|
|
Applications of integrative OMICs approaches to gene regulation studies |
Jing Qin1, Bin Yan2,3, Yaohua Hu4, Panwen Wang5, Junwen Wang5,6( ) |
1. School of Life Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China 2. Laboratory for Food Safety and Environmental Technology, Institutes of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China 3. School of Biomedical Sciences, The University of Hong Kong, Hong Kong SAR 999077, China 4. College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China 5. Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA 6. Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA |
|
|
Abstract 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.
|
Author Summary Dozens of OMICs technologies have been developed to dissect the functions of DNA, RNA and protein in gene regulation. Each of these technologies is powerful on their own. However, like the parable of blind men and an elephant, any single technology has a limited ability to depict the whole complex regulatory system. Integrative approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to elucidate the regulatory mechanisms. This article reviews the popular OMICs technologies, OMICs data integration strategies, and the bioinformatics tools used for multi-dimensional data integration. |
Keywords
gene regulatory networks
integrative analysis
OMICs
ChIP-seq
RNA-seq
|
Corresponding Author(s):
Junwen Wang
|
About author: Tongcan Cui and Yizhe Hou contributed equally to this work. |
Just Accepted Date: 30 September 2016
Online First Date: 21 November 2016
Issue Date: 01 December 2016
|
|
1 |
K. L. Lee, , S. K. Lim, , Y. L. Orlov, , Y. Yit, , H. Yang, , L. T. Ang, , L. Poellinger, and B. Lim, (2011) Graded Nodal/Activin signaling titrates conversion of quantitative phospho-Smad2 levels into qualitative embryonic stem cell fate decisions. PLoS Genet., 7, e1002130
https://doi.org/10.1371/journal.pgen.1002130
pmid: 21731500
|
2 |
R. H. Newman, , J. Hu, , H. S. Rho, , Z. Xie, , C. Woodard, , J. Neiswinger, , C. Cooper, , M. Shirley, , H. M. Clark, , S. Hu, , et al. (2013) Construction of human activity-based phosphorylation networks. Mol. Syst. Biol., 9, 655
https://doi.org/10.1038/msb.2013.12
pmid: 23549483
|
3 |
A. C. Gavin, , M. Bösche, , R. Krause, , P. Grandi, , M. Marzioch, , A. Bauer, , J. Schultz, , J. M. Rick, , A. M. Michon, , C. M. Cruciat, , et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature, 415, 141–147
https://doi.org/10.1038/415141a
pmid: 11805826
|
4 |
S. Fields, and O. Song, (1989) A novel genetic system to detect protein-protein interactions. Nature, 340, 245–246
https://doi.org/10.1038/340245a0
pmid: 2547163
|
5 |
T. Chen, and S. Y. Dent, (2014) Chromatin modifiers and remodellers: regulators of cellular differentiation. Nat. Rev. Genet., 15, 93–106
https://doi.org/10.1038/nrg3607
pmid: 24366184
|
6 |
J. Dekker, , M. A. Marti-Renom, and L. A. Mirny, (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet., 14, 390–403
https://doi.org/10.1038/nrg3454
pmid: 23657480
|
7 |
J. T. Witten, and J. Ule, (2011) Understanding splicing regulation through RNA splicing maps. Trends Genet., 27, 89–97
https://doi.org/10.1016/j.tig.2010.12.001
pmid: 21232811
|
8 |
N. T. Ingolia, , S. Ghaemmaghami, , J. R. Newman, and J. S. Weissman, (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science, 324, 218–223
https://doi.org/10.1126/science.1168978
pmid: 19213877
|
9 |
S. Geisler, and J. Coller, (2013) RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat. Rev. Mol. Cell Biol., 14, 699–712
https://doi.org/10.1038/nrm3679
pmid: 24105322
|
10 |
L. L. Chen, (2016) The biogenesis and emerging roles of circular RNAs. Nat. Rev. Mol. Cell Biol., 17, 205–211
https://doi.org/10.1038/nrm.2015.32
pmid: 26908011
|
11 |
J. J. Quinn, , I. A. Ilik, , K. Qu, , P. Georgiev, , C. Chu, , A. Akhtar, and H. Y. Chang, (2014) Revealing long noncoding RNA architecture and functions using domain-specific chromatin isolation by RNA purification. Nat. Biotechnol., 32, 933–940
https://doi.org/10.1038/nbt.2943
pmid: 24997788
|
12 |
A. Di Ruscio, , A. K. Ebralidze, , T. Benoukraf, , G. Amabile, , L. A. Goff, , J. Terragni, , M. E. Figueroa, , L. L. De Figueiredo Pontes, , M. Alberich-Jorda, , P. Zhang, , et al. (2013) DNMT1-interacting RNAs block gene-specific DNA methylation. Nature, 503, 371–376
https://doi.org/10.1038/nature12598
pmid: 24107992
|
13 |
E. Gómez-Orte, , B. Sáenz-Narciso, , S. Moreno, and J. Cabello, (2013) Multiple functions of the noncanonical Wnt pathway. Trends Genet., 29, 545–553
https://doi.org/10.1016/j.tig.2013.06.003
pmid: 23846023
|
14 |
J. Liang, , M. Wan, , Y. Zhang, , P. Gu, , H. Xin, , S. Y. Jung, , J. Qin, , J. Wong, , A. J. Cooney, , D. Liu, , et al. (2008) Nanog and Oct4 associate with unique transcriptional repression complexes in embryonic stem cells. Nat. Cell Biol., 10, 731–739
https://doi.org/10.1038/ncb1736
pmid: 18454139
|
15 |
T. Ito, , T. Chiba, , R. Ozawa, , M. Yoshida, , M. Hattori, and Y. Sakaki, (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA, 98, 4569–4574
https://doi.org/10.1073/pnas.061034498
pmid: 11283351
|
16 |
M. Jain, , R. Nilsson, , S. Sharma, , N. Madhusudhan, , T. Kitami, , A. L. Souza, , R. Kafri, , M. W. Kirschner, , C. B. Clish, and V. K. Mootha, (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science, 336, 1040–1044
