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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Bibliographic Details
Published in:Nature biotechnology Vol. 31; no. 2; pp. 126 - 134
Main Authors: 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
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.02.2013
Nature Publishing Group
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/nbt.2486