Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching

Martin Slawski,Barbara Gregorius,Andreas Hildebrandt,Thomas Jakoby,Andreas Tholey,Rene Hussong,Matthias Hein

doi:10.1186/1471-2105-13-291

Abstract

BackgroundThe robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context.ResultsWe propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns.ConclusionsWe find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html).

Highlights

The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications
For the assessment of the pattern picking performance, in total eight spectra generated by two different ionization methods, matrix assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), respectively, form the basis of the evaluation
While MALDI has been coupled to a time-of-flight (TOF) mass analyzer, ESI MS spectra have been recorded on both a linear ion trap (LTQ) and an Orbitrap mass analyzer

Summary

Introduction

The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. Given a dramatic increase in occurrence of high-dimensional datasets in recent years and the resulting need for feature selection, the lasso, due to computationally and theoretically appealing properties, has become so popular that it can be regarded as a standard tool of modern data analysis [11]. In this respect, NITPICK follows the usual paradigm suggesting that 1regularization is the method of choice. See Section “Sparse recovery with non-negativity constraints: non-negative least squares + thresholding vs. the non-negative lasso” for a detailed discussion

Methods

Results

Conclusion