Abstract
BackgroundA major challenge in computational genomics is the development of methodologies that allow accurate genome-wide prediction of the regulatory targets of a transcription factor. We present a method for target identification that combines experimental characterization of binding requirements with computational genomic analysis.ResultsOur method identified potential target genes of the transcription factor Ndt80, a key transcriptional regulator involved in yeast sporulation, using the combined information of binding affinity, positional distribution, and conservation of the binding sites across multiple species. We have also developed a mathematical approach to compute the false positive rate and the total number of targets in the genome based on the multiple selection criteria.ConclusionWe have shown that combining biochemical characterization and computational genomic analysis leads to accurate identification of the genome-wide targets of a transcription factor. The method can be extended to other transcription factors and can complement other genomic approaches to transcriptional regulation.
Highlights
A major challenge in computational genomics is the development of methodologies that allow accurate genome-wide prediction of the regulatory targets of a transcription factor
The availability of genome sequences for multiple species and large-scale gene expression data has led to the development of computational genomic approaches to transcriptional regulation
ChIP-chip experiments performed under a specific condition may not identify the correct targets of a factor if that factor is not activated, or may only identify a subset of the target genes if the factor works with other transcription factors (TF) in a combinatorial fashion, and searching for all conditions under which a factor may be functional is a daunting task
Summary
A major challenge in computational genomics is the development of methodologies that allow accurate genome-wide prediction of the regulatory targets of a transcription factor. The availability of genome sequences for multiple species and large-scale gene expression data has led to the development of computational genomic approaches to transcriptional regulation. Gene expression profiling of cells in which a transcription factor is either overexpressed or deleted has been used to identify targets [3,4,5,6,7,8]. While these are powerful approaches to systematically identifying target genes, they have limitations. Physical binding to a promoter as detected by a ChIP-chip experiment may not necessarily imply regula-
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