Epigenetic priors for identifying active transcription factor binding sites
Gabriel Cuellar-Partida1, Fabian A Buske, Robert C McLeay
1Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.
Bioinformatics (Oxford, England)
|November 11, 2011
Summary
This study introduces a new probabilistic method to identify active transcription factor binding sites (TFBSs) by integrating epigenetic data with DNA sequence motifs. The method outperforms existing filters and offers a simpler, general approach for TFBS prediction.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Understanding genome-wide transcription factor binding is crucial for transcriptional regulation.
- Epigenetic data, including histone modifications and DNase I sensitivity, can enhance in silico prediction of transcription factor binding sites (TFBSs).
Purpose of the Study:
- To develop and validate a probabilistic method for improving the identification of active TFBSs by integrating epigenetic data with DNA sequence motif models.
- To compare the performance of the proposed method against existing approaches.
Main Methods:
- A probabilistic method was developed to combine multiple epigenetic data tracks (H3K4me1, H3K4me3, H3K9ac, H3K27ac, DNase I sensitivity) with DNA sequence motif models.
- Each data type was converted into a position-specific probabilistic prior, which was then combined with a traditional motif model to compute a log-posterior odds score.
- The FIMO software, part of the MEME Suite, was updated to support this log-posterior odds scoring.
Main Results:
- The log-posterior odds score consistently outperformed a simple binary filter using the same epigenetic data.
- The proposed method demonstrated competitive performance compared to the more complex CENTIPEDE method.
- The method effectively identifies active transcription factor binding sites using DNA sequence and epigenetic evidence.
Conclusions:
- The developed probabilistic method provides a robust and general approach for identifying functional TFBSs.
- The simplicity of the log-posterior odds scoring makes it an appealing tool for researchers in transcriptional regulation.
- Integration of epigenetic data significantly improves the accuracy of in silico TFBS prediction.
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