title: Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
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 < 10(-5). The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator "number of matched ions between the experimental and theoretical spectra" highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×105 are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.
authors.affiliation: Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
authors.forename: Malik N
authors.initials: MN
authors.lastname: Akhtar
authors.affiliation: Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Southey
authors.affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
authors.forename: Per E
authors.initials: PE
authors.lastname: Andrén
authors.affiliation: Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
authors.forename: Jonathan V
authors.initials: JV
authors.lastname: Sweedler
authors.affiliation: Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America; Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America; Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Rodriguez-Zas
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D030562:Databases, Protein; D013058:Mass Spectrometry; D051379:Mice; D009010:Monte Carlo Method; D009479:Neuropeptides / Q000737:chemistry / Q000302:isolation & purification*; D040901:Proteomics; D012984:Software
chemical_list: D009479:Neuropeptides