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Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.

Publication ,  Journal Article
Reddy, TE; Shakhnovich, BE; Roberts, DS; Russek, SJ; DeLisi, C
Published in: Nucleic Acids Res
2007

Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mechanism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A gamma-aminobutyric acid receptor (GABA(A)R) subunit genes. Positional clustering is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABA(A)R subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system.

Duke Scholars

Published In

Nucleic Acids Res

DOI

EISSN

1362-4962

Publication Date

2007

Volume

35

Issue

3

Start / End Page

e20

Location

England

Related Subject Headings

  • Transcription Factors
  • Sequence Analysis, DNA
  • Saccharomyces cerevisiae
  • Receptors, GABA-A
  • Rats
  • Protein Subunits
  • Promoter Regions, Genetic
  • Neurons
  • Mice
  • Developmental Biology
 

Citation

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MLA
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Reddy, T. E., Shakhnovich, B. E., Roberts, D. S., Russek, S. J., & DeLisi, C. (2007). Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes. Nucleic Acids Res, 35(3), e20. https://doi.org/10.1093/nar/gkl1062
Reddy, Timothy E., Boris E. Shakhnovich, Daniel S. Roberts, Shelley J. Russek, and Charles DeLisi. “Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.Nucleic Acids Res 35, no. 3 (2007): e20. https://doi.org/10.1093/nar/gkl1062.
Reddy, Timothy E., et al. “Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABAA receptor subunit genes.Nucleic Acids Res, vol. 35, no. 3, 2007, p. e20. Pubmed, doi:10.1093/nar/gkl1062.
Journal cover image

Published In

Nucleic Acids Res

DOI

EISSN

1362-4962

Publication Date

2007

Volume

35

Issue

3

Start / End Page

e20

Location

England

Related Subject Headings

  • Transcription Factors
  • Sequence Analysis, DNA
  • Saccharomyces cerevisiae
  • Receptors, GABA-A
  • Rats
  • Protein Subunits
  • Promoter Regions, Genetic
  • Neurons
  • Mice
  • Developmental Biology