Towards precise reconstruction of gene regulatory networks by data integration

Abstract

Background: More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks.

Results: In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences.

Conclusions: We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.

Graphical abstract

Keywords

gene regulatory network / computational inference / data integration / bioinformatics

References

[1] Marx, V. (2013) Biology: the big challenges of big data. Nature, 498, 255–260

[2] Babu, M. M., Luscombe, N. M., Aravind, L., Gerstein, M. and Teichmann, S. A. (2004) Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol., 14, 283–291

[3] Liu, Z. P. (2015) Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr. Genomics, 16, 3–22

[4] Lee, T. I. and Young, R. A. (2013) Transcriptional regulation and its misregulation in disease. Cell, 152, 1237–1251

[5] Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M. K., Chuang, R., Jaehnig, E. J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., (2010) Rewiring of genetic networks in response to DNA damage. Science, 330, 1385–1389

[6] Johnson, D. S., Mortazavi, A., Myers, R. M. and Wold, B. (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science, 316, 1497–1502

[7] Park, P. J. (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet., 10, 669–680

[8] Edgar, R., Domrachev, M. and Lash, A. E. (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res., 30, 207–210

[9] Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J., Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P., Lara, G. G., (2003) ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res., 31, 68–71

[10] Jaenisch, R. and Bird, A. (2003) Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat. Genet., 33, 245–254

[11] Song, C. X., Yi, C. and He, C. (2012) Mapping recently identified nucleotide variants in the genome and transcriptome. Nat. Biotechnol., 30, 1107–1116

[12] Schena, M., Shalon, D., Davis, R. W. and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467–470

[13] Wang, Z., Gerstein, M. and Snyder, M. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57–63

[14] Blackham, S., Baillie, A., Al-Hababi, F., Remlinger, K., You, S., Hamatake, R. and McGarvey, M. J. (2010) Gene expression profiling indicates the roles of host oxidative stress, apoptosis, lipid metabolism, and intracellular transport genes in the replication of hepatitis C virus. J. Virol., 84, 5404–5414

[15] Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., Kawasaki, E. S., Lee, K. Y., . (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151–1161

[16] Wu, S., Liu, Z. P., Qiu, X. and Wu, H. (2014) Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations. PLoS One, 9, e95276

[17] Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U. and Timmer, J. (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25, 1923–1929

[18] Leinonen, R., Sugawara, H. and Shumway, M., and the International Nucleotide Sequence Database Collaboration. (2011) The sequence read archive. Nucleic Acids Res., 39, D19–D21

[19] The Cancer Genome Atlas Research Network. (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455, 1061–1068

[20] Hudson, T. J., Anderson, W., Artez, A., Barker, A. D., Bell, C., Bernabé R. R., Bhan, M. K., Calvo, F., Eerola, I., Gerhard, D. S., (2010) International network of cancer genome projects. Nature, 464, 993–998

[21] Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. and Sayers, E. W. (2013) GenBank. Nucleic Acids Res., 41, D36–D42

[22] Maher, B. (2012) ENCODE: the human encyclopaedia. Nature, 489, 46–48

[23] Muers, M. (2011) Functional genomics: the modENCODE guide to the genome. Nat. Rev. Genet., 12, 80

[24] Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., (2010) The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol., 28, 1045–1048

[25] Fingerman, I. M., McDaniel, L., Zhang, X., Ratzat, W., Hassan, T., Jiang, Z., Cohen, R. F. and Schuler, G. D. (2011) NCBI Epigenomics: a new public resource for exploring epigenomic data sets. Nucleic Acids Res., 39, D908–D912

[26] Cantara, W. A., Crain, P. F., Rozenski, J., McCloskey, J. A., Harris, K. A., Zhang, X., Vendeix, F. A., Fabris, D. and Agris, P. F. (2011) The RNA Modification Database, RNAMDB: 2011 update. Nucleic Acids Res., 39, D195–D201

[27] Machnicka, M. A., Milanowska, K., Osman Oglou, O., Purta, E., Kurkowska, M., Olchowik, A., Januszewski, W., Kalinowski, S., Dunin-Horkawicz, S., Rother, K. M., (2013) MODOMICS: a database of RNA modification pathways—2013 update. Nucleic Acids Res., 41, D262–D267

[28] Bujold, D., de Lima Morais, D.A., Gauthier, C., Côté C., Caron, M., Kwan, T., Chen, K.T., Laperle, J., Markovits, A. N., Pastinen, T., (2016) The International Human Epigenome Consortium Data Portal. Cell Syst., 3, 496–499

[29] Ardlie, K. G., Deluca, D. S., Segre, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., Lek, M., (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science, 348, 648–660

[30] Matys, V., Fricke, E., Geffers, R., Gössling, E., Haubrock, M., Hehl, R., Hornischer, K., Karas, D., Kel, A. E., Kel-Margoulis, O. V., (2003) TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res., 31, 374–378

