Inference, validation, and dynamic modeling of transcription networks in multipotent hematopoietic cells.

Identