PeakDecoder: A Metabolite Identification Algorithm for Multidimensional Mass Spectrometry Measurements and its Application in Synthetic Biology
PeakDecoder is a machine learning-based metabolite identification algorithm for multidimensional mass spectrometry measurements incorporating liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and collecting extensive fragmentation spectra with data-independent acquisition (DIA) methods. The algorithm learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates.
The 3 subfolder contain input and output files to run the PeakDecoder steps for the synthetic biology datasets:
- Asper: Aspergillus pseudoterreus and Aspergillus niger strains
- Pput: Pseudomonas putida strains
- Rhodo: Rhodosporidium toruloides strains
If you use PeakDecoder or any portions of this code please cite: Bilbao et al. "PeakDecoder: A Machine Learning-Based Metabolite Identification Algorithm for Multidimensional Mass Spectrometry Measurements and its Application in Synthetic Biology". Submitted.