Workshop Description (Introductory Course)

The application of omics (i.e., metabolomics, proteomics, transcriptomics, genomics) has become greatly popular in the life sciences. These technologies unlock the investigation of metabolism across thousands of genes, proteins, and metabolites, and provide a holistic perspective on the system of interest. However, to gather a systems-level understanding of biological functions in health and disease, omics data must be integrated in the relevant metabolic networks, in the context of allosteric, signaling and other regulatory layers. Metabolites can be regarded as a functional readout for the activity of a biological system. Therefore, their measured abundances can be leveraged to guide the interpretation of other omics layers of information. In this workshop, we provide an introduction and hands-on practical exercises on state-of-the art workflows and tools for the integration and interpretation of metabolomics data sets in the context of transcriptomics, proteomics, and genomics data. We will first introduce tools for processing metabolomics data at high throughput, such as the XCMS, MetaboAnalyst, METLIN, LIPID MAPS, The Human Metabolome Database, and the GNPS. We will further present tools for multi-omics integration and interpretation, including PathView, Reactome, MixOmics, PARADIGM, among others. The objective of this workshop is to i) provide a comprehensive overview on tools and resources for metabolomics data analysis and multi-omics interpretation, ii) provide hands-on training on open-source tools for systems biology of metabolism. This workshop is addressed to empirical biologists and computational biologists interested in metabolism.

Workshop Materials


Technical Requirements

A computer with access to R and MATLAB, internet connection, and access to campus network.


Amelia Palermo is a postdoctoral researcher in the Graeber Lab within the UCLA Metabolomics Center. Her research revolves around the study of metabolism in health and disease using metabolomics and high-throughput systems biology approaches. Her areas of focus include the discovery of novel metabolic vulnerabilities of resistant cancer phenotypes, the study of metabolic changes induced by COVID-19, as well as the development of novel tools for the analysis and interpretation of big data for systems biology.Amelia has extensive experience in the use and development of bioinformatics tools for systems biology of metabolism by metabolomics and other omics approaches complemented by data modeling tools including those based on machine learning (ML), data and molecular repositories for meta-analysis and high-throughput interpretation.


Workshop Details

Prerequisites: Basic knowledge of metabolomics, proteomics, genomics, transcriptomics; basic knowledge of R and MATLAB, univariate and multivariate statistics.
Length: 2 hours
Level: Introductory
Location: Boyer 529, and ZOOM upon request.
Seats Available: 28

Winter 2022 Dates

March 7, 2022
1:00 PM – 3:00 PM