ArticlePLoS computational biology2021
POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed.
- Metabolite Biomarkers Linking a High-Fiber Rye Intervention with Cardiometabolic Risk Factors: The RyeWeight Study.Journal of agricultural and food chemistry · 2025Trial
- Metabolomic Fingerprints of Medical Therapy Versus Bariatric Surgery in Patients With Obesity and Type 2 Diabetes: The STAMPEDE Trial.Diabetes care · 2024Trial
- Distinct metabolomic and proteomic signatures in Parkinson's disease patients with REM sleep behavior disorder.Signal transduction and targeted therapy · 2026Article
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- GAUDI: interpretable multi-omics integration with UMAP embeddings and density-based clustering.Nature communications · 2025Article
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.Scientific reports · 2025Article
- Article
- Optimizing gene selection and module identification via ontology-based scoring and deep learning.Bioinformatics advances · 2025Article
- Prediagnostic Plasma Nutrimetabolomics and Prostate Cancer Risk: A Nested Case-Control Analysis Within the EPIC Study.Cancers · 2024Article
- Metabolomic profiles differentiate between porto-sinusoidal vascular disorder, cirrhosis, and healthy individuals.JHEP reports : innovation in hepatology · 2024Article
- Differential Anti-Inflammatory Effects of Electrostimulation in a Standardized Setting.International journal of molecular sciences · 2024Article
- Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence.Science advances · 2024Article
- SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.Briefings in bioinformatics · 2024Article
- CURTAIN-A unique web-based tool for exploration and sharing of MS-based proteomics data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A fat- and sucrose-enriched diet causes metabolic alterations in mdx mice.American journal of physiology. Regulatory, integrative and comparative physiology · 2023Article
- BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.PLoS computational biology · 2023Article
- Article
- Dietary Sources of Anthocyanins and Their Association with Metabolome Biomarkers and Cardiometabolic Risk Factors in an Observational Study.Nutrients · 2023Observational
- The activity of the aryl hydrocarbon receptor in T cells tunes the gut microenvironment to sustain autoimmunity and neuroinflammation.PLoS biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.