STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results

doi:https://doi.org/10.1002/pmic.201200096

Title: STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results

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Abstract

For bottom-up proteomics there are a wide variety of database searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection - referred to as STEPS - utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.

Authors:: Piehowski, Paul D.; Petyuk, Vladislav A.; Sandoval, John D.; Burnum, Kristin E.; Kiebel, Gary R.; Monroe, Matthew E.; Anderson, Gordon A.; Camp, David G.; Smith, Richard D.

Publication Date:: 2013-03-01

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1076691

Report Number(s):: PNNL-SA-86051
40072; 400412000

DOE Contract Number:: AC05-76RL01830

Resource Type:: Journal Article

Journal Name:: Proteomics, 13(5):766-770

Additional Journal Information:: Journal Name: Proteomics, 13(5):766-770

Country of Publication:: United States

Language:: English

Subject:: Environmental Molecular Sciences Laboratory

Citation Formats


                    Piehowski, Paul D., Petyuk, Vladislav A., Sandoval, John D., Burnum, Kristin E., Kiebel, Gary R., Monroe, Matthew E., Anderson, Gordon A., Camp, David G., and Smith, Richard D. STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results.  United States: N. p., 2013. 
        Web.  doi:10.1002/pmic.201200096.

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                    Piehowski, Paul D., Petyuk, Vladislav A., Sandoval, John D., Burnum, Kristin E., Kiebel, Gary R., Monroe, Matthew E., Anderson, Gordon A., Camp, David G., & Smith, Richard D. STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results.  United States.  https://doi.org/10.1002/pmic.201200096

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                    Piehowski, Paul D., Petyuk, Vladislav A., Sandoval, John D., Burnum, Kristin E., Kiebel, Gary R., Monroe, Matthew E., Anderson, Gordon A., Camp, David G., and Smith, Richard D. Fri .  
        "STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results".  United States.  https://doi.org/10.1002/pmic.201200096.

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@article{osti_1076691,

  title        = {STEPS: A Grid Search Methodology for Optimized Peptide Identification Filtering of MS/MS Database Search Results},

  author       = {Piehowski, Paul D. and Petyuk, Vladislav A. and Sandoval, John D. and Burnum, Kristin E. and Kiebel, Gary R. and Monroe, Matthew E. and Anderson, Gordon A. and Camp, David G. and Smith, Richard D.},

  abstractNote = {For bottom-up proteomics there are a wide variety of database searching algorithms in use for matching peptide sequences to tandem MS spectra. Likewise, there are numerous strategies being employed to produce a confident list of peptide identifications from the different search algorithm outputs. Here we introduce a grid search approach for determining optimal database filtering criteria in shotgun proteomics data analyses that is easily adaptable to any search. Systematic Trial and Error Parameter Selection - referred to as STEPS - utilizes user-defined parameter ranges to test a wide array of parameter combinations to arrive at an optimal "parameter set" for data filtering, thus maximizing confident identifications. The benefits of this approach in terms of numbers of true positive identifications are demonstrated using datasets derived from immunoaffinity-depleted blood serum and a bacterial cell lysate, two common proteomics sample types.},

  doi          = {10.1002/pmic.201200096},

  url          = {https://www.osti.gov/biblio/1076691},
  journal      = {Proteomics, 13(5):766-770},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2013},

  month        = {3}

}

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