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INTEGRATED BIOINFORMATICS PLATFORM

Pathway Analysis

Pathway analysis can guide experimental design, ensuring efficient resource utilization and focused exploration of biologically relevant areas.

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Pathway Analysis Overview

Pathway analysis is a powerful tool in bioinformatics and systems biology. It involves the interpretation of various biological pathways to understand the complex interplay of genes, proteins, metabolites, and other molecular entities. By analyzing these pathways, researchers can uncover how different biological processes operate and interact, leading to new insights into disease mechanisms, therapeutic targets, and more.

Pathway analysis can guide experimental design, ensuring efficient resource utilization and focused exploration of biologically relevant areas. The identification of key regulators within perturbed pathways opens avenues for potential therapeutic interventions. Visual representations provided by pathway analysis tools simplify the communication of findings, enhancing the accessibility of metabolomics research across interdisciplinary audiences. In essence, pathway analysis in metabolomics is indispensable for extracting meaningful biological knowledge from intricate metabolite networks, contributing to advancements in both basic science and translational research.

Pathway Analysis within Metabolon’s Integrated Bioinformatics Platform

Pathway analysis is crucial for effectively analyzing metabolomics data.

Discovery of Biological Insights
Pathway analysis enables the identification of significant pathways that can reveal novel biological hypotheses, potentially leading to groundbreaking scientific discoveries.

Disease Understanding
Understanding the mechanisms of diseases at the molecular level can guide the development of new therapeutic strategies.

Personalized Medicine
Pathway analysis plays a key role in the development of personalized medicine, allowing for more targeted and effective treatments based on individual genetic profiles.

 

Comprehensive and User-Friendly Pathway Analysis

Highly Curated Metabolon Pathways
Enrichment Calculation for Pathway Highlighting
Comprehensive and Interactive Pathway Diagrams
Exploration of Disease-Related Pathways

Highly Curated Metabolon Pathways

Metabolon’s Bioinformatics Platform offers access to a collection of meticulously curated pathways. These pathways are the result of extensive research and validation, ensuring that you are working with accurate and relevant biological information.

Enrichment Calculation for Pathway Highlighting

We employ sophisticated enrichment calculations to help you highlight the most interesting and statistically significant pathways in your datasets. This feature is crucial for directing your focus to the most impactful areas of your research.

Comprehensive and Interactive Pathway Diagrams

Utilizing WikiPathways, the Pathway Enrichment tool provides comprehensive pathway diagrams. These diagrams are not only detailed but also interactive, allowing you to toggle different elements on or off for a more tailored view. For users familiar with our existing tools, we offer support for legacy Metabolon pathway visualizations, ensuring a smooth transition and continued access to trusted resources.

Exploration of Disease-Related Pathways

A unique aspect of the Pathway Enrichment tool is the ability to explore diseases in relation to specific pathways as informed by scientific literature. This feature is invaluable for researchers focusing on disease mechanisms, offering insights into how molecular changes are linked to various health conditions.

Pathways Enrichment Features

Enrichment Results Tab

PLSDA Plot

The ‘Enrichment Results’ feature is the default view, showcasing a sunburst (circular) visualization that categorizes pathways such as ‘Amino acids,’ ‘Lipids,’ and ‘Energy.’ This visualization employs a color gradient to reflect the log(p-value), providing an immediate visual cue to the statistical significance of each pathway. Below the visualization is the ‘Enrichment Results’ table, which lists pathways with columns for Level, Parent, Name, p-value, log(p-value), Coverage, Size, and direct links to ‘Pathways’ and ‘Volcano Plot.’ This table is interactive, allowing for sorting and filtering to aid in the examination of specific results.

Explore Pathways

Cluster Embedding

Upon selecting the “Explore Pathways” feature, you are presented with a rich, interactive diagram page. This page offers a visualization of biological pathways, providing an intuitive and engaging way to explore the intricate web of metabolic and signaling pathways. Here you can delve deeper into the data, gaining insights into how different metabolites, enzymes, and cofactors interact within a pathway.

Explore Associations

Explore Associations

The “Explore Associations” feature utilizes a Sankey diagram to illustrate the intricate relationships between metabolic pathways and diseases. A Sankey diagram is a specific type of flow diagram, in which the width of the arrows or bands is proportional to the flow rate or quantity of the entities through the system. This visualization effectively conveys the magnitude of connections, allowing us to discern the relative significance of different pathways and their association with various diseases.

Bioinformatics Platform

Overview
Principal Component Analysis
Partial Least Squares Discriminant Analysis (PLS-DA)
Volcano Plot
Clustering Analysis
Pathway Analysis
Biomarker Lenses®
Statistics
Multiomics
Microbiome Analysis Tool

Demo the Bioinformatics Platform

Advanced analysis and data enrichment tools, curated pathways, statistics, and customizable visualizations all included within our Integrated Bioinformatics Platform.

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Demo our Bioinformatics Platform

Advanced analysis and data enrichment tools, curated pathways, statistics, and customizable visualizations all included within our Integrated Bioinformatics Platform.

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References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.