Coupling Large-Scale Omics Data for Deciphering Systems Complexity

  • Ali Nehme
  • Zahraa Awada
  • Firas Kobeissy
  • Frédéric Mazurier
  • Kazem ZibaraEmail author
Part of the RNA Technologies book series (RNATECHN)


Recent development in high-throughput experiments has provided great amount of data that is being used in translational personalized medicine. Data available in public databases is increasing exponentially as a result of the progress in omics technologies including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Advancements in computing power and machine intelligence are affecting large-scale data analysis and integration. Two types of data integration are often considered: horizontal and vertical meta-analysis. The former integrates multiple studies of the same type, while the latter integrates data at different biological levels. This integrative approach provides a better understanding of systems complexity as a result of the global view that it offers from a biological point of view. This chapter describes the different types of omics analysis and discusses the methods of integrating multi-omics data using a case study.


Omics Integration High-throughput Large-scale Systems biology 


  1. 1000 Genomes Project Consortium, Auton A, Brooks LD et al (2015) A global reference for human genetic variation. Nature 526:68–74Google Scholar
  2. Abaffy T, Möller MG, Riemer DD et al (2013) Comparative analysis of volatile metabolomics signals from melanoma and benign skin: a pilot study. Metabolomics 9:998–1008CrossRefPubMedPubMedCentralGoogle Scholar
  3. Abou-Abbass H, Abou-El-Hassan H, Bahmad H et al (2016) Glycosylation and other PTMs alterations in neurodegenerative diseases: current status and future role in neurotrauma. Electrophoresis 37:1549–1561CrossRefPubMedPubMedCentralGoogle Scholar
  4. Adams MD, Kelley JM, Gocayne JD et al (1991) Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252:1651–1656CrossRefPubMedGoogle Scholar
  5. Alberts B (1998) The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92:291–294CrossRefPubMedGoogle Scholar
  6. Allfrey VG, Faulkner R, Mirsky AE (1964) Acetylation and methylation of histones and their possible role in the regulation of RNA synthesis. Proc Natl Acad Sci USA 51:786–794CrossRefPubMedGoogle Scholar
  7. Anderson S (1981) Shotgun DNA sequencing using cloned DNase I-generated fragments. Nucleic Acids Res 9:3015–3027CrossRefPubMedPubMedCentralGoogle Scholar
  8. Arnes L, Akerman I, Balderes DA et al (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502–507CrossRefPubMedPubMedCentralGoogle Scholar
  9. Assfalg M, Bortoletti E, D’Onofrio M et al (2012) An exploratory 1H-nuclear magnetic resonance metabolomics study reveals altered urine spectral profiles in infants with atopic dermatitis. Br J Dermatol 166:1123–1125CrossRefPubMedGoogle Scholar
  10. Atlas SA (2007) The renin-angiotensin aldosterone system: pathophysiological role and pharmacologic inhibition. J Manag Care Pharm JMCP 13:9–20PubMedGoogle Scholar
  11. Balog J, Sasi-Szabo L, Kinross J et al (2013) Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med 5:194ra93–194ra93CrossRefPubMedGoogle Scholar
  12. Barrett T, Wilhite SE, Ledoux P et al (2013) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991–D995CrossRefPubMedGoogle Scholar
  13. Bird A, Taggart M, Frommer M et al (1985) A fraction of the mouse genome that is derived from islands of nonmethylated, CpG-rich DNA. Cell 40:91–99CrossRefPubMedGoogle Scholar
  14. Buermans HPJ, den Dunnen JT (2014) Next generation sequencing technology: advances and applications. Biochim Biophys Acta 1842:1932–1941CrossRefPubMedGoogle Scholar
  15. Byron SA, Van Keuren-Jensen KR, Engelthaler DM et al (2016) Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet 17:257–271CrossRefPubMedGoogle Scholar
  16. Cantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22CrossRefPubMedPubMedCentralGoogle Scholar
  17. Carichon M, Pallet N, Schmitt C et al (2014) Urinary metabolic fingerprint of acute intermittent porphyria analyzed by 1H NMR spectroscopy. Anal Chem 86:2166–2174CrossRefPubMedGoogle Scholar
  18. Carithers LJ, Moore HM (2015) The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank 13:307–308CrossRefPubMedPubMedCentralGoogle Scholar
  19. Caskey CT, Gonzalez-Garay ML, Pereira S, McGuire AL (2014) Adult genetic risk screening. Annu Rev Med 65:1–17CrossRefPubMedGoogle Scholar
