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Quantitative Biology

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Quant. Biol.    2016, Vol. 4 Issue (4) : 283-301    https://doi.org/10.1007/s40484-016-0085-y
REVIEW
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
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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
 Cite this article:   
Jing Qin,Bin Yan,Yaohua Hu, et al. Applications of integrative OMICs approaches to gene regulation studies[J]. Quant. Biol., 2016, 4(4): 283-301.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-016-0085-y
https://academic.hep.com.cn/qb/EN/Y2016/V4/I4/283
Fig.1  High-throughput OMICs data describing the complex gene regulatory system.
TechnologyData typeDescription
Protein arrayKinase-substrate interactomeProtein array tracks the interactions and activities of proteins [2]
IP-MSProtein-protein interactomeImmunoprecipitation followed by mass spectrometry identifies interacting partners of a protein of interests [14]
Y2HProtein-protein interactomeYeast two-hybrid screening discovers protein-protein interactions [4,15]
TAP-MSProtein-protein interactomeTandem affinity purification combined with mass spectrometry identifies components of protein complexes [3]
MSMetabolomeMass spectrometry measures the consumption and release metabolites [16]
ChIP-seq/chipProtein-DNA interactomeChromatin immunoprecipitation coupled with sequencing or microarray reveals the repertoire of in vivo protein (TF, chromatin modifier, etc.) binding sites on the genome [17]
ChIP-exoProtein-DNA interactomeChromatin immunoprecipitation combines with the use of exonucleases to achieve a high resolution of protein binding sites [18]
BS-seqDNA methylomeBisulfite sequencing uses bisulfite treatment of DNA to determine its pattern of methylation [19]
MSCCDNA methylomeMethyl sensitive cut counting, a whole genome methylation profiling method based on the sensitivity of CCGG sites to the restriction enzymes [20]
MeDIPDNA methylomeMethylated DNA immunoprecipitation coupled with microarray or sequencing enriches methylated DNA sequences which then detected by microarray or sequencing [21]
BSPPDNA methylomeBisulfite padlock probes, a targeted method that isolates selected locations for methylation profiling [20]
RRBSDNA methylomeReduced representation bisulfite sequencing combines restriction enzymes and bisulfite sequencing to enrich high CpG sequence and measure their methylation levels [22]
Methyl-MAPSDNA methylomeMethylation mapping analysis by paired-end sequencing [23]
DNase-seqOpen chromatin regionDNase I hypersensitive sites sequencing identifies the location of regulatory regions, based on the genome-wide sequencing of regions super sensitive to cleavage by DNase I [24]
Sono-seqOpen chromatin regionMethod for isolating protein-free genomic regions by sonication of chromatin combined with a size-selection step and massively parallel short-read sequencing [25]
ATAC-seqOpen chromatin regionAssay for detecting transposase-accessible chromatin using sequencing [26]
FAIRE-SeqOpen chromatin regionFormaldehyde-assisted isolation of regulatory elements determines the sequences of regulatory region [27]
NOMe-seqNucleosome occupancy and methylomeA high-resolution single-molecule mapping approach to simultaneously investigate endogenous DNA methylation and nucleosome occupancies [28]
MNase-seqNucleosome occupancyPaired-end sequencing of micrococcal nuclease-digested chromatin determines genome-wide nucleosome occupancy [29]
Hi-C/4C/5CLong-range DNA interactomeTechniques to detect chromatin interactions based on chromosome conformation capture [6]
ChIA-PETLong-range DNA interactomeChromatin interaction analysis by paired-end tag sequencing determines long-range interactions [6]
RNA-seqTranscriptomeRNA sequencing measures the abundance of RNA transcripts [30]
MicroarrayTranscriptomeMicroarray contains probes designed for RNA transcripts, on which hybridization signals indicate the abundance of RNA transcripts [31]
GRO-seqTranscriptomeGlobal run-on sequencing identifies the genes that are being transcribed [32]
CLIP-seqProtein-RNA interactomeSequencing of RNA isolated by crosslinking immunoprecipitation maps protein-RNA binding sites in vivo [33]
Degradome-seqProtein-RNA interactomeDegradome sequencing, also known as parallel analysis of RNA ends (PARE) sequencing, detects cDNA ends, which implies mRNAs that are degraded under miRNA regulations [34]
RIP-seqProtein-RNA interactomeRNA-immunoprecipitation sequencing captures the protein-bound RNAs [12]
CLASHmiRNA-RNA interactomeCrosslinking, ligation, and sequencing of hybrids directly map miRNA-RNA interactions [35]
dChIRP-seqRNA-RNA, RNA-DNA and Protein-RNA interactomeDomain-specific chromatin isolation by RNA purification followed by sequencing dissects pairwise RNA-RNA, RNA-protein and RNA-chromatin interactions that reveal lncRNA architecture and function [11]
Structure-seqRNA structureA high-throughput and quantitative method detects genome-wide information on RNA structure at single-nucleotide resolution [36]
CGHGenomeComparative genomic hybridization surveys copy number variations [37]
DNA-seqGenomeDNA sequencing for genome assembly or resequencing for detection of variations [38]
Exome-seqGenomeDNA sequencing of exomes for detection of variations [39]
SILACProteomeStable isotope labeling with amino acids in cell culture quantitatively detects differences in protein abundance among samples using non-radioactive isotopic labeling [40]
PRISMProteomeProteomic investigation strategy for mammals [41]
DEEP SEQ MSProteomeDeep efficient peptide sequencing and quantification mass spectrometry [42]
Ribo-seqProteomeRibosome profiling determines the mRNAs that are being actively translated and measures the translation efficiency [8]
Tab.1  
Fig.2  Statistics of OMICs studies.
