Document Detail


Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.
MedLine Citation:
PMID:  23289783     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications. We present a novel probabilistic scoring algorithm called Context-Sensitive Peptide Identification (CSPI) based on highly flexible Input-Output Hidden Markov Models (IO-HMM) that capture the influence of peptide physicochemical properties on their observed MS/MS spectra. We use several local and global properties of peptides and their fragment ions from literature. Comparison with two popular algorithms, Crux (re-implementation of SEQUEST) and X!Tandem, on multiple datasets of varying complexity, shows that peptide identification scores from our models are able to achieve greater discrimination between true and false peptides, identifying up to ∼25% more peptides at a False Discovery Rate (FDR) of 1%. We evaluated two alternative normalization schemes for fragment ion-intensities, a global rank-based and a local window-based. Our results indicate the importance of appropriate normalization methods for learning superior models. Further, combining our scores with Crux using a state-of-the-art procedure, Percolator, we demonstrate the utility of using scoring features from intensity-based models, identifying ∼4-8 % additional identifications over Percolator at 1% FDR. IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data.
Authors:
Himanshu Grover; Garrick Wallstrom; Christine C Wu; Vanathi Gopalakrishnan
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2013-01-05
Journal Detail:
Title:  Omics : a journal of integrative biology     Volume:  17     ISSN:  1557-8100     ISO Abbreviation:  OMICS     Publication Date:  2013 Feb 
Date Detail:
Created Date:  2013-02-04     Completed Date:  2013-07-18     Revised Date:  2014-02-04    
Medline Journal Info:
Nlm Unique ID:  101131135     Medline TA:  OMICS     Country:  United States    
Other Details:
Languages:  eng     Pagination:  94-105     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Databases, Protein
Markov Chains*
Peptides / analysis*
Reproducibility of Results
Sensitivity and Specificity
Software
Tandem Mass Spectrometry*
Grant Support
ID/Acronym/Agency:
K25 GM071951/GM/NIGMS NIH HHS; K25GM071951/GM/NIGMS NIH HHS; R01 LM010950/LM/NLM NIH HHS; R01LM010950/LM/NLM NIH HHS
Chemical
Reg. No./Substance:
0/Peptides
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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