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Discriminative Motif Finding for Predicting Protein Subcellular Localization
March/April 2011 (vol. 8 no. 2)
pp. 441-451
ASCII Text
x 
 
Tien-ho Lin, Robert F. Murphy, Ziv Bar-Joseph, "Discriminative Motif Finding for Predicting Protein Subcellular Localization," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 441-451, March/April, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2009.82,
author = {Tien-ho Lin and Robert F. Murphy and Ziv Bar-Joseph},
title = {Discriminative Motif Finding for Predicting Protein Subcellular Localization},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {8},
number = {2},
issn = {1545-5963},
year = {2011},
pages = {441-451},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.82},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - Discriminative Motif Finding for Predicting Protein Subcellular Localization
IS - 2
SN - 1545-5963
SP441
EP451
EPD - 441-451
A1 - Tien-ho Lin,
A1 - Robert F. Murphy,
A1 - Ziv Bar-Joseph,
PY - 2011
KW - Hidden Markov models
KW - maximal mutual information estimate
KW - discriminative motif finding
KW - protein localization.
VL - 8
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Tien-ho Lin, Carnegie Mellon University, Pittsburgh
Robert F. Murphy, Carnegie Mellon University, Pittsburgh
Ziv Bar-Joseph, Carnegie Mellon University, Pittsburgh
Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.

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Index Terms:
Hidden Markov models, maximal mutual information estimate, discriminative motif finding, protein localization.
Citation:
Tien-ho Lin, Robert F. Murphy, Ziv Bar-Joseph, "Discriminative Motif Finding for Predicting Protein Subcellular Localization," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 441-451, March-April 2011, doi:10.1109/TCBB.2009.82
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