Targeted Metabolomics Data Analysis. Unlocking insights with Machine Learning, AI and Statistics

Overview

Using high-throughput technologies, life science researchers can identify and characterize all the small molecules or metabolites in a given cell, tissue, or organism.

This non-profit course covers a wide range of topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Hands-on practical tutorials using various data sets and tools will assist participants in learning metabolomics analysis techniques.

Organizers

The course is organized by the Faculty of Biology and Faculty of Pharmacy and Food Sciences of the University of Barcelona, The Metabolics Innovation Center (TMIC) of the University of Alberta and the Frailty and Healthy Ageing-CIBERFES.

  • David Wishart, University of Alberta, Canadá

  • Alex Sanchez, University of Barcelona, CIBERFES-ISCIII

  • Cristina Andres-Lacueva, University of Barcelona, CIBERFES-ISCIII

  • Xavier Nogués, IMIM-Hospital del Mar, CIBERFES-ISCIII

  • Pedro Abizanda, Servicio de Salud de Castilla la Mancha (SESCAM), CIBERFES-ISCIII

  • Anna Guadall, University of Barcelona

  • Miriam Martínez Huélamo, University of Barcelona, CIBERFES-ISCIII

Speakers

The course will be taught by professors:

  • David Wishart (University of Alberta, Canada),
  • Alex Sanchez-Pla (University of Barcelona, Spain)
  • Xavier Nogués (Hospital del Mar, Spain)
  • Cristina Andrés-Lacueva ((University of Barcelona, Spain)

Objectives

  • Learn to correctly design metabolome-focused experiments.

  • Understand advantages and limitations of metabolomic data analysis.

  • Present bioinformatics workflows for metabolomic data.

  • Learn how to select and apply Statistics and Machine learning to undertake rigorous data analysis.

  • Visualize datasets to gain intuitive insights into the composition and/or activity of their metabolome

Contents

  1. Fundamentals of metabolomics.

  2. Targeted metabolomics with MEGA and LC-Autofit.

  3. Statistics for metabolomics: From tests to projections.

  4. Metabolomics data analysis with MetaboAnalyst.

  5. Machine learning and Artificial Intelligence applied to metabolomics.

  6. Pathway Analysis and Biological Significance.

Target Audience

This course is intended for graduate students, post-doctoral fellows, clinical fellows and investigators who are interested in learning about both bioinformatic and cheminformatic tools to analyze and interpret metabolomics data.

Registration, Dates and Number of participants

  • Number of participants: 25

  • Duration: 11th - 14th June 2024

  • Registration fee:

    • 60 Euros (+21% VAT) for academic researchers (10% discount if agreed),
    • 500€ (+21% VAT) for industry.

It only includes participation in workshop and coffee breaks. Materials will be provided online

Late registration

There are a few places left so, in case you still want to register you may try this link

Prerrequisites

The attendants are expected to be familar with at least one language such as R or Python for data analysis.

You will require your own laptop computer. Minimum requirements: 1024×768 screen resolution, 2.4GHz CPU, 8GB RAM, 100GB free disk space, recent versions of Windows, Mac OS X or Linux (Most computers purchased in the past 3-4 years likely meet these requirements).

There workshop requires participants to complete pre-workshop tasks and readings, to be available form this website soon.