Bioinformatic analysis of transcription regulation.. (Promoter predictions)
Bioinformatic analysis of transcription regulation: a modeling approach
Start date: Mar 1, 2011,
End date: Feb 28, 2015
Transcription is both the first step and a major regulatory checkpoint in gene expression. Transcription start sites are locations in genome where RNA polymerase initiates transcription, while transcription binding sites are locations where transcription factors bind to regulate transcription. Knowledge of both transcription start sites, and transcription factor binding sites, is crucial for understanding transcription. However, methods for bioinformatic detection of these sites, which are mainly based on information theory, are typically characterized by low accuracy. Major underlying problems are: i) transcription initiation is a complex process that is characterized by both binding of RNA polymerase and opening of two DNA strands, ii) transcription factor binding sites usually have to be discovered/aligned within longer DNA fragments, which is often technically demanding and unreliable, iii) discovery of direct target genes of a transcription factor is complicated by random occurrence of binding sites that have high binding energy, but are not functional in regulating transcription.The main goal of our proposal is to develop bioinformatic methods for accurate detection of transcription signals. To address the above problems, we will use biophysical modeling to i) Develop a novel method for transcription start site detection in bacteria, which is based on explicit calculation of transcription initiation rates and takes into account both RNA polymerase binding and opening of two DNA strands, ii) Develop a method for inferring transcription factor-DNA interaction parameters directly from DNA fragments selected through high-throughput in-vitro selection experiments, iii) Develop a method for detection of target genes of a transcription factor, which detects an overrepresentation of binding energy distribution upstream of genes. We expect that these methods will significantly improve accuracy of analyzing transcription regulation.
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