BACKGROUND: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. RESULTS: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. CONCLUSIONS: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.

Faculty of Informatics Masaryk University Botanicka 68a Brno Czech Republic

RECETOX Faculty of Science Masaryk University Kotlarska 2 Brno Czech Republic

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BMC Bioinformatics. 2022 Sep 15;23(1):376. doi: 10.1186/s12859-022-04931-4. PubMed

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Djebbari A, Quackenbush J. Seeded Bayesian Networks: constructing genetic networks from microarray data. BMC Syst Biol. 2008 doi: 10.1186/1752-0509-2-57. PubMed DOI PMC

Gao S, Wang X. Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data. BMC Bioinform. 2011 doi: 10.1186/1471-2105-12-359. PubMed DOI PMC

Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S. Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J. Bioinform. Comput. Biol. 2004;2:77–98. doi: 10.1142/s021972000400048x. PubMed DOI

Werhli AV, Husmeier D. Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. Stat Appl Genet Mol Biol. 2007 doi: 10.2202/1544-6115.1282. PubMed DOI

de Campos LM, Cano A, Castellano JG, Moral S. Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions. Stat Appl Genet Mol Biol. 2019;18. PubMed

Calkhoven CF, Ab G. Multiple steps in the regulation of transcription-factor level and activity. Biochem J. 1996;317:329–342. doi: 10.1042/bj3170329. PubMed DOI PMC

Shlien A, Malkin D. Copy number variations and cancer. Genome Med. 2009 doi: 10.1186/gm62. PubMed DOI PMC

Herman JG. Hypermethylation of tumor suppressor genes in cancer. Semin Cancer Biol. 1999;9:359–367. doi: 10.1006/scbi.1999.0138. PubMed DOI

Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo Q-M, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462:315–322. doi: 10.1038/nature08514. PubMed DOI PMC

Brenet F, Moh M, Funk P, Feierstein E, Viale AJ, Socci ND, Scandura JM. DNA methylation of the first exon is tightly linked to transcriptional silencing. PLoS ONE. 2011 doi: 10.1371/journal.pone.0014524. PubMed DOI PMC

Yang X, Han H, de Carvalho DD, Lay FD, Jones PA, Liang G. Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell. 2014;26:577–590. doi: 10.1016/j.ccr.2014.07.028. PubMed DOI PMC

Xu Y, Zhang J, Yuan Y, Mitra R, Müller P, Ji Y. A Bayesian graphical model for integrative analysis of TCGA data. IEEE Int Workshop Genomic Signal Process Stat. 2012 doi: 10.1109/GENSIPS.2012.6507747. PubMed DOI PMC

Ni Y, Stingo FC, Baladandayuthapani V. Integrative Bayesian network analysis of genomic data. Cancer Inform. 2014;13:39–48. doi: 10.4137/CIN.S13786. PubMed DOI PMC

Kim D-C, Kang M, Zhang B, Wu X, Liu C, Gao J. Integration of DNA methylation, copy number variation, and gene expression for gene regulatory network inference and application to psychiatric disorders. In: 2014 IEEE international conference on bioinformatics and bioengineering, 2014;238–42. 10.1109/BIBE.2014.71.

Zarayeneh N, Ko E, Oh JH, Suh S, Liu C, Gao J, Kim D, Kang M. Integration of multi-omics data for integrative gene regulatory network inference. Int J Data Min Bioinform. 2017;18:223–239. doi: 10.1504/IJDMB.2017.10008266. PubMed DOI PMC

Yuan L, Guo L-H, Yuan C-A, Zhang Y-H, Han K, Nandi A, Honig B, Huang D-S. Integration of Multi-omics Data for Gene Regulatory Network Inference and Application to Breast Cancer. EEE/ACM Trans Comput Biol Bioinform. 2018;16:782–791. doi: 10.1109/TCBB.2018.2866836. PubMed DOI

Zhao Y, Hoang TH, Joshi P, Hong S-H, Giardina C, Shin D-G. A route-based pathway analysis framework integrating mutation information and gene expression data. Methods. 2017;124:3–12. doi: 10.1016/j.ymeth.2017.06.016. PubMed DOI

