A Guide to Multi-omics Data Collection and Integration for Translational Medicine

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2022 Dec 22

PMID 36544480

Authors

Efi Athieniti

George M Spyrou

Affiliations

Soon will be listed here.

Abstract

The emerging high-throughput technologies have led to the shift in the design of translational medicine projects towards collecting multi-omics patient samples and, consequently, their integrated analysis. However, the complexity of integrating these datasets has triggered new questions regarding the appropriateness of the available computational methods. Currently, there is no clear consensus on the best combination of omics to include and the data integration methodologies required for their analysis. This article aims to guide the design of multi-omics studies in the field of translational medicine regarding the types of omics and the integration method to choose. We review articles that perform the integration of multiple omics measurements from patient samples. We identify five objectives in translational medicine applications: (i) detect disease-associated molecular patterns, (ii) subtype identification, (iii) diagnosis/prognosis, (iv) drug response prediction, and (v) understand regulatory processes. We describe common trends in the selection of omic types combined for different objectives and diseases. To guide the choice of data integration tools, we group them into the scientific objectives they aim to address. We describe the main computational methods adopted to achieve these objectives and present examples of tools. We compare tools based on how they deal with the computational challenges of data integration and comment on how they perform against predefined objective-specific evaluation criteria. Finally, we discuss examples of tools for downstream analysis and further extraction of novel insights from multi-omics datasets.

Citing Articles

Epigenomic Echoes-Decoding Genomic and Epigenetic Instability to Distinguish Lung Cancer Types and Predict Relapse.

Baumann A, Buribayev Z, Wolkenhauer O, Salybekov A, Wolfien M Epigenomes. 2025; 9(1).

PMID: 39982247 PMC: 11843950. DOI: 10.3390/epigenomes9010005.

Comprehensive Evaluation of Multi-Omics Clustering Algorithms for Cancer Molecular Subtyping.

Wang J, Wang L, Liu Y, Li X, Ma J, Li M Int J Mol Sci. 2025; 26(3).

PMID: 39940732 PMC: 11816650. DOI: 10.3390/ijms26030963.

AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.

Boyd S, Slawson C, Thompson J BMC Bioinformatics. 2025; 26(1):39.

PMID: 39910456 PMC: 11800622. DOI: 10.1186/s12859-025-06063-x.

Exome sequencing of UK birth cohorts.

Koko M, Fabian L, Popov I, Eberhardt R, Zakharov G, Huang Q Wellcome Open Res. 2025; 9:390.

PMID: 39839975 PMC: 11747307. DOI: 10.12688/wellcomeopenres.22697.2.

BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data.

Iperi C, Fernandez-Ochoa A, Barturen G, Pers J, Foulquier N, Bettacchioli E BMC Bioinformatics. 2025; 26(1):8.

PMID: 39794699 PMC: 11721463. DOI: 10.1186/s12859-024-06022-y.

References

Chervova O, Conde L, Guerra-Assuncao J, Moghul I, Webster A, Berner A . The Personal Genome Project-UK, an open access resource of human multi-omics data. Sci Data. 2019; 6(1):257. PMC: 6823446. DOI: 10.1038/s41597-019-0205-4. View

Gligorijevic V, Przulj N . Methods for biological data integration: perspectives and challenges. J R Soc Interface. 2015; 12(112). PMC: 4685837. DOI: 10.1098/rsif.2015.0571. View

Xicota L, Ichou F, Lejeune F, Colsch B, Tenenhaus A, Leroy I . Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study. EBioMedicine. 2019; 47:518-528. PMC: 6796577. DOI: 10.1016/j.ebiom.2019.08.051. View

Pang Z, Chong J, Zhou G, Morais D, Chang L, Barrette M . MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021; 49(W1):W388-W396. PMC: 8265181. DOI: 10.1093/nar/gkab382. View

Rohart F, Gautier B, Singh A, Le Cao K . mixOmics: An R package for 'omics feature selection and multiple data integration. PLoS Comput Biol. 2017; 13(11):e1005752. PMC: 5687754. DOI: 10.1371/journal.pcbi.1005752. View

Speicher N, Pfeifer N . Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics. 2015; 31(12):i268-75. PMC: 4765854. DOI: 10.1093/bioinformatics/btv244. View

Li J, Lu Q, Wen Y . Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data. Bioinformatics. 2019; 36(6):1785-1794. PMC: 7523642. DOI: 10.1093/bioinformatics/btz822. View

Metwaly A, Dunkel A, Waldschmitt N, Durai Raj A, Lagkouvardos I, Corraliza A . Integrated microbiota and metabolite profiles link Crohn's disease to sulfur metabolism. Nat Commun. 2020; 11(1):4322. PMC: 7456324. DOI: 10.1038/s41467-020-17956-1. View

Wang T, Shao W, Huang Z, Tang H, Zhang J, Ding Z . MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun. 2021; 12(1):3445. PMC: 8187432. DOI: 10.1038/s41467-021-23774-w. View

10.

Meng C, Helm D, Frejno M, Kuster B . moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. J Proteome Res. 2015; 15(3):755-65. DOI: 10.1021/acs.jproteome.5b00824. View

11.

Li W, Zhang S, Liu C, Zhou X . Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics. 2012; 28(19):2458-66. PMC: 3463121. DOI: 10.1093/bioinformatics/bts476. View

12.

Paczkowska M, Barenboim J, Sintupisut N, Fox N, Zhu H, Abd-Rabbo D . Integrative pathway enrichment analysis of multivariate omics data. Nat Commun. 2020; 11(1):735. PMC: 7002665. DOI: 10.1038/s41467-019-13983-9. View

13.

Hasin Y, Seldin M, Lusis A . Multi-omics approaches to disease. Genome Biol. 2017; 18(1):83. PMC: 5418815. DOI: 10.1186/s13059-017-1215-1. View

14.

Chu J, Sun N, Hu W, Chen X, Yi N, Shen Y . The Application of Bayesian Methods in Cancer Prognosis and Prediction. Cancer Genomics Proteomics. 2021; 19(1):1-11. PMC: 8717957. DOI: 10.21873/cgp.20298. View

15.

Frost H, Li Z, Moore J . Principal component gene set enrichment (PCGSE). BioData Min. 2015; 8:25. PMC: 4543476. DOI: 10.1186/s13040-015-0059-z. View

16.

Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal K . Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol. 2021; 17(1):e9730. PMC: 7838823. DOI: 10.15252/msb.20209730. View

17.

Garali I, Adanyeguh I, Ichou F, Perlbarg V, Seyer A, Colsch B . A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia. Brief Bioinform. 2017; 19(6):1356-1369. DOI: 10.1093/bib/bbx060. View

18.

Hofree M, Shen J, Carter H, Gross A, Ideker T . Network-based stratification of tumor mutations. Nat Methods. 2013; 10(11):1108-15. PMC: 3866081. DOI: 10.1038/nmeth.2651. View

19.

Yan Z, An J, Peng Y, Kong S, Liu Q, Yang M . DevOmics: an integrated multi-omics database of human and mouse early embryo. Brief Bioinform. 2021; 22(6). DOI: 10.1093/bib/bbab208. View

20.

Olivier M, Asmis R, Hawkins G, Howard T, Cox L . The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci. 2019; 20(19). PMC: 6801754. DOI: 10.3390/ijms20194781. View