  Mixture - modeling of mass spectra is an approach with many potential
applications including peak detection and quantification, smoothing,
de-noising, feature extraction and spectral signal compression. However,
existing algorithms do not allow for automatic analyses of whole spectra.
Therefore, despite highlighting potential advantages of mixture modeling of
mass spectra of peptide/protein mixtures and some preliminary results presented
in several papers, the mixture modeling approach was so far not developed to
the stage enabling systematic comparisons with existing software packages for
proteomic mass spectra analyses. In this paper we present an efficient
algorithm for Gaussian mixture modeling of proteomic mass spectra of different
types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automatic
partitioning of protein mass spectral signal into fragments. The obtained
fragments are separately decomposed into Gaussian mixture models. The
parameters of the mixture models of fragments are then aggregated to form the
mixture model of the whole spectrum. We compare the elaborated algorithm to
existing algorithms for peak detection and we demonstrate improvements of peak
detection efficiency obtained by using Gaussian mixture modeling. We also show
applications of the elaborated algorithm to real proteomic datasets of low and
high resolution.
