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A flexible integrative approach based on random forest improves prediction of transcription factor binding sites

Bart Hooghe (UGent) , Stefan Broos (UGent) , Frans Van Roy (UGent) and Pieter De Bleser (UGent)
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Abstract
Transcription factor binding sites (TFBSs) are DNA sequences of 6-15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.
Keywords
TATA BOX, B-DNA, IN-VITRO, HUMAN GENOME, TARGET SITES, STRUCTURAL-ANALYSIS, MOLECULAR-DYNAMICS SIMULATIONS, ESCHERICHIA-COLI, PROTEIN-DNA RECOGNITION, SEQUENCE

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MLA
Hooghe, Bart, Stefan Broos, Frans Van Roy, et al. “A Flexible Integrative Approach Based on Random Forest Improves Prediction of Transcription Factor Binding Sites.” NUCLEIC ACIDS RESEARCH 40.14 (2012): n. pag. Print.
APA
Hooghe, B., Broos, S., Van Roy, F., & De Bleser, P. (2012). A flexible integrative approach based on random forest improves prediction of transcription factor binding sites. NUCLEIC ACIDS RESEARCH, 40(14).
Chicago author-date
Hooghe, Bart, Stefan Broos, Frans Van Roy, and Pieter De Bleser. 2012. “A Flexible Integrative Approach Based on Random Forest Improves Prediction of Transcription Factor Binding Sites.” Nucleic Acids Research 40 (14).
Chicago author-date (all authors)
Hooghe, Bart, Stefan Broos, Frans Van Roy, and Pieter De Bleser. 2012. “A Flexible Integrative Approach Based on Random Forest Improves Prediction of Transcription Factor Binding Sites.” Nucleic Acids Research 40 (14).
Vancouver
1.
Hooghe B, Broos S, Van Roy F, De Bleser P. A flexible integrative approach based on random forest improves prediction of transcription factor binding sites. NUCLEIC ACIDS RESEARCH. 2012;40(14).
IEEE
[1]
B. Hooghe, S. Broos, F. Van Roy, and P. De Bleser, “A flexible integrative approach based on random forest improves prediction of transcription factor binding sites,” NUCLEIC ACIDS RESEARCH, vol. 40, no. 14, 2012.
@article{3009502,
  abstract     = {Transcription factor binding sites (TFBSs) are DNA sequences of 6-15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.},
  articleno    = {e106},
  author       = {Hooghe, Bart and Broos, Stefan and Van Roy, Frans and De Bleser, Pieter},
  issn         = {0305-1048},
  journal      = {NUCLEIC ACIDS RESEARCH},
  keywords     = {TATA BOX,B-DNA,IN-VITRO,HUMAN GENOME,TARGET SITES,STRUCTURAL-ANALYSIS,MOLECULAR-DYNAMICS SIMULATIONS,ESCHERICHIA-COLI,PROTEIN-DNA RECOGNITION,SEQUENCE},
  language     = {eng},
  number       = {14},
  pages        = {15},
  title        = {A flexible integrative approach based on random forest improves prediction of transcription factor binding sites},
  url          = {http://dx.doi.org/10.1093/nar/gks283},
  volume       = {40},
  year         = {2012},
}

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