Evaluation of methods for modeling transcription factor sequence specificity
- Weirauch, M. T.
- Cote, A.
- Norel, R.
- Annala, M.
- Zhao, Y.
- Riley, T. R.
- Saez-Rodriguez, J.
- Cokelaer, T.
- Vedenko, A.
- Talukder, S.
- Bussemaker, H. J.
- Quaid, M. D.
- Bulyk, M. L.
- Stolovitzky, G.
- Hughes, T. R.
- Agius, P.
- Arvey, A.
- Bucher, P.
- Callan Jr, C. G.
- Chang, C. W.
- Chen, C. Y.
- Chen, Y. S.
- Chu, Y. W.
- Grau, J.
- Grosse, I.
- Jagannathan, V.
- Keilwagen, J.
- Kiebasa, S. M.
- Kinney, J. B.
- Klein, H.
- Kursa, M. B.
- Lähdesmäki, H.
- Laurila, K.
- Lei, C.
- Leslie, C.
- Linhart, C.
- Murugan, A.
- Myšičková, A.
- Noble, W. S.
- Nykter, M.
- Orenstein, Y.
- Posch, S.
- Ruan, J.
- Rudnicki, W. R.
- Schmid, C. D.
- Shamir, R.
- Sung, W. K.
- Vingron, M.
- Zhang, Z.
DOIAbstract
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 similar...
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