NDLI: Context-Sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

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Context-Sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

Year: 2013, Volume: 17, Issue: 1

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Context-Sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

Content Provider	PubMed Central
Author	Grover, Himanshu Wallstrom, Garrick Wu, Christine C. Gopalakrishnan, Vanathi
Copyright Year	2013
Abstract	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.
Related Links	http://dx.doi.org/10.1089/omi.2012.0073
Ending Page	105
Page Count	12
Starting Page	94
File Format	PDF
ISSN	15362310
e-ISSN	15578100
Journal	OMICS : a Journal of Integrative Biology
Issue Number	2
Volume Number	17
Language	English
Publisher	Mary Ann Liebert, Inc.
Publisher Date	2013-02-01
Access Restriction	Open
Rights Holder	Mary Ann Liebert, Inc.
Subject Keyword	Biotechnology Molecular Medicine Genetics Biochemistry Molecular Biology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Molecular Biology Biochemistry Molecular Medicine Biotechnology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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