ToolReferenceDescription
Signaling pathway/network analysis
iPEAP [79]Integrative Pathway Enrichment Analysis Platform aggregates transcriptome, proteome, metabolome and GWAS data to detect enriched signaling pathways
PathFinder [80]With known PPI network, PathFinder utilizes transcriptome data, protein subcellular localization and sequence information to filter the false positives, and incorporates protein families to fix false negative pairs
bioPIXIE [81]An integrative system combines PPI network and gene expression data to find pathways
SPINE [82]Signaling-regulatory Pathway INferencE constructs PPI and protein-DNA interaction networks, and uses an integer programming solver to get final pathways from knockout expression data
ReponseNet [83,84]ReponseNet uses a network-optimization approach to detect both signaling and regulatory networks from the integration of genetic screen, transcriptome data and PPI networks
MINDy [85]Modulator inference by network dynamics (MINDy) facilitates genome-wide identification of cell-specific post-translational modulators of TF activity, which dissect the cross-talk between signaling pathways and transcriptional regulations
Zhu and Guan [86]A Markov chain theory is applied to detect signaling networks using a known phosphorylation networks
CEASAR [2]A comprehensive integrative approach incorporating functional protein microarrays, mass spectrometry and bioinformatics to detect cell-specific genome-wide phosphorylation network
SNPLS [87]Sparse Network-regularized Partial Least Square identifies gene-drug modules from large-scale pairwise gene-expression and drug-response data
TF-gene regulation
ChIP-Array [88]A web server that integrates ChIP-seq/chip and expression data to detect direct and indirect target genes of a TF of interest
ChIP-Array 2 [89]An enhanced version of ChIP-Array integrates long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components
BETA [90]Binding and Expression Target Analysis ranks the direct targets of a TF based on two probability ranking derived from ChIP-seq/chip and transcriptome data
LpRGNI [91]Inferring gene regulatory networks by integrating ChIP- seq/chip and transcriptome via LASSO-type regularization methods
EMBER [92]Expectation Maximization of Binding and Expression pRofiles, a unsupervised machine learning algorithm to search enriched gene expression pattern around TFBSs
ChIPXpress [93]An R package that rank TF targets by integrating ChIP-seq/chip data with large amounts of Publicly available gene Expression Data
Tang et al. [94]Bayesian statistical modeling and modularity analysis integrates time-series ChIP-seq and gene expression data to construct dynamic regulatory network for any given TF
Yan et al. [95]A bioinformatics method to uncover interactive relationships between TFs or microRNAs and genes based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity
Pique-Regi et al. [96]A probabilistic framework that integrates epigenetic data with genomic information to draw a genome-wide map of tissue specific TFBS
miRNA-gene regulation
GenMiR++ [97]GenMir++ uses a Bayesian model to predict miRNA targets based on both mRNA 3′ UTR region sequence features and the correlation between expressions of miRNA and its targets
mirAct [98]A tool designed to investigate miRNA activity based on gene-expression data by using the negative regulation relationship between miRNAs and their target genes
MMIA [99]MiRNA and MRNA Integrated Analysis is a web server that integrates miRNA and mRNA expression data with predicted miRNA target information for analyzing miRNA-associated phenotypes and biological functions
MAGIA[100]MiRNA and Genes Integrated Analysis is a web tool for the integrative analysis of miRNA target predictions
ProteoMirExpress [101]A web server that combines proteome and transcriptome data to infer miRNA-centered regulatory networks
Epigenetic regulation
EpiRegNet [102]A web server that detects important histone modifications affecting the expression of genes
CMGRN [103]Constructing Multilevel Gene Regulatory Networks uses the Bayesian network modeling to infer causal interrelationships among transcription factors and epigenetic modifications
Multi-dimensional integration
mirConnX [104]Condition-specific mRNA-miRNA network integrator uses TF binding in the promoter regions of miRNAs and mRNA, as well as predicted miRNA targets, to construct TF-miRNA-gene regulatory network
SNMNMF [105]A Sparse Network-Regularized Multiple Non-negative Matrix Factorization framework integrates miRNA and mRNA profiles, as well as miRNA-mRNA, TF-gene and PPI networks, for achieving modular patterns
jNMF [106]Joint Non-negative Matrix Factorization framework identifies multi-dimensional modules
sMBPLS [107]Sparse Multi-Block Partial Least Squares regression method identifies regulatory modules from multiple OMICs data
PTHGRN [108]Post-translational hierarchical GRN constructs multi-layer network by virtue of a graphical Gaussian model with partial least squares regression-based methodology
LRAcluster [109]A method using low-rank approximation based integrative probabilistic model to perform fast dimension reduction and unsupervised clustering of large-scale multi-OMICs data
Tab.2  Current bioinformatics tools for integrative analysis of OMICs data.
Fig.3  Integrative OMICs have profoundly changed the strategy on basic biological research and is playing significant roles in medical fields.
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
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