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Discovery Panel Report

View relevant data in insightful ways. Get an introduction to your metabolomics results—see the “story” in the data and find areas of interest.

Visualization Legend

Metabolon SmartPanel Report Visual

The Discovery Panel Report visualization shows Detected Metabolites. Within the Detected Metabolites, we further classify these results into Significantly More Abundant, Significantly Less Abundant, and (statistically) Nonsignificant Differences.

Each metabolite is represented by a circle, which is sized by p-value (smaller p-value, larger circle) and colored by the direction and intensity of change. A more intense color indicates a larger fold change difference. If ANOVA Main Effects are included in your study design, results will be indicated by a dot in the center of the circle with more information on hover.

Choose Your View

There are a few ways you can adjust the view of your Discovery Panel Report:

Metabolon SmartPanel Report ChooseView
Select Comparison
p-Value Threshold
Find a Metabolite

Your Discovery Panel report visualizes statistical comparisons (one group compared to another). Select which comparison to view by using the “Select Comparison” drop-down menu. Here you will see the group comparisons defined by your study design.

This slider gives you the ability to adjust the limit of significance (p-value) shown in the visualization and classification tables. Sliding to the right will widen the range of significance, while sliding to left will narrow the range to only the most statistically significant metabolites for the chosen comparison.p-values for each comparison are derived from the Natural Log-Transformed Data using the corresponding statistical analysis (e.g., t-Test, ANOVA, etc.).

Use this drop-down to find or search for a particular metabolite from within the Discovery Panel. Once selected, the metabolite will be highlighted within the visualization.

Learn About Your Study Results

The Discovery Panel Report visualization is designed to give you both an “at-a-glance” view of the areas of significance within your study results and also enable deeper insight.

Hovering
Selecting
Sorting

Interacting with your data by hovering over each metabolite circle will bring up more detailed information about that metabolite within your chosen comparison, such as metabolite name, p-value, fold change, and main effect (if applicable). Hovering over a metabolite name within a table will temporarily highlight the metabolite’s location within the visualization.

Selecting a metabolite circle (or name within a table) will highlight it and allow you to track how it changes across multiple comparisons.

You can view tabular data by ascending or descending values by clicking on the table header by which you wish to sort. Sort order will be reflected on all tables with related values.

Explore Pathways

Start digging into the meaning behind your study results by leveraging our deep annotation.

Each table shows the association name, total number of metabolites of association, number of associated metabolites with a significant result inyour study, and the percentage of significant metabolites to the total number associated.

pathways

Pathways

View the interconnected power of the metabolome by exploring associated biochemical pathways that reveal biological function.

See how Metabolon can advance your path to preclinical and clinical insights

Contact Us

Talk with an expert

Request a quote for our services, get more information on sample types and handling procedures, request a letter of support, or submit a question about how metabolomics can advance your research.

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Research Triangle Park, NC 27709

+1 (919) 572-1721

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.