NITPICK: Peak identification for mass spectrometry data. 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/.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Qin, Shanshan; Ding, Hao; Wu, Yuehua; Liu, Feng: High-dimensional sign-constrained feature selection and grouping (2021)
- Yuan, Zheng; Shi, Jinhong; Lin, Wenjun; Chen, Bolin; Wu, Fang-Xiang: Features-based deisotoping method for tandem mass spectra (2011) ioport