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