[31] Bryne, J. C., Valen, E., Tang, M. H., Marstrand, T., Winther, O., da Piedade, I., Krogh, A., Lenhard, B. and Sandelin, A. (2008) JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update. Nucleic Acids Res., 36, D102–D106

[32] Liu, Z. P., Wu, C., Miao, H. and Wu, H. (2015) RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford), 2015, bav095

[33] Xie, C., Yuan, J., Li, H., Li, M., Zhao, G., Bu, D., Zhu, W., Wu, W., Chen, R. and Zhao, Y. (2014) NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res., 42, D98–D103

[34] The RNAcentral Consortium. (2015) RNAcentral: an international database of ncRNA sequences. Nucleic Acids Res., 43, D123–D129

[35] Sethupathy, P., Corda, B. and Hatzigeorgiou, A. G. (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA, 12, 192–197

[36] Volders, P. J., Helsens, K., Wang, X., Menten, B., Martens, L., Gevaert, K., Vandesompele, J. and Mestdagh, P. (2013) LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res., 41, D246–D251

[37] Amaral, P. P., Clark, M. B., Gascoigne, D. K., Dinger, M. E. and Mattick, J. S. (2011) lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res., 39, D146–D151

[38] Griffiths-Jones, S., Saini, H. K., van Dongen, S. and Enright, A. J. (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res., 36, D154–D158

[39] Glažar, P., Papavasileiou, P. and Rajewsky, N. (2014) circBase: a database for circular RNAs. RNA, 20, 1666–1670

[40] Yang, J. H., Li, J. H., Jiang, S., Zhou, H. and Qu, L. H. (2013) ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res., 41, D177–D187

[41] Wang, Q., Huang, J., Sun, H., Liu, J., Wang, J., Wang, Q., Qin, Q., Mei, S., Zhao, C., Yang, X., (2014) CR Cistrome: a ChIP-Seq database for chromatin regulators and histone modification linkages in human and mouse. Nucleic Acids Res., 42, D450–D458

[42] Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. and Bourne, P. E. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235–242

[43] The UniProt Consortium. (2008) The universal protein resource (UniProt). Nucleic Acids Res., 36, D190–D195

[44] von Mering, C., Jensen, L. J., Kuhn, M., Chaffron, S., Doerks, T., Krüger, B., Snel, B. and Bork, P. (2007) STRING 7—recent developments in the integration and prediction of protein interactions. Nucleic Acids Res., 35, D358–D362

[45] Zhang, X., Zhao, X. M., He, K., Lu, L., Cao, Y., Liu, J., Hao, J. K., Liu, Z. P. and Chen, L. (2012) Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics, 28, 98–104

[46] Zhang, B. and Horvath, S. (2005) A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 4, Article17

[47] Meyer, P. E., Lafitte, F. and Bontempi, G. (2008) minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics, 9, 461

[48] Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R. and Califano, A. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7, S7

[49] Wilczyński, B. and Dojer, N. (2009) BNFinder: exact and efficient method for learning Bayesian networks. Bioinformatics, 25, 286–287

[50] Scutari, M. (2010) Learning Bayesian Networks with the bnlearn R Package. J. Stat. Softw., 35, 1–22

[51] Shmulevich, I., Dougherty, E. R., Kim, S. and Zhang, W. (2002) Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18, 261–274

[52] Müssel, C., Hopfensitz, M. and Kestler, H. A. (2010) BoolNe—an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics, 26, 1378–1380

[53] Schaffter, T., Marbach, D. and Floreano, D. (2011) GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 27, 2263–2270

[54] Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L., Baliga, N. S. and Thorsson, V. (2006) The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol., 7, R36

[55] Liu, Z. P., Zhang, W., Horimoto, K. and Chen, L. (2013) Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data. IET Syst. Biol., 7, 143–152

[56] Liu, Z. P., Wu, H., Zhu, J. and Miao, H. (2014) Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection. BMC Bioinformatics, 15, 336

[57] Haury, A. C., Mordelet, F., Vera-Licona, P. and Vert, J. P. (2012) TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst. Biol., 6, 145

[58] Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. and Geurts, P. (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS One, 5, e12776

[59] Langfelder, P. and Horvath, S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559

[60] Liu, Z. P. (2017) Quantifying gene regulatory relationships with association measures: a comparative study. Front. Genet., 8, 96

[61] Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U., Dalla-Favera, R. and Califano, A. (2005) Reverse engineering of regulatory networks in human B cells. Nat. Genet., 37, 382–390

[62] Friedman, N. (2004) Inferring cellular networks using probabilistic graphical models. Science, 303, 799–805

[63] Zou, M. and Conzen, S. D. (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71–79

[64] Amit, I., Garber, M., Chevrier, N., Leite, A. P., Donner, Y., Eisenhaure, T., Guttman, M., Grenier, J. K., Li, W., Zuk, O., (2009) Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science, 326, 257–263

[65] Thomas, R. (1973) Boolean formalization of genetic control circuits. J. Theor. Biol., 42, 563–585

