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This article is part of the supplement: The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)

Open Access Research

ICPD-A New Peak Detection Algorithm for LC/MS

Jianqiu Zhang1* and William Haskins2

Author affiliations

1 Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, Texas, USA

2 Department of Biology, University of Texas at San Antonio, San Antonio, Texas, USA

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Citation and License

BMC Genomics 2010, 11(Suppl 3):S8  doi:10.1186/1471-2164-11-S3-S8

Published: 1 December 2010

Abstract

Background

The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery.

Results

In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection.

Conclusions

The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.