The statistical significance of protein identification results as a function of the number of protein sequences searched

J Proteome Res. Sep-Oct 2004;3(5):979-82. doi: 10.1021/pr0499343.

Abstract

The potential for obtaining a true mass spectrometric protein identification result depends on the choice of algorithm as well as on experimental factors that influence the information content in the mass spectrometric data. Current methods can never prove definitively that a result is true, but an appropriate choice of algorithm can provide a measure of the statistical risk that a result is false, i.e., the statistical significance. We recently demonstrated an algorithm, Probity, which assigns the statistical significance to each result. For any choice of algorithm, the difficulty of obtaining statistically significant results depends on the number of protein sequences in the sequence collection searched. By simulations of random protein identifications and using the Probity algorithm, we here demonstrate explicitly how the statistical significance depends on the number of sequences searched. We also provide an example on how the practitioner's choice of taxonomic constraints influences the statistical significance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Caenorhabditis elegans / genetics
  • Computational Biology / statistics & numerical data*
  • Computer Simulation
  • Databases, Protein
  • Haemophilus influenzae / genetics
  • Humans
  • Least-Squares Analysis
  • Mass Spectrometry / statistics & numerical data
  • Models, Statistical*
  • Peptide Fragments / analysis
  • Proteins / analysis*
  • Proteins / genetics
  • Proteomics / statistics & numerical data*
  • Saccharomyces cerevisiae / genetics

Substances

  • Peptide Fragments
  • Proteins