SpADS: An R Script for Mass Spectrometry Data Preprocessing before Data MiningLuca Belmonte1, Rosanna Spera1 and Claudio Nicolini1,2,3,4*
- *Corresponding Author:
- Claudio Nicolini
Laboratories of Biophysics and Nanobiotechnology
Department of Experimental Medicine
University of Genova, Italy
Received date: July 17, 2013; Accepted date: September 16, 2013; Published date: September 23, 2013
Citation: Belmonte L, Spera R, Nicolini C (2013) SpADS: An R Script for Mass Spectrometry Data Preprocessing before Data Mining. J Comput Sci Syst Biol 6:298-304. doi:10.4172/jcsb.1000125
Copyright: © 2013 Belmonte L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The recent application of Mass Spectrometry (MS) to Nucleic Acid Programmable Protein Array (NAPPA) technique for proteins identification by non-classical methods leads to the needs of more sophisticated algorithm for peak recognition. NAPPA technique allows for functional proteins to be synthesized in situ directly from printed cDNAs but faces the difficulty generated by the presence of master mix and lysate molecules peaks appearing as background in the overall spectra. A wide range of tools are available to analyze proteins conventional mass spectra corresponding to few molecular species. None of them is optimized for background subtraction. Moreover, peak identification is performed by statistical analysis on characteristics peaks and thus background subtraction can alter outcome by erasing characteristic peaks. A first attempt to overcome the so far discussed problem is here discussed. The result of this effort is the development of SpADS: Spectrum Analyzer and Data Set manager-an R script for MS data preprocessing-therein discussed. SpADS provides useful preprocessing functions such binning and peak extractions, as available tools, and provides functions of spectra background subtraction and dataset managing. It is entirely developed in R, thus free of charge. A cluster k means implementation is here used to improve results of SpADS preprocessing on test datasets and on NAPPA expressed proteins.