Proteomic and metabolomic data analysis

Proteomics and metabolomics reveal the functional state of a biological system.

Computational analysis of Proteins and metabolites addresses fundamental questions of biochemistry: Which reactions take place? What is being built? How is energy produced and used?

While transcriptomics is commonly used to infer activities of signaling and metabolic pathways, proteomics and metabolomics give a more direct view into the key molecules of such pathways and individual reactions.

Proteins and metabolites are typically identified and quantified by the means of mass-spectrometry (MS). Other methods, relying on antibodies (for proteins) or nuclear magnetic resonance (NMR; for metabolites) provide lower-throughput or less quantitative data, often with lower costs compared to MS.

In addition to pathway analyses, proteomic and metabolomic data from patient samples are particularly suitable for biomarker discovery.

Scroll down to learn more and to browse our references in proteomics, metabolomics and lipidomics.

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Proteomic data analysis

Bioinformatic analysis of proteomes starts with identifying proteins and quantifying their abundances — absolute or relative, depending on the experiment.

As with the analysis of any gene expression data, the next step is an exploratory analysis to study the variance and grouping of the data set with principal component analysis (PCA) or similar dimensionality reduction approaches.

More focused analyses may consist of differential expression and pathway analyses to characterize differences between samples.

These analyses can also be performed on proteins enriched for specific post-translational modifications such as phosphorylation. Quantitative data from the total proteome and enriched subset (e.g., phosphoproteome) can be processed in parallel or in an integrative manner to gain a more detailed view of pathway activities.

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Metabolomic data analysis

The metabolome consists of an almost endless catalogue of endogenous and exogenous small molecules that partake in reactions in an organism.

While proteomics studies the catalysts of these reactions, metabolomics is concerned with their substrates, intermediates and products.

As with proteomics, high-throughput metabolomic data is typically used to either quantify and study metabolic pathways or to identify clinically revelant molecules such as biomarkers. Thus, the explorative and statistical analyses are very similar to those of proteomics.

A special case of metabolomics, lipidomics focuses on the vast diversity of lipid molecules in an organism. Lipidomic analyses often aim at characterizing the (dys)function of lipid metabolism and trafficking, particularly in metabolic diseases.

Meet some of our experts in proteomics and metabolomics

I am a senior data scientist with several years of experience in computational biology and biostatistics. I have analyzed and integrated countless types of NGS and mass-spec data in different biomedical applications.

My scientific interests include designing and developing data analysis methods, drug development and biomarker discovery in metabolic diseases as well as studying post-transcriptional regulation of gene expression. In the last few years, I have also applied machine learning methods to cluster, classify and make predictions based on complex biological data.

I am at home in interdisciplinary and translational research, at the crossroads of computer science, biology and clinical research.

Davide Chiarugi Scientific Project Manager Genevia Technologies Oy

I am a senior bioinformatician with extensive experience in analysing data types including mRNA-seq, smallRNA-seq, DNA-seq, ChIP-seq, proteomics and methylation data.

During the years working on customer projects I have gained experience in e.g. gene and protein expression analysis, germline and somatic variant analysis, genome-wide association studies and polygenic risk score analysis, population genomics, epigenetic analysis, using public ‘omics datasets such as TCGA and applying machine learning methods for patient prognosis.

I am also very experienced in leveraging the various pathway and gene-regulatory analyses of the Ingenuity Pathway Analysis (IPA) software. In my data analysis role, I also benefit from my 10+ years of experience as a wet-lab molecular biologist as well as my experience in biocuration and database content creation.

