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  Evaluation of methods for modeling transcription factor sequence specificity

Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., et al. (2013). Evaluation of methods for modeling transcription factor sequence specificity. Nature biotechnology, 31(2), 126-34. doi:10.1038/nbt.2486.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-7B8E-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-7B9E-E
Genre: Journal Article

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 Creators:
Weirauch, M. T., Author
Cote, A., Author
Norel, R., Author
Annala, M., Author
Zhao, Y., Author
Riley, T. R., Author
Saez-Rodriguez, J., Author
Cokelaer, T., Author
Vedenko, A., Author
Talukder, S., Author
Consortium, Dream, Author
Agius, P., Author
Arvey, A., Author
Bucher, P., Author
Callan, C. G., Jr., Author
Chang, C. W., Author
Chen, C. Y., Author
Chen, Y. S., Author
Chu, Y. W., Author
Grau, J., Author
Grosse, I., AuthorJagannathan, V., AuthorKeilwagen, J., AuthorKielbasa, S. M.1, Author              Kinney, J. B., AuthorKlein, H., AuthorKursa, M. B., AuthorLahdesmaki, H., AuthorLaurila, K., AuthorLei, C., AuthorLeslie, C., AuthorLinhart, C., AuthorMurugan, A., AuthorMysickova, A., AuthorNoble, W. S., AuthorNykter, M., AuthorOrenstein, Y., AuthorPosch, S., AuthorRuan, J., AuthorRudnicki, W. R., AuthorSchmid, C. D., AuthorShamir, R., AuthorSung, W. K., AuthorVingron, M.2, Author              Zhang, Z., AuthorBussemaker, H. J., AuthorMorris, Q. D., AuthorBulyk, M. L., AuthorStolovitzky, G., AuthorHughes, T. R., Author more..
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, Berlin, Germany, ou_1479639              

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 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.

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Language(s): eng - English
 Dates: 2013-02
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/nbt.2486
ISSN: 1546-1696 (Electronic)1087-0156 (Print)
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Title: Nature biotechnology
Source Genre: Journal
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Publ. Info: New York : Gale Group Inc.
Pages: - Volume / Issue: 31 (2) Sequence Number: - Start / End Page: 126 - 34 Identifier: ISSN: 1087-0156
CoNE: https://pure.mpg.de/cone/journals/resource/954926980065