Algorithms for the analysis of MALDI peptide mass fingerprint spectra for proteomics

Monigatti, Flavio. Algorithms for the analysis of MALDI peptide mass fingerprint spectra for proteomics. 2005, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: http://edoc.unibas.ch/diss/DissB_7167

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In this study we present a simple algorithm which allows accurate estimates of the similarity between peptide fingerprint mass spectra from matrix assisted laser desorption/ionization (MALDI) spectrometers. The algorithm, which is a combination of mass correlation and intensity rank correlation, was used to cluster similar spectra and to generate consensus spectra from a data store of more than 100,000 spectra. The resulting first spectra library of 1248 unambiguously identified different protein digests was used to search for missed cleavage patterns that have not been reported so far and to shed light on some peptide ionization characteristics. The findings of this study could directly be applied to a peptide mass fingerprint search algorithm to decrease the false positive error rate to <0.25%. Furthermore, the results contribute to the understanding of the peptide ionization process in MALDI experiments. The reference library of consensus spectra was also used to identify MALDI peptide mass fingerprint spectra by comparison of the experimental spectra with the spectra in the library. We report the potential of this method to achieve an identification rate of almost 100%. In a second step, the information derived from the clustering of similar spectra was used to match similar spectra content on different two-dimensional polyacrylamid gel electrophoresis (2D-PAGE) gels. This is to our knowledge the first attempt to match different gels on the level of mass spectrometric information. A newly established method that makes use of the new techniques is compared to a proteomics study carried out employing traditional proteomics strategies.
Advisors:Schwede, Torsten
Committee Members:Langen, Hanno and Jenö, Paul
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)
UniBasel Contributors:Schwede, Torsten and Jenö, Paul
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:7167
Thesis status:Complete
Number of Pages:83
Identification Number:
edoc DOI:
Last Modified:22 Apr 2018 04:30
Deposited On:13 Feb 2009 15:08

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