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Coupling Large-Scale Omics Data for Deciphering Systems Complexity

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

Abstract

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.

Keywords

Omics Integration High-throughput Large-scale Systems biology 

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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
  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

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