Applications of integrative OMICs approaches to gene regulation studies
Jing Qin, Bin Yan, Yaohua Hu, Panwen Wang, Junwen Wang
Applications of integrative OMICs approaches to gene regulation studies
Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.
Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms.
Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.
gene regulatory networks / integrative analysis / OMICs / ChIP-seq / RNA-seq
[1] |
Lee, K. L., Lim, S. K., Orlov, Y. L., Yit, Y., Yang, H., Ang, L. T., Poellinger, L. and Lim, B. (2011) Graded Nodal/Activin signaling titrates conversion of quantitative phospho-Smad2 levels into qualitative embryonic stem cell fate decisions. PLoS Genet., 7, e1002130
CrossRef
Pubmed
Google scholar
|
[2] |
Newman, R. H., Hu, J., Rho, H. S., Xie, Z., Woodard, C., Neiswinger, J., Cooper, C., Shirley, M., Clark, H. M., Hu, S.,
CrossRef
Pubmed
Google scholar
|
[3] |
Gavin, A. C., Bösche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A. M., Cruciat, C. M.,
CrossRef
Pubmed
Google scholar
|
[4] |
Fields, S. and Song, O. (1989) A novel genetic system to detect protein-protein interactions. Nature, 340, 245–246
CrossRef
Pubmed
Google scholar
|
[5] |
Chen, T. and Dent, S. Y. (2014) Chromatin modifiers and remodellers: regulators of cellular differentiation. Nat. Rev. Genet., 15, 93–106
CrossRef
Pubmed
Google scholar
|
[6] |
Dekker, J., Marti-Renom, M. A. and Mirny, L. A. (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet., 14, 390–403
CrossRef
Pubmed
Google scholar
|
[7] |
Witten, J. T. and Ule, J. (2011) Understanding splicing regulation through RNA splicing maps. Trends Genet., 27, 89–97
CrossRef
Pubmed
Google scholar
|
[8] |
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. and Weissman, J. S. (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science, 324, 218–223
CrossRef
Pubmed
Google scholar
|
[9] |
Geisler, S. and Coller, J. (2013) RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat. Rev. Mol. Cell Biol., 14, 699–712
CrossRef
Pubmed
Google scholar
|
[10] |
Chen, L. L. (2016) The biogenesis and emerging roles of circular RNAs. Nat. Rev. Mol. Cell Biol., 17, 205–211
CrossRef
Pubmed
Google scholar
|
[11] |
Quinn, J. J., Ilik, I. A., Qu, K., Georgiev, P., Chu, C., Akhtar, A. and Chang, H. Y. (2014) Revealing long noncoding RNA architecture and functions using domain-specific chromatin isolation by RNA purification. Nat. Biotechnol., 32, 933–940
CrossRef
Pubmed
Google scholar
|
[12] |
Di Ruscio, A., Ebralidze, A. K., Benoukraf, T., Amabile, G., Goff, L. A., Terragni, J., Figueroa, M. E., De Figueiredo Pontes, L. L., Alberich-Jorda, M., Zhang, P.,
CrossRef
Pubmed
Google scholar
|
[13] |
Gómez-Orte, E., Sáenz-Narciso, B., Moreno, S. and Cabello, J. (2013) Multiple functions of the noncanonical Wnt pathway. Trends Genet., 29, 545–553
CrossRef
Pubmed
Google scholar
|
[14] |
Liang, J., Wan, M., Zhang, Y., Gu, P., Xin, H., Jung, S. Y., Qin, J., Wong, J., Cooney, A. J., Liu, D.,
CrossRef
Pubmed
Google scholar
|
[15] |
Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M. and Sakaki, Y. (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA, 98, 4569–4574
CrossRef
Pubmed
Google scholar
|
[16] |
Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., Kafri, R., Kirschner, M. W., Clish, C. B. and Mootha, V. K. (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science, 336, 1040–1044
CrossRef
Pubmed
Google scholar
|
[17] |
Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680
CrossRef
Pubmed
Google scholar
|
[18] |
Rhee, H. S. and Pugh, B. F. (2012) ChIP-exo method for identifying genomic location of DNA-binding proteins with near-single-nucleotide accuracy. In Current Protocols In Molecular Biology, Chapter 21, Unit 21–24. Wiley
|
[19] |
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q. M.,
CrossRef
Pubmed
Google scholar
|
[20] |
Ball, M. P., Li, J. B., Gao, Y., Lee, J. H., LeProust, E. M., Park, I. H., Xie, B., Daley, G. Q. and Church, G. M. (2009) Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol., 27, 361–368
CrossRef
Pubmed
Google scholar
|
[21] |