  20. Chen R, Mias GI, Li-Pook-Than J et al (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148:1293–1307CrossRefPubMedPubMedCentralGoogle Scholar
  21. Civelek M, Lusis AJ (2013) Systems genetics approaches to understand complex traits. Nat Rev Genet 15:34–48CrossRefPubMedPubMedCentralGoogle Scholar
  22. Clark SJ, Lee HJ, Smallwood SA et al (2016) Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol 17:72CrossRefPubMedPubMedCentralGoogle Scholar
  23. Consortium IH 3 (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58CrossRefGoogle Scholar
  24. Consortium IHGS (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921CrossRefGoogle Scholar
  25. Crutchfield CA, Thomas SN, Sokoll LJ, Chan DW (2016) Advances in mass spectrometry-based clinical biomarker discovery. Clin Proteomics 13:1CrossRefPubMedPubMedCentralGoogle Scholar
  26. Das MK, Arya R, Debnath S et al (2016) Global urine metabolomics in patients treated with first-line tuberculosis drugs and identification of a novel metabolite of ethambutol. Antimicrob Agents Chemother 60:2257–2264CrossRefPubMedPubMedCentralGoogle Scholar
  27. DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498CrossRefPubMedPubMedCentralGoogle Scholar
  28. Dirks RAM, Stunnenberg HG, Marks H (2016) Genome-wide epigenomic profiling for biomarker discovery. Clin Epigenetics 8:122CrossRefPubMedPubMedCentralGoogle Scholar
  29. Eid J, Fehr A, Gray J et al (2009) Real-time DNA sequencing from single polymerase molecules. Science 323:133–138CrossRefPubMedGoogle Scholar
  30. ENCODE Project Consortium {fname} (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74CrossRefGoogle Scholar
  31. Farley AR, Link AJ (2009) Identification and quantification of protein posttranslational modifications. Methods Enzymol 463:725–763CrossRefPubMedGoogle Scholar
  32. Farrah T, Deutsch EW, Omenn GS et al (2014) State of the human proteome in 2013 as viewed through peptideatlas: comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven human proteome project. J Proteome Res 13:60–75CrossRefPubMedGoogle Scholar
  33. Fiehn O (2002) Metabolomics—the link between genotyopes and phenotypes. Plant Mol Biol 48:155–171CrossRefPubMedGoogle Scholar
  34. Friedrich N (2012) Metabolomics in diabetes research. J Endocrinol 215:29–42CrossRefPubMedGoogle Scholar
  35. García-Cañaveras JC, Jiménez N, Gómez-Lechón MJ et al (2015) LC-MS untargeted metabolomic analysis of drug-induced hepatotoxicity in HepG2 cells. Electrophoresis 36:2294–2302CrossRefPubMedGoogle Scholar
  36. GTEx Consortium TGte (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45:580–585CrossRefGoogle Scholar
  37. Guo L, Milburn MV, Ryals JA et al (2015) Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci USA 112:E4901–E4910CrossRefPubMedGoogle Scholar
  38. Gupta RA, Shah N, Wang KC et al (2010) Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464:1071–1076CrossRefPubMedPubMedCentralGoogle Scholar
  39. Heller MJ (2002) DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng 4:129–153CrossRefPubMedGoogle Scholar
  40. Holliday R, Pugh JE (1975) DNA modification mechanisms and gene activity during development. Science 187:226–232CrossRefPubMedGoogle Scholar
  41. Hotchkiss RD (1948) The quantitative separation of purines, pyrimidines, and nucleosides by paper chromatography. J Biol Chem 175:315–332PubMedGoogle Scholar
  42. International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome. Nature 431:931–945CrossRefGoogle Scholar
  43. Ishii N, Ozaki K, Sato H et al (2006) Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J Hum Genet 51:1087–1099CrossRefPubMedGoogle Scholar
  44. Jung J, Kim SH, Lee HS et al (2013) Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin Exp Allergy 43:425–433CrossRefPubMedGoogle Scholar
  45. Khare SP, Habib F, Sharma R et al (2012) HIstome—a relational knowledgebase of human histone proteins and histone modifying enzymes. Nucleic Acids Res 40:D337–D342CrossRefPubMedGoogle Scholar
  46. Khurana E, Fu Y, Chakravarty D et al (2016) Role of non-coding sequence variants in cancer. Nat Rev Genet 17:93–108CrossRefPubMedGoogle Scholar
  47. Kim K, Aronov P, Zakharkin SO et al (2009) Urine metabolomics analysis for kidney cancer detection and biomarker discovery. Mol Cell Proteomics MCP 8:558–570CrossRefPubMedGoogle Scholar
