Evaluation of methods for modeling transcription factor sequence specificity

The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in vitro data. Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specifi...

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Vydáno v:	Nature biotechnology Ročník 31; číslo 2; s. 126 - 134
Hlavní autoři:	Weirauch, Matthew T, Cote, Atina, Norel, Raquel, Annala, Matti, Zhao, Yue, Riley, Todd R, Saez-Rodriguez, Julio, Cokelaer, Thomas, Vedenko, Anastasia, Talukder, Shaheynoor, Bussemaker, Harmen J, Morris, Quaid D, Bulyk, Martha L, Stolovitzky, Gustavo, Hughes, Timothy R
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.02.2013 Nature Publishing Group
Témata:	631/114 631/45/612/822 631/61/191 Agriculture Algorithms analysis Animals Binding sites Bioinformatics Biomedical Engineering/Biotechnology Biomedicine Biotechnology Computational Biology Deoxyribonucleic acid DNA DNA binding proteins DNA sequencing DNA-Binding Proteins - chemistry DNA-Binding Proteins - genetics Genome Genomics Health aspects Life Sciences Mice Nucleotide Motifs - genetics Nucleotide sequencing Physiological aspects Position-Specific Scoring Matrices Protein Array Analysis Proteins Transcription factors Transcription Factors - genetics Transcription Factors - metabolism United States
ISSN:	1087-0156, 1546-1696, 1546-1696
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Abstract	The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in vitro data. 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.
AbstractList	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.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. The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in vitro data. 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. 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. 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 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 9 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 in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10%). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
Audience	Academic
Author	Cokelaer, Thomas Stolovitzky, Gustavo Morris, Quaid D Weirauch, Matthew T Talukder, Shaheynoor Saez-Rodriguez, Julio Norel, Raquel Zhao, Yue Hughes, Timothy R Bussemaker, Harmen J Riley, Todd R Annala, Matti Vedenko, Anastasia Bulyk, Martha L Cote, Atina
AuthorAffiliation	11 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 6 Department of Biological Sciences, Columbia University, and Center for Computational Biology and Bioinformatics, Columbia University Medical Center, New York, NY 10 Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA 8 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 9 Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 2 Center for Autoimmune Genomics and Etiology (CAGE) and Divisions of Rheumatology and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA 4 Department of Signal Processing, Tampere University of Technology, Tampere, Finland 1 Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto, Toronto, ON, Canada 3 IBM Computational Biology Center, Yorktown Heights, New York,
AuthorAffiliation_xml	– name: 9 Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA – name: 4 Department of Signal Processing, Tampere University of Technology, Tampere, Finland – name: 2 Center for Autoimmune Genomics and Etiology (CAGE) and Divisions of Rheumatology and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA – name: 7 EMBL-EBI European Bioinformatics Institute, Cambridge, UK – name: 6 Department of Biological Sciences, Columbia University, and Center for Computational Biology and Bioinformatics, Columbia University Medical Center, New York, NY – name: 11 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada – name: 3 IBM Computational Biology Center, Yorktown Heights, New York, NY, USA – name: 5 Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA – name: 1 Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto, Toronto, ON, Canada – name: 10 Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA – name: 8 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
Author_xml	– sequence: 1 givenname: Matthew T surname: Weirauch fullname: Weirauch, Matthew T organization: Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto, Center for Autoimmune Genomics and Etiology (CAGE) and Divisions of Rheumatology and Biomedical Informatics, Cincinnati Children's Hospital Medical Center – sequence: 2 givenname: Atina surname: Cote fullname: Cote, Atina organization: Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto – sequence: 3 givenname: Raquel surname: Norel fullname: Norel, Raquel organization: IBM Computational Biology Center, Yorktown Heights – sequence: 4 givenname: Matti surname: Annala fullname: Annala, Matti organization: Department of Signal Processing, Tampere University of Technology – sequence: 5 givenname: Yue surname: Zhao fullname: Zhao, Yue organization: Department of Genetics, University of Pennsylvania – sequence: 6 givenname: Todd R surname: Riley fullname: Riley, Todd R organization: Department of Biological Sciences, Columbia University, and Center for Computational Biology and Bioinformatics, Columbia University Medical Center – sequence: 7 givenname: Julio surname: Saez-Rodriguez fullname: Saez-Rodriguez, Julio organization: EMBL-EBI European Bioinformatics Institute – sequence: 8 givenname: Thomas surname: Cokelaer fullname: Cokelaer, Thomas organization: EMBL-EBI European Bioinformatics Institute – sequence: 9 givenname: Anastasia surname: Vedenko fullname: Vedenko, Anastasia organization: Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School – sequence: 10 givenname: Shaheynoor surname: Talukder fullname: Talukder, Shaheynoor organization: Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto – sequence: 12 givenname: Harmen J surname: Bussemaker fullname: Bussemaker, Harmen J organization: Department of Biological Sciences, Columbia University, and Center for Computational Biology and Bioinformatics, Columbia University Medical Center – sequence: 13 givenname: Quaid D surname: Morris fullname: Morris, Quaid D organization: Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto, Department of Molecular Genetics, University of Toronto – sequence: 14 givenname: Martha L surname: Bulyk fullname: Bulyk, Martha L organization: Department of Medicine, Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School – sequence: 15 givenname: Gustavo surname: Stolovitzky fullname: Stolovitzky, Gustavo organization: IBM Computational Biology Center, Yorktown Heights – sequence: 16 givenname: Timothy R surname: Hughes fullname: Hughes, Timothy R email: t.hughes@utoronto.ca organization: Banting and Best Department of Medical Research and Donnelly Centre, University of Toronto, Department of Molecular Genetics, University of Toronto
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Snippet	The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in... Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins.... Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins....
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SubjectTerms	631/114 631/45/612/822 631/61/191 Agriculture Algorithms analysis Animals Binding sites Bioinformatics Biomedical Engineering/Biotechnology Biomedicine Biotechnology Computational Biology Deoxyribonucleic acid DNA DNA binding proteins DNA sequencing DNA-Binding Proteins - chemistry DNA-Binding Proteins - genetics Genome Genomics Health aspects Life Sciences Mice Nucleotide Motifs - genetics Nucleotide sequencing Physiological aspects Position-Specific Scoring Matrices Protein Array Analysis Proteins Transcription factors Transcription Factors - genetics Transcription Factors - metabolism
Title	Evaluation of methods for modeling transcription factor sequence specificity
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