Computational approaches for network-based integrative multi-omics analysis
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.967205/full |
| _version_ | 1817971027765886976 |
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| author | Francis E. Agamah Francis E. Agamah Jumamurat R. Bayjanov Anna Niehues Kelechi F. Njoku Michelle Skelton Gaston K. Mazandu Gaston K. Mazandu Gaston K. Mazandu Thomas H. A. Ederveen Nicola Mulder Emile R. Chimusa Peter A. C. 't Hoen |
| author_facet | Francis E. Agamah Francis E. Agamah Jumamurat R. Bayjanov Anna Niehues Kelechi F. Njoku Michelle Skelton Gaston K. Mazandu Gaston K. Mazandu Gaston K. Mazandu Thomas H. A. Ederveen Nicola Mulder Emile R. Chimusa Peter A. C. 't Hoen |
| author_sort | Francis E. Agamah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T20:41:35Z |
| format | Article |
| id | doaj.art-498c307c503d4bb98e87531edd4dfa4e |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-13T20:41:35Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-498c307c503d4bb98e87531edd4dfa4e2022-12-22T02:30:50ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-11-01910.3389/fmolb.2022.967205967205Computational approaches for network-based integrative multi-omics analysisFrancis E. Agamah0Francis E. Agamah1Jumamurat R. Bayjanov2Anna Niehues3Kelechi F. Njoku4Michelle Skelton5Gaston K. Mazandu6Gaston K. Mazandu7Gaston K. Mazandu8Thomas H. A. Ederveen9Nicola Mulder10Emile R. Chimusa11Peter A. C. 't Hoen12Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South AfricaComputational 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 AfricaCenter for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, NetherlandsCenter for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, NetherlandsDivision of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South AfricaComputational 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 AfricaDivision of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South AfricaComputational 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 AfricaAfrican Institute for Mathematical Sciences, Cape Town, South AfricaCenter for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, NetherlandsComputational 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 AfricaDepartment of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, United KingdomCenter for Molecular and Biomolecular Informatics (CMBI), Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, NetherlandsAdvances 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.https://www.frontiersin.org/articles/10.3389/fmolb.2022.967205/fullmulti-omicsdata integrationmulti-modal networkmachine learningnetwork diffusion/propagationnetwork causal inference |
| spellingShingle | Francis E. Agamah Francis E. Agamah Jumamurat R. Bayjanov Anna Niehues Kelechi F. Njoku Michelle Skelton Gaston K. Mazandu Gaston K. Mazandu Gaston K. Mazandu Thomas H. A. Ederveen Nicola Mulder Emile R. Chimusa Peter A. C. 't Hoen Computational approaches for network-based integrative multi-omics analysis Frontiers in Molecular Biosciences multi-omics data integration multi-modal network machine learning network diffusion/propagation network causal inference |
| title | Computational approaches for network-based integrative multi-omics analysis |
| title_full | Computational approaches for network-based integrative multi-omics analysis |
| title_fullStr | Computational approaches for network-based integrative multi-omics analysis |
| title_full_unstemmed | Computational approaches for network-based integrative multi-omics analysis |
| title_short | Computational approaches for network-based integrative multi-omics analysis |
| title_sort | computational approaches for network based integrative multi omics analysis |
| topic | multi-omics data integration multi-modal network machine learning network diffusion/propagation network causal inference |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.967205/full |
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