Welcome to LipidOne 2.5

User-friendly lipidomic data analysis tool
for a deeper interpretation
in a systems biology scenario

Explore your lipidomic dataset through four thematic modules

Statistics
Three levels of lipids analysis
  • Classes
  • Molecular species
  • Building blocks
Biochemistry
Functional Lipid Analysis
  • Energy
  • Signaling
  • Structures
Foodomics
Lipid Food Profile
  • Comparative analysis
  • Food lipid characterization
  • Selective in-silico hydrolysis
  • Food origin and authenticity assessment
Metabolism
Nutritional-Metabolic Lipid Profile
  • Diet- and disease-related changes
  • Group trajectories and stratification
  • Translational interpretation
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LipidOne performs various univariate analyses and generates multiple visualizations, including grouped and stacked bar charts, pie charts, and heatmaps, based on lipid characteristics and user-selected experimental groups.
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LipidOne performs comprehensive univariate analyses, including volcano plots, lipid class summaries, boxplots, sequence analysis, and t-test/ANOVA, providing statistical insights into lipidomic variations across experimental groups.
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LipidOne includes clustering analysis functions such as k-means clustering, hierarchical clustering (dendrogram), and heatmaps, allowing for the identification of lipidomic patterns and similarities among experimental groups.
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LipidOne provides comprehensive PCA analysis, generating score plots, loading plots, scree plots, outlier detection plots, and top-loading bar charts, enabling users to explore variance, sample distribution, and key contributing lipid species across principal components.
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LipidOne enables pathway analysis at three levels: lipid classes, molecular species, and lipid building blocks (Acyl, Alkyl, and Alkenyl chains). It identifies metabolic transformations, interactions, and activation states, while also listing the enzymes involved in each pathway. This analysis is available for eight user-selectable model organisms, providing a comprehensive view of lipid metabolism across different biological systems.
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LipidOne projects the proteins predicted by pathway analysis onto the STRING database to identify neighboring proteins and construct interaction networks. These networks are then used to perform enrichment analyses, providing functional insights into lipid-related biological processes.
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LipidOne performs PLS-DA analysis, generating multiple visualizations for model interpretation:
  • Score Plot: Displays sample separation across components, highlighting group clustering and variance.
  • Loading Plot: Identifies key lipid variables contributing to group differentiation.
  • VIP Score Plot with Heatmap: Ranks the top 20 most influential lipid species (VIPs) and visualizes their expression patterns.
  • R2Y, Q2Y, and Accuracy Plot: Evaluates model performance and predictive ability across components.
  • Confusion Matrix: Assesses classification accuracy by comparing predicted vs. actual sample groups.
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LipidOne performs sPLS-DA analysis, generating multiple visualizations to enhance model interpretation and feature selection:
  • Score Plot: Shows the separation of groups based on sparse latent components.
  • VIP Score Plot with Heatmap: Highlights the top 15 most discriminant lipid species and their expression patterns.
  • Performance Evaluation Plot: Assesses classification performance based on the number of selected variables using different distance metrics.
  • Error Rate Plot: Displays classification error rates across components and distance metrics.
  • Confusion Matrix: Evaluates classification accuracy by comparing predicted vs. actual groups.
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LipidOne performs OPLS-DA analysis, generating multiple visualizations for model interpretation and validation:
  • Score Plot: Displays group separation by removing orthogonal variation, enhancing discrimination.
  • VIP Score Plot with Heatmap: Identifies the top 20 most discriminant lipid species and their expression trends.
  • Observation Diagnostics Plot: Detects potential outliers based on orthogonal and score distances.
  • S-Plot: Highlights key lipid features contributing to group differentiation.
  • Permutation Tests (R2Y and Q2Y): Validates model robustness by comparing actual vs. permuted data.
  • Model Performance Plot: Summarizes predictive ability using R2X, R2Y, and Q2 values.
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LipidOne enables biomarker discovery through multiple statistical approaches:
  • Univariate ROC Analysis: Evaluates the diagnostic performance of individual lipid species using AUC.
  • Multivariate ROC Analysis (Random Forest Model): Assesses the combined predictive power of multiple lipid species.
  • Candidate Biomarker Table: Lists the top 20 lipid biomarkers ranked by p-value, AUC, Cohen's d effect size, and statistical power, helping prioritize robust and clinically relevant candidates.
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Functional Lipid Analysis turns complex lipid profiles into 42 biochemically-defined indices, highlighting functional shifts in energy, structural and signalling lipids between groups.
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Each analysis result in LipidOne is accompanied by two key features:
