title: NITPICK: peak identification for mass spectrometry data.
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 averaging, a novel extension to Senko's well-known averaging 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.
  
  
  CONCLUSIONS
  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/).
authors.affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de
authors.forename: Bernhard Y
authors.initials: BY
authors.lastname: Renard
authors.forename: Marc
authors.initials: M
authors.lastname: Kirchner
authors.forename: Hanno
authors.initials: H
authors.lastname: Steen
authors.forename: Judith A J
authors.initials: JA
authors.lastname: Steen
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht