New methodologies for analyzing metabolomic data

• Autores: Javier López-Ibañez Infante
• Directores de la Tesis: Florencio Pazos (dir. tes.), Mónica Chagoyen Quiles (dir. tes.)
• Lectura: En la Universidad Autónoma de Madrid ( España ) en 2021
• Idioma: inglés
• Número de páginas: 82
• Títulos paralelos:
  • Nuevas metodologías para el análisis de datos metabolómicos
• Tribunal Calificador de la Tesis: Alfonso Valencia Herrera (presid.), Enrique Carrillo de Santa Pau (secret.), Alberto Valdés Tabernero (voc.)
• Programa de doctorado: Programa de Doctorado en Biociencias Moleculares por la Universidad Autónoma de Madrid
• Materias:
  • Ciencias de la vida
    • Biomatemáticas
      • Bioestadística
• Enlaces
  • Tesis en acceso abierto en: Biblos-e Archivo
• Resumen

  • Computational approaches for analyzing metabolite-related data have not evolved at the same pace than those for dealing with protein and nucleic acid data. As an example, the number of tools available for performing functional analysis of metabolomics data is very small compared with the plethora of approaches for carrying out the same analysis on genomics and proteomics data. Since the first bioinformatic resources for performing this kind of analysis with metabolomic data were published, new metabolic databases have appeared and the amount of biological data related with chemical compounds has increased. One of these resources was MBROLE, a webserver for performing enrichment analysis of metabolomic data. It allows reducing a raw list of metabolites (e.g. those showing up in a metabolomics experiment) to a meaningful set of biological terms. A new version, MBROLE 2 has been developed with new features such as the automatic conversion of identifiers (IDs), a redesigned graphical user interface and an improved reporting of results. Updated data from databases included in the previous version and data from new metabolic and chemical databases has been integrated in MBROLE 2. Another aspect that illustrates the gap between protein/DNA and metabolite bioinformatics is the lack of an equivalent of the powerful sequence profile based approaches. In this work, a new method for predicting biological functions of chemical compounds from their 2D structure has been developed. Inspired in protein sequence profiles, this method extracts the characteristic features of a related set of chemical compounds, and use them to make predictions about others sharing these features. The performance of this profile-inspired method predicting involvement of compounds in biological pathways has been assessed and compared with previous methods with the same goal. This methodology has been implemented in a public webserver so it can be accessed through an interactive graphical interface