Abstract
Mass spectrometry is a well-known technology used for the analysis of pure compounds as well as mixtures, widely applied in large-scale studies such proteomic studies. The result of mass spectrometric analyses is a mass spectrum, a profile of mass/charge values and corresponding intensity values originated from the analyzed compounds. In the case of large-scale analyses, raw mass spectra comparisons are difficult due to different drawback typologies: data defects, unusual distributions, underlying disturbs and noise, bad data calibration. A bunch of data elaborations is essential, from data processing to feature extraction, in order to obtain a list of peaks from different mass spectra. In this work, a workflow has been developed to process raw mass spectra and compare the new tidy ones with the aim of defining a robust procedure, suitable for real applications and reusable for different kind of studies. A similarity measure has been used for comparison purposes, in order to verify similarity among replicates and differences among analyzed samples, and a clustering method has been performed on fish species, in order to discover how they cluster statistically. A case study is shown with the application of the processing method to data obtained from the analysis of different fish species.
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Acknowledgments
This work is partially supported by the Flagship InterOmics Project (PB.P05), funded and supported by the Italian Ministry of Education, University and Research and Italian National Research Council organizations. This work is also partially supported by a dedicated grant from the Italian Ministry of Economy and Finance to CNR and ENEA for the Project “Innovazione e Sviluppo del Mezzogiorno e Conoscenze Integrate per Sostenibilità ed Innovazione del Made in Italy Agroalimentare (CISIA)” Legge n.191/2009.
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Del Prete, E., d’Esposito, D., Mazzeo, M.F., Siciliano, R.A., Facchiano, A. (2016). Comparative Analysis of MALDI-TOF Mass Spectrometric Data in Proteomics: A Case Study. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_12
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