Pelizzola, M., Koga, Y., Urban, A. E., Krauthammer, M., Weissman, S., Halaban, R. and Molinaro, A. M. (2008) MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res., 18, 1652–1659
CrossRef
Pubmed
Google scholar
|
[22] |
Meissner, A., Gnirke, A., Bell, G. W., Ramsahoye, B., Lander, E. S. and Jaenisch, R. (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res., 33, 5868–5877
CrossRef
Pubmed
Google scholar
|
[23] |
Edwards, J. R., O’Donnell, A. H., Rollins, R. A., Peckham, H. E., Lee, C., Milekic, M. H., Chanrion, B., Fu, Y., Su, T., Hibshoosh, H.,
CrossRef
Pubmed
Google scholar
|
[24] |
He, H. H., Meyer, C. A., Hu, S. S., Chen, M. W., Zang, C., Liu, Y., Rao, P. K., Fei, T., Xu, H., Long, H.,
CrossRef
Pubmed
Google scholar
|
[25] |
Auerbach, R. K., Euskirchen, G., Rozowsky, J., Lamarre-Vincent, N., Moqtaderi, Z., Lefrançois, P., Struhl, K., Gerstein, M. and Snyder, M. (2009) Mapping accessible chromatin regions using Sono-Seq. Proc. Natl. Acad. Sci. USA, 106, 14926–14931
CrossRef
Pubmed
Google scholar
|
[26] |
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. and Greenleaf, W. J. (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods, 10, 1213–1218
CrossRef
Pubmed
Google scholar
|
[27] |
Gaulton, K. J., Nammo, T., Pasquali, L., Simon, J. M., Giresi, P. G., Fogarty, M. P., Panhuis, T. M., Mieczkowski, P., Secchi, A., Bosco, D.,
CrossRef
Pubmed
Google scholar
|
[28] |
You, J. S., Kelly, T. K., De Carvalho, D. D., Taberlay, P. C., Liang, G. and Jones, P. A. (2011) OCT4 establishes and maintains nucleosome-depleted regions that provide additional layers of epigenetic regulation of its target genes. Proc. Natl. Acad. Sci. USA, 108, 14497–14502
CrossRef
Pubmed
Google scholar
|
[29] |
Schones, D. E., Cui, K., Cuddapah, S., Roh, T. Y., Barski, A., Wang, Z., Wei, G. and Zhao, K. (2008) Dynamic regulation of nucleosome positioning in the human genome. Cell, 132, 887–898
CrossRef
Pubmed
Google scholar
|
[30] |
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. and Wold, B. (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods, 5, 621–628
CrossRef
Pubmed
Google scholar
|
[31] |
Schena, M., Shalon, D., Davis, R. W. and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467–470
CrossRef
Pubmed
Google scholar
|
[32] |
Core, L. J., Waterfall, J. J. and Lis, J. T. (2008) Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science, 322, 1845–1848
CrossRef
Pubmed
Google scholar
|
[33] |
Chi, S. W., Zang, J. B., Mele, A. and Darnell, R. B. (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 460, 479–486
Pubmed
|
[34] |
German, M. A., Pillay, M., Jeong, D. H., Hetawal, A., Luo, S., Janardhanan, P., Kannan, V., Rymarquis, L. A., Nobuta, K., German, R.,
CrossRef
Pubmed
Google scholar
|
[35] |
Helwak, A., Kudla, G., Dudnakova, T. and Tollervey, D. (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell, 153, 654–665
CrossRef
Pubmed
Google scholar
|
[36] |
Ding, Y., Tang, Y., Kwok, C. K., Zhang, Y., Bevilacqua, P. C. and Assmann, S. M. (2014)In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 505, 696–700
CrossRef
Pubmed
Google scholar
|
[37] |
Pinkel, D., Segraves, R., Sudar, D., Clark, S., Poole, I., Kowbel, D., Collins, C., Kuo, W. L., Chen, C., Zhai, Y.,
CrossRef
Pubmed
Google scholar
|
[38] |
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R.,
CrossRef
Pubmed
Google scholar
|
[39] |
Ng, S. B., Turner, E. H., Robertson, P. D., Flygare, S. D., Bigham, A. W., Lee, C., Shaffer, T., Wong, M., Bhattacharjee, A., Eichler, E. E.,
CrossRef
Pubmed
Google scholar
|
[40] |
Krüger, M., Moser, M., Ussar, S., Thievessen, I., Luber, C. A., Forner, F., Schmidt, S., Zanivan, S., Füssler, R. and Mann, M. (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell, 134, 353–364
CrossRef
Pubmed
Google scholar
|
[41] |
Kislinger, T., Rahman, K., Radulovic, D., Cox, B., Rossant, J. and Emili, A. (2003) PRISM, a generic large scale proteomic investigation strategy for mammals. Mol. Cell. Proteomics, 2, 96–106
CrossRef
Pubmed
Google scholar
|
[42] |
Zhou, F., Lu, Y., Ficarro, S. B., Adelmant, G., Jiang, W., Luckey, C. J. and Marto, J. A. (2013) Genome-scale proteome quantification by DEEP SEQ mass spectrometry. Nat. Commun., 4, 2171
