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Predicting tryptic cleavage from proteomics data using decision tree ensembles

(2013) JOURNAL OF PROTEOME RESEARCH. 12(5). p.2253-2259
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Bioinformatics: from nucleotids to networks (N2N)
Abstract
Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in Aim In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a decision tree ensemble that was learned on publicly available peptide identification data from the PRIDE repository. We demonstrate that CP-DT is able to accurately predict tryptic cleavage: tests on three independent data sets show that CP-DT significantly outperforms the Keil rules that are currently used to predict tryptic cleavage. Moreover, the trees generated by CP-DT can make predictions efficiently and are interpretable by domain experts.
Keywords
PERFORMANCE, TRYPSIN, SPECTRA, SHOTGUN PROTEOMICS, PROTEIN IDENTIFICATION, QUANTITATIVE PROTEOMICS, TANDEM MASS-SPECTROMETRY, decision tree, machine learning, PRIDE, trypsin, mass spectrometry, COMPLEXITY, PEPTIDES, RESIDUES

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Citation

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Chicago
Fannes, Thomas, Elien Vandermarliere, Leander Schietgat, Sven Degroeve, Lennart Martens, and Jan Ramon. 2013. “Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles.” Journal of Proteome Research 12 (5): 2253–2259.
APA
Fannes, T., Vandermarliere, E., Schietgat, L., Degroeve, S., Martens, L., & Ramon, J. (2013). Predicting tryptic cleavage from proteomics data using decision tree ensembles. JOURNAL OF PROTEOME RESEARCH, 12(5), 2253–2259.
Vancouver
1.
Fannes T, Vandermarliere E, Schietgat L, Degroeve S, Martens L, Ramon J. Predicting tryptic cleavage from proteomics data using decision tree ensembles. JOURNAL OF PROTEOME RESEARCH. 2013;12(5):2253–9.
MLA
Fannes, Thomas, Elien Vandermarliere, Leander Schietgat, et al. “Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles.” JOURNAL OF PROTEOME RESEARCH 12.5 (2013): 2253–2259. Print.
@article{4089849,
  abstract     = {Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in Aim In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a decision tree ensemble that was learned on publicly available peptide identification data from the PRIDE repository. We demonstrate that CP-DT is able to accurately predict tryptic cleavage: tests on three independent data sets show that CP-DT significantly outperforms the Keil rules that are currently used to predict tryptic cleavage. Moreover, the trees generated by CP-DT can make predictions efficiently and are interpretable by domain experts.},
  author       = {Fannes, Thomas and Vandermarliere, Elien and Schietgat, Leander and Degroeve, Sven and Martens, Lennart and Ramon, Jan},
  issn         = {1535-3893},
  journal      = {JOURNAL OF PROTEOME RESEARCH},
  keyword      = {PERFORMANCE,TRYPSIN,SPECTRA,SHOTGUN PROTEOMICS,PROTEIN IDENTIFICATION,QUANTITATIVE PROTEOMICS,TANDEM MASS-SPECTROMETRY,decision tree,machine learning,PRIDE,trypsin,mass spectrometry,COMPLEXITY,PEPTIDES,RESIDUES},
  language     = {eng},
  number       = {5},
  pages        = {2253--2259},
  title        = {Predicting tryptic cleavage from proteomics data using decision tree ensembles},
  url          = {http://dx.doi.org/10.1021/pr4001114},
  volume       = {12},
  year         = {2013},
}

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