High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

Détails

ID Serval
serval:BIB_AD8B977B18DC
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.
Périodique
Nature Biotechnology
Auteur(s)
Roulet E., Busso S., Camargo A.A., Simpson A.J., Mermod N., Bucher P.
ISSN
1087-0156[print], 1087-0156[linking]
Statut éditorial
Publié
Date de publication
2002
Volume
20
Numéro
8
Pages
831-835
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
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.
Mots-clé
Base Sequence, Binding Sites, CCAAT-Enhancer-Binding Proteins/metabolism, Computational Biology/methods, Computer Simulation, Consensus Sequence/genetics, DNA/genetics, DNA/metabolism, DNA-Binding Proteins/metabolism, Gene Expression Regulation, Genome, Genomics/methods, Ligands, Models, Biological, NFI Transcription Factors, Protein Binding, Response Elements/genetics, Substrate Specificity, Transcription Factors/metabolism
Pubmed
Web of science
Création de la notice
24/01/2008 10:41
Dernière modification de la notice
20/08/2019 15:17
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