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Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data

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Cancer Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1711))

Abstract

With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator.

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References

  1. Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19:A68–A77. https://doi.org/10.5114/wo.2014.47136

    Google Scholar 

  2. Encode Consortium (2013) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. https://doi.org/10.1038/nature11247

  3. Malo N, Hanley JA, Cerquozzi S et al (2006) Statistical practice in high-throughput screening data analysis. Nat Biotechnol 24:167–175. https://doi.org/10.1038/nbt1186

    Article  CAS  PubMed  Google Scholar 

  4. Huang S-SC, Fraenkel E (2009) Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci Signal 2:ra40. https://doi.org/10.1126/scisignal.2000350

    PubMed  PubMed Central  Google Scholar 

  5. Ideker T, Thorsson V, Ranish JA et al (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292:929–934. https://doi.org/10.1126/science.292.5518.929

    Article  CAS  PubMed  Google Scholar 

  6. Huang SSC, Clarke DC, Gosline SJC et al (2013) Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling. PLoS Comput Biol 9(2):e1002887. https://doi.org/10.1371/journal.pcbi.1002887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113. https://doi.org/10.1038/nrg1272

    Article  PubMed  Google Scholar 

  8. Razick S, Magklaras G, Donaldson IM (2008) iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9:405. https://doi.org/10.1186/1471-2105-9-405

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tyers M, Breitkreutz A, Stark C et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539. https://doi.org/10.1093/nar/gkj109

    Article  PubMed  Google Scholar 

  10. Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452. https://doi.org/10.1093/nar/gku1003

    Article  CAS  PubMed  Google Scholar 

  11. Wishart DS, Jewison T, Guo AC et al (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807. https://doi.org/10.1093/nar/gks1065

    CAS  PubMed  Google Scholar 

  12. Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425. https://doi.org/10.1038/nbt.2488

    Article  CAS  PubMed  Google Scholar 

  13. Kuhn M, Szklarczyk D, Pletscher-Frankild S et al (2014) STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res 42(Database issue):D401–D407. https://doi.org/10.1093/nar/gkt1207

    Article  CAS  PubMed  Google Scholar 

  14. Valcárcel B, Würtz P, al Basatena NKS et al (2011) A differential network approach to exploring differences between biological states: an application to prediabetes. PLoS One 6(9):e24702. https://doi.org/10.1371/journal.pone.0024702

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kotze HL, Armitage EG, Sharkey KJ et al (2013) A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions. BMC Syst Biol 7:107. https://doi.org/10.1186/1752-0509-7-107

    Article  PubMed  PubMed Central  Google Scholar 

  16. Tuncbag N, Braunstein A, Pagnani A et al (2013) Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J Comput Biol 20:124–136. https://doi.org/10.1089/cmb.2012.0092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tuncbag N, Gosline SJ, Kedaigle AJ et al (2016) Network-based interpretation of diverse high-throughput datasets through the Omics Integrator software package. PLoS Comput Biol 12(4):e1004879

    Article  PubMed  PubMed Central  Google Scholar 

  18. Aoki-Kinoshita KF, Kanehisa M (2007) Gene annotation and pathway mapping in KEGG. Methods Mol Biol 396:71–91. https://doi.org/10.1007/978-1-59745-515-2_6

    Article  CAS  PubMed  Google Scholar 

  19. Maier T, Güell M, Serrano L (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583:3966–3973. https://doi.org/10.1016/j.febslet.2009.10.036

    Article  CAS  PubMed  Google Scholar 

  20. Bernstein BE, Stamatoyannopoulos JA, Costello JF et al (2010) The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol 28:1045–1048. https://doi.org/10.1038/nbt1010-1045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Matys V, Kel-Margoulis OV, Fricke E et al (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34:D108–D110. https://doi.org/10.1093/nar/gkj143

    Article  CAS  PubMed  Google Scholar 

  22. Neph S, Vierstra J, Stergachis AB et al (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489:83–90. https://doi.org/10.1038/nature11212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Blankenberg D, Von Kuster G, Coraor N et al (2010) Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc Mol Biol. https://doi.org/10.1002/0471142727.mb1910s89

  24. Villaveces JM, Jiménez RC, Porras P et al (2015) Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study. Database 2015:bau131. https://doi.org/10.1093/database/bau131

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Smoot ME, Ono K, Ruscheinski J et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432. https://doi.org/10.1093/bioinformatics/btq675

    Article  CAS  PubMed  Google Scholar 

  27. Love MI, Anders S, Huber W (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8

  28. Trapnell C, Hendrickson DG, Sauvageau M et al (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53. https://doi.org/10.1038/nbt.2450

    Article  CAS  PubMed  Google Scholar 

  29. Bantscheff M, Lemeer S, Savitski MM, Kuster B (2012) Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem 404:939–965. https://doi.org/10.1007/s00216-012-6203-4

    Article  CAS  PubMed  Google Scholar 

  30. Saito R, Smoot ME, Ono K et al (2012) A travel guide to Cytoscape plugins. Nat Methods 9:1069–1076. https://doi.org/10.1038/nmeth.2212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by grants from National Institute of Health (R01-NS089076, T32-GM008334, and U01-CA184898). We thank Tobias Ehrenberger and Renan Escalante-Chong for helpful comments on the manuscript.

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Correspondence to Ernest Fraenkel .

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Kedaigle, A.J., Fraenkel, E. (2018). Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data. In: von Stechow, L. (eds) Cancer Systems Biology. Methods in Molecular Biology, vol 1711. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7493-1_2

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  • DOI: https://doi.org/10.1007/978-1-4939-7493-1_2

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7492-4

  • Online ISBN: 978-1-4939-7493-1

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