  • INFO Button: Provides a detailed explanation of the selected function, including its methodology and guidelines for interpreting the results.
  • DOWNLOAD Button: Allows users to export high-resolution graphics and CSV tables, ensuring easy integration of results into reports and publications.
Reference (please cite as:)
  • Nutritional-Metabolic Lipid Profiling with LipidOne for plasma lipidomics interpretation in metabolic health, Dorotea Frongia Mancini, Husam B R Alabed, Roberto Maria Pellegrino, bioRxiv 2026.05.14.725104; https://doi.org/10.64898/2026.05.14.725104
    This article is a preprint and has not been certified by peer review
  • Husam B R Alabed, Dorotea Frongia Mancini, Martina Pergola, Luigina Romani, Sabata Martino, Albert Koulman, Roberto Maria Pellegrino, Functional lipid analysis via index-based lipidomics profile: a new computational module in LipidOne, Bioinformatics, Volume 42, Issue 3, March 2026, btag090 https://doi.org/10.1093/bioinformatics/btag090
  • Alabed, H. B. R., Mancini, D. F., Buratta, S., Calzoni, E., Giacomo, D. D., Emiliani, C., Martino, S., Urbanelli, L., & Pellegrino, R. M. (2024). LipidOne: A web tool for discovering biological meanings hidden in lipidomic data. Current Protocols, 4, e70009. doi: 10.1002/cpz1.70009
  • Roberto Maria Pellegrino, Matteo Giulietti, Husam B.R. Alabed, Sandra Buratta, Lorena Urbanelli, Francesco Piva, Carla Emiliani, LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario, Bioinformatics, Volume 38, Issue 6, March 2022, Pages 1767–1769, https://doi.org/10.1093/bioinformatics/btab867
Recent Publications Using LipidOne
  • Arsic, Aleksandra, Ales Kvasnicka, David Friedecky, Nebojsa Ivanovic, Maja Milosevic, and Vesna Vucic. 2026. "Aromatase Inhibitor Therapy Is Associated with Distinct Plasma Lipidomic Profiles in Postmenopausal Breast Cancer Patients". International Journal of Molecular Sciences 27, no. 4: 1926. https://doi.org/10.3390/ijms27041926
  • Narimanfar, G., Alabed, H.B.R., Brancolini, A. et al. Multi-parametric profiling of plasma-derived extracellular vesicles reveals a disease-associated molecular signature supporting a liquid biopsy approach in myelofibrosis. Mol Med (2026). https://doi.org/10.1186/s10020-026-01477-6
  • Arad, M., & Foster, L. J. (2026). Toward large-scale mass spectrometry-based omics for clinical applications. Expert Review of Proteomics, 23(3), 63–77. https://doi.org/10.1080/14789450.2026.2646657
  • Garcia-Gimenez, A., Ditcham, J.E., Azazi, D.M.A. et al. CREBBP inactivation sensitizes B cell acute lymphoblastic leukemia to ferroptotic cell death upon BCL2 inhibition. Nat Commun 16, 4274 (2025). https://doi.org/10.1038/s41467-025-59531-6
  • Michael G. Atser, Chelsea D. Wenyonu, Elyn M.et al., Pyruvate dehydrogenase kinase 1 controls triacylglycerol hydrolysis in cardiomyocytes, Journal of Biological Chemistry, Volume 301, Issue 4, 2025, 108398, ISSN 0021-9258, https://doi.org/10.1016/j.jbc.2025.108398.
  • Volante, V.S.; Watson, F.L.; Bhattacharya, S.K. A Comparative Bioinformatic Analysis of Optic Nerve Axon Regeneration Lipidomes Using the Xenopus laevis as a Model System. Methods Protoc. 2025, 8, 110. https://doi.org/10.3390/mps8050110
  • Amrutkar, M., Guttorm, S.J.T., Labori, K.J. et al. Reduced lipid metabolite abundance in human pancreatic cancer and matched serum samples following neoadjuvant FOLFIRINOX treatment. Metabolomics 22, 18 (2026). https://doi.org/10.1007/s11306-025-02388-z
NEW Unipg
Our latest updates
Discover the new features:
  • Nutritional-Metabolic Lipid Profile: a new LipidOne module that turns plasma lipidomics into intuitive nutritional and metabolic indices, helping reveal hidden signatures of diet, metabolism and human health.
  • Enhanced shorthand translation: LipidOne now also integrates the Rgoslin* library and automatically converts classical fatty-acid names into shorthand notation, making lipid nomenclature harmonisation more robust and flexible.
    (*) Kopczynski, D., Hoffmann, N., Peng, B., & Ahrends, R. (2022). Goslin 2.0 Implements the Recent Lipid Shorthand Nomenclature for MS-Derived Lipid Structures. Analytical Chemistry, 94(15), 6097–6101. https://doi.org/10.1021/acs.analchem.1c05430
New features from v.2.4:
  • New data upload workflow: a redesigned input pipeline now detects the nomenclature level of your dataset and guides you through a smoother, more robust import—reducing formatting friction and making uploads more reliable across different lipidomics outputs.
  • Chain Space: a new analysis view that projects your results into “chain space”, helping you quickly spot which acyl/alkyl chain signatures (e.g., unsaturation and chain-length patterns) drive differences between experimental groups.
  • Functional Lipid Analysis: our innovative module for functional lipid profiling is now online. It calculates 42 functional indices, grounded in well-documented biochemical principles, to capture how energy, structural and signaling functions change between experimental groups.
  • Improved Predicted Proteins Network: a new network architecture better links lipid changes to their regulatory proteins, making it easier to move from lipid signatures to mechanistic hypotheses.
  • PERMANOVA for lipid molecular species: a new function lets you test group differences directly on distance matrices, with support for covariates and blocking factors.
  • Shorthand lipid translator upgraded: the utility now understands Thermo’s LipidSearch nomenclature, allowing LipidSearch users to feed their lipidomics tables directly into LipidOne.
  • LipidOne on ELIXIR-IT: LipidOne is now hosted on the ELIXIR-IT infrastructure, offering faster analyses, higher uptime and seamless integration with other European bioinformatics services.
Stay Connected with LipidOne!
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