Evaluation of methods for modeling transcription factor sequence specificity

Matthew T. Weirauch, Atina Cote, Raquel Norel, Matti Annala, Yue Zhao, Todd R. Riley, Julio Saez-Rodriguez, Thomas Cokelaer, Anastasia Vedenko, Shaheynoor Talukder, Harmen J. Bussemaker, Morris D. Quaid, Martha L. Bulyk, Gustavo Stolovitzky, Timothy R. Hughes, Phaedra Agius, Aaron Arvey, Philipp Bucher, Curtis G. Callan, Cheng Wei ChangChien Yu Chen, Yong Syuan Chen, Yu Wei Chu, Jan Grau, Ivo Grosse, Vidhya Jagannathan, Jens Keilwagen, Szymon M. Kiebasa, Justin B. Kinney, Holger Klein, Miron B. Kursa, Harri Lähdesmäki, Kirsti Laurila, Chengwei Lei, Christina Leslie, Chaim Linhart, Anand Murugan, Alena Myšičková, William Stafford Noble, Matti Nykter, Yaron Orenstein, Stefan Posch, Jianhua Ruan, Witold R. Rudnicki, Christoph D. Schmid, Ron Shamir, Wing Kin Sung, Martin Vingron, Zhizhuo Zhang

Research output: Contribution to journalArticlepeer-review

276 Scopus citations


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.

Original languageEnglish
Pages (from-to)126-134
Number of pages9
JournalNature Biotechnology
Issue number2
StatePublished - Feb 2013
Externally publishedYes

Bibliographical note

Funding Information:
were supported by the Academy of Finland (project 260403) and EU ERASysBio ERA-NET. Y.O., C.L. and R.S. were funded by the European Community’s Seventh Framework Programme under grant agreement no. HEALTH-F4-2009-223575 for the TRIREME project, and by the Israel Science Foundation (grant no. 802/08). Y.O. was supported in part by a fellowship from the Edmond J. Safra Bioinformatics Program at Tel Aviv University. J.G., I.G., S.P. and J.K. were supported by grant XP3624HP/0606T by the Ministry of Culture of Saxony-Anhalt. A.M. was supported by US National Science Foundation (NSF) grant PHY-1022140. C.C. was supported by NSF grant PHY-0957573. J.B.K. was supported by the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory.

Funding Information:
We thank H. van Bakel and M. Albu for database assistance, and members of the Hughes laboratory for helpful discussion. M.T.W. was supported by fellowships from the Canadian Institutes of Health Research (CIHR) and the Canadian Institute for Advanced Research (CIFAR) Junior Fellows Genetic Networks Program. This work was supported in part by the Ontario Research Fund and Genome Canada through the Ontario Genomics Institute, and the March of Dimes (T.R.H.). Funding was also provided by Operating Grant MOP-77721 from CIHR to T.R.H. and M.L.B., and grant no. R01 HG003985 from the US National Institutes of Health/National Human Genome Research Institute to M.L.B., as well as US National Institutes of Health grants R01HG003008 and U54CA121852 and a John Simon Guggenheim Foundation Fellowship to H.J.B. M.A., K.L., H.L. and M.L.


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