A computational platform for MALDI-TOF mass spectrometry data: application to serum and plasma samples.

Journal article

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 http://www.unich.it/proteomica/bioinf.

Authors: Mantini, D; Petrucci, F; Pieragostino, D; Del Boccio, P; Sacchetta, P

• Journal: Journal of proteomics
• Volume: 73
• Issue: 3
• Pages: 562-570
• Publication date: 2010-01-01
• DOI: 10.1016/j.jprot.2009.11.004
• EISSN: 1876-7737
• ISSN: 1874-3919
• Language: English
• Keywords: Biological Markers; Data Interpretation, Statistical; Computational Biology; Cluster Analysis; Serum; Humans; Blood Chemical Analysis; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Autoimmune Diseases; Algorithms; Plasma; Blood Proteins