Combinatorial Approaches for Mass Spectra Recalibration

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Sebastian Böcker and Veli Mäkinen: Combinatorial Approaches for Mass Spectra Recalibration. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5(1):91-100, 2008.

Title: Combinatorial Approaches for Mass Spectra Recalibration
Author: Böcker, Sebastian; Mäkinen, Veli
Contributor organization: Department of Computer Science
Tietojenkäsittelytieteen laitos
Datavetenskap, Institutionen för
Publisher: IEEE/ACM
Date: 2008
Language: eng
Abstract: Mass spectrometry has become one of the most popular analysis techniques in Proteomics and Systems Biology. With the creation of larger datasets, the automated recalibration of mass spectra becomes important to ensure that very peak in the sample spectrum is correctly assigned to some peptide and protein. Algorithms for recalibrating mass spectra have to be robust with respect to wrongly assigned peaks, as well as efficient due to the amount of mass spectrometry data. The recalibration of mass spectra leads us to the problem of finding an optimal matching between mass spectra under measurement errors. We have developed two deterministic methods that allow robust computation of such a matching: The first approach uses a computational geometry interpretation of the problem, and tries to find two parallel lines with constant distance that stab a maximal number of points in the plane. The second approach is based on finding a maximal common approximate subsequence, and improves existing algorithms by one order of magnitude exploiting the sequential nature of the matching problem. We compare our results to a computational geometry algorithm using a topological line-sweep.
Subject: Biotechnology
mass spectrometry
combinatorial pattern matching
computational geometry

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