Integration of Transcriptomic and Proteomic Data for Disease Insights

  • Ravi Sirdeshmukh
  • Savita Jayaram
  • Manoj Kumar Gupta
  • Pranali Sonpatki
  • Manika Singh
  • Raksha A. Ganesh
  • Chaitra B. Amaresha
  • Nameeta Shah
Protocol
Part of the Neuromethods book series (NM, volume 127)

Abstract

Omics technologies have dominated molecular analysis of the biological processes in normal and disease contexts in the post-genomic era, and large data is continuously accumulating. Data repositories, new databases, data mining, and analysis approaches are being developed. One of the approaches that is emerging in recent years is integration of gene expression data generated at different levels of regulation. Transcriptomic and proteomic data available at present and metabolic data in the future hold strong promise to understand molecular changes associated with the disease processes. Integration of these data would offer an interconnected view of these changes to add further value to the single ome datasets. We have been interested to explore known methods or develop new ways for integrating transcriptomic and proteomics data and have developed strategies that appear promising. The chapter describes these methods with examples in the context of glioblastomas—the major primary tumors of the central nervous system (CNS). The pipelines shown are however not rigid but indicative of the feasibility of approaches for the purpose of transcriptomic and proteomic data integration and can be adapted suitably for use in other tumor types as well as any clinical conditions in general based on the specific end objectives.

Key words

Transcriptomics Proteomics Proteogenomics Data integration Glioblastoma Glioma Neurological diseases 

Notes

Acknowledgments

RS lab is funded under projects from the Department of Biotechnology and Indian Council of Medical Research, Government of India. We gratefully acknowledge Ravindra Varma Polisetty for generating the GBM membrane proteomics data and his initial contribution toward proteogenomics analysis and Anil K. Madugundu and Jyoti Sharma for technical support for RNA-Seq and proteogenomic data analysis from the Institute of Bioinformatics.

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Ravi Sirdeshmukh
    • 1
    • 2
  • Savita Jayaram
    • 2
    • 3
  • Manoj Kumar Gupta
    • 2
    • 3
  • Pranali Sonpatki
    • 1
  • Manika Singh
    • 1
  • Raksha A. Ganesh
    • 1
  • Chaitra B. Amaresha
    • 1
  • Nameeta Shah
    • 1
  1. 1.Neuro-Oncology ProgramMazumdar Shaw Centre for Translational ResearchBangaloreIndia
  2. 2.Institute of BioinformaticsBangaloreIndia
  3. 3.Manipal UniversityManipalIndia

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