Motivation: High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse.
Results: To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e.g. different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views, and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user.
Availability and implementation: The methodology presented here is implemented in PathwayNexus, an open-source add-on for Vanted available at www.cls.uni-konstanz.de/software/pathway-nexus.
Contact: falk.schreiber@unikonstanz.de.
Supplementary information: Website: www.cls.uni-konstanz.de/software/pathway-nexus.
© The Author(s) 2024. Published by Oxford University Press.