https://doi.org/10.1126/science.1218595
pmid: 22628656
|
17 |
P. J. Park, (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680
https://doi.org/10.1038/nrg2641
pmid: 19736561
|
18 |
H. S. Rhee, and B. F. Pugh, (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 |
R. Lister, , M. Pelizzola, , R. H. Dowen, , R. D. Hawkins, , G. Hon, , J. Tonti-Filippini, , J. R. Nery, , L. Lee, , Z. Ye, , Q. M. Ngo, , et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462, 315–322
https://doi.org/10.1038/nature08514
pmid: 19829295
|
20 |
M. P. Ball, , J. B. Li, , Y. Gao, , J. H. Lee, , E. M. LeProust, , I. H. Park, , B. Xie, , G. Q. Daley, and G. M. Church, (2009) Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol., 27, 361–368
https://doi.org/10.1038/nbt.1533
pmid: 19329998
|
21 |
M. Pelizzola, , Y. Koga, , A. E. Urban, , M. Krauthammer, , S. Weissman, , R. Halaban, and A. M. Molinaro, (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
https://doi.org/10.1101/gr.080721.108
pmid: 18765822
|
22 |
A. Meissner, , A. Gnirke, , G. W. Bell, , B. Ramsahoye, , E. S. Lander, and R. Jaenisch, (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res., 33, 5868–5877
https://doi.org/10.1093/nar/gki901
pmid: 16224102
|
23 |
J. R. Edwards, , A. H. O’Donnell, , R. A. Rollins, , H. E. Peckham, , C. Lee, , M. H. Milekic, , B. Chanrion, , Y. Fu, , T. Su, , H. Hibshoosh, , et al. (2010) Chromatin and sequence features that define the fine and gross structure of genomic methylation patterns. Genome Res., 20, 972–980
https://doi.org/10.1101/gr.101535.109
pmid: 20488932
|
24 |
H. H. He, , C. A. Meyer, , S. S. Hu, , M. W. Chen, , C. Zang, , Y. Liu, , P. K. Rao, , T. Fei, , H. Xu, , H. Long, , et al. (2014) Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat. Methods, 11, 73–78
https://doi.org/10.1038/nmeth.2762
pmid: 24317252
|
25 |
R. K. Auerbach, , G. Euskirchen, , J. Rozowsky, , N. Lamarre-Vincent, , Z. Moqtaderi, , P. Lefrançois, , K. Struhl, , M. Gerstein, and M. Snyder, (2009) Mapping accessible chromatin regions using Sono-Seq. Proc. Natl. Acad. Sci. USA, 106, 14926–14931
https://doi.org/10.1073/pnas.0905443106
pmid: 19706456
|
26 |
J. D. Buenrostro, , P. G. Giresi, , L. C. Zaba, , H. Y. Chang, and W. J. Greenleaf, (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
https://doi.org/10.1038/nmeth.2688
pmid: 24097267
|
27 |
K. J. Gaulton, , T. Nammo, , L. Pasquali, , J. M. Simon, , P. G. Giresi, , M. P. Fogarty, , T. M. Panhuis, , P. Mieczkowski, , A. Secchi, , D. Bosco, , et al. (2010) A map of open chromatin in human pancreatic islets. Nat. Genet., 42, 255–259
https://doi.org/10.1038/ng.530
pmid: 20118932
|
28 |
J. S. You, , T. K. Kelly, , D. D. De Carvalho, , P. C. Taberlay, , G. Liang, and P. A. Jones, (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
https://doi.org/10.1073/pnas.1111309108
pmid: 21844352
|
29 |
D. E. Schones, , K. Cui, , S. Cuddapah, , T. Y. Roh, , A. Barski, , Z. Wang, , G. Wei, and K. Zhao, (2008) Dynamic regulation of nucleosome positioning in the human genome. Cell, 132, 887–898
https://doi.org/10.1016/j.cell.2008.02.022
pmid: 18329373
|
30 |
A. Mortazavi, , B. A. Williams, , K. McCue, , L. Schaeffer, and B. Wold, (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods, 5, 621–628
https://doi.org/10.1038/nmeth.1226
pmid: 18516045
|
31 |
M. Schena, , D. Shalon, , R. W. Davis, and P. O. Brown, (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467–470
https://doi.org/10.1126/science.270.5235.467
pmid: 7569999
|
32 |
L. J. Core, , J. J. Waterfall, and J. T. Lis, (2008) Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science, 322, 1845–1848
https://doi.org/10.1126/science.1162228
pmid: 19056941
|
33 |
S. W. Chi, , J. B. Zang, , A. Mele, and R. B. Darnell, (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature, 460, 479–486
pmid: 19536157
|
34 |
M. A. German, , M. Pillay, , D. H. Jeong, , A. Hetawal, , S. Luo, , P. Janardhanan, , V. Kannan, , L. A. Rymarquis, , K. Nobuta, , R. German, , et al. (2008) Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat. Biotechnol., 26, 941–946
https://doi.org/10.1038/nbt1417
pmid: 18542052
|
35 |
A. Helwak, , G. Kudla, , T. Dudnakova, and D. Tollervey, (2013) Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell, 153, 654–665
https://doi.org/10.1016/j.cell.2013.03.043
pmid: 23622248
|
36 |
Y. Ding, , Y. Tang, , C. K. Kwok, , Y. Zhang, , P. C. Bevilacqua, and S. M. Assmann, (2014)In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 505, 696–700
https://doi.org/10.1038/nature12756
pmid: 24270811
|
37 |
D. Pinkel, , R. Segraves, , D. Sudar, , S. Clark, , I. Poole, , D. Kowbel, , C. Collins, , W. L. Kuo, , C. Chen, , Y. Zhai, , et al. (1998) High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet., 20, 207–211
https://doi.org/10.1038/2524
pmid: 9771718
|
38 |
D. R. Bentley, , S. Balasubramanian, , H. P. Swerdlow, , G. P. Smith, , J. Milton, , C. G. Brown, , K. P. Hall, , D. J. Evers, , C. L. Barnes, , H. R. Bignell, , et al. (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature, 456, 53–59
https://doi.org/10.1038/nature07517
pmid: 18987734
|
39 |
S. B. Ng, , E. H. Turner, , P. D. Robertson, , S. D. Flygare, , A. W. Bigham, , C. Lee, , T. Shaffer, , M. Wong, , A. Bhattacharjee, , E. E. Eichler, , et al. (2009) Targeted capture and massively parallel sequencing of 12 human exomes. Nature, 461, 272–276
https://doi.org/10.1038/nature08250
pmid: 19684571
|
40 |
M. Krüger, , M. Moser, , S. Ussar, , I. Thievessen, , C. A. Luber, , F. Forner, , S. Schmidt, , S. Zanivan, , R. Füssler, and M. Mann, (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell, 134, 353–364
https://doi.org/10.1016/j.cell.2008.05.033
pmid: 18662549
|
41 |
T. Kislinger, , K. Rahman, , D. Radulovic, , B. Cox, , J. Rossant, and A. Emili, (2003) PRISM, a generic large scale proteomic investigation strategy for mammals. Mol. Cell. Proteomics, 2, 96–106
https://doi.org/10.1074/mcp.M200074-MCP200
pmid: 12644571
|
42 |
F. Zhou, , Y. Lu, , S. B. Ficarro, , G. Adelmant, , W. Jiang, , C. J. Luckey, and J. A. Marto, (2013) Genome-scale proteome quantification by DEEP SEQ mass spectrometry. Nat. Commun., 4, 2171
https://doi.org/10.1038/ncomms3171
pmid: 23863870
|
43 |
T. Jewison, , Y. Su, , F. M. Disfany, , Y. Liang, , C. Knox, , A. Maciejewski, , J. Poelzer, , J. Huynh, , Y. Zhou, , D. Arndt, , et al.(2014) SMPDB 2.0: big improvements to the Small Molecule Pathway Database. Nucleic Acids Res., 42, D478–D484
https://doi.org/10.1093/nar/gkt1067
pmid: 24203708
|
44 |
C. Song, , M. Ye, , Z. Liu, , H. Cheng, , X. Jiang, , G. Han, , Z. Songyang, , Y. Tan, , H. Wang, , J. Ren, , et al. (2012) Systematic analysis of protein phosphorylation networks from phosphoproteomic data. Mol. Cell. Proteomics, 11, 1070–1083
https://doi.org/10.1074/mcp.M111.012625
pmid: 22798277
|
45 |
A. Meissner, , T. S. Mikkelsen, , H. Gu, , M. Wernig, , J. Hanna, , A. Sivachenko, , X. Zhang, , B. E. Bernstein, , C. Nusbaum, , D. B. Jaffe, , et al. (2008) Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature, 454, 766–770
pmid: 18600261
|
46 |
S. Neph, , J. Vierstra, , A. B. Stergachis, , A. P. Reynolds, , E. Haugen, , B. Vernot, , R. E. Thurman, , S. John, , R. Sandstrom, , A. K. Johnson, , et al. (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature, 489, 83–90
https://doi.org/10.1038/nature11212
pmid: 22955618
|
47 |
I. Dunham, , A. Kundaje, , S. F. Aldred, , P. J. Collins, , C. A. Davis, , F. Doyle, , C. B. Epstein, , S. Frietze, , J. Harrow, , R. Kaul, , et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74
https://doi.org/10.1038/nature11247
pmid: 22955616
|
48 |
G. Ramaswami, , R. Zhang, , R. Piskol, , L. P. Keegan, , P. Deng, , M. A. O’Connell, and J. B. Li, (2013) Identifying RNA editing sites using RNA sequencing data alone. Nat. Methods, 10, 128–132
https://doi.org/10.1038/nmeth.2330
pmid: 23291724
|
49 |
G. Ramaswami, , W. Lin, , R. Piskol, , M. H. Tan, , C. Davis, and J. B. Li, (2012) Accurate identification of human Alu and non-Alu RNA editing sites. Nat. Methods, 9, 579–581
https://doi.org/10.1038/nmeth.1982
pmid: 22484847
|
50 |
X. D. Fu, and M. Ares, Jr. (2014) Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet., 15, 689–701
https://doi.org/10.1038/nrg3778
pmid: 25112293
|
51 |
Z. Wang, , M. Gerstein, and M. Snyder, (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57–63
https://doi.org/10.1038/nrg2484
pmid: 19015660
|
52 |
L. Yang, , M. O. Duff, , B. R. Graveley, , G. G. Carmichael, and L. L. Chen, (2011) Genomewide characterization of non-polyadenylated RNAs. Genome Biol., 12, R16
https://doi.org/10.1186/gb-2011-12-2-r16
pmid: 21324177
|
53 |
J. H. Yang, , J. H. Li, , P. Shao, , H. Zhou, , Y. Q. Chen, and L. H. Qu, (2011) starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res., 39, D202–D209
https://doi.org/10.1093/nar/gkq1056
pmid: 21037263
|
54 |
E. Lau, (2014) Non-coding RNA: zooming in on lncRNA functions. Nat. Rev. Genet., 15, 574–575
https://doi.org/10.1038/nrg3795
pmid: 25048169
|
55 |
M. T. Venø, , T. B. Hansen, , S. T. Venø, , B. H. Clausen, , M. Grebing, , B. Finsen, , I. E. Holm, and J. Kjems, (2015) Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol., 16, 245
https://doi.org/10.1186/s13059-015-0801-3
pmid: 26541409
|
56 |
Y. Gao, , J. Wang, and F. Zhao, (2015) CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol., 16, 4
https://doi.org/10.1186/s13059-014-0571-3
pmid: 25583365
|
57 |
J. Salzman, , C. Gawad, , P. L. Wang, , N. Lacayo, and P. O. Brown, (2012) Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One, 7, e30733
https://doi.org/10.1371/journal.pone.0030733
pmid: 22319583
|
58 |
Y. Qin, , H.K. Yalamanchili, , J. Qin, , B. Yan, and J. Wang, (2015) The current status and challenges in computational analysis of genomic big data. Big data research, 2, 12–18
|
59 |
Y. Kluger, , H. Yu, , J. Qian, and M. Gerstein, (2003) Relationship between gene co-expression and probe localization on microarray slides. BMC Genomics, 4, 49
https://doi.org/10.1186/1471-2164-4-49
pmid: 14667251
|
60 |
K. Robasky, , N. E. Lewis, and G. M. Church, (2014) The role of replicates for error mitigation in next-generation sequencing. Nat. Rev. Genet., 15, 56–62
https://doi.org/10.1038/nrg3655
pmid: 24322726
|
61 |
L. Teytelman, , D. M. Thurtle, , J. Rine, and A. van Oudenaarden, (2013) Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Proc. Natl. Acad. Sci. USA, 110, 18602–18607
https://doi.org/10.1073/pnas.1316064110
pmid: 24173036
|
62 |
C. von Mering, , R. Krause, , B. Snel, , M. Cornell, , S. G. Oliver, , S. Fields, and P. Bork, (2002) Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417, 399–403
https://doi.org/10.1038/nature750
pmid: 12000970
|
63 |
M. El Gazzar, and C. E. McCall, (2012) MicroRNAs regulatory networks in myeloid lineage development and differentiation: regulators of the regulators. Immunol. Cell Biol., 90, 587–593
https://doi.org/10.1038/icb.2011.74
pmid: 21912420
|
64 |
W. H. Hudson, and E. A. Ortlund, (2014) The structure, function and evolution of proteins that bind DNA and RNA. Nat. Rev. Mol. Cell Biol., 15, 749–760
https://doi.org/10.1038/nrm3884
pmid: 25269475
|
65 |
L. Poliseno, , L. Salmena, , J. Zhang, , B. Carver, , W. J. Haveman, and P. P. Pandolfi, (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature, 465, 1033–1038
https://doi.org/10.1038/nature09144
pmid: 20577206
|
66 |
T. B. Hansen, , T. I. Jensen, , B. H. Clausen, , J. B. Bramsen, , B. Finsen, , C. K. Damgaard, and J. Kjems, (2013) Natural RNA circles function as efficient microRNA sponges. Nature, 495, 384–388
https://doi.org/10.1038/nature11993
pmid: 23446346
|
67 |
modEncode Consortium, S. Roy, , J. Ernst, J., P.V. Kharchenko, , Kheradpour, P., N. Negre, , M.L. Eaton, , J.M. Landolin, , C.A. Bristow, , L. Ma, et al. (2010) Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science, 330, 1787–1797
https://doi.org/10.1126/science.1198374
|
68 |
N. Nègre, , C. D. Brown, , L. Ma, , C. A. Bristow, , S. W. Miller, , U. Wagner, , P. Kheradpour, , M. L. Eaton, , P. Loriaux, , R. Sealfon, , et al. (2011) A cis-regulatory map of the Drosophila genome. Nature, 471, 527–531
https://doi.org/10.1038/nature09990
pmid: 21430782
|
69 |
M. B. Gerstein, , Z. J. Lu, , E. L. Van Nostrand, , C. Cheng, , B. I. Arshinoff, , T. Liu, , K. Y. Yip, , R. Robilotto, , A. Rechtsteiner, , K. Ikegami, , et al. (2010) Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science, 330, 1775–1787
https://doi.org/10.1126/science.1196914
pmid: 21177976
|
70 |
A. Kundaje, , W. Meuleman, , J. Ernst, , M. Bilenky, , A. Yen, , A. Heravi-Moussavi, , P. Kheradpour, , Z. Zhang, , J. Wang, , M. J. Ziller, , et al. (2015) Integrative analysis of 111 reference human epigenomes. Nature, 518, 317–330
https://doi.org/10.1038/nature14248
pmid: 25693563
|
71 |
D. Bell, , A. Berchuck, , M. Birrer, , J. Chien, , D. W. Cramer, , F. Dao, , R. Dhir, , P. DiSaia, , H. Gabra, , P. Glenn, , et al. (2011) Integrated genomic analyses of ovarian carcinoma. Nature, 474, 609–615
https://doi.org/10.1038/nature10166
pmid: 21720365
|
72 |
A. J. Bass, , V. Thorsson, , I. Shmulevich, , S. M. Reynolds, , M. Miller, , B. Bernard, , T. Hinoue, , P. W. Laird, , C. Curtis, , H. Shen, , et al. (2014) Comprehensive molecular characterization of gastric adenocarcinoma. Nature, 513, 202–209
https://doi.org/10.1038/nature13480
pmid: 25079317
|
73 |
H. Xu, , C. Baroukh, , R. Dannenfelser, , E.Y. Chen, , C. M. Tan, , Y. Kou, , Y. E. Kim, , I. R. Lemischka, and A. Ma’ayan, (2013) ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells. Database, 2013, bat045
https://doi.org/10.1093/database/bat045
|
74 |
S. J. Marygold, , P. C. Leyland, , R. L. Seal, , J. L. Goodman, , J. Thurmond, , V. B. Strelets, and R. J. Wilson, , and the FlyBase consortium. (2013) FlyBase: improvements to the bibliography. Nucleic Acids Res., 41, D751–D757
https://doi.org/10.1093/nar/gks1024
pmid: 23125371
|
75 |
M. C. Costanzo, , S. R. Engel, , E. D. Wong, , P. Lloyd, , K. Karra, , E. T. Chan, , S. Weng, , K. M. Paskov, , G. R. Roe, , G. Binkley, , et al. (2014) Saccharomyces genome database provides new regulation data. Nucleic Acids Res., 42, D717–D725
https://doi.org/10.1093/nar/gkt1158
pmid: 24265222
|
76 |
D. H. Phanstiel, , J. Brumbaugh, , C. D. Wenger, , S. Tian, , M. D. Probasco, , D. J. Bailey, , D. L. Swaney, , M. A. Tervo, , J. M. Bolin, , V. Ruotti, , et al.(2011) Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Methods, 8, 821–827
https://doi.org/10.1038/nmeth.1699
pmid: 21983960
|
77 |
D. Swarbreck, , C. Wilks, , P. Lamesch, , T. Z. Berardini, , M. Garcia-Hernandez, , H. Foerster, , D. Li, , T. Meyer, , R. Muller, , L. Ploetz, , et al. (2008) The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res., 36, D1009–D1014
https://doi.org/10.1093/nar/gkm965
pmid: 17986450
|
78 |
A. Suzuki, , H. Wakaguri, , R. Yamashita, , S. Kawano, , K. Tsuchihara, , S. Sugano, , Y. Suzuki, and K. Nakai, (2015) DBTSS as an integrative platform for transcriptome, epigenome and genome sequence variation data. Nucleic Acids Res., 43, D87–D91
https://doi.org/10.1093/nar/gku1080
pmid: 25378318
|
79 |
H. Sun, , H. Wang, , R. Zhu, , K. Tang, , Q. Gong, , J. Cui, , Z. Cao, and Q. Liu, (2014) iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics, 30, 737–739
https://doi.org/10.1093/bioinformatics/btt576
pmid: 24092766
|
80 |
G. Bebek, and J. Yang, (2007) PathFinder: mining signal transduction pathway segments from protein-protein interaction networks. BMC Bioinformatics, 8, 335
https://doi.org/10.1186/1471-2105-8-335
pmid: 17854489
|
81 |
C. L. Myers, , D. Robson, , A. Wible, , M. A. Hibbs, , C. Chiriac, , C. L. Theesfeld, , K. Dolinski, and O. G. Troyanskaya, (2005) Discovery of biological networks from diverse functional genomic data. Genome Biol., 6, R114
https://doi.org/10.1186/gb-2005-6-13-r114
pmid: 16420673
|
82 |
O. Ourfali, , T. Shlomi, , T. Ideker, , E. Ruppin, and R. Sharan, (2007) SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics, 23, i359–i366
https://doi.org/10.1093/bioinformatics/btm170
pmid: 17646318
|
83 |
O. Basha, , S. Tirman, , A. Eluk, and E. Yeger-Lotem, (2013) ResponseNet2.0: revealing signaling and regulatory pathways connecting your proteins and genes—now with human data. Nucleic Acids Res., 41, W198–W203
https://doi.org/10.1093/nar/gkt532
pmid: 23761447
|
84 |
A. Lan, , I. Y. Smoly, , G. Rapaport, , S. Lindquist, , E. Fraenkel, and E. Yeger-Lotem, (2011) ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res., 39, W424–W429
https://doi.org/10.1093/nar/gkr359
pmid: 21576238
|
85 |
K. Wang, , M. Saito, , B. C. Bisikirska, , M. J. Alvarez, , W. K. Lim, , P. Rajbhandari, , Q. Shen, , I. Nemenman, , K. Basso, , A. A. Margolin, , et al. (2009) Genome-wide identification of post-translational modulators of transcription factor activity in human B cells. Nat. Biotechnol., 27, 829–837
https://doi.org/10.1038/nbt.1563
pmid: 19741643
|
86 |
F. Zhu, and Y. Guan, (2014) Predicting dynamic signaling network response under unseen perturbations. Bioinformatics, 30, 2772–2778
https://doi.org/10.1093/bioinformatics/btu382
pmid: 24919880
|
87 |
J. Chen, and S. Zhang, (2016) Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics, 32, 1724–1732
https://doi.org/10.1093/bioinformatics/btw059
pmid: 26833341
|
88 |
J. Qin, , M. J. Li, , P. Wang, , M. Q. Zhang, and J. Wang, (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
https://doi.org/10.1093/nar/gkr332
pmid: 21586587
|
89 |
P. Wang, , J. Qin, , Y. Qin, , Y. Zhu, , L. Y. Wang, , M. J. Li, , M. Q. Zhang, and J. Wang, (2015) ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks. Nucleic Acids Res., 43, W264–W269
pmid: 25916854
|
90 |
S. Wang, , H. Sun, , J. Ma, , C. Zang, , C. Wang, , J. Wang, , Q. Tang, , C. A. Meyer, , Y. Zhang, and X. S. Liu, (2013) Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc., 8, 2502–2515
https://doi.org/10.1038/nprot.2013.150
pmid: 24263090
|
91 |
J. Qin, , Y. Hu, , F. Xu, , H. K. Yalamanchili, and J. Wang, (2014) Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods. Methods, 67, 294–303
https://doi.org/10.1016/j.ymeth.2014.03.006
pmid: 24650566
|
92 |
M. Maienschein-Cline, , J. Zhou, , K. P. White, , R. Sciammas, and A. R. Dinner, (2012) Discovering transcription factor regulatory targets using gene expression and binding data. Bioinformatics, 28, 206–213
https://doi.org/10.1093/bioinformatics/btr628
pmid: 22084256
|
93 |
G. Wu, and H. Ji, (2013) ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking. BMC Bioinformatics, 14, 188
https://doi.org/10.1186/1471-2105-14-188
pmid: 23758851
|
94 |
B. Tang, , H. K. Hsu, , P. Y. Hsu, , R. Bonneville, , S. S. Chen, , T. H. Huang, and V. X. Jin, (2012) Hierarchical modularity in ERα transcriptional network is associated with distinct functions and implicates clinical outcomes. Sci. Rep., 2, 875
https://doi.org/10.1038/srep00875
pmid: 23166858
|
95 |
B. Yan, , H. Li, , X. Yang, , J. Shao, , M. Jang, , D. Guan, , S. Zou, , C. Van Waes, , Z. Chen, and M. Zhan, (2013) Unraveling regulatory programs for NF-kappaB, p53 and microRNAs in head and neck squamous cell carcinoma. PLoS One, 8, e73656
https://doi.org/10.1371/journal.pone.0073656
pmid: 24069219
|
96 |
R. Pique-Regi, , J. F. Degner, , A. A. Pai, , D. J. Gaffney, , Y. Gilad, and J. K. Pritchard, (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res., 21, 447–455
https://doi.org/10.1101/gr.112623.110
pmid: 21106904
|
97 |
J. C. Huang, , T. Babak, , T. W. Corson, , G. Chua, , S. Khan, , B. L. Gallie, , T. R. Hughes, , B. J. Blencowe, , B. J. Frey, and Q. D. Morris, (2007) Using expression profiling data to identify human microRNA targets. Nat. Methods, 4, 1045–1049
https://doi.org/10.1038/nmeth1130
pmid: 18026111
|
98 |
Z. Liang, , H. Zhou, , Z. He, , H. Zheng, and J. Wu, (2011) mirAct: a web tool for evaluating microRNA activity based on gene expression data. Nucleic Acids Res., 39, W139–144
https://doi.org/10.1093/nar/gkr351
pmid: 21596785
|
99 |
S. Nam, , M. Li, , K. Choi, , C. Balch, , S. Kim, and K. P. Nephew, (2009) MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res., 37, W356–362
https://doi.org/10.1093/nar/gkp294
pmid: 19420067
|
100 |
Sales, G., A. Coppe, , A. Bisognin, , M. Biasiolo, , S. Bortoluzzi, and C. Romualdi, (2010) MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res., 38W352–W359
https://doi.org/10.1093/nar/gkq423
|
101 |
J. Qin, , M. J. Li, , P. Wang, , N. S. Wong, , M. P. Wong, , Z. Xia, , G. S. Tsao, , M. Q. Zhang, and J. Wang, (2013) ProteoMirExpress: inferring microRNA and protein-centered regulatory networks from high-throughput proteomic and mRNA expression data. Mol. Cell. Proteomics, 12, 3379–3387
https://doi.org/10.1074/mcp.O112.019851
pmid: 23924514
|
102 |
L.Y. Wang, , P. Wang, , M.J. Li, , J. Qin, , X. Wang, , M.Q. Zhang, and J. Wang, (2011) EpiRegNet: constructing epigenetic regulatory network from high throughput gene expression data for humans. Epigenetics, 6, 1505–1512
|
103 |
D. Guan, , J. Shao, , Y. Deng, , P. Wang, , Z. Zhao, , Y. Liang, , J. Wang, and B. Yan, (2014) CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data. Bioinformatics, 30, 1190–1192
https://doi.org/10.1093/bioinformatics/btt761
pmid: 24389658
|
104 |
G. T. Huang, , C. Athanassiou, and P. V. Benos, (2011) mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic Acids Res., 39, W416–W423
https://doi.org/10.1093/nar/gkr276
pmid: 21558324
|
105 |
S. Zhang, , Q. Li, , J. Liu, and X. J. Zhou, (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 27, i401–i409
https://doi.org/10.1093/bioinformatics/btr206
