Computational Analysis of Phosphoproteomics Data in Multi‐Omics Cancer Studies. Issue 3 (8th October 2020)

• Record Type: Journal Article
• Authors: Mantini, Giulia; Pham, Thang V.; Piersma, Sander R.; Jimenez, Connie R.
• Other Names: Veenstra Timothy D. guestEditor.
• Is Part Of: Proteomics. Volume 21:Issue 3/4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-08
• Subjects: bioinformatics -- cancer -- data integration -- multi‐omics -- phosphoproteomics; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900312
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi‐omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi‐omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi‐omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi‐omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi‐omics data integration and other algorithms for multi‐omics data integration promising for future application to phosphoproteomics data.