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Methods to Calculate Spectrum Similarity.

Şule Yilmaz1,2,3, Elien Vandermarliere1,2,3, Lennart Martens4,5,6

  • 1Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent, 9000, Belgium.

Methods in Molecular Biology (Clifton, N.J.)
|December 16, 2016
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Summary
This summary is machine-generated.

This study evaluates scoring functions for mass spectrometry (MS/MS) spectrum similarity. We assessed functions for comparing experimental spectra against theoretical spectra and against other experimental spectra.

Keywords:
Database searchingMass spectrometryScoring functionsSpectrum librarySpectrum similarity

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Area of Science:

  • Computational mass spectrometry
  • Bioinformatics
  • Analytical chemistry

Background:

  • Scoring functions are vital for computational mass spectrometry algorithms.
  • These functions compare experimental (MS/MS) spectra against theoretical or other experimental spectra.
  • Applications include database searching, spectrum clustering, and spectral library searching.

Purpose of the Study:

  • To describe and evaluate various scoring functions for assessing MS/MS spectrum similarity.
  • To compare the performance of scoring functions for acquired versus theoretical spectra.
  • To compare the performance of scoring functions for acquired versus acquired spectra.

Main Methods:

  • Evaluation of cross-correlation and probability-derived scoring functions for acquired versus theoretical spectra.
  • Assessment of normalized dot product for acquired versus acquired spectra.
  • Analysis of correlation coefficients (Pearson's, Spearman's), mean squared error, and median absolute deviation.

Main Results:

  • The study details the performance of different scoring functions in assessing spectrum similarity.
  • Key scoring functions like cross-correlation, probability-derived methods, and normalized dot product are analyzed.
  • Alternative metrics such as correlation coefficients and error measures are also considered.

Conclusions:

  • The choice of scoring function depends on whether comparing acquired to theoretical or acquired to acquired spectra.
  • This evaluation aids in selecting appropriate scoring functions for specific mass spectrometry applications.
  • Understanding scoring function performance is crucial for accurate computational analysis of MS/MS data.