SIMAT: GC-SIM-MS data analysis tool
| dc.contributor.author | Nezami Ranjbar, Mohammad R. | en |
| dc.contributor.author | Poto, Cristina D. | en |
| dc.contributor.author | Wang, Yue | en |
| dc.contributor.author | Ressom, Habtom W. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2015-08-18T06:02:07Z | en |
| dc.date.available | 2015-08-18T06:02:07Z | en |
| dc.date.issued | 2015-08-19 | en |
| dc.date.updated | 2015-08-18T06:02:07Z | en |
| dc.description.abstract | Background Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GC-SIM-MS data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats. Results We evaluated the performance of SIMAT using two GC-SIM-MS datasets obtained by targeted analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by analysis of GC-SIM-MS data. Conclusions We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based analysis. Also, various functions and algorithms are implemented in the tool to: (1) read and import raw data and spectral libraries; (2) perform GC-SIM-MS data preprocessing; and (3) plot and visualize EICs and TICs. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | BMC Bioinformatics. 2015 Aug 19;16(1):259 | en |
| dc.identifier.doi | https://doi.org/10.1186/s12859-015-0681-2 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/56484 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | Nezami Ranjbar et al. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | SIMAT: GC-SIM-MS data analysis tool | en |
| dc.title.serial | BMC Bioinformatics | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |