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POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis

Author

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  • Pol Castellano-Escuder
  • Raúl González-Domínguez
  • Francesc Carmona-Pontaque
  • Cristina Andrés-Lacueva
  • Alex Sánchez-Pla

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.Author summary: Metabolomics and proteomics are two growing areas in human health and personalized medicine fields. Often, one of the main applications of metabolomics and proteomics is the discovery of novel biomarkers and new therapeutic targets in these areas. However, these data are extremely complex and hard to analyse, since they have a large number of features, several missing values, and often important clinical variables to consider in the analyses. Therefore, powerful and versatile tools are needed to provide efficient methods for data visualization and exploration, as well as a wide range of robust statistical methods to meet all data and users requirements. Although powerful tools do exist for the analysis of these data, many of them are still limiting the analyses in terms of visualization and statistical analysis. To address this limitation and complement the existing tools, we have developed a web-based application, named POMAShiny, for the data analysis of metabolomics and proteomics. This novel and versatile tool offers a wholly interactive and easy-to-use environment for the analysis of these data, including numerous methods for preprocessing, data visualization and statistical analysis. The POMAShiny open-source tool is extremely flexible and portable, as it can be installed locally and freely accessed online at https://webapps.nutrimetabolomics.com/POMAShiny.

Suggested Citation

  • Pol Castellano-Escuder & Raúl González-Domínguez & Francesc Carmona-Pontaque & Cristina Andrés-Lacueva & Alex Sánchez-Pla, 2021. "POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-15, July.
    • Handle: RePEc:plo:pcbi00:1009148
    DOI: 10.1371/journal.pcbi.1009148
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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Yan, Dongyang & Li, Keping & Ye, Jingjing, 2019. "Correlation analysis of short text based on network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
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