Cargando…

SIMAT: GC-SIM-MS data analysis tool

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Nezami Ranjbar, Mohammad R., Poto, Cristina Di, Wang, Yue, Ressom, Habtom W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539696/
https://www.ncbi.nlm.nih.gov/pubmed/26283310
http://dx.doi.org/10.1186/s12859-015-0681-2
_version_ 1782386146177712128
author Nezami Ranjbar, Mohammad R.
Poto, Cristina Di
Wang, Yue
Ressom, Habtom W.
author_facet Nezami Ranjbar, Mohammad R.
Poto, Cristina Di
Wang, Yue
Ressom, Habtom W.
author_sort Nezami Ranjbar, Mohammad R.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0681-2) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4539696
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-45396962015-08-19 SIMAT: GC-SIM-MS data analysis tool Nezami Ranjbar, Mohammad R. Poto, Cristina Di Wang, Yue Ressom, Habtom W. BMC Bioinformatics Software 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0681-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-19 /pmc/articles/PMC4539696/ /pubmed/26283310 http://dx.doi.org/10.1186/s12859-015-0681-2 Text en © Nezami Ranjbar et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Nezami Ranjbar, Mohammad R.
Poto, Cristina Di
Wang, Yue
Ressom, Habtom W.
SIMAT: GC-SIM-MS data analysis tool
title SIMAT: GC-SIM-MS data analysis tool
title_full SIMAT: GC-SIM-MS data analysis tool
title_fullStr SIMAT: GC-SIM-MS data analysis tool
title_full_unstemmed SIMAT: GC-SIM-MS data analysis tool
title_short SIMAT: GC-SIM-MS data analysis tool
title_sort simat: gc-sim-ms data analysis tool
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539696/
https://www.ncbi.nlm.nih.gov/pubmed/26283310
http://dx.doi.org/10.1186/s12859-015-0681-2
work_keys_str_mv AT nezamiranjbarmohammadr simatgcsimmsdataanalysistool
AT potocristinadi simatgcsimmsdataanalysistool
AT wangyue simatgcsimmsdataanalysistool
AT ressomhabtomw simatgcsimmsdataanalysistool