HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data

mass spectrometry
software
Authors

Michael G Leeming

Andrew P Isaac

Luke Zappia

Richard A J O’Hair

William A Donald

Bernard J Pope

Date

July 7, 2020

Links
Citation stats
Abstract

The identification of metabolites plays an important role in understanding drug efficacy and safety however these compounds are often difficult to identify in complex mixtures. One approach to identify drug metabolites involves utilising differentially isotopically labelled drug compounds to create unique isotopic signals that can be detected by liquid chromatography-mass spectrometry (LC-MS). User-friendly, efficient, computational tools that allow selective detection of these signals are lacking. We have developed an efficient open-source software tool called HiTIME (High-Resolution Twin-Ion Metabolite Extraction) which filters twin-ion signals in LC-MS data. The intensity of each data point in the input is replaced by a Z-score describing how well the point matches an idealised twin-ion signal versus alternative ion signatures. Here we provide a detailed description of the algorithm and demonstrate its performance on simulated and experimental data.

Citation

BibTeX citation:
@article{g_leeming2020,
  author = {G Leeming, Michael and P Isaac, Andrew and Zappia, Luke and
    A J O’Hair, Richard and A Donald, William and J Pope, Bernard},
  title = {HiTIME: {An} Efficient Model-Selection Approach for the
    Detection of Unknown Drug Metabolites in {LC-MS} Data},
  journal = {SoftwareX},
  pages = {100559},
  date = {2020-07-07},
  url = {https://lazappi.id.au/publications/2020-leeming-HiTIME/},
  doi = {10.1016/j.softx.2020.100559},
  langid = {en},
  abstract = {The identification of metabolites plays an important role
    in understanding drug efficacy and safety however these compounds
    are often difficult to identify in complex mixtures. One approach to
    identify drug metabolites involves utilising differentially
    isotopically labelled drug compounds to create unique isotopic
    signals that can be detected by liquid chromatography-mass
    spectrometry (LC-MS). User-friendly, efficient, computational tools
    that allow selective detection of these signals are lacking. We have
    developed an efficient open-source software tool called HiTIME
    (High-Resolution Twin-Ion Metabolite Extraction) which filters
    twin-ion signals in LC-MS data. The intensity of each data point in
    the input is replaced by a Z-score describing how well the point
    matches an idealised twin-ion signal versus alternative ion
    signatures. Here we provide a detailed description of the algorithm
    and demonstrate its performance on simulated and experimental data.}
}
For attribution, please cite this work as:
G Leeming, Michael, Andrew P Isaac, Luke Zappia, Richard A J O’Hair, William A Donald, and Bernard J Pope. 2020. “HiTIME: An Efficient Model-Selection Approach for the Detection of Unknown Drug Metabolites in LC-MS Data.” SoftwareX, July, 100559. https://doi.org/10.1016/j.softx.2020.100559.