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Using a spike-in experiment to evaluate analysis of LC-MS data

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5 Author(s)
Tuli, L. ; Lombardi Comprehensive Cancer Center, Georgetown Univ., Washington, DC, USA ; Tsai, T. ; Varghese, Rency S. ; Cheema, A.
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Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to newer approaches for measuring changes in peptide/protein abundances. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization, and transformation. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. In this paper, we use a spike-in experiment to evaluate the performance of three software tools in accurately detecting changes in peptide abundances from LC-MS data obtained by a label-free LC-MS method. We observe that tools that incorporate peptide isotope cluster and multiple charge information lead to more accurate difference detection with fewer false positives.

Published in:

Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on

Date of Conference:

18-18 Dec. 2010

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