Ion-networks: a sparse data format capturing full data integrity of data independent acquisition mass spectrometry
Sander WillemsiD, Simon DalediD, Bart Van PuyveldeiD, Laura De ClerckiD, Sofie Vande CasteeleiD, Filip Van NieuwerburghiD, Dieter L. D. DeforceiD, Maarten DhaenensiD
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has introduced deter-ministic, periodic and simultaneous acquisition of all fragment ions. Despite the chimeric side-effects associated with this unprecedented data integrity, DIA data analysis approaches still use conventional spectra and extracted ion chromatograms (XICs) that represent individual precursors and fragments. Here, we introduce ion-networks, an alternative data format wherein nodes correspond to reproducible fragment ions from multiple runs and edges correspond to consistent co-elution. Each ion-network represents a complete experiment and computationally eliminates chimericy based on reproducibility without sacrificing data integrity.
02 /Specification & Classification
Click any tag to browse matching papersReproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- 2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Study
Benjamin A. Neely, Paul M. Stemmer, … Joanna M Kirkpatrick·bioRxiv·Nov 20, 2020
- Dynamic Data Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment
Lilian R. Heil, Philip M. Remes, … Michael J. MacCoss·Analytical ChemistryPeer-reviewed·Aug 1, 2023
- Rapid Development of Improved Data-dependent Acquisition Strategies.
Vinny Davies, Joe Wandy, … Simon Rogers·Analytical ChemistryPeer-reviewed·Mar 31, 2021
- Parallel accumulation - serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage
Florian Meier, Andreas‐David Brunner, … Matthias Mann·bioRxiv·Mar 11, 2020
- DIA-NN: Neural networks and interference correction enable deep coverage in high-throughput proteomics
Vadim Demichev, Christoph B. Messner, … Markus Ralser·Nature MethodsPeer-reviewed·Nov 25, 2019
- Ion Identity Molecular Networking in the GNPS Environment
Robin Schmid, Daniel Petras, … Pieter C. Dorrestein·Nature CommunicationsPeer-reviewed·Jun 22, 2021