Advances in computational ChIA-PET data analysis

Abstract

Genome-wide chromatin interaction analysis has become important for understanding 3D topological structure of a genome as well as for linking distal cis-regulatory elements to their target genes. Compared to the Hi-C method, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is unique, in that one can interrogate thousands of chromatin interactions (in a genome) mediated by a specific protein of interest at high resolution and reasonable cost. However, because of the noisy nature of the data, efficient analytical tools have become necessary. Here, we review some new computational methods recently developed by us and compare them with other existing methods. Our intention is to help readers to better understand ChIA-PET results and to guide the users on selection of the most appropriate tools for their own projects.

Graphical abstract

References

[1] Fullwood, M. J., Liu, M. H., Pan, Y. F., Liu, J., Xu, H., Mohamed, Y. B., Orlov, Y. L., Velkov, S., Ho, A., Mei, P. H., (2009) An oestrogen-receptor-alpha-bound human chromatin interactome. Nature, 462, 58–64

[2] Li, G., Ruan, X., Auerbach, R. K., Sandhu, K. S., Zheng, M., Wang, P., Poh, H. M., Goh, Y., Lim, J., Zhang, J., (2012) Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell, 148, 84–98

[3] Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326, 289–293.

[4] Rao, S. S., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., (2014) A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159, 1665–1680.

[5] He, C., Zhang, M. Q. and Wang, X. (2015) MICC: an R package for identifying chromatin interactions from ChIA-PET data. Bioinformatics, 31, 3832–3834

[6] He, C., Wang, X. and Zhang, M. Q. (2014) Nucleosome eviction and multiple co-factor binding predict estrogen-receptor-alpha-associated long-range interactions. Nucleic Acids Res., 42, 6935–6944

[7] Djekidel, M. N., Liang, Z., Wang, Q., Hu, Z., Li, G., Chen, Y. and Zhang, M. Q. (2015) 3CPET: finding co-factor complexes from ChIA-PET data using a hierarchical Dirichlet process. Genome Biol., 16, 288

[8] Tang, Z., Luo, O. J., Li, X., Zheng, M., Zhu, J. J., Szalaj, P., Trzaskoma, P., Magalska, A., Wlodarczyk, J., Ruszczycki, B., (2015) CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell, 163, 1611–1627

[9] Li, G., Fullwood, M. J., Xu, H., Mulawadi, F. H., Velkov, S., Vega, V., Ariyaratne, P. N., Mohamed, Y. B., Ooi, H. S., Tennakoon, C., (2010) ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol., 11, R22

[10] Paulsen, J., Rødland, E. A., Holden, L., Holden, M. and Hovig, E. (2014) A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions. Nucleic Acids Res., 42, e143

[11] Phanstiel, D. H., Boyle, A. P., Heidari, N. and Snyder, M. P. (2015) Mango: a bias-correcting ChIA-PET analysis pipeline. Bioinformatics, 31, 3092–3098

[12] Heyse, J. (2011) A false discovery rate procedure for categorical data. In Resent Advances in Biostatistics: False Discovery Rates, Survival Analysis, and Related Topics, 43–58, World Scientific Publishing Company

[13] Benjamini YaH, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol. 57, 289–300

[14] Jessen, B. and Wintner, A. (1935) Distribution functions and the Riemann ZETA function. Trans. Am. Math. Soc., 38, 48–88

[15] Sanyal, A., Lajoie, B. R., Jain, G. and Dekker, J. (2012) The long-range interaction landscape of gene promoters. Nature, 489, 109–113

[16] Fullwood, M. J., Wei, C. L., Liu, E. T. and Ruan, Y. (2009) Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res., 19, 521–532

[17] He, H. H., Meyer, C. A., Chen, M. W., Jordan, V. C., Brown, M. and Liu, X. S. (2012) Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics. Genome Res., 22, 1015–1025

[18] Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S. and Ren, B. (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485, 376–380

[19] Marsman J., Horsfield, J.(2012) Long distance relationships: enhancer–promoter communication and dynamic gene transcription. Biochim. Biophys. Acta, 1819:1217–1227

[20] Phillips-Cremins, J. E., Sauria, M. E., Sanyal, A., Gerasimova, T. I., Lajoie, B. R., Bell, J. S., Ong, C. T., Hookway, T. A., Guo, C., Sun, Y., (2013) Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell, 153, 1281–1295

[21] Kagey, M. H., Newman, J. J., Bilodeau, S., Zhan, Y., Orlando, D. A., van Berkum, N. L., Ebmeier, C. C., Goossens, J., Rahl, P. B., Levine, S. S., (2010) Mediator and cohesin connect gene expression and chromatin architecture. Nature, 467, 430–435

[22] Lan, X., Witt, H., Katsumura, K., Ye, Z., Wang, Q., Bresnick, E. H., Farnham, P. J. and Jin, V. X. (2012) Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages. Nucleic Acids Res., 40, 7690–7704

[23] Deng, W., Lee, J., Wang, H., Miller, J., Reik, A., Gregory, P. D., Dean, A. and Blobel, G. A. (2012) Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell, 149, 1233–1244

[24] Teha, Y.W., Jordana, M. I., Beala, M. J. and Bleia, D. M. ( 2006) Hierarchical Dirichlet processes. J. Am. Stat. Assoc., 101, 1566–1581

[25] Mohammed, H., D’Santos, C., Serandour, A. A., Ali, H. R., Brown, G. D., Atkins, A., Rueda, O. M., Holmes, K. A., Theodorou, V., Robinson, J. L., (2013) Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor. Cell Reports, 3, 342–349

[26] Li, M. J., Wang, L.Y., Xia, Z., Sham, P.C., Wang, J. (2013) GWAS3D: Detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucl. Acids Res. 41, W150–W158

[27] Grubert, F., Zaugg, J. B., Kasowski, M., Ursu, O., Spacek, D. V., Martin, A. R., Greenside, P., Srivas, R., Phanstiel, D. H., Pekowska, A., (2015) Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell, 162, 1051–1065

[28] Higgins, G. A., Allyn-Feuer, A. and Athey, B. D. (2015) Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response. Pharmacogenomics, 16, 1565–1583

[29] Smemo, S., Tena, J. J., Kim, K. H., Gamazon, E. R., Sakabe, N. J., Gómez-Marín, C., Aneas, I., Credidio, F. L., Sobreira, D. R., Wasserman, N. F., (2014) Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature, 507, 371–375

[30] Hnisz, D., Weintraub, A. S., Day, D. S., Valton, A. L., Bak, R. O., Li, C. H., Goldmann, J., Lajoie, B. R., Fan, Z. P., Sigova, A. A., (2016) Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science, 351, 1454–1458

[31] Heidari, N., Phanstiel, D. H., He, C., Grubert, F., Jahanbani, F., Kasowski, M., Zhang, M. Q. and Snyder, M. P. (2014) Genome-wide map of regulatory interactions in the human genome. Genome Res., 24, 1905–1917

[32] Li, G., Cai, L., Chang, H., Hong, P., Zhou, Q., Kulakova, E. V., Kolchanov, N. A. and Ruan, Y. (2014) Chromatin interaction analysis with paired-end tag (ChIA-PET) sequencing technology and application. BMC Genomics, 15, S11

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