Dataset Information

Automated peak detection and matching algorithm for gas chromatography-differential mobility spectrometry.

ABSTRACT: A gas chromatography-differential mobility spectrometer (GC-DMS) involves a portable and selective mass analyzer that may be applied to chemical detection in the field. Existing approaches examine whole profiles and do not attempt to resolve peaks. A new approach for peak detection in the 2D GC-DMS chromatograms is reported. This method is demonstrated on three case studies: a simulated case study; a case study of headspace gas analysis of Mycobacterium tuberculosis (MTb) cultures consisting of three matching GC-DMS and GC-MS chromatograms; a case study consisting of 41 GC-DMS chromatograms of headspace gas analysis of MTb culture and media.

SUBMITTER: Fong SS

PROVIDER: S-EPMC3694766 | biostudies-other | 2011 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Automated peak detection and matching algorithm for gas chromatography-differential mobility spectrometry.

Fong Sim S SS Rearden Preshious P Kanchagar Chitra C Sassetti Christopher C Trevejo Jose J Brereton Richard G RG

Analytical chemistry 20110104 5

A gas chromatography-differential mobility spectrometer (GC-DMS) involves a portable and selective mass analyzer that may be applied to chemical detection in the field. Existing approaches examine whole profiles and do not attempt to resolve peaks. A new approach for peak detection in the 2D GC-DMS chromatograms is reported. This method is demonstrated on three case studies: a simulated case study; a case study of headspace gas analysis of Mycobacterium tuberculosis (MTb) cultures consisting of ...[more]

PMID: 21204557

Similar Datasets

Project description:BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Project description:BackgroundComprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.ResultsWe developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.ConclusionsThe proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.

Dataset Information

Automated peak detection and matching algorithm for gas chromatography-differential mobility spectrometry.

Publications

Automated peak detection and matching algorithm for gas chromatography-differential mobility spectrometry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets