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MALDI-ToF analysis software

  • Geena 2
    Pre-processing of lists of peaks from MALDI-ToF spectra.
    For each peak list, it:
    • joins isotopic abundances for a given molecular species,
    • normalizes signals against an internal standard, if available,
    • removes background noise,
    • averages multiple peak lists from the same sample,
    • aligns peak lists from different samples.
    As output, it provides the alignment of common signals found in the peak lists.
    Link: http://proteomics.hsanmartino.it/geena2/

  • GeenaR
    GeenaR is an extension of Geena 2 based on shared R libraries. Moreover, it provides support for reproducibility by reporting the analysis with R code.
    The following pre-processing elaborations are available and can be executed in pipeline:
    • Variance stabilization, implemented by using one among the following methods: SQRT, LOG, LOG2, LOG10
    • Smoothing, implemented by using one among the following methods: Savitzky-Golay, Moving average
    • Baseline removal, implemented by using one among the following methods: Statistics-sensitive Non-linear Iterative Peak-clipping (SNIP), fast algorithm on kernels (TopHat), convex hull, median.
    • Normalization, implemented by using one among the following methods: Total Ion Current (TIC), Probabilistic Quotient Normalization (PQN), median.
    • Averaging, implemented by using one among the following methods: mean, median, sum.
    • Alignment of technical replicates, including estimating the noise and aligning the spectra by correcting the phase. The following methods are available for noise estimation: Median Absolute Deviation (MAD), Friedman’s Super Smoother. The following methods are available for phase correction: Local Weight Scatterplot Smoothing (LOWESS), linear, quadratic, cubic warping methods.
    GeenaR may also execute some clustering analysis and support tools for visualization of data, as follows:
    • Clustering may be carried out by using one of the following link function: average, complete, ward, median. The number of clusters can be defined by using gap statistics or silhouette analyis or it may be given by the user.
    • Heatmap can be shown. The heatmap can be reordered along its dimensions by similarity either on samples only, on signals only, or on both samples and signals.
    • Principal Component Analysis (PCA) can also be carried out.

    Link: http://proteomics.hsanmartino.it/geenar/

  • Seradeg
    A software tool to assess the integrity level of serum samples by evaluating their fibrinopeptide contents. It processes spectra to extract peak lists, which are then elaborated for noise reduction. Finally, fpA related peaks are compared in order to compute their overall abundance and the percent contribution of each peptide. Abundances are expressed as percent of the abundance of a reference spectrum. If a reference spectrum is not provided by the user, the spectrum with the greatest overall abundance for fpA peptides is taken as the reference.
    A quality score is assigned to spectra by taking into account both their overall fpA abundance and the ratio between abundances of more and less degraded forms.
    Link: http://proteomics.hsanmartino.it/seradeg/