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Accurate Assignment of Significance to Neuropeptide Identifications Using Monte Carlo K-Permuted Decoy Databases

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  • Malik N Akhtar
  • Bruce R Southey
  • Per E Andrén
  • Jonathan V Sweedler
  • Sandra L Rodriguez-Zas

Abstract

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value

Suggested Citation

  • Malik N Akhtar & Bruce R Southey & Per E Andrén & Jonathan V Sweedler & Sandra L Rodriguez-Zas, 2014. "Accurate Assignment of Significance to Neuropeptide Identifications Using Monte Carlo K-Permuted Decoy Databases," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-13, October.
    • Handle: RePEc:plo:pone00:0111112
    DOI: 10.1371/journal.pone.0111112
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    1. Gelio Alves & Aleksey Y Ogurtsov & Yi-Kuo Yu, 2010. "RAId_aPS: MS/MS Analysis with Multiple Scoring Functions and Spectrum-Specific Statistics," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-18, November.
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