000196477 001__ 196477
000196477 005__ 20181203023423.0
000196477 022__ $$a1087-0156
000196477 02470 $$2PMID$$a23354101
000196477 02470 $$2ISI$$a000315322100023
000196477 0247_ $$2doi$$a10.1038/nbt.2486
000196477 037__ $$aARTICLE
000196477 245__ $$aEvaluation of methods for modeling transcription factor sequence specificity
000196477 269__ $$a2013
000196477 260__ $$bNature Publishing Group$$c2013
000196477 336__ $$aJournal Articles
000196477 520__ $$aGenomic 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
000196477 700__ $$aWeirauch, Matthew T.
000196477 700__ $$aCote, Atina
000196477 700__ $$aNorel, Raquel
000196477 700__ $$aAnnala, Matti
000196477 700__ $$aZhao, Yue
000196477 700__ $$aRiley, Todd R.
000196477 700__ $$aSaez-Rodriguez, Julio
000196477 700__ $$aCokelaer, Thomas
000196477 700__ $$aVedenko, Anastasia
000196477 700__ $$aTalukder, Shaheynoor
000196477 700__ $$aBussemaker, Harmen J.
000196477 700__ $$aMorris, Quaid D.
000196477 700__ $$aBulyk, Martha L.
000196477 700__ $$aStolovitzky, Gustavo
000196477 700__ $$aHughes, Timothy R.
000196477 700__ $$aDREAM5, Consortium
000196477 773__ $$j31$$tNature Biotechnology$$k2$$q126-134
000196477 909C0 $$xU11780$$0252244$$pGR-BUCHER
000196477 909CO $$pSV$$particle$$ooai:infoscience.tind.io:196477
000196477 917Z8 $$x182396
000196477 937__ $$aEPFL-ARTICLE-196477
000196477 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000196477 980__ $$aARTICLE