Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor binding sites

@article{Kim2007FindingSM,
  title={Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor binding sites},
  author={Nak-Kyeong Kim and Kannan Tharakaraman and Leonardo Mari{\~n}o-Ram{\'i}rez and John L. Spouge},
  journal={BMC Bioinformatics},
  year={2007},
  volume={9},
  pages={262 - 262}
}
BackgroundBiologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream of a transcription start site (TSS). Although some programs for identifying sequence motifs exploit positional information, most of them model it only implicitly and with ad hoc methods, making them unsuitable for general motif searches.ResultsA-GLAM, a user… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 25 CITATIONS

De-Novo Discovery of Differentially Abundant Transcription Factor Binding Sites Including Their Positional Preference

  • PLoS Computational Biology
  • 2011
VIEW 7 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Appendix A : Component models and parameterization

  • 2010
VIEW 5 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 35 REFERENCES

DBTSS: DataBase of Human Transcription Start Sites, progress report 2006

  • Nucleic Acids Research
  • 2006
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

The evolution of transcriptional regulation in eukaryotes.

  • Molecular biology and evolution
  • 2003
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Analysis of computational approaches for motif discovery

  • Algorithms for Molecular Biology
  • 2006
VIEW 1 EXCERPT