IPPD package vignette

نویسندگان

  • Martin Slawski
  • Rene Hussong
  • Matthias Hein
چکیده

This is the vignette of the Bioconductor add-on package IPPD which implements automatic isotopic pattern extraction from a raw protein mass spectrum. Basically, the user only has to provide mass/charge channels and corresponding intensities, which are automatically decomposed into a list of monoisotopic peaks. IPPD can handle several charge states as well as overlaps of peak patterns. 1 Aims and scope of IPPD A crucial challenge in the analysis of protein mass spectrometry data is to automatically process the raw spectrum to a list of peptide masses. IPPD is tailored to spectra where peptides emerge in the form of isotope patterns, i.e. one observes several peaks for each peptide mass at a given charge state due to the natural abundance of heavy isotopes. Datasets with a size of up to 100,000 mass/charge channels and the presence of isotope patterns at multiple charge states frequently exhibiting overlap make the manual annotation of a raw spectrum a tedious task. IPPD provides functionality to perform this task in a fully automatic, transparent and user-customizable way. Basically, one feeds the raw spectrum into one single function to obtain a list of monoisotopic peaks described by a mass/charge channel, a charge and an intensity. What makes our approach particularly user-friendly is its dependence on only a small set of easily interpretable parameters. We also offer a method to display the decomposition of the spectrum graphically, thereby facilitating a manual validation of the output. 2 Methodology 2.1 Template model In the context of this package, a protein mass spectrum is understood as a sequence of pairs {xi, yi}i=1, where xi = mi/zi is a mass (mi) per charge (zi) value (measured in Thomson) and yi is the intensity, i.e. the abundance of a particular mass (modulo charge state), observed at xi, i = 1, . . . , n, which are assumed to be in an increasing order. The yi are modeled as a linear combination of template functions representing prior knowledge about

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تاریخ انتشار 2013