pmid: 21685098
|
106 |
S. Zhang, , C. C. Liu, , W. Li, , H. Shen, , P. W. Laird, and X. J. Zhou, (2012) Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res., 40, 9379–9391
https://doi.org/10.1093/nar/gks725
pmid: 22879375
|
107 |
W. Li, , S. Zhang, , C. C. Liu, and X. J. Zhou, (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics, 28, 2458–2466
https://doi.org/10.1093/bioinformatics/bts476
pmid: 22863767
|
108 |
D. Guan, , J. Shao, , Z. Zhao, , P. Wang, , J. Qin, , Y. Deng, , K. R. Boheler, , J. Wang, and B. Yan, (2014) PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data. Nucleic Acids Res., 42, W130–136
https://doi.org/10.1093/nar/gku471
pmid: 24875471
|
109 |
D. Wu, , D. Wang, , M. Q. Zhang, and J. Gu, (2015) Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics, 16, 1022
https://doi.org/10.1186/s12864-015-2223-8
pmid: 26626453
|
110 |
M. Oyama, , H. Kozuka-Hata, , S. Tasaki, , K. Semba, , S. Hattori, , S. Sugano, , J. Inoue, and T. Yamamoto, (2009) Temporal perturbation of tyrosine phosphoproteome dynamics reveals the system-wide regulatory networks. Mol. Cell. Proteomics, 8, 226–231
https://doi.org/10.1074/mcp.M800186-MCP200
pmid: 18815124
|
111 |
B. Bodenmiller, , S. Wanka, , C. Kraft, , J. Urban, , D. Campbell, , P. G. Pedrioli, , B. Gerrits, , P. Picotti, , H. Lam, , O. Vitek, , et al. (2010) Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci. Signal., 3, rs4
https://doi.org/10.1126/scisignal.2001182
pmid: 21177495
|
112 |
C. Caldana, , A. R. Fernie, , L. Willmitzer, and D. Steinhauser, (2012) Unraveling retrograde signaling pathways: finding candidate signaling molecules via metabolomics and systems biology driven approaches. Front. Plant Sci., 3, 267
https://doi.org/10.3389/fpls.2012.00267
pmid: 23227029
|
113 |
J. Zhu, , P. Sova, , Q. Xu, , K. M. Dombek, , E. Y. Xu, , H. Vu, , Z. Tu, , R. B. Brem, , R. E. Bumgarner, and E. E. Schadt, (2012) Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol., 10, e1001301
https://doi.org/10.1371/journal.pbio.1001301
pmid: 22509135
|
114 |
A. K. Jha, , S. C. Huang, , A. Sergushichev, , V. Lampropoulou, , Y. Ivanova, , E. Loginicheva, , K. Chmielewski, , K. M. Stewart, , J. Ashall, , B. Everts, , et al. (2015) Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity, 42, 419–430
https://doi.org/10.1016/j.immuni.2015.02.005
pmid: 25786174
|
115 |
D. Marbach, , J. C. Costello, , R. Küffner, , N. M. Vega, , R. J. Prill, , D. M. Camacho, , K. R. Allison, , The DREAM5 Consortium,M. Kellis, , J. J. Collins, et al. (2012) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796–804
https://doi.org/10.1038/nmeth.2016
pmid: 22796662
|
116 |
Z. Hu, , P. J. Killion, and V. R. Iyer, (2007) Genetic reconstruction of a functional transcriptional regulatory network. Nat. Genet., 39, 683–687
https://doi.org/10.1038/ng2012
pmid: 17417638
|
117 |
L. Song, , Z. Zhang, , L. L. Grasfeder, , A. P. Boyle, , P. G. Giresi, , B. K. Lee, , N. C. Sheffield, , S. Gräf, , M. Huss, , D. Keefe, , et al. (2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res., 21, 1757–1767
https://doi.org/10.1101/gr.121541.111
pmid: 21750106
|
118 |
T. K. Kelly, , Y. Liu, , F. D. Lay, , G. Liang, , B. P. Berman, and P. A. Jones, (2012) Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res., 22, 2497–2506
https://doi.org/10.1101/gr.143008.112
pmid: 22960375
|
119 |
A. Natarajan, , G. G. Yardimci, , N. C. Sheffield, , G. E. Crawford, and U. Ohler, (2012) Predicting cell-type-specific gene expression from regions of open chromatin. Genome Res., 22, 1711–1722
https://doi.org/10.1101/gr.135129.111
pmid: 22955983
|
120 |
X. Lan, , H. Witt, , K. Katsumura, , Z. Ye, , Q. Wang, , E. H. Bresnick, , P. J. Farnham, and V. X. Jin, (2012) Integration of Hi-C and ChIP-seq data reveals distinct types of chromatin linkages. Nucleic Acids Res., 40, 7690–7704
https://doi.org/10.1093/nar/gks501
pmid: 22675074
|
121 |
J. G. Doench, and P. A. Sharp, (2004) Specificity of microRNA target selection in translational repression. Genes Dev., 18, 504–511
https://doi.org/10.1101/gad.1184404
pmid: 15014042
|
122 |
X. Chen, (2004) A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science, 303, 2022–2025
https://doi.org/10.1126/science.1088060
pmid: 12893888
|
123 |
B. Wightman, , I. Ha, and G. Ruvkun, (1993) Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855–862
https://doi.org/10.1016/0092-8674(93)90530-4
pmid: 8252622
|
124 |
S. Vasudevan, , Y. Tong, and J. A. Steitz, (2007) Switching from repression to activation: microRNAs can up-regulate translation. Science, 318, 1931–1934
https://doi.org/10.1126/science.1149460
pmid: 18048652
|
125 |
S. Vasudevan, and J. A. Steitz, (2007) AU-rich-element-mediated upregulation of translation by FXR1 and Argonaute 2. Cell, 128, 1105–1118
https://doi.org/10.1016/j.cell.2007.01.038
pmid: 17382880
|
126 |
Y. Chen, , Y. Wang, , Z. Xuan, , M. Chen, and M. Q. Zhang, (2016) De novo deciphering three-dimensional chromatin interaction and topological domains by wavelet transformation of epigenetic profiles. Nucleic Acids Res., 44, e106
https://doi.org/10.1093/nar/gkw225
pmid: 27060148
|
127 |
M. N. Djekidel, , Z. Liang, , Q. Wang, , Z. Hu, , G. Li, , Y. Chen, and M. Q. Zhang, (2015) 3CPET: finding co-factor complexes from ChIA-PET data using a hierarchical Dirichlet process. Genome Biol., 16, 288
https://doi.org/10.1186/s13059-015-0851-6
pmid: 26694485
