ, Liu H, Bae HJ, Highfill SL, Jin P . Single cell sequencing reveals gene expression signatures associated with bone marrow stromal cell subpopulations and time in culture. . Transl Med, 2019,17(1):23. doi: 10.1186/s12967-018-1766-2 pmid: 30635013
[76]	Wang Q, Sun Q, Czajkowsky DM, Shao ZF . Sub-kb Hi-C in D. melanogaster reveals conserved characteristics of TADs between insect and mammalian cells. Nat Commun, 2018,9(1):188. doi: 10.1038/s41467-017-02526-9 pmid: 29335463

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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

基于生物信息学的Hi-C研究现状与发展趋势

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

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