Smith-Waterman peak alignment for comprehensive two-dimensional gas chromatography-mass spectrometry

BMC Bioinformatics

Methodology Article

Seongho Kim¹  Imhoi Koo²  Xiang Zhang³  Aiqin Fang³ 

[1] Department of Bioinformatics and Biostatistics, University of Louisville, 40292, Louisville, KY, USA;Department of Bioinformatics and Biostatistics, University of Louisville, 40292, Louisville, KY, USA;Department of Chemistry, University of Louisville, 40292, Louisville, KY, USA;Department of Chemistry, University of Louisville, 40292, Louisville, KY, USA;

关键词: Positive Predictive Value;    True Positive Rate;    Peak List;    Peak Pair;    Peak Alignment;   

DOI  :  10.1186/1471-2105-12-235

 received in 2011-01-31, accepted in 2011-06-15,  发布年份 2011

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Kim et al; licensee BioMed Central Ltd. 2011