Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
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- Supplementary Text and Figures (9 MB)
Supplementary Notes 1–9, Supplementary Tables 1–8 and Supplementary Figures 1–4
- Supplementary Table 1 (35 KB)
Information on transcription factors and associated experiments
- Supplementary Table 3 (93 KB)
Full evaluations for all algorithms, by TF
- Supplementary Table 6 (49 KB)
Improvement of secondary over primary motifs, for each TF
- Supplementary Table 7 (27 KB)
Full Comparison to ChIP-seq and ChIP-exo data
- Supplementary Table 8 (46 KB)
Information on plasmids used for PBMs in this study