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Agamah FE, Bayjanov JR, Niehues A, Njoku KF, Skelton M, Mazandu GK, Ederveen THA, Mulder N, Chimusa ER, 't Hoen PAC. Computational approaches for network-based integrative multi-omics analysis. Front Mol Biosci 2022; 9:967205. [PMID: 36452456 PMCID: PMC9703081 DOI: 10.3389/fmolb.2022.967205] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 10/20/2022] [Indexed: 08/27/2023] Open
Abstract
Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.
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Affiliation(s)
- Francis E. Agamah
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jumamurat R. Bayjanov
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anna Niehues
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Kelechi F. Njoku
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Michelle Skelton
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston K. Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- African Institute for Mathematical Sciences, Cape Town, South Africa
| | - Thomas H. A. Ederveen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, CIDRI-Africa Wellcome Trust Centre, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Emile R. Chimusa
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, United Kingdom
| | - Peter A. C. 't Hoen
- Center for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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