Comparative Analysis of MALDI-TOF Mass Spectrometric Data in Proteomics: A Case Study

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• 关键词：Proteomics ; Mass spectrometry ; MALDI ; TOF ; Similarity measures ; R environment
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9874
• 期：1
• 页码：154-164
• 全文大小：3,593 KB
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• 作者单位：Eugenio Del Prete (16) (17)
  Diego d’Esposito (16)
  Maria Fiorella Mazzeo (16)
  Rosa Anna Siciliano (16)
  Angelo Facchiano (16)
  
  16. Istituto di Scienze dell’Alimentazione, CNR, Via Roma 64, 83100, Avellino, Italy
  17. Dipartimento di Scienze, Università della Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy
  
• 丛书名：Computational Intelligence Methods for Bioinformatics and Biostatistics
• ISBN：978-3-319-44332-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9874

文摘

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.