CrossRef
Pubmed
Google scholar
|
[43] |
Jewison, T., Su, Y., Disfany, F. M., Liang, Y., Knox, C., Maciejewski, A., Poelzer, J., Huynh, J., Zhou, Y., Arndt, D.,
CrossRef
Pubmed
Google scholar
|
[44] |
Song, C., Ye, M., Liu, Z., Cheng, H., Jiang, X., Han, G., Songyang, Z., Tan, Y., Wang, H., Ren, J.,
CrossRef
Pubmed
Google scholar
|
[45] |
Meissner, A., Mikkelsen, T. S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B. E., Nusbaum, C., Jaffe, D. B.,
Pubmed
|
[46] |
Neph, S., Vierstra, J., Stergachis, A. B., Reynolds, A. P., Haugen, E., Vernot, B., Thurman, R. E., John, S., Sandstrom, R., Johnson, A. K.,
CrossRef
Pubmed
Google scholar
|
[47] |
Dunham, I., Kundaje, A., Aldred, S. F., Collins, P. J., Davis, C. A., Doyle, F., Epstein, C. B., Frietze, S., Harrow, J., Kaul, R.,
CrossRef
Pubmed
Google scholar
|
[48] |
Ramaswami, G., Zhang, R., Piskol, R., Keegan, L. P., Deng, P., O’Connell, M. A. and Li, J. B. (2013) Identifying RNA editing sites using RNA sequencing data alone. Nat. Methods, 10, 128–132
CrossRef
Pubmed
Google scholar
|
[49] |
Ramaswami, G., Lin, W., Piskol, R., Tan, M. H., Davis, C. and Li, J. B. (2012) Accurate identification of human Alu and non-Alu RNA editing sites. Nat. Methods, 9, 579–581
CrossRef
Pubmed
Google scholar
|
[50] |
Fu, X. D. and Ares, M. Jr. (2014) Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet., 15, 689–701
CrossRef
Pubmed
Google scholar
|
[51] |
Wang, Z., Gerstein, M. and Snyder, M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57–63
CrossRef
Pubmed
Google scholar
|
[52] |
Yang, L., Duff, M. O., Graveley, B. R., Carmichael, G. G. and Chen, L. L. (2011) Genomewide characterization of non-polyadenylated RNAs. Genome Biol., 12, R16
CrossRef
Pubmed
Google scholar
|
[53] |
Yang, J. H., Li, J. H., Shao, P., Zhou, H., Chen, Y. Q. and Qu, L. H. (2011) starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res., 39, D202–D209
CrossRef
Pubmed
Google scholar
|
[54] |
Lau, E. (2014) Non-coding RNA: zooming in on lncRNA functions. Nat. Rev. Genet., 15, 574–575
CrossRef
Pubmed
Google scholar
|
[55] |
Venø, M. T., Hansen, T. B., Venø, S. T., Clausen, B. H., Grebing, M., Finsen, B., Holm, I. E. and Kjems, J. (2015) Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol., 16, 245
CrossRef
Pubmed
Google scholar
|
[56] |
Gao, Y., Wang, J. and Zhao, F. (2015) CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol., 16, 4
CrossRef
Pubmed
Google scholar
|
[57] |
Salzman, J., Gawad, C., Wang, P. L., Lacayo, N. and Brown, P. O. (2012) Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One, 7, e30733
CrossRef
Pubmed
Google scholar
|
[58] |
Qin, Y., Yalamanchili, H.K., Qin, J., Yan, B. and Wang, J. (2015) The current status and challenges in computational analysis of genomic big data. Big data research, 2, 12–18
|
[59] |
Kluger, Y., Yu, H., Qian, J. and Gerstein, M. (2003) Relationship between gene co-expression and probe localization on microarray slides. BMC Genomics, 4, 49
CrossRef
Pubmed
Google scholar
|
[60] |
Robasky, K., Lewis, N. E. and Church, G. M. (2014) The role of replicates for error mitigation in next-generation sequencing. Nat. Rev. Genet., 15, 56–62
CrossRef
Pubmed
Google scholar
|
[61] |
Teytelman, L., Thurtle, D. M., Rine, J. and van Oudenaarden, A. (2013) Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Proc. Natl. Acad. Sci. USA, 110, 18602–18607
CrossRef
Pubmed
Google scholar
|
[62] |
von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S. G., Fields, S. and Bork, P. (2002) Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417, 399–403
CrossRef
Pubmed
Google scholar
|
[63] |
El Gazzar, M. and McCall, C. E. (2012) MicroRNAs regulatory networks in myeloid lineage development and differentiation: regulators of the regulators. Immunol. Cell Biol., 90, 587–593
CrossRef
Pubmed
Google scholar
|
[64] |
Hudson, W. H. and Ortlund, E. A. (2014) The structure, function and evolution of proteins that bind DNA and RNA. Nat. Rev. Mol. Cell Biol., 15, 749–760
CrossRef
Pubmed
Google scholar
|
[65] |
Poliseno, L., Salmena, L., Zhang, J., Carver, B., Haveman, W. J. and Pandolfi, P. P. (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 465, 1033–1038
CrossRef
Pubmed
Google scholar
|
[66] |
Hansen, T. B., Jensen, T. I., Clausen, B. H., Bramsen, J. B., Finsen, B., Damgaard, C. K. and Kjems, J. (2013) Natural RNA circles function as efficient microRNA sponges. Nature, 495, 384–388
CrossRef
Pubmed
Google scholar
|
[67] |
modEncode Consortium, Roy, S., Ernst, J.J., Kharchenko, P.V., Kheradpour,
CrossRef
Google scholar
|
[68] |
Nègre, N., Brown, C. D., Ma, L., Bristow, C. A., Miller, S. W., Wagner, U., Kheradpour, P., Eaton, M. L., Loriaux, P., Sealfon, R.,
CrossRef
Pubmed
Google scholar
|
[69] |
Gerstein, M. B., Lu, Z. J., Van Nostrand, E. L., Cheng, C., Arshinoff, B. I., Liu, T., Yip, K. Y., Robilotto, R., Rechtsteiner, A., Ikegami, K.,
CrossRef
Pubmed
Google scholar
|
[70] |
Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., Ziller, M. J.,
CrossRef
Pubmed
Google scholar
|
[71] |
Bell, D., Berchuck, A., Birrer, M., Chien, J., Cramer, D. W., Dao, F., Dhir, R., DiSaia, P., Gabra, H., Glenn, P.,
CrossRef
Pubmed
Google scholar
|
[72] |
Bass, A. J., Thorsson, V., Shmulevich, I., Reynolds, S. M., Miller, M., Bernard, B., Hinoue, T., Laird, P. W., Curtis, C., Shen, H.,
CrossRef
Pubmed
Google scholar
|
[73] |
Xu, H., Baroukh, C., Dannenfelser, R., Chen, E.Y., Tan, C. M., Kou, Y., Kim, Y. E., Lemischka, I. R. and Ma’ayan, A. (2013) ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database, 2013, bat045
CrossRef
Google scholar
|
[74] |
Marygold, S. J., Leyland, P. C., Seal, R. L., Goodman, J. L., Thurmond, J., Strelets, V. B. and Wilson, R. J., and the FlyBase consortium. (2013) FlyBase: improvements to the bibliography. Nucleic Acids Res., 41, D751–D757
CrossRef
Pubmed
Google scholar
|
[75] |
Costanzo, M. C., Engel, S. R., Wong, E. D., Lloyd, P., Karra, K., Chan, E. T., Weng, S., Paskov, K. M., Roe, G. R., Binkley, G.,
CrossRef
Pubmed
Google scholar
|
[76] |
Phanstiel, D. H., Brumbaugh, J., Wenger, C. D., Tian, S., Probasco, M. D., Bailey, D. J., Swaney, D. L., Tervo, M. A., Bolin, J. M., Ruotti, V.,
CrossRef
Pubmed
Google scholar
|
[77] |
Swarbreck, D., Wilks, C., Lamesch, P., Berardini, T. Z., Garcia-Hernandez, M., Foerster, H., Li, D., Meyer, T., Muller, R., Ploetz, L.,
CrossRef
Pubmed
Google scholar
|
[78] |
Suzuki, A., Wakaguri, H., Yamashita, R., Kawano, S., Tsuchihara, K., Sugano, S., Suzuki, Y. and Nakai, K. (2015) DBTSS as an integrative platform for transcriptome, epigenome and genome sequence variation data. Nucleic Acids Res., 43, D87–D91
CrossRef
Pubmed
Google scholar
|
[79] |
Sun, H., Wang, H., Zhu, R., Tang, K., Gong, Q., Cui, J., Cao, Z. and Liu, Q. (2014) iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics, 30, 737–739
CrossRef
Pubmed
Google scholar
|
[80] |
Bebek, G. and Yang, J. (2007) PathFinder: mining signal transduction pathway segments from protein-protein interaction networks. BMC Bioinformatics, 8, 335
CrossRef
Pubmed
Google scholar
|
[81] |
Myers, C. L., Robson, D., Wible, A., Hibbs, M. A., Chiriac, C., Theesfeld, C. L., Dolinski, K. and Troyanskaya, O. G. (2005) Discovery of biological networks from diverse functional genomic data. Genome Biol., 6, R114
CrossRef
Pubmed
Google scholar
|
[82] |
Ourfali, O., Shlomi, T., Ideker, T., Ruppin, E. and Sharan, R. (2007) SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics, 23, i359–i366
CrossRef
Pubmed
Google scholar
|
[83] |
Basha, O., Tirman, S., Eluk, A. and Yeger-Lotem, E. (2013) ResponseNet2.0: revealing signaling and regulatory pathways connecting your proteins and genes—now with human data. Nucleic Acids Res., 41, W198–W203
CrossRef
Pubmed
Google scholar
|
[84] |
Lan, A., Smoly, I. Y., Rapaport, G., Lindquist, S., Fraenkel, E. and Yeger-Lotem, E. (2011) ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res., 39, W424–W429
CrossRef
Pubmed
Google scholar
|
[85] |
Wang, K., Saito, M., Bisikirska, B. C., Alvarez, M. J., Lim, W. K., Rajbhandari, P., Shen, Q., Nemenman, I., Basso, K., Margolin, A. A.,
CrossRef
Pubmed
Google scholar
|
[86] |
