A practical data processing workflow for multi-OMICS projects
Open accessAbstract
Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
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Many multi-omic reviews concentrate on narrow sub-topics, including study design, workflows, software selection, and overfitting evaluation.
“Currently, many of the multi-omic reviews are focussed on individual sub-topics.For example, designing studies (Haas et al., 2017;Hasin et al., 2017), setting up workflows (Kohl et al., 2014), choosing software tools (Misra et al., 2019) and evaluating overfitted performance (McCabe et al., 2020).”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Kohl et al., 2014
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- Authors
- Kohl, M., Megger, D. A., Trippler, M., Meckel, H., Ahrens, M., Bracht, T., Weber, F., Hoffmann, A.-C., Baba, H. A., Sitek, B., Schlaak, J. F., Meyer, H. E., Stephan, C., & Eisenacher, M.
- Affiliations
- Ruhr University Bochum (DE)
- Essen University Hospital (DE)
- Corresponding author
- Kohl, Michael
- ORCID
- Venue
- Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 1844
- Issue
- 1
- Pages
- 52-62
- ISSN
- 1570-9639 (ISSN-L)
- OpenAlex
- W2091076764
- Cited by
- 60
- References
- 68
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 8 · 2021: 5 · 2020: 1 · 2019: 6 · 2018: 6 · 2017: 5 · 2016: 9 · 2015: 6 · 2014: 6 · 2013: 3
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- BioNRW.PROFILE, Förderkennzeichen (005-1006-0050)
- European Union (European Regional Development Fund - Investing in your future)
- German federal state North Rhine-Westphalia (NRW)
- P.U.R.E. (Protein Unit for Research in Europe)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics integration, data processing workflow, biomarker discovery, therapeutic target, liver disease, hepatocellular carcinoma, transcriptomics, proteomics, linear regression limitations, feature selection, classification
- MeSH terms
- Chromatography, Liquid, Mass Spectrometry, Biomarkers, Proteomics, Workflow, Transcriptome
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