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De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways
Open accessPublished version of a preprint
Abstract
New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.
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Metadata
- Authors
- Vaparanta, K., Merilahti, J. A. M., Ojala, V. K., & Elenius, K.
- Affiliations
- Åbo Akademi University (FI)
- Corresponding author
- Vaparanta, Katri
- Elenius, Klaus
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 23
- Issue
- 7
- Pages
- 100780
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC11259815
- OpenAlex
- W4396622959
- Cited by
- 3
- References
- 81
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Åbo Akademi University Research Portal (submittedVersion, cc-by)
- UTUPub (University of Turku) (submittedVersion, cc-by)
- Funders
- Foundation for Cardiovascular Research
- Maud Kuistilan Muistosäätiö
- Aarne Koskelon Säätiö
- Academy of Finland
- Suomen Kulttuurirahasto
- Emil Aaltosen Säätiö
- Sydäntutkimussäätiö
- Oskar Öflunds Stiftelse
- Orionin Tutkimussäätiö
- Paavo Nurmen Säätiö
- Syöpäsäätiö
- Turun Yliopistosäätiö
- Jenny ja Antti Wihurin Rahasto
- Varsinais-Suomen Rahasto
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- multi-omics integration, protein-protein interaction network, phosphoproteome, transcriptome, methylome, metabolomics, transcription factor prediction, functional prediction, melanoma, gamma-secretase, Tyro3, receptor tyrosine kinase signaling
- MeSH terms
- Multiomics, Algorithms, Humans, Phosphoproteins, Signal Transduction, Computational Biology, Proteome, Proteomics, Metabolomics, Transcriptome
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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