Reija Hieta
Reija Hieta Senior Bioinformatician Genevia Technologies Oy

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References and case studies

All references

Selected publications from our customers

  • Singh, A. et al. (2022). Urolithin A improves muscle strength, exercise performance, and biomarkers of mitochondrial health in a randomized trial in middle-aged adults. Cell reports. Medicine, 3(5), 100633. https://doi.org/10.1016/j.xcrm.2022.100633
  • Pihlström, S. et al. (2022). A multi-omics study to characterize the transdifferentiation of human dermal fibroblasts to osteoblast-like cells. Frontiers in molecular biosciences, 9, 1032026. https://doi.org/10.3389/fmolb.2022.1032026
  • Kundu, S. et al. (2021). Common and mutation specific phenotypes of KRAS and BRAF mutations in colorectal cancer cells revealed by integrative -omics analysis. Journal of experimental & clinical cancer research : CR, 40(1), 225. https://doi.org/10.1186/s13046-021-02025-2
  • Chaudhary, P., et al. (2021). An exploratory analysis of comparative plasma metabolomic and lipidomic profiling in salt-sensitive and salt-resistant individuals from The Dietary Approaches to Stop Hypertension Sodium Trial. Journal of hypertension, 39(10), 1972–1981. https://doi.org/10.1097/HJH.0000000000002904
  • Tiihonen, J. et al. (2020). Neurobiological roots of psychopathy. Molecular psychiatry, 25(12), 3432–3441. https://doi.org/10.1038/s41380-019-0488-z
  • Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599. https://doi.org/10.1002/glia.23741
  • Tiihonen, J. et al. (2019). Sex-specific transcriptional and proteomic signatures in schizophrenia. Nature communications, 10(1), 3933. https://doi.org/10.1038/s41467-019-11797-3

Selected publications from our team

  • Zijlmans, D. W. et al. (2022). Integrated multi-omics reveal polycomb repressive complex 2 restricts human trophoblast induction. Nature cell biology, 24(6), 858–871. https://doi.org/10.1038/s41556-022-00932-w
  • Pellegrinelli, V. et al. (2022). Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance. Nature metabolism, 4(4), 476–494. https://doi.org/10.1038/s42255-022-00561-5
  • Furse, S. et al. (2022). Dietary PUFAs drive diverse system-level changes in lipid metabolism. Molecular metabolism, 59, 101457. https://doi.org/10.1016/j.molmet.2022.101457
  • Furse, S. et al. (2022). Paternal nutritional programming of lipid metabolism is propagated through sperm and seminal plasma. Metabolomics : Official journal of the Metabolomic Society, 18(2), 13. https://doi.org/10.1007/s11306-022-01869-9
  • Furse, S. et al. (2022). A mouse model of gestational diabetes shows dysregulated lipid metabolism post-weaning, after return to euglycaemia. Nutrition & diabetes, 12(1), 8. https://doi.org/10.1038/s41387-022-00185-4
  • Stefos, G. C. et al. (2021). aniFOUND: analysing the associated proteome and genomic landscape of the repaired nascent non-replicative chromatin. Nucleic acids research, 49(11), e64. https://doi.org/10.1093/nar/gkab144
  • Carobbio, S. et al. (2021). Unraveling the Developmental Roadmap toward Human Brown Adipose Tissue. Stem cell reports, 16(3), 641–655. https://doi.org/10.1016/j.stemcr.2021.01.013
  • Alvarez-Guaita, A. et al. (2021). Phenotypic characterization of Adig null mice suggests roles for adipogenin in the regulation of fat mass accrual and leptin secretion. Cell reports, 34(10), 108810. https://doi.org/10.1016/j.celrep.2021.108810
  • Hall, Z. et al. (2021). Lipid Remodeling in Hepatocyte Proliferation and Hepatocellular Carcinoma. Hepatology (Baltimore, Md.), 73(3), 1028–1044. https://doi.org/10.1002/hep.31391
  • Furse, S. et al. (2021). Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings' lipid metabolism. Communications biology, 4(1), 163. https://doi.org/10.1038/s42003-021-01686-1
  • Furse, S. et al. (2021). Lipid Metabolism Is Dysregulated before, during and after Pregnancy in a Mouse Model of Gestational Diabetes. International journal of molecular sciences, 22(14), 7452. https://doi.org/10.3390/ijms22147452
  • Roberts, G. P. et al. (2019). Comparison of Human and Murine Enteroendocrine Cells by Transcriptomic and Peptidomic Profiling. Diabetes, 68(5), 1062–1072. https://doi.org/10.2337/db18-0883
  • Jylhä, A. et al. (2018). Comparison of iTRAQ and SWATH in a clinical study with multiple time points. Clinical proteomics, 15, 24. https://doi.org/10.1186/s12014-018-9201-5
  • Latonen, L. et al. (2018). Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression. Nature communications, 9(1), 1176. https://doi.org/10.1038/s41467-018-03573-6

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Antti Ylipää
Antti Ylipää CEO, co-founder Genevia Technologies Oy +358 40 747 7672

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