  48. Kim Y, Jeon J, Mejia S et al (2016) Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun 7:11906CrossRefPubMedPubMedCentralGoogle Scholar
  49. Klein RJ, Zeiss C, Chew EY et al (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308:385–389CrossRefPubMedPubMedCentralGoogle Scholar
  50. Koussounadis A, Langdon SP, Um IH et al (2015) Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Sci Rep 5:10775CrossRefPubMedPubMedCentralGoogle Scholar
  51. Kouzarides T (2007) Chromatin modifications and their function. Cell 128:693–705CrossRefPubMedPubMedCentralGoogle Scholar
  52. Kulis M, Esteller M (2010) DNA methylation and cancer. Adv Genet 70:27–56PubMedGoogle Scholar
  53. Lehmann S, Brede C, Lescuyer P et al (2017) Clinical mass spectrometry proteomics (cMSP) for medical laboratory: what does the future hold? Clin Chim Acta 467:51–58CrossRefPubMedGoogle Scholar
  54. Li S, Todor A, Luo R (2016) Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14:1–7CrossRefPubMedGoogle Scholar
  55. Licata L, Briganti L, Peluso D et al (2012) MINT, the molecular interaction database: 2012 Update. Nucleic Acids Res 40:D857–D861CrossRefGoogle Scholar
  56. Lindskog C (2015) The potential clinical impact of the tissue-based map of the human proteome. Expert Rev Proteomics 12:213–215CrossRefPubMedGoogle Scholar
  57. Lister R, Pelizzola M, Kida YS et al (2011) Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471:68–73CrossRefPubMedPubMedCentralGoogle Scholar
  58. Maier T, Güell M, Serrano L (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583:3966–3973CrossRefPubMedGoogle Scholar
  59. Manolio TA, Collins FS (2009) The HapMap and genome-wide association studies in diagnosis and therapy. Annu Rev Med 60:443–456CrossRefPubMedPubMedCentralGoogle Scholar
  60. Maraganore DM, de Andrade M, Lesnick TG et al (2005) High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet 77:685–693CrossRefPubMedPubMedCentralGoogle Scholar
  61. Mun J-H, Lee H, Yoon D et al (2016) Discrimination of basal cell carcinoma from normal skin tissue using high-resolution magic angle spinning 1H NMR spectroscopy. PLoS One 11:e0150328CrossRefPubMedPubMedCentralGoogle Scholar
  62. Nehme A, Zibara K (2017a) Cellular distribution and interaction between extended renin-angiotensin-aldosterone system pathways in atheroma. Atherosclerosis 263:334–342CrossRefPubMedGoogle Scholar
  63. Nehme A, Zibara K (2017b) Efficiency and specificity of RAAS inhibitors in cardiovascular diseases: how to achieve better end-organ protection? Hypertens Res 40:903–909CrossRefPubMedGoogle Scholar
  64. Nehme A, Cerutti C, Dhaouadi N et al (2015) Atlas of tissue renin-angiotensin-aldosterone system in human: a transcriptomic meta-analysis. Sci Rep 5:10035CrossRefPubMedPubMedCentralGoogle Scholar
  65. Nehme A, Cerutti C, Zibara K (2016a) Transcriptomic analysis reveals novel transcription factors associated with renin–angiotensin–aldosterone system in human atheroma. Hypertension HYPERTENSIONAHA.116.08070Google Scholar
  66. Nehme A, Marcelo P, Nasser R et al (2016b) The kinetics of angiotensin-I metabolism in human carotid atheroma: an emerging role for angiotensin (1-7). Vascul Pharmacol 85:50–56CrossRefPubMedGoogle Scholar
  67. Ng SB, Turner EH, Robertson PD et al (2009) Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461:272–276CrossRefPubMedPubMedCentralGoogle Scholar
  68. Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–D363CrossRefPubMedGoogle Scholar
  69. Palmer ND, Stevens RD, Antinozzi PA et al (2015) Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J Clin Endocrinol Metab 100:E463–E468CrossRefPubMedGoogle Scholar
  70. Parkinson H, Kapushesky M, Shojatalab M et al (2007) ArrayExpress—a public database of microarray experiments and gene expression profiles. Nucleic Acids Res 35:D747–D750CrossRefGoogle Scholar
  71. Polak P, Karlić R, Koren A et al (2015) Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518:360–364CrossRefPubMedPubMedCentralGoogle Scholar
  72. Qin W, Kozlowski P, Taillon BE et al (2010) Ultra deep sequencing detects a low rate of mosaic mutations in tuberous sclerosis complex. Hum Genet 127:573–582CrossRefPubMedPubMedCentralGoogle Scholar
  73. Rhodes DR, Yu J, Shanker K et al (2004) ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 6(1)Google Scholar
  74. Rhodes DR, Kalyana-Sundaram S, Mahavisno V et al (2007) Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9:166–180CrossRefPubMedPubMedCentralGoogle Scholar
