Reanalysis of Data User Interface (ReDU)
ReDU is a community- and data-driven approach to find and reuse public data containing tandem MS data at the repository scale. ReDU is a launchpad for co- or re-analysis of public data via the Global Natural Product Social Molecular Networking Platform (GNPS). Our aim is to empower researchers to put their data in the context of public data as well as explore questions using public data at the repository scale.
The full documentation can be found here.
This is a community effort and everyone is encouraged to participate by submitting their own data and sample information instructions. The sharing of new applications (and code) which use ReDU is highly encouraged.
Analyze Your Data
Compare Your Data to Public Data via Multivariate Analysis - Projection of your data onto a precalculated principal components analysis score plot of public data.
Co-analyze Your Data with Public Data at GNPS - Select files using sample information and assemble public data in groups as desired using the file selector. Launching an analysis loads the files from MassIVE at which point users can add their own data. The following co-analyses can be launched: * Molecular Networking at GNPS * Library Search at GNPS
Analyze Public Data
Explore Multivariate Analysis of Public Data - Explore precalculated principal components analysis score plot of public data.
Explore Chemical Annotations and Associated Sample Information in all Public Data - Precalculated using public data and default GNPS parameters
Re-analyze Public Data at GNPS - Select files using sample information and assemble public data in groups as desired using the file selector. The following co-analyses can be launched: * Molecular Networking at GNPS * Library Search at GNPS * Chemical Enrichment Analysis * Sample Information Association
All sample information can be downloaded from the ReDU homepage by clicking "Download Database". The ReDU identification database is publicly available and accessible via GNPS/MassIVE (gnps.ucsd.edu), MSV000084206.
Read our recent submitted preprint: Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data.
Issues and Suggestions
Alan K. Jarmusch (UCSD), Mingxun Wang (UCSD), Christine M. Aceves (UCSD)