Process / pipelineBioinformatics / omics

Multi-omics proteomics analysis — Integrative Proteomics

Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss.

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Sources

  1. Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752. DOI: 10.1371/journal.pcbi.1005752
  2. Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M., Tebbutt, S. J., & Le Cao, K.-A. (2019). DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics, 35(17), 3055–3062. DOI: 10.1093/bioinformatics/bty1054

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Referenced by

ScholarGateMulti-omics proteomics analysis (Multi-Omics Integrative Proteomics Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/bioinformatics/multi-omics-proteomics-analysis