Li Y, Liang M, Zhang Z. Regression analysis of combined gene expression regulation in acute Myeloid Leukemia. PLOS Comput Biol. 2014 doi: 10.1371/journal.pcbi.1003908. PubMed DOI PMC

Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker-Jensen DB, Kranz J, Bindels EMJ, Costa ASH, Sousa A, Beltrao P, Rocha M, Olsen JV, Frezza C, Kramann R, Saez-Rodriguez J. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol. 2021 doi: 10.15252/msb.20209730. PubMed DOI PMC

Liu A, Trairatphisan P, Gjerga E, Didangelos A, Barratt J, Saez-Rodriguez J. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. npj Syst Biol Appl. 2019 doi: 10.1038/s41540-019-0118-z. PubMed DOI PMC

Ogris C, Hu Y, Arloth J, Müller NS. Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data. Sci Rep. 2021 doi: 10.1038/s41598-021-85544-4. PubMed DOI PMC

Simon N, Friedman J, Hastie T, Tibshirani R. A Sparse-Group Lasso. J Comput Graph Stat. 2013;22:231–245. doi: 10.1080/10618600.2012.681250. DOI

Siebert JC, Saint-Cyr M, Borengasser SJ, Wagner BD, Lozupone CA, Görg C. CANTARE: finding and visualizing network-based multi-omic predictive models. BMC Bioinform. 2021 doi: 10.1186/s12859-021-04016-8. PubMed DOI PMC

Lappalainen I, Lopez J, Skipper L, Hefferon T, Spalding JD, Garner J, Chen C, Maguire M, Corbett M, Zhou G, Paschall J, Ananiev V, Flicek P, Church DM. DbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 2013;41:936–941. doi: 10.1093/nar/gks1213. PubMed DOI PMC

Komaki S, Shiwa Y, Furukawa R, Hachiya T, Ohmomo H, Otomo R, Satoh M, Hitomi J, Sobue K, Sasaki M, Shimizu A. iMETHYL: an integrative database of human DNA methylation, gene expression, and genomic variation. Human Genome Var. 2018 doi: 10.1038/hgv.2018.8. PubMed DOI PMC

Su C, Borsuk ME. Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling. J Mach Learn Res. 2016;17:1–20.

Cooper GF, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn. 1992;9:309–347. doi: 10.1007/BF00994110. DOI

Geiger D, Heckerman D. Learning Gaussian networks. In: Proceedings of the 10th conference on uncertainty in artificial intelligence, 1994;235–43.

Grzegorczyk M. Comparative evaluation of different graphical models for the analysis of gene expression data. PhD thesis, The Department of Statistics of the University Dortmund. 2006. 10.17877/DE290R-607.

Yang J, Rosenthal JS. Automatically tuned general-purpose MCMC via new adaptive diagnostics. Comput Stat. 2017;32:315–348. doi: 10.1007/s00180-016-0682-2. DOI

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27:29–34. doi: 10.1093/nar/27.1.29. PubMed DOI PMC

Consortium, E.P. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004;306:636–40. 10.1126/science.1105136. PubMed

E.P., Consortium. A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011;9. 10.1371/journal.pbio.1001046. PubMed PMC

Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, Ma’ayan A. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database. 2016 doi: 10.1093/database/baw100. PubMed DOI PMC

Madigan D, York J, Allard D. Bayesian graphical models for discrete data. Int Stat Rev/Revue Internationale De Statistique. 1995;63:215–232. doi: 10.2307/1403615. DOI

Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 1970;51:97–109. doi: 10.2307/2334940. DOI

Peterson RA, Cavanaugh JE. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J Appl Stat. 2020;47:2312–2327. doi: 10.1080/02664763.2019.1630372. PubMed DOI PMC

Agostinho NB, Machado KS, Werhli AV. Inference of regulatory networks with a convergence improved MCMC sampler. BMC Bioinform. 2015 doi: 10.1186/s12859-015-0734-6. PubMed DOI PMC

Leclerc RD. Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol. 2008 doi: 10.1038/msb.2008.52. PubMed DOI PMC

Stolovitzky G, Monroe D, Califano A. Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann N Y Acad Sci. 2007;1115:1–22. doi: 10.1196/annals.1407.021. PubMed DOI

Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16:229–239. doi: 10.1089/cmb.2008.09TT. PubMed DOI

Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, Clarke ND, Altan-Bonnet G, Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE. 2010 doi: 10.1371/journal.pone.0009202. PubMed DOI PMC

Marbach D, Costello JC, Kffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9:796–804. doi: 10.1038/nmeth.2016. PubMed DOI PMC

Barker N, Clevers H. Mining the Wnt pathway for cancer therapeutics. Nat Rev Drug Discov. 2006;5:997–1014. doi: 10.1038/nrd2154. PubMed DOI

Sebio A, Kahn M, Lenz H-J. The potential of targeting Wnt/ PubMed DOI

Novellasdemunt L, Antas P, Li VSW. Targeting Wnt signaling in colorectal cancer. A Review in the Theme: Cell Signaling: Proteins, Pathways and Mechanisms. Am J Physiol Cell Physiol. 2015;309:511–521. doi: 10.1152/ajpcell.00117.2015. PubMed DOI PMC

Duchartre Y, Kim Y-M, Kahn M. The Wnt signaling pathway in cancer. Crit Rev Oncol Hematol. 2016;99:141–149. doi: 10.1016/j.critrevonc.2015.12.005. PubMed DOI PMC

Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacol. 2013;38:23–38. doi: 10.1038/npp.2012.112. PubMed DOI PMC

Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, Lin SM. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 2010 doi: 10.1186/1471-2105-11-587. PubMed DOI PMC

Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SFA, Hakonarson H, Bucan M. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007;17:1665–1674. doi: 10.1101/gr.6861907. PubMed DOI PMC

Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The cancer genome atlas pan-cancer analysis project. Nat Genet. 2013;45:1113–1120. doi: 10.1038/ng.2764. PubMed DOI PMC

Pačínková A, Popovici V. Cross-platform data analysis reveals a generic gene expression signature for microsatellite instability in colorectal cancer. Biomed Res Int. 2019 doi: 10.1155/2019/6763596. PubMed DOI PMC

Van Cutsem E, Labianca R, Bodoky G, Barone C, Aranda E, Nordlinger B, Topham C, Tabernero J, André T, Sobrero AF, Mini E, Greil R, Costanzo FD, Collette L, Cisar L, Zhang X, Khayat D, Bokemeyer C, Roth AD, Cunningham D. Randomized Phase III Trial Comparing Biweekly Infusional Fluorouracil/Leucovorin Alone or With Irinotecan in the Adjuvant Treatment of Stage III Colon Cancer: PETACC-3. J Clin Oncol. 2009;27:3117–3125. doi: 10.1200/JCO.2008.21.6663. PubMed DOI

Xie T, d’Ario G, Lamb JR, Martin E, Wang K, Tejpar S, Delorenzi M, Bosman FT, Roth AD, Yan P, Bougel S, Narzo AFD, Popovici V, Budinská E, Mao M, Weinrich SL, Rejto PA, Hodgson JG. A comprehensive characterization of genome-wide copy number aberrations in colorectal cancer reveals novel oncogenes and patterns of alterations. PLoS ONE. 2012 doi: 10.1371/journal.pone.0042001. PubMed DOI PMC

Goldstein H, Healy MJR. The graphical presentation of a collection of means. J R Stat Soc. 1995;158:175–177. doi: 10.2307/2983411. DOI

Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004;32:452–455. doi: 10.1093/nar/gkh052. PubMed DOI PMC

Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34:535–539. doi: 10.1093/nar/gkj109. PubMed DOI PMC

Wu G, Haw R. Functional interaction network construction and analysis for disease discovery. Methods Mol Biol. 2017;1558:235–253. doi: 10.1007/978-1-4939-6783-4_11. PubMed DOI

Mirabelli-Primdahl L, Gryfe R, Kim H, Millar A, Luceri C, Dale D, Holowaty E, Bapat B, Gallinger S, Redston M. PubMed

Fukushima H, Yamamoto H, Itoh F, Horiuchi S, Min Y, Iku S, Imai K. Frequent alterations of the beta-catenin and TCF-4 genes, but not of the APC gene, in colon cancers with high-frequency microsatellite instability. J Exp Clin Cancer Res. 2001;20:553–559. PubMed