Zhu, F. and Guan, Y. (2014) Predicting dynamic signaling network response under unseen perturbations. Bioinformatics, 30, 2772–2778
CrossRef
Pubmed
Google scholar
|
[87] |
Chen, J. and Zhang, S. (2016) Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics, 32, 1724–1732
CrossRef
Pubmed
Google scholar
|
[88] |
Qin, J., Li, M. J., Wang, P., Zhang, M. Q. and Wang, J. (2011) ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor. Nucleic Acids Res., 39, W430–W436
CrossRef
Pubmed
Google scholar
|
[89] |
Wang, P., Qin, J., Qin, Y., Zhu, Y., Wang, L. Y., Li, M. J., Zhang, M. Q. and Wang, J. (2015) ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks. Nucleic Acids Res., 43, W264–W269
Pubmed
|
[90] |
Wang, S., Sun, H., Ma, J., Zang, C., Wang, C., Wang, J., Tang, Q., Meyer, C. A., Zhang, Y. and Liu, X. S. (2013) Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc., 8, 2502–2515
CrossRef
Pubmed
Google scholar
|
[91] |
Qin, J., Hu, Y., Xu, F., Yalamanchili, H. K. and Wang, J. (2014) Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. Methods, 67, 294–303
CrossRef
Pubmed
Google scholar
|
[92] |
Maienschein-Cline, M., Zhou, J., White, K. P., Sciammas, R. and Dinner, A. R. (2012) Discovering transcription factor regulatory targets using gene expression and binding data. Bioinformatics, 28, 206–213
CrossRef
Pubmed
Google scholar
|
[93] |
Wu, G. and Ji, H. (2013) ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking. BMC Bioinformatics, 14, 188
CrossRef
Pubmed
Google scholar
|
[94] |
Tang, B., Hsu, H. K., Hsu, P. Y., Bonneville, R., Chen, S. S., Huang, T. H. and Jin, V. X. (2012) Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes. Sci. Rep., 2, 875
CrossRef
Pubmed
Google scholar
|
[95] |
Yan, B., Li, H., Yang, X., Shao, J., Jang, M., Guan, D., Zou, S., Van Waes, C., Chen, Z. and Zhan, M. (2013) Unraveling regulatory programs for NF-kappaB, p53 and microRNAs in head and neck squamous cell carcinoma. PLoS One, 8, e73656
CrossRef
Pubmed
Google scholar
|
[96] |
Pique-Regi, R., Degner, J. F., Pai, A. A., Gaffney, D. J., Gilad, Y. and Pritchard, J. K. (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res., 21, 447–455
CrossRef
Pubmed
Google scholar
|
[97] |
Huang, J. C., Babak, T., Corson, T. W., Chua, G., Khan, S., Gallie, B. L., Hughes, T. R., Blencowe, B. J., Frey, B. J. and Morris, Q. D. (2007) Using expression profiling data to identify human microRNA targets. Nat. Methods, 4, 1045–1049
CrossRef
Pubmed
Google scholar
|
[98] |
Liang, Z., Zhou, H., He, Z., Zheng, H. and Wu, J. (2011) mirAct: a web tool for evaluating microRNA activity based on gene expression data. Nucleic Acids Res., 39, W139–144
CrossRef
Pubmed
Google scholar
|
[99] |
Nam, S., Li, M., Choi, K., Balch, C., Kim, S. and Nephew, K. P. (2009) MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res., 37, W356–362
CrossRef
Pubmed
Google scholar
|
[100] |
Sales,
CrossRef
Google scholar
|
[101] |
Qin, J., Li, M. J., Wang, P., Wong, N. S., Wong, M. P., Xia, Z., Tsao, G. S., Zhang, M. Q. and Wang, J. (2013) ProteoMirExpress: inferring microRNA and protein-centered regulatory networks from high-throughput proteomic and mRNA expression data. Mol. Cell. Proteomics, 12, 3379–3387
CrossRef
Pubmed
Google scholar
|
[102] |
Wang, L.Y., Wang, P., Li, M.J., Qin, J., Wang, X., Zhang, M.Q. and Wang, J. (2011) EpiRegNet: constructing epigenetic regulatory network from high throughput gene expression data for humans. Epigenetics, 6, 1505–1512
|
[103] |
Guan, D., Shao, J., Deng, Y., Wang, P., Zhao, Z., Liang, Y., Wang, J. and Yan, B. (2014) CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data. Bioinformatics, 30, 1190–1192
CrossRef
Pubmed
Google scholar
|
[104] |
Huang, G. T., Athanassiou, C. and Benos, P. V. (2011) mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic Acids Res., 39, W416–W423
CrossRef
Pubmed
Google scholar
|
[105] |
Zhang, S., Li, Q., Liu, J. and Zhou, X. J. (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 27, i401–i409
CrossRef
Pubmed
Google scholar
|
[106] |
Zhang, S., Liu, C. C., Li, W., Shen, H., Laird, P. W. and Zhou, X. J. (2012) Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res., 40, 9379–9391
CrossRef
Pubmed
Google scholar
|
[107] |
Li, W., Zhang, S., Liu, C. C. and Zhou, X. J. (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics, 28, 2458–2466
CrossRef
Pubmed
Google scholar
|
[108] |
Guan, D., Shao, J., Zhao, Z., Wang, P., Qin, J., Deng, Y., Boheler, K. R., Wang, J. and Yan, B. (2014) PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data. Nucleic Acids Res., 42, W130–136
CrossRef
Pubmed
Google scholar
|
[109] |
Wu, D., Wang, D., Zhang, M. Q. and Gu, J. (2015) Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics, 16, 1022
CrossRef
Pubmed
Google scholar
|
[110] |
Oyama, M., Kozuka-Hata, H., Tasaki, S., Semba, K., Hattori, S., Sugano, S., Inoue, J. and Yamamoto, T. (2009) Temporal perturbation of tyrosine phosphoproteome dynamics reveals the system-wide regulatory networks. Mol. Cell. Proteomics, 8, 226–231
CrossRef
Pubmed
Google scholar
|
[111] |
Bodenmiller, B., Wanka, S., Kraft, C., Urban, J., Campbell, D., Pedrioli, P. G., Gerrits, B., Picotti, P., Lam, H., Vitek, O.,
CrossRef
Pubmed
Google scholar
|
[112] |
Caldana, C., Fernie, A. R., Willmitzer, L. and Steinhauser, D. (2012) Unraveling retrograde signaling pathways: finding candidate signaling molecules via metabolomics and systems biology driven approaches. Front. Plant Sci., 3, 267
CrossRef
Pubmed
Google scholar
|
[113] |
Zhu, J., Sova, P., Xu, Q., Dombek, K. M., Xu, E. Y., Vu, H., Tu, Z., Brem, R. B., Bumgarner, R. E. and Schadt, E. E. (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol., 10, e1001301
CrossRef
Pubmed
Google scholar
|
[114] |
Jha, A. K., Huang, S. C., Sergushichev, A., Lampropoulou, V., Ivanova, Y., Loginicheva, E., Chmielewski, K., Stewart, K. M., Ashall, J., Everts, B.,
CrossRef
Pubmed
Google scholar
|
[115] |
Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., The DREAM5 Consortium, Kellis, M., Collins, J. J.
CrossRef
Pubmed
Google scholar
|
[116] |
Hu, Z., Killion, P. J. and Iyer, V. R. (2007) Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet., 39, 683–687
CrossRef
Pubmed
Google scholar
|
[117] |
Song, L., Zhang, Z., Grasfeder, L. L., Boyle, A. P., Giresi, P. G., Lee, B. K., Sheffield, N. C., Gräf, S., Huss, M., Keefe, D.,
CrossRef
Pubmed
Google scholar
|
[118] |
Kelly, T. K., Liu, Y., Lay, F. D., Liang, G., Berman, B. P. and Jones, P. A. (2012) Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res., 22, 2497–2506
CrossRef
Pubmed
Google scholar
|
[119] |
Natarajan, A., Yardimci, G. G., Sheffield, N. C., Crawford, G. E. and Ohler, U. (2012) Predicting cell-type-specific gene expression from regions of open chromatin. Genome Res., 22, 1711–1722
CrossRef
Pubmed
Google scholar
|
[120] |
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
CrossRef
Pubmed
Google scholar
|
[121] |
Doench, J. G. and Sharp, P. A. (2004) Specificity of microRNA target selection in translational repression. Genes Dev., 18, 504–511
CrossRef
Pubmed
Google scholar
|
[122] |
Chen, X. (2004) A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science, 303, 2022–2025
CrossRef
Pubmed
Google scholar
|
[123] |
Wightman, B., Ha, I. and Ruvkun, G. (1993) Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855–862
CrossRef
Pubmed
Google scholar
|
[124] |
Vasudevan, S., Tong, Y. and Steitz, J. A. (2007) Switching from repression to activation: microRNAs can up-regulate translation. Science, 318, 1931–1934
CrossRef
Pubmed
Google scholar
|
[125] |
Vasudevan, S. and Steitz, J. A. (2007) AU-rich-element-mediated upregulation of translation by FXR1 and Argonaute 2. Cell, 128, 1105–1118
CrossRef
Pubmed
Google scholar
|
[126] |
Chen, Y., Wang, Y., Xuan, Z., Chen, M. and Zhang, M. Q. (2016) De novo deciphering three-dimensional chromatin interaction and topological domains by wavelet transformation of epigenetic profiles. Nucleic Acids Res., 44, e106
CrossRef
Pubmed
Google scholar
|
[127] |
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
CrossRef
Pubmed
Google scholar
|
[128] |
Whalen, S., Truty, R. M. and Pollard, K. S. (2016) Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet., 48, 488–496
CrossRef
Pubmed
Google scholar
|
[129] |
Wang, Z., Zang, C., Rosenfeld, J. A., Schones, D. E., Barski, A., Cuddapah, S., Cui, K., Roh, T. Y., Peng, W., Zhang, M. Q.,
CrossRef
Pubmed
Google scholar
|
[130] |
Karlić, R., Chung, H. R., Lasserre, J., Vlahovicek, K. and Vingron, M. (2010) Histone modification levels are predictive for gene expression. Proc. Natl. Acad. Sci. USA, 107, 2926–2931
CrossRef
Pubmed
Google scholar
|
[131] |
Zhu, Y., Sun, L., Chen, Z., Whitaker, J. W., Wang, T. and Wang, W. (2013) Predicting enhancer transcription and activity from chromatin modifications. Nucleic Acids Res., 41, 10032–10043
CrossRef
Pubmed
Google scholar
|
[132] |
Khalil, A. M., Guttman, M., Huarte, M., Garber, M., Raj, A., Rivea Morales, D., Thomas, K., Presser, A., Bernstein, B. E., van Oudenaarden, A.,
CrossRef
Pubmed
Google scholar
|
[133] |
Knouf, E. C., Garg, K., Arroyo, J. D., Correa, Y., Sarkar, D., Parkin, R. K., Wurz, K., O’Briant, K. C., Godwin, A. K., Urban, N. D.,
CrossRef
Pubmed
Google scholar
|
[134] |
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. and Kim, D. (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet., 16, 85–97
CrossRef
Pubmed
Google scholar
|
[135] |
Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K., Frigessi, A. and Børresen-Dale, A. L. (2014) Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer, 14, 299–313
CrossRef
Pubmed
Google scholar
|
[136] |
Marbach, D., Roy, S., Ay, F., Meyer, P. E., Candeias, R., Kahveci, T., Bristow, C. A. and Kellis, M. (2012) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks. Genome Res., 22, 1334–1349
CrossRef
Pubmed
Google scholar
|
[137] |
Sintupisut, N., Liu, P. L. and Yeang, C. H. (2013) An integrative characterization of recurrent molecular aberrations in glioblastoma genomes. Nucleic Acids Res., 41, 8803–8821
CrossRef
Pubmed
Google scholar
|
[138] |
Palsson, B. and Zengler, K. (2010) The challenges of integrating multi-omic data sets. Nat. Chem. Biol., 6, 787–789
CrossRef
Pubmed
Google scholar
|
[139] |
Boyer, L. A., Lee, T. I., Cole, M. F., Johnstone, S. E., Levine, S. S., Zucker, J. P., Guenther, M. G., Kumar, R. M., Murray, H. L., Jenner, R. G.,
CrossRef
Pubmed
Google scholar
|
[140] |
Marson, A., Levine, S. S., Cole, M. F., Frampton, G. M., Brambrink, T., Johnstone, S., Guenther, M. G., Johnston, W. K., Wernig, M., Newman, J.,
CrossRef
Pubmed
Google scholar
|
[141] |
Boyer, L. A., Plath, K., Zeitlinger, J., Brambrink, T., Medeiros, L. A., Lee, T. I., Levine, S. S., Wernig, M., Tajonar, A., Ray, M. K.,
CrossRef
Pubmed
Google scholar
|
[142] |
Takahashi, K. and Yamanaka, S. (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126, 663–676
CrossRef
Pubmed
Google scholar
|
[143] |
Anokye-Danso, F., Trivedi, C. M., Juhr, D., Gupta, M., Cui, Z., Tian, Y., Zhang, Y., Yang, W., Gruber, P. J., Epstein, J. A.,
CrossRef
Pubmed
Google scholar
|
[144] |
Wu, S. M. and Hochedlinger, K. (2011) Harnessing the potential of induced pluripotent stem cells for regenerative medicine. Nat. Cell Biol., 13, 497–505
CrossRef
Pubmed
Google scholar
|
[145] |
Buganim, Y., Faddah, D. A. and Jaenisch, R. (2013) Mechanisms and models of somatic cell reprogramming. Nat. Rev. Genet., 14, 427–439
CrossRef
Pubmed
Google scholar
|
[146] |
Gifford, C. A., Ziller, M. J., Gu, H., Trapnell, C., Donaghey, J., Tsankov, A., Shalek, A. K., Kelley, D. R., Shishkin, A. A., Issner, R.,
CrossRef
Pubmed
Google scholar
|
[147] |
Mohn, F., Weber, M., Rebhan, M., Roloff, T. C., Richter, J., Stadler, M. B., Bibel, M. and Schübeler, D. (2008) Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. Mol. Cell, 30, 755–766
CrossRef
Pubmed
Google scholar
|
[148] |
Tsankov, A. M., Gu, H., Akopian, V., Ziller, M. J., Donaghey, J., Amit, I., Gnirke, A. and Meissner, A. (2015) Transcription factor binding dynamics during human ES cell differentiation. Nature, 518, 344–349
CrossRef
Pubmed
Google scholar
|
[149] |
Choukrallah, M. A., Song, S., Rolink, A. G., Burger, L. and Matthias, P. (2015) Enhancer repertoires are reshaped independently of early priming and heterochromatin dynamics during B cell differentiation. Nat. Commun., 6, 8324