  75. Ritchie MD, Holzinger ER, Li R et al (2015) Methods of integrating data to uncover genotype–phenotype interactions. Nat Rev Genet 16:85–97CrossRefGoogle Scholar
  76. Roy B, Haupt LM, Griffiths LR (2013) Review: alternative splicing (AS) of genes as an approach for generating protein complexity. Curr Genomics 14:182–194CrossRefPubMedPubMedCentralGoogle Scholar
  77. Sanger F, Coulson AR (1975) A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol 94:441–448CrossRefPubMedGoogle Scholar
  78. Schaefer C, Meier A, Rost B, Bromberg Y (2012) Snpdbe: constructing an nsSnp functional impacts database. Bioinformatics 28:601–602CrossRefPubMedGoogle Scholar
  79. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470CrossRefGoogle Scholar
  80. Schmitz SU, Grote P, Herrmann BG (2016) Mechanisms of long noncoding RNA function in development and disease. Cell Mol Life Sci 73:2491–2509CrossRefPubMedPubMedCentralGoogle Scholar
  81. Schoen C, Kischkies L, Elias J, Ampattu BJ (2014) Metabolism and virulence in Neisseria meningitidis. Front Cell Infect Microbiol 4:114CrossRefPubMedPubMedCentralGoogle Scholar
  82. Shabman RS, Jabado OJ, Mire CE et al (2014) Deep sequencing identifies noncanonical editing of Ebola and Marburg virus RNAs in infected cells. mBio 5:e02011CrossRefPubMedPubMedCentralGoogle Scholar
  83. Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:1135–1145CrossRefPubMedGoogle Scholar
  84. Smith LM, Kelleher NL, Linial M et al (2013) Proteoform: a single term describing protein complexity. Nat Methods 10:186–187CrossRefPubMedPubMedCentralGoogle Scholar
  85. Stadler ZK, Thom P, Robson ME et al (2010) Genome-wide association studies of cancer. J Clin Oncol Off J Am Soc Clin Oncol 28:4255–4267CrossRefGoogle Scholar
  86. Tessarz P, Kouzarides T (2014) Histone core modifications regulating nucleosome structure and dynamics. Nat Rev Mol Cell Biol 15:703–708CrossRefPubMedGoogle Scholar
  87. Tomescu OA, Mattanovich D, Thallinger GG (2014) Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data. BMC Syst Biol 8:S4CrossRefPubMedPubMedCentralGoogle Scholar
  88. Trushina E, Mielke MM (2014) Recent advances in the application of metabolomics to Alzheimer’s disease. Biochim Biophys Acta Mol Basis Dis 1842:1232–1239CrossRefGoogle Scholar
  89. Uhlén M, Fagerberg L, Hallström BM et al (2015) Proteomics. Tissue-based map of the human proteome. Science 347:1260419CrossRefPubMedGoogle Scholar
  90. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial analysis of gene expression. Science 270:484–487CrossRefPubMedGoogle Scholar
  91. Verdin E, Ott M (2014) 50 years of protein acetylation: from gene regulation to epigenetics, metabolism and beyond. Nat Rev Mol Cell Biol 16:258–264CrossRefPubMedGoogle Scholar
  92. Vizcaíno JA, Deutsch EW, Wang R et al (2014) ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat Biotechnol 32:223–226CrossRefPubMedPubMedCentralGoogle Scholar
  93. Waddington CH (1942) The epigenotype. Endeavour 1:18–20. CrossRefGoogle Scholar
  94. Weis JH, Tan SS, Martin BK, Wittwer CT (1992) Detection of rare mRNAs via quantitative RT-PCR. Trends Genet 8:263–264. CrossRefPubMedGoogle Scholar
  95. Welter D, MacArthur J, Morales J et al (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42:D1001–D1006CrossRefPubMedGoogle Scholar
  96. Worrall JA, Kolczak U, Canters GW, Ubbink M (2001) Interaction of yeast iso-1-cytochrome c with cytochrome c peroxidase investigated by [15N, 1H] heteronuclear NMR spectroscopy. Biochemistry (Mosc) 40:7069–7076CrossRefGoogle Scholar
  97. Wu JR, Zeng R (2012) Molecular basis for population variation: from SNPs to SAPs. FEBS Letters.:2841–2845Google Scholar
  98. Xenarios I (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30:303–305CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ali Nehme
    • 1
    • 2
  • Zahraa Awada
    • 2
  • Firas Kobeissy
    • 3
  • Frédéric Mazurier
    • 1
  • Kazem Zibara
    • 2
    • 4
    Email author
  1. 1.Université François Rabelais, CNRS UMR 7292, LNOx TeamToursFrance
  2. 2.ER045, PRASE, DSSTLebanese UniversityBeirutLebanon
  3. 3.Department of Biochemistry and Molecular Genetics, Faculty of MedicineAmerican University of BeirutBeirutLebanon
  4. 4.Biology Department, Faculty of Sciences-ILebanese UniversityBeirutLebanon

Personalised recommendations