A noise model for mass spectrometry based proteomics

Peicheng Du, Gustavo Stolovitzky, Peter Horvatovich, Rainer Bischoff, Jihyeon Lim, Frank Suits

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

Motivation: Mass spectrometry data are subjected to considerable noise. Good noise models are required for proper detection and quantification of peptides. We have characterized noise in both quadrupole time-of-flight (Q-TOF) and ion trap data, and have constructed models for the noise. Results: We find that the noise in Q-TOF data from Applied Biosystems QSTAR fits well to a combination of multinomial and Poisson model with detector dead-time correction. In comparison, ion trap noise from Agilent MSD-Trap-SL is larger than the Q-TOF noise and is proportional to Poisson noise. We then demonstrate that the noise model can be used to improve deisotoping for peptide detection, by estimating appropriate cutoffs of the goodness of fit parameter at prescribed error rates. The noise models also have implications in noise reduction, retention time alignment and significance testing for biomarker discovery.

Original languageEnglish (US)
Pages (from-to)1070-1077
Number of pages8
JournalBioinformatics
Volume24
Issue number8
DOIs
StatePublished - Apr 1 2008

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Du, P., Stolovitzky, G., Horvatovich, P., Bischoff, R., Lim, J., & Suits, F. (2008). A noise model for mass spectrometry based proteomics. Bioinformatics, 24(8), 1070-1077. https://doi.org/10.1093/bioinformatics/btn078