CrossRef
Pubmed
Google scholar
|
[150] |
Sancho-Martinez, I., Baek, S. H. and Izpisua Belmonte, J. C. (2012) Lineage conversion methodologies meet the reprogramming toolbox. Nat. Cell Biol., 14, 892–899
CrossRef
Pubmed
Google scholar
|
[151] |
Heinäniemi, M., Nykter, M., Kramer, R., Wienecke-Baldacchino, A., Sinkkonen, L., Zhou, J. X., Kreisberg, R., Kauffman, S. A., Huang, S. and Shmulevich, I. (2013) Gene-pair expression signatures reveal lineage control. Nat. Methods, 10, 577–583
CrossRef
Pubmed
Google scholar
|
[152] |
Cahan, P., Li, H., Morris, S. A., Lummertz da Rocha, E., Daley, G. Q. and Collins, J. J. (2014) CellNet: network biology applied to stem cell engineering. Cell, 158, 903–915
CrossRef
Pubmed
Google scholar
|
[153] |
Rackham, O. J., Firas, J., Fang, H., Oates, M. E., Holmes, M. L., Knaupp, A. S., Suzuki, H., Nefzger, C. M., Daub, C. O., Shin, J. W.,
CrossRef
Pubmed
Google scholar
|
[154] |
Li, M. J., Wang, P., Liu, X., Lim, E. L., Wang, Z., Yeager, M., Wong, M. P., Sham, P. C., Chanock, S. J. and Wang, J. (2012) GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res., 40, D1047–D1054
CrossRef
Pubmed
Google scholar
|
[155] |
Li, M. J., Sham, P. C. and Wang, J. (2012) Genetic variant representation, annotation and prioritization in the post-GWAS era. Cell Res., 22, 1505–1508
CrossRef
Pubmed
Google scholar
|
[156] |
Li, M. J., Yan, B., Sham, P. C. and Wang, J. (2015) Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression. Brief. Bioinform.16, 393–412
CrossRef
Pubmed
Google scholar
|
[157] |
Alfaro, J. A., Sinha, A., Kislinger, T. and Boutros, P. C. (2014) Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat. Methods, 11, 1107–1113
CrossRef
Pubmed
Google scholar
|
[158] |
The Cancer Genome Atlas Research Network
CrossRef
Pubmed
Google scholar
|
[159] |
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B. and Goldenberg, A. (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods, 11, 333–337
CrossRef
Pubmed
Google scholar
|
[160] |
Yuan, Y., Van Allen, E. M., Omberg, L., Wagle, N., Amin-Mansour, A., Sokolov, A., Byers, L. A., Xu, Y., Hess, K. R., Diao, L.,
CrossRef
Pubmed
Google scholar
|
[161] |
Ernst, J. and Kellis, M. (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol., 33, 364–376
CrossRef
Pubmed
Google scholar
|
[162] |
Marchini, J. and Howie, B. (2010) Genotype imputation for genome-wide association studies. Nat. Rev. Genet., 11, 499–511
CrossRef
Pubmed
Google scholar
|
[163] |
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W., Jansen, R., de Geus, E. J., Boomsma, D. I., Wright, F. A.,
CrossRef
Pubmed
Google scholar
|
[164] |
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. and Smyth, G. K. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, e47
CrossRef
Pubmed
Google scholar
|
[165] |
Orlando, D. A., Chen, M. W., Brown, V. E., Solanki, S., Choi, Y. J., Olson, E. R., Fritz, C. C., Bradner, J. E. and Guenther, M. G. (2014) Quantitative ChIP-Seq normalization reveals global modulation of the epigenome. Cell Reports, 9, 1163–1170
CrossRef
Pubmed
Google scholar
|
[166] |
Diaz, A., Park, K., Lim, D.A. and Song, J.S. (2012) Normalization, bias correction, and peak calling for ChIP-seq. Stat. Appl. Genet. Mol. Biol. 11, Article 9
CrossRef
Google scholar
|
[167] |
Sysi-Aho, M., Katajamaa, M., Yetukuri, L. and Oresic, M. (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics, 8, 93
CrossRef
Pubmed
Google scholar
|
[168] |
Johnson, W. E., Li, C. and Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127
CrossRef
Pubmed
Google scholar
|
[169] |
Leek, J. T. and Storey, J. D. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3, e161
CrossRef
Pubmed
Google scholar
|
[170] |
Thompson, J.A., Tan, J. and Greene, C.S. (2016) Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ, 4, e1621
|
[171] |
Zang, C., Wang, T., Deng, K., Li, B., Hu, S., Qin, Q., Xiao, T., Zhang, S., Meyer, C. A., He, H. H.,
CrossRef
Pubmed
Google scholar
|
[172] |
Rudy, J. and Valafar, F. (2011) Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics, 12, 467
CrossRef
Pubmed
Google scholar
|
/
〈 | 〉 |