SOFTWARE DEVELOPMENT#

OwlStatApp#

Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this omic discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow, being a time-consuming issue which usually requires detailed knowledge of bioinformatics, statistics and specialized software. We have developed OWL Stat App, an easy-to-use web application for metabolomics data analysis. It combines powerful univariate and multivariate data analysis with pathway mapping tools and visualization capacities to facilitate interpretation of the results.
| OwlStatApp |

AgingAnalysis#

Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this omic discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled Enhancing metabolomics research through data mining, which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.
| AgingAnalysis |

PyGenMet#

A Python package for Genetics Algorithms.
| PyGenMet | Manual |