POMA: User-friendly Workflow for Metabolomics and Proteomics Data Analysis
Pol Castellano Escuder,Cristina Andrés-Lacueva,Alex Sánchez-Pla University of Barcelona
Abstract
Mass spectrometry, like other high-throughput technologies, usually faces a data mining challenge to provide an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the hard points and it’s critical in the subsequent biological interpretation of the results. Due to this fact combined with the computational programming skills needed for this type of analysis, several bioinformatic tools have emerged to simplify mass spectrometry data analysis. However, some of these tools limit the analysis to a few hidebound statistical methods and to a low-flexible datasets. POMA introduces a structured, reproducible and easy-use workflow for the visualization, exploration and statistical analysis of mass spectrometry data that integrates several statistical methods, some of them widely used in other type of omics. In summary, POMA enables a flexible data cleaning and statistical analysis in one comprehensible R/Bioconductor package.
Keywords: mass spectrometry,metabolomics,proteomics,workflow,statistical analysis,visualization