AI Article Synopsis
- Metabolomics and proteomics encounter challenges in data mining, particularly in statistical analysis, which is essential for biological interpretation and biomarker discovery.
- To aid in these analyses, bioinformatic tools have been developed, but many still rely on limited statistical methods and inflexible data sets.
- POMAShiny is a user-friendly web tool that enhances the statistical analysis and visualization of metabolomics and proteomics data, integrating various methods for improved reproducibility and flexibility, and is available for free online.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279420 | PMC |
| http://dx.doi.org/10.1371/journal.pcbi.1009148 | DOI Listing |
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