Large datasets generated by researchers using liquid chromatography-mass spectrometry to identify unknown drug metabolites can now be processed more quickly and efficiently by using HiTIME, a novel memory efficient and scalable parallelisation algorithm which allows timely processing on commodity computing hardware.
Computer scientists and chemists from the University of Melbourne and University of NSW have now published a paper documenting their successful work in developing this algorithm at Software X, Elsevier’s open access journal: Michael G. Leeming, Andrew P. Isaac, Luke Zappia, Richard A.J. O’Hair, William A. Donald and Bernard J. Pope, 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. Our computer scientists 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.
HiTIME is a sensitive tool for the detection of twin-ion signals in LC-MS data that has been successfully demonstrated for the detection of paracetamol (APAP) metabolites in blood plasma of APAP-treated rats and endogenous proteins covalently bound to electrophilic APAP metabolites. HiTIME accepts inputs and produces outputs in standard mzML format, facilitating integration with other tools and workflows. A significant advantage of HiTIME is that it supports inputs in both profile and centroid modes, and its novel memory efficient and scalable parallelisation algorithm allows timely processing of large data sets on commodity computing hardware.
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This work was completed with support from a University of Melbourne Interdisciplinary Seed Grant, the Victorian Life Sciences Computation Initiative (now Melbourne Bioinformatics), Assoc. Prof. Pope’s Victorian Health and Medical Research Fellowship, Australia and Dr Michael Leeming’s Elizabeth and Vernon Puzey PhD scholarship and The University of Melbourne’s Norma Hilda Schuster scholarship.