A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research
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A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research. / Vlachavas, Efstathios Iason; Bohn, Jonas; Ückert, Frank; Nürnberg, Sylvia.
In: INT J MOL SCI, Vol. 22, No. 6, 2822, 10.03.2021.Research output: SCORING: Contribution to journal › SCORING: Review article › Research
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TY - JOUR
T1 - A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research
AU - Vlachavas, Efstathios Iason
AU - Bohn, Jonas
AU - Ückert, Frank
AU - Nürnberg, Sylvia
PY - 2021/3/10
Y1 - 2021/3/10
N2 - 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.
AB - 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.
KW - Biomarkers, Tumor/genetics
KW - Computational Biology
KW - Genomics
KW - Humans
KW - Metabolomics
KW - Neoplasms/genetics
KW - Proteomics
KW - Translational Medical Research
U2 - 10.3390/ijms22062822
DO - 10.3390/ijms22062822
M3 - SCORING: Review article
C2 - 33802234
VL - 22
JO - INT J MOL SCI
JF - INT J MOL SCI
SN - 1661-6596
IS - 6
M1 - 2822
ER -