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BACKGROUND: Mass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data. RESULTS: MASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population. CONCLUSIONS: The analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at

Original publication




Journal article


J Proteomics

Publication Date





562 - 570


Algorithms, Autoimmune Diseases, Biomarkers, Blood Chemical Analysis, Blood Proteins, Cluster Analysis, Computational Biology, Data Interpretation, Statistical, Humans, Plasma, Serum, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization