Linked Life Data

12101405

Source:http://linkedlifedata.com/resource/pubmed/id/12101405

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2002-7-30
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1087-0156
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
831-5
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed-meshheading:12101405-Base Sequence, pubmed-meshheading:12101405-Binding Sites, pubmed-meshheading:12101405-CCAAT-Enhancer-Binding Proteins, pubmed-meshheading:12101405-Computational Biology, pubmed-meshheading:12101405-Computer Simulation, pubmed-meshheading:12101405-Consensus Sequence, pubmed-meshheading:12101405-DNA, pubmed-meshheading:12101405-DNA-Binding Proteins, pubmed-meshheading:12101405-Gene Expression Regulation, pubmed-meshheading:12101405-Genome, pubmed-meshheading:12101405-Genomics, pubmed-meshheading:12101405-Ligands, pubmed-meshheading:12101405-Models, Biological, pubmed-meshheading:12101405-NFI Transcription Factors, pubmed-meshheading:12101405-Protein Binding, pubmed-meshheading:12101405-Response Elements, pubmed-meshheading:12101405-Substrate Specificity, pubmed-meshheading:12101405-Transcription Factors
pubmed:year
2002
pubmed:articleTitle
High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.
pubmed:affiliation
Laboratory of Molecular Biotechnology, Center for Biotechnology UNIL-EPFL, and Institute of Animal Biology, University of Lausanne, 1015 Lausanne, Switzerland.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't