Last updated: 5 October 2024
In the Saez Lab over the recent years, we started to develop a number of tools for metabolomics data processing and analysis. Within our prior knowledge processing framework, OmniPath, we provide access to database knowledge about metabolites, drug compounds and metabolism, and we create utilities to make use of this knowledge in data processing and analysis. In this page we maintain a list of our already available tools and resources.
The MetaProViz R package is a collection of utilities for metabolomics data analysis. These include quality control, identifier translation, differential analysis, functional enrichment analysis, and a set of versatile, publication quality visualizations.
The COSMOS method, implemented in the cosmosR R/Bioconductor package, combines multiple omics by inferring molecular activities from each omics modality, and mapping the activities to a causal network covering both signalling and metabolism. In the preprint linked above, we present the application of the method in rich case studies.
MetalinksDB is an integrated database of interactions between edogenous, small molecule ligands and proteins. Using its web application, it is contextualisable by subcellular location, tissue, biospecimen and pathway.
Git repo |
Docs |
Database clients |
ID translation
The pypath.inputs module includes Python clients for almost 200 databases. Metabolomics related databases include HMDB, RaMP, SwissLipids, Lipid Maps LMSD and PubChem. The pypath.utils.mapping module supports identifier translation, using HMDB and RaMP identifier translation data.
The OmnipathR R/Bioconductor package provides direct access to several databases, such as HMDB, RaMP, the Chalmers Sysbio GEMs and STITCH. It also supports identifier translation, and contains utilities to build the prior knowledge network for COSMOS.
Chrisina Schmidt,
Aurelien Dugourd,
Macabe Daley,
Forrest Hyde,
Elias Farr,
Igor Bulanov,
Dénes Türei,
Leila Gul,
Saez Lab &
Korcsmaros Lab,
2016-2024.
Feedback: omnipathdb@gmail.com