[66] Akutsu, T., Miyano, S. and Kuhara, S. (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput, 99, 17–28

[67] Saito, S., Aburatani, S. and Horimoto, K. (2008) Network evaluation from the consistency of the graph structure with the measured data. BMC Syst. Biol., 2, 84

[68] Jordan, M. I. and Mitchell, T. M. (2015) Machine learning: trends, perspectives, and prospects. Science, 349, 255–260

[69] Marbach, D., Roy, S., Ay, F., Meyer, P. E., Candeias, R., Kahveci, T., Bristow, C. A. and Kellis, M. (2012) Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks. Genome Res., 22, 1334–1349

[70] Bartel, D. P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281–297

[71] Pefanis, E., Wang, J., Rothschild, G., Lim, J., Kazadi, D., Sun, J., Federation, A., Chao, J., Elliott, O., Liu, Z. P., (2015) RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity. Cell, 161, 774–789

[72] Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., (2013) Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 495, 333–338

[73] Garber, M., Grabherr, M. G., Guttman, M. and Trapnell, C. (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods, 8, 469–477

[74] Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U. and Speed, T. P. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249–264

[75] Elowitz, M. B., Levine, A. J., Siggia, E. D. and Swain, P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186

[76] Gibcus, J. H. and Dekker, J. (2012) The context of gene expression regulation. F1000 Biol. Rep., 4, 8

[77] Ideker, T., Dutkowski, J. and Hood, L. (2011) Boosting signal-to-noise in complex biology: prior knowledge is power. Cell, 144, 860–863

[78] de la Fuente, A., Bing, N., Hoeschele, I. and Mendes, P. (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics, 20, 3565–3574

[79] Zheng, G., Xu, Y., Zhang, X., Liu, Z. P., Wang, Z., Chen, L. and Zhu, X. G. (2016) CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data. BMC Bioinformatics, 17, 535

[80] Burchard, J., Zhang, C., Liu, A. M., Poon, R. T., Lee, N. P., Wong, K. F., Sham, P. C., Lam, B. Y., Ferguson, M. D., Tokiwa, G., (2010) microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma. Mol. Syst. Biol., 6, 402

[81] Liu, Z. P. (2014) Systematic identification of local structure binding motifs in protein-RNA recognition. In: Proceedings of 8th International Conference on Systems Biology, pp. 74–80

[82] Cheng, C., Yan, K. K., Hwang, W., Qian, J., Bhardwaj, N., Rozowsky, J., Lu, Z. J., Niu, W., Alves, P., Kato, M., (2011) Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data. PLoS Comput. Biol., 7, e1002190

[83] The ENCODE Project Consortium. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74

[84] Amaral, P. P., Dinger, M. E., Mercer, T. R. and Mattick, J. S. (2008) The eukaryotic genome as an RNA machine. Science, 319, 1787–1789

[85] Spitz, F. and Furlong, E. E. (2012) Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet., 13, 613–626

[86] Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. and Guthke, R. (2009) Gene regulatory network inference: data integration in dynamic models-a review. Biosystems, 96, 86–103

[87] Jensen, S. T., Chen, G. and Stoeckert, C. J. Jr (2007) Bayesian variable selection and data integration for biological regulatory networks. Ann. Appl. Stat., 1, 612–633

[88] Yeung, M. K., Tegnér, J. and Collins, J. J. (2002) Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA, 99, 6163–6168

[89] Tegner, J., Yeung, M. K., Hasty, J. and Collins, J. J. (2003) Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. USA, 100, 5944–5949

[90] Lam, K. Y., Westrick, Z. M., Müller, C. L., Christiaen, L. and Bonneau, R. (2016) Fused regression for multi-source gene regulatory network inference. PLoS Comput. Biol., 12, e1005157

[91] Werhli, A. V. and Husmeier, D. (2007) Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol., 6, Article15

[92] Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E. and Schadt, E. E. (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet., 40, 854–861

[93] Santra, T. (2014) A Bayesian framework that integrates heterogeneous data for inferring gene regulatory networks. Front. Bioeng. Biotechnol., 2, 13

[94] De Smet, R. and Marchal, K. (2010) Advantages and limitations of current network inference methods. Nat. Rev. Microbiol., 8, 717–729

[95] Mordelet, F. and Vert, J. P. (2008) SIRENE: supervised inference of regulatory networks. Bioinformatics, 24, i76–i82

[96] Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344, 1396–1401

[97] Djebali, S., Davis, C. A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., (2012) Landscape of transcription in human cells. Nature, 489, 101–108

[98] Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S. and Elowitz, M. B. (2005) Gene regulation at the single-cell level. Science, 307, 1962–1965

[99] Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J. and Stolovitzky, G., (2012) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796–804

[100] Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., (2015) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol., 33, 269–276

[101] Graham, J. E. Marians, K. J.and Kowalczykowski, S. C. (2017) Independent and stochastic action of DNA polymerases in the replisome. Cell, 169, 1201–1213

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature