Journal Article (Review Article)

DKFZ-2021-00813

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.

Vlachavas, E.-I. (First author)DKFZ* ; Bohn, J. (First author)DKFZ* ; Ückert, F.DKFZ* ; Nürnberg, S. (Last author)DKFZ*

2021
Molecular Diversity Preservation International Basel

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Please use a persistent id in citations: doi:10.3390/ijms22062822

Abstract: Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Keyword(s): analysis tools ; integrative methods ; literature review ; multi-omics data integration ; oncology ; personalized medicine ; supervised data integration ; translational cancer research ; unsupervised data integration

Note: #EA:B050#EA:E240#LA:E240#

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Contributing Institute(s):

1. B050 Molekulare Genomanalyse (B050)
2. Med. Informatik in der Translationalen Onkologie (E240)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2021

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Database coverage:
; ; ; Article Processing Charges ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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