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Published on: February 7, 2019
Assessing phylogenetic motif models for predicting transcription factor binding sites
John Hawkins1, Charles Grant, William Stafford Noble
1Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au
Bioinformatics (Oxford, England)
|May 30, 2009
Summary
Phylogenetic motif models (PMMs) for predicting transcription factor binding sites (TFBSs) were surprisingly outperformed by simple scanning methods using validated yeast TFBSs. However, PMMs excelled when assessing statistical significance, suggesting limitations in current TFBS prediction benchmarks.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Transcription factor binding sites (TFBSs) are crucial genomic elements.
- Evolutionary information from multiple genome alignments aids TFBS prediction.
- Phylogenetic motif models (PMMs) incorporate evolutionary data but lack rigorous benchmarking.
Purpose of the Study:
- To benchmark PMM-based TFBS prediction algorithms against non-phylogenetic methods.
- To investigate the impact of different gapped alignment treatments in PMMs.
- To re-evaluate TFBS prediction accuracy metrics and develop improved methods.
Main Methods:
- Evaluated three PMM algorithms with varying gapped alignment handling.
- Compared PMMs against a standard position weight matrix scanning approach.
- Utilized a gold standard of validated yeast TFBSs and statistically significant site predictions.
Main Results:
- PMMs were inferior to simple scanning using a validated TFBS gold standard.
- PMMs significantly outperformed simple scanning when assessing statistical significance.
- A refined theoretical model improved accuracy bounds and enabled new genome-wide site prediction.
- The MONKEY algorithm demonstrated the highest accuracy among tested PMMs for yeast TFBSs.
Conclusions:
- Current TFBS prediction benchmarks using known sites may be misleading due to missing 'weak' sites.
- PMMs show promise when evaluated with appropriate statistical significance measures.
- The number of true TFBSs in yeast genomes likely exceeds current database annotations.
- The MONKEY algorithm is a promising tool for yeast TFBS prediction.
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