Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer
Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the gen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/11/3/184 |
| _version_ | 1827697160943042560 |
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| author | Takoua Jendoubi |
| author_facet | Takoua Jendoubi |
| author_sort | Takoua Jendoubi |
| collection | DOAJ |
| description | Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline. |
| first_indexed | 2024-03-10T13:02:22Z |
| format | Article |
| id | doaj.art-d23bf2f243c149b08ac9ea8590ca28df |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T13:02:22Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-d23bf2f243c149b08ac9ea8590ca28df2023-11-21T11:24:36ZengMDPI AGMetabolites2218-19892021-03-0111318410.3390/metabo11030184Approaches to Integrating Metabolomics and Multi-Omics Data: A PrimerTakoua Jendoubi0Department of Statistical Science, University College London, London WC1E 6BT, UKMetabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.https://www.mdpi.com/2218-1989/11/3/184data integrationmulti-omicsintegration strategiesgenomics |
| spellingShingle | Takoua Jendoubi Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer Metabolites data integration multi-omics integration strategies genomics |
| title | Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer |
| title_full | Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer |
| title_fullStr | Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer |
| title_full_unstemmed | Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer |
| title_short | Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer |
| title_sort | approaches to integrating metabolomics and multi omics data a primer |
| topic | data integration multi-omics integration strategies genomics |
| url | https://www.mdpi.com/2218-1989/11/3/184 |
| work_keys_str_mv | AT takouajendoubi approachestointegratingmetabolomicsandmultiomicsdataaprimer |