The technique of transcriptional profiling on microarrays is now a widely-used and valuable tool in functional genomics. The goal of this project is to develop rigorous statistical tests and computational analysis tools for extracting the meaningful information from transcriptional profiling data. Much software is already available to detect patterns in profiling data. However, approaches to assess the reliability of the observed patterns can be further optimized. This proposal suggests a method to detect differentially regulated genes, whose significance estimates are relatively insensitive to minor variations in the analysis parameters and protocol. [unreadable] The combination of profiling data with the genomic sequence can yield insights into the principles of gene regulation at the whole-genome level. Expression profiling data generated by collaborators have led to the identification of genes whose expression is dependent on the activity of a transcription factor of interest. Computational searches for the consensus sequence of the transcription factor has yielded candidate regulatory elements near a subset of these genes. Subsequent experimental testing of these candidates will lead to deeper understanding of the sequence requirements for a functional regulatory element. [unreadable] [unreadable]
NIH ExPorter
NIH ExPorter