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. (February 2013) Evaluation of methods for modeling transcription factor sequence specificity. Nature Biotechnology, 31 (2). pp. 126-134. ISSN 10870156 (ISSN)
Abstract
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. © 2013 Nature America, Inc. All rights reserved.
| Item Type: | Paper |
|---|---|
| Subjects: | bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification > protein types > DNA binding protein bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification > protein types bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification > protein types > transcription factor |
| CSHL Authors: | |
| Communities: | CSHL labs > Kinney lab |
| Depositing User: | Matt Covey |
| Date: | February 2013 |
| Date Deposited: | 01 Apr 2013 16:31 |
| Last Modified: | 18 Jan 2017 17:00 |
| PMCID: | PMC3687085 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/28044 |
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