3.8 Article

Omics Data Complementarity Underlines Functional Cross-Communication in Yeast

Journal

JOURNAL OF INTEGRATIVE BIOINFORMATICS
Volume 14, Issue 2, Pages -

Publisher

WALTER DE GRUYTER GMBH
DOI: 10.1515/jib-2017-0018

Keywords

Molecular omics data; cell's functional organization; data-integration

Funding

  1. European Research Council (ERC) [278212]
  2. Serbian Ministry of Education and Science [III44006]
  3. Medical Research Council
  4. Arthritis Research UK
  5. British Heart Foundation
  6. Cancer Research UK
  7. Chief Scientist Office
  8. Economic and Social Research Council
  9. Engineering and Physical Sciences Research Council
  10. National Institute for Health Research
  11. National Institute for Social Care and Health Research
  12. Wellcome Trust [MR/K006584/1]
  13. European Research Council (ERC) [278212] Funding Source: European Research Council (ERC)

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Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

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