A predictive model for identifying proteins by a single peptide match.

A predictive model for identifying proteins by a single peptide match. Research Abstract Details 

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A predictive model for identifying proteins by a single peptide match. Abstract Text:

Roger Higdon,Eugene Kolker,

MOTIVATION: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A predictive model for identifying proteins by a single peptide match. Publishing Authors By Initials

R Higdon,E Kolker,

For similar investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research abstracts see: investigative techniques: genetic techniques: sequence analysis: sequence analysis, protein research

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A predictive model for identifying proteins by a single peptide match. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Bioinformatics (Oxford, England)

VOLUME: 23

Page Numbers: 277-80

Journal Abbreviation: Bioinformatics

ISSN: 1460-2059

DAY: 22

MONTH: 11

YEAR: 2006

A predictive model for identifying proteins by a single peptide match. Information

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LANGUAGE: eng

NlmUniqueID: 9808944

A predictive model for identifying proteins by a single peptide match. Keywords Mesh Terms:

KEYWORDS: Sequence Analysis, Protein

MESH TERMS: methods

Chemical & Substance for Abstract: A predictive model for identifying proteins by a single peptide match. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for A predictive model for identifying proteins by a single peptide match.

AFFILIATION: The BIATECH Institute, Bothell, WA 98011, USA.

Country: England

AGENCY: United States NIGMS

GRANT: R01 GM076680-01A1

ACRONYM: GM

MEDLINETA: Bioinformatics

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