High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.
The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.
Roulet
Emmanuelle
E
Laboratory of Molecular Biotechnology, Center for Biotechnology UNIL-EPFL, and Institute of Animal Biology, University of Lausanne, 1015 Lausanne, Switzerland.
Busso
Stéphane
S
Camargo
Anamaria A
AA
Simpson
Andrew J G
AJ
Mermod
Nicolas
N
Bucher
Philipp
P
D001483:Base Sequence; D001665:Binding Sites; D022762:CCAAT-Enhancer-Binding Proteins / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016384:Consensus Sequence / Q000235:genetics; D004247:DNA / Q000235:genetics / Q000378:metabolism; D004268:DNA-Binding Proteins / Q000378:metabolism; D005786:Gene Expression Regulation; D016678:Genome; D023281:Genomics / Q000379:methods*; D008024:Ligands; D008954:Models, Biological; D051839:NFI Transcription Factors; D011485:Protein Binding; D020218:Response Elements / Q000235:genetics*; D013379:Substrate Specificity; D014157:Transcription Factors / Q000378:metabolism*
D022762:CCAAT-Enhancer-Binding Proteins; C077305:CTF-1 transcription factor; D004268:DNA-Binding Proteins; D008024:Ligands; D051839:NFI Transcription Factors; D014157:Transcription Factors; D004247:DNA