Kim S, Jeong S. Mutation hotspots in the PubMed DOI PMC

Zhao Z, Wang L, Bartom E, Marshall S, Rendleman E, Ryan C, Shilati A, Savas J, Chandel N, Shilatifard A. PubMed DOI PMC

Herman JG, Umar A, Polyak K, Graff JR, Ahuja N, Issa J-PJ, Markowitz S, Willson JKV, Hamilton SR, Kinzler KW, Kane MF, Kolodner RD, Vogelstein B, Kunkel TA, Baylin SB. Incidence and functional consequences of hmlh1 promoter hypermethylation in colorectal carcinoma. Proc Natl Acad Sci USA. 1998;95:6870–6875. doi: 10.1073/pnas.95.12.6870. PubMed DOI PMC

Kuismanen SA, Holmberg MT, Salovaara R, de la Chapelle A, Peltomäki P. Genetic and epigenetic modification of mlh1 accounts for a major share of microsatellite-unstable colorectal cancers. Am J Pathol. 2000;156:1773–1779. doi: 10.1016/S0002-9440(10)65048-1. PubMed DOI PMC

Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, Teschendorff AE. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33:3982–3984. doi: 10.1093/bioinformatics/btx513. PubMed DOI PMC

Bray SJ. Notch signalling: a simple pathway becomes complex. Nat Rev Mol Cell Biol. 2006;7:678–689. doi: 10.1038/nrm2009. PubMed DOI

Stenvang J, Budinská E, Van Cutsem E, Bosman F, Brünner VPN. An Explorative Analysis of ABCG2/TOP-1 mRNA Expression as a Biomarker Test for FOLFIRI Treatment in Stage III Colon Cancer Patients: Results from Retrospective Analyses of the PETACC-3 Trial. Cancers. 2020 doi: 10.3390/cancers12040977. PubMed DOI PMC

Doyle LA, Douglas RD. Multidrug resistance mediated by the breast cancer resistance protein BCRP (ABCG2) Oncogene. 2003;22:7340–7358. doi: 10.1038/sj.onc.1206938. PubMed DOI

Porro A, Haber M, Diolaiti D, Iraci N, Henderson M, Gherardi S, Valli E, Munoz MA, Xue C, Flemming C, Schwab M, Wong JH, Marshall GM, Valle GD, Norris MD, Perini G. Direct and coordinate regulation of ATP-binding cassette transporter genes by Myc factors generates specific transcription signatures that significantly affect the chemoresistance phenotype of cancer cells. J Biol Chem. 2010;285:19532–19543. doi: 10.1074/jbc.M109.078584. PubMed DOI PMC

zu Schwabedissen HEM, Grube M, Dreisbach A, Jedlitschky G, Meissner K, Linnemann K, Fusch C, Ritter CA, Völker U, Kroemer HK. Epidermal growth factor-mediated activation of the map kinase cascade results in altered expression and function of ABCG2 (BCRP) Drug Metab Dispos. 2006;34:524–33. doi: 10.1124/dmd.105.007591. PubMed DOI

Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, Silliman N, Szabo S, Dezso Z, Ustyanksky V, Nikolskaya T, Nikolsky Y, Karchin R, Wilson PA, Kaminker JS, Zhang Z, Croshaw R, Willis J, Dawson D, Shipitsin M, Willson JKV, Sukumar S, Polyak K, Park BH, Pethiyagoda CL, Pant PVK, Ballinger DG, Sparks AB, Hartigan J, Smith DR, Suh E, Papadopoulos N, Buckhaults P, Markowitz SD, Parmigiani G, Kinzler KW, Velculescu VE, Vogelstein B. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–13. doi: 10.1126/science.1145720. PubMed DOI

Burghel GJ, Lin W-Y, Whitehouse H, Brock I, Hammond D, Bury J, Stephenson Y, George R, Cox A. Identification of candidate driver genes in common focal chromosomal aberrations of microsatellite stable colorectal cancer. PLoS ONE. 2013 doi: 10.1371/journal.pone.0083859. PubMed DOI PMC

Fletcher JI, Haber M, Henderson MJ, Norris MD. ABC transporters in cancer: more than just drug efflux pumps. Nat Rev Cancer. 2010;10:147–156. doi: 10.1038/nrc2789. PubMed DOI

IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data

Correction: Using empirical biological knowledge to infer regulatory networks from multi-omics data