|
128 |
S. Whalen, , R. M. Truty, and K. S. Pollard, (2016) Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet., 48, 488–496
https://doi.org/10.1038/ng.3539
pmid: 27064255
|
129 |
Z. Wang, , C. Zang, , J. A. Rosenfeld, , D. E. Schones, , A. Barski, , S. Cuddapah, , K. Cui, , T. Y. Roh, , W. Peng, , M. Q. Zhang, , et al.(2008) Combinatorial patterns of histone acetylations and methylations in the human genome. Nat. Genet., 40, 897–903
https://doi.org/10.1038/ng.154
pmid: 18552846
|
130 |
R. Karlić, , H. R. Chung, , J. Lasserre, , K. Vlahovicek, and M. Vingron, (2010) Histone modification levels are predictive for gene expression. Proc. Natl. Acad. Sci. USA, 107, 2926–2931
https://doi.org/10.1073/pnas.0909344107
pmid: 20133639
|
131 |
Y. Zhu, , L. Sun, , Z. Chen, , J. W. Whitaker, , T. Wang, and W. Wang, (2013) Predicting enhancer transcription and activity from chromatin modifications. Nucleic Acids Res., 41, 10032–10043
https://doi.org/10.1093/nar/gkt826
pmid: 24038352
|
132 |
A. M. Khalil, , M. Guttman, , M. Huarte, , M. Garber, , A. Raj, , D. Rivea Morales, , K. Thomas, , A. Presser, , B. E. Bernstein, , A. van Oudenaarden, , et al. (2009) Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc. Natl. Acad. Sci. USA, 106, 11667–11672
https://doi.org/10.1073/pnas.0904715106
pmid: 19571010
|
133 |
E. C. Knouf, , K. Garg, , J. D. Arroyo, , Y. Correa, , D. Sarkar, , R. K. Parkin, , K. Wurz, , K. C. O’Briant, , A. K. Godwin, , N. D. Urban, , et al. (2012) An integrative genomic approach identifies p73 and p63 as activators of miR-200 microRNA family transcription. Nucleic Acids Res., 40, 499–510
https://doi.org/10.1093/nar/gkr731
pmid: 21917857
|
134 |
M. D. Ritchie, , E. R. Holzinger, , R. Li, , S. A. Pendergrass, and D. Kim, (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet., 16, 85–97
https://doi.org/10.1038/nrg3868
pmid: 25582081
|
135 |
V. N. Kristensen, , O. C. Lingjærde, , H. G. Russnes, , H. K. Vollan, , A. Frigessi, and A. L. Børresen-Dale, (2014) Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer, 14, 299–313
https://doi.org/10.1038/nrc3721
pmid: 24759209
|
136 |
D. Marbach, , S. Roy, , F. Ay, , P. E. Meyer, , R. Candeias, , T. Kahveci, , C. A. Bristow, and M. Kellis, (2012) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks. Genome Res., 22, 1334–1349
https://doi.org/10.1101/gr.127191.111
pmid: 22456606
|
137 |
N. Sintupisut, , P. L. Liu, and C. H. Yeang, (2013) An integrative characterization of recurrent molecular aberrations in glioblastoma genomes. Nucleic Acids Res., 41, 8803–8821
https://doi.org/10.1093/nar/gkt656
pmid: 23907387
|
138 |
B. Palsson, and K. Zengler, (2010) The challenges of integrating multi-omic data sets. Nat. Chem. Biol., 6, 787–789
https://doi.org/10.1038/nchembio.462
pmid: 20976870
|
139 |
L. A. Boyer, , T. I. Lee, , M. F. Cole, , S. E. Johnstone, , S. S. Levine, , J. P. Zucker, , M. G. Guenther, , R. M. Kumar, , H. L. Murray, , R. G. Jenner, , et al. (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell, 122, 947–956
https://doi.org/10.1016/j.cell.2005.08.020
pmid: 16153702
|
140 |
A. Marson, , S. S. Levine, , M. F. Cole, , G. M. Frampton, , T. Brambrink, , S. Johnstone, , M. G. Guenther, , W. K. Johnston, , M. Wernig, , J. Newman, , et al.(2008) Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell, 134, 521–533
https://doi.org/10.1016/j.cell.2008.07.020
pmid: 18692474
|
141 |
L. A. Boyer, , K. Plath, , J. Zeitlinger, , T. Brambrink, , L. A. Medeiros, , T. I. Lee, , S. S. Levine, , M. Wernig, , A. Tajonar, , M. K. Ray, , et al. (2006) Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature, 441, 349–353
https://doi.org/10.1038/nature04733
pmid: 16625203
|
142 |
K. Takahashi, and S. Yamanaka, (2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126, 663–676
https://doi.org/10.1016/j.cell.2006.07.024
pmid: 16904174
|
143 |
F. Anokye-Danso, , C. M. Trivedi, , D. Juhr, , M. Gupta, , Z. Cui, , Y. Tian, , Y. Zhang, , W. Yang, , P. J. Gruber, , J. A. Epstein, , et al. (2011) Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell, 8, 376–388
https://doi.org/10.1016/j.stem.2011.03.001
pmid: 21474102
|
144 |
S. M. Wu, and K. Hochedlinger, (2011) Harnessing the potential of induced pluripotent stem cells for regenerative medicine. Nat. Cell Biol., 13, 497–505
https://doi.org/10.1038/ncb0511-497
pmid: 21540845
|
145 |
Y. Buganim, , D. A. Faddah, and R. Jaenisch, (2013) Mechanisms and models of somatic cell reprogramming. Nat. Rev. Genet., 14, 427–439
https://doi.org/10.1038/nrg3473
pmid: 23681063
|
146 |
C. A. Gifford, , M. J. Ziller, , H. Gu, , C. Trapnell, , J. Donaghey, , A. Tsankov, , A. K. Shalek, , D. R. Kelley, , A. A. Shishkin, , R. Issner, , et al. (2013) Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell, 153, 1149–1163
https://doi.org/10.1016/j.cell.2013.04.037
pmid: 23664763
|
147 |
F. Mohn, , M. Weber, , M. Rebhan, , T. C. Roloff, , J. Richter, , M. B. Stadler, , M. Bibel, and D. Schübeler, (2008) Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. Mol. Cell, 30, 755–766
https://doi.org/10.1016/j.molcel.2008.05.007
pmid: 18514006
|
148 |
A. M. Tsankov, , H. Gu, , V. Akopian, , M. J. Ziller, , J. Donaghey, , I. Amit, , A. Gnirke, and A. Meissner, (2015) Transcription factor binding dynamics during human ES cell differentiation. Nature, 518, 344–349
https://doi.org/10.1038/nature14233
pmid: 25693565
|
149 |
M. A. Choukrallah, , S. Song, , A. G. Rolink, , L. Burger, and P. Matthias, (2015) Enhancer repertoires are reshaped independently of early priming and heterochromatin dynamics during B cell differentiation. Nat. Commun., 6, 8324
https://doi.org/10.1038/ncomms9324
pmid: 26477271
|
150 |
I. Sancho-Martinez, , S. H. Baek, and J. C. Izpisua Belmonte, (2012) Lineage conversion methodologies meet the reprogramming toolbox. Nat. Cell Biol., 14, 892–899
https://doi.org/10.1038/ncb2567
pmid: 22945254
|
151 |
M. Heinäniemi, , M. Nykter, , R. Kramer, , A. Wienecke-Baldacchino, , L. Sinkkonen, , J. X. Zhou, , R. Kreisberg, , S. A. Kauffman, , S. Huang, and I. Shmulevich, (2013) Gene-pair expression signatures reveal lineage control. Nat. Methods, 10, 577–583
https://doi.org/10.1038/nmeth.2445
pmid: 23603899
|
152 |
P. Cahan, , H. Li, , S. A. Morris, , E. Lummertz da Rocha, , G. Q. Daley, and J. J. Collins, (2014) CellNet: network biology applied to stem cell engineering. Cell, 158, 903–915
https://doi.org/10.1016/j.cell.2014.07.020
pmid: 25126793
|
153 |
O. J. Rackham, , J. Firas, , H. Fang, , M. E. Oates, , M. L. Holmes, , A. S. Knaupp, , H. Suzuki, , C. M. Nefzger, , C. O. Daub, , J. W. Shin, , et al. (2016) A predictive computational framework for direct reprogramming between human cell types. Nat. Genet., 48, 331–335
https://doi.org/10.1038/ng.3487
pmid: 26780608
|
154 |
M. J. Li, , P. Wang, , X. Liu, , E. L. Lim, , Z. Wang, , M. Yeager, , M. P. Wong, , P. C. Sham, , S. J. Chanock, and J. Wang, (2012) GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res., 40, D1047–D1054
https://doi.org/10.1093/nar/gkr1182
pmid: 22139925
|
155 |
M. J. Li, , P. C. Sham, and J. Wang, (2012) Genetic variant representation, annotation and prioritization in the post-GWAS era. Cell Res., 22, 1505–1508
https://doi.org/10.1038/cr.2012.106
pmid: 22801476
|
156 |
M. J. Li, , B. Yan, , P. C. Sham, and J. Wang, (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
https://doi.org/10.1093/bib/bbu018
pmid: 24916300
|
157 |
J. A. Alfaro, , A. Sinha, , T. Kislinger, and P. C. Boutros, (2014) Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat. Methods, 11, 1107–1113
https://doi.org/10.1038/nmeth.3138
pmid: 25357240
|
158 |
The Cancer Genome Atlas Research Network (2013) Integrated genomic characterization of endometrial carcinoma. Nature, 497, 67–73
https://doi.org/10.1038/nature12113
pmid: 23636398
|
159 |
B. Wang, , A. M. Mezlini, , F. Demir, , M. Fiume, , Z. Tu, , M. Brudno, , B. Haibe-Kains, and A. Goldenberg, (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods, 11, 333–337
https://doi.org/10.1038/nmeth.2810
pmid: 24464287
|
160 |
Y. Yuan, , E. M. Van Allen, , L. Omberg, , N. Wagle, , A. Amin-Mansour, , A. Sokolov, , L. A. Byers, , Y. Xu, , K. R. Hess, , L. Diao, , et al. (2014) Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol., 32, 644–652
https://doi.org/10.1038/nbt.2940
pmid: 24952901
|
161 |
J. Ernst, and M. Kellis, (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol., 33, 364–376
https://doi.org/10.1038/nbt.3157
pmid: 25690853
|
162 |
J. Marchini, and B. Howie, (2010) Genotype imputation for genome-wide association studies. Nat. Rev. Genet., 11, 499–511
https://doi.org/10.1038/nrg2796
pmid: 20517342
|
163 |
A. Gusev, , A. Ko, , H. Shi, , G. Bhatia, , W. Chung, , B. W. Penninx, , R. Jansen, , E. J. de Geus, , D. I. Boomsma, , F. A. Wright, , et al. (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 48, 245–252
https://doi.org/10.1038/ng.3506
pmid: 26854917
|
164 |
M. E. Ritchie, , B. Phipson, , D. Wu, , Y. Hu, , C. W. Law, , W. Shi, and G. K. Smyth, (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43, e47
https://doi.org/10.1093/nar/gkv007
pmid: 25605792
|
165 |
D. A. Orlando, , M. W. Chen, , V. E. Brown, , S. Solanki, , Y. J. Choi, , E. R. Olson, , C. C. Fritz, , J. E. Bradner, and M. G. Guenther, (2014) Quantitative ChIP-Seq normalization reveals global modulation of the epigenome. Cell Reports, 9, 1163–1170
https://doi.org/10.1016/j.celrep.2014.10.018
pmid: 25437568
|
166 |
A. Diaz, , K. Park, , D.A. Lim, and J.S. Song, (2012) Normalization, bias correction, and peak calling for ChIP-seq. Stat. Appl. Genet. Mol. Biol. 11, Article 9
https://doi.org/10.1515/1544-6115.1750
|
167 |
M. Sysi-Aho, , M. Katajamaa, , L. Yetukuri, and M. Oresic, (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics, 8, 93
https://doi.org/10.1186/1471-2105-8-93
pmid: 17362505
|
168 |
W. E. Johnson, , C. Li, and A. Rabinovic, (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127
https://doi.org/10.1093/biostatistics/kxj037
pmid: 16632515
|
169 |
J. T. Leek, and J. D. Storey, (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3, e161
https://doi.org/10.1371/journal.pgen.0030161
pmid: 17907809
|
170 |
J.A. Thompson, , J. Tan, and C.S. Greene, (2016) Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ, 4, e1621
|
171 |
C. Zang, , T. Wang, , K. Deng, , B. Li, , S. Hu, , Q. Qin, , T. Xiao, , S. Zhang, , C. A. Meyer, , H. H. He, , et al. (2016) High-dimensional genomic data bias correction and data integration using MANCIE. Nat. Commun., 7, 11305
https://doi.org/10.1038/ncomms11305
pmid: 27072482
|
172 |
J. Rudy, and F. Valafar, (2011) Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics, 12, 467
https://doi.org/10.1186/1471-2105-12-467
pmid: 22151536
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|