Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.

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 rang...

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Main Authors: Malik N Akhtar, Bruce R Southey, Per E Andrén, Jonathan V Sweedler, Sandra L Rodriguez-Zas
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4201571?pdf=render
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author Malik N Akhtar
Bruce R Southey
Per E Andrén
Jonathan V Sweedler
Sandra L Rodriguez-Zas
author_facet Malik N Akhtar
Bruce R Southey
Per E Andrén
Jonathan V Sweedler
Sandra L Rodriguez-Zas
author_sort Malik N Akhtar
collection DOAJ
description 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.
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spelling doaj.art-8532b6fb0da442c9b582dc38743916702022-12-22T00:50:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e11111210.1371/journal.pone.0111112Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.Malik N AkhtarBruce R SoutheyPer E AndrénJonathan V SweedlerSandra L Rodriguez-ZasIn 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.http://europepmc.org/articles/PMC4201571?pdf=render
spellingShingle Malik N Akhtar
Bruce R Southey
Per E Andrén
Jonathan V Sweedler
Sandra L Rodriguez-Zas
Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
PLoS ONE
title Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
title_full Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
title_fullStr Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
title_full_unstemmed Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
title_short Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.
title_sort accurate assignment of significance to neuropeptide identifications using monte carlo k permuted decoy databases
url http://europepmc.org/articles/PMC4201571?pdf=render
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