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...
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Elsevier
2020-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711019303796 |
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doaj-9d262ac641b94230a8142455002a4fc92020-12-19T05:08:23ZengElsevierSoftwareX2352-71102020-07-0112100559HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS dataMichael G. Leeming0Andrew P. Isaac1Luke Zappia2Richard A.J. O’Hair3William A. Donald4Bernard J. Pope5School of Chemistry and Bio21 Molecular Science & Biotechnology Institute, The University of Melbourne, AustraliaMelbourne Bioinformatics, The University of Melbourne, Australia; The Walter and Eliza Hall Institute of Medical Research, AustraliaSchool of Biosciences, The University of Melbourne, Australia; Murdoch Children’s Research Institute, AustraliaSchool of Chemistry and Bio21 Molecular Science & Biotechnology Institute, The University of Melbourne, AustraliaSchool of Chemistry, University of New South Wales, AustraliaMelbourne Bioinformatics, The University of Melbourne, Australia; Department of Clinical Pathology, The University of Melbourne, Australia; Department of Medicine, Central Clinical School, Monash University, Australia; Corresponding author at: Melbourne Bioinformatics, The University of Melbourne, Victoria, Australia, 3010, Australia.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.http://www.sciencedirect.com/science/article/pii/S2352711019303796Liquid chromatography-mass spectrometryTwin ionsMetabolites |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Michael G. Leeming Andrew P. Isaac Luke Zappia Richard A.J. O’Hair William A. Donald Bernard J. Pope |
| spellingShingle |
Michael G. Leeming Andrew P. Isaac Luke Zappia Richard A.J. O’Hair William A. Donald Bernard J. Pope HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data SoftwareX Liquid chromatography-mass spectrometry Twin ions Metabolites |
| author_facet |
Michael G. Leeming Andrew P. Isaac Luke Zappia Richard A.J. O’Hair William A. Donald Bernard J. Pope |
| author_sort |
Michael G. Leeming |
| title |
HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data |
| title_short |
HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data |
| title_full |
HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data |
| title_fullStr |
HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data |
| title_full_unstemmed |
HiTIME: An efficient model-selection approach for the detection of unknown drug metabolites in LC-MS data |
| title_sort |
hitime: an efficient model-selection approach for the detection of unknown drug metabolites in lc-ms data |
| publisher |
Elsevier |
| series |
SoftwareX |
| issn |
2352-7110 |
| publishDate |
2020-07-01 |
| description |
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. |
| topic |
Liquid chromatography-mass spectrometry Twin ions Metabolites |
| url |
http://www.sciencedirect.com/science/article/pii/S2352711019303796 |
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