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Open Access Highly Accessed Methodology article

NITPICK: peak identification for mass spectrometry data

Bernhard Y Renard12, Marc Kirchner12, Hanno Steen3, Judith AJ Steen4 and Fred A Hamprecht12*

Author Affiliations

1 Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany

2 Department of Pathology, Children’s Hospital Boston, Boston, MA, USA

3 Department of Pathology, Harvard Medical School and Children’s Hospital Boston, Boston, MA, USA

4 Department of Neurobiology, Harvard Medical School and Department of Neurology, Children’s Hospital Boston, Boston, MA, USA

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BMC Bioinformatics 2008, 9:355  doi:10.1186/1471-2105-9-355

Published: 28 August 2008

Abstract

Background

The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.

Results

This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averagine, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.

Conclusion

Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from http://hci.iwr.uni-heidelberg.de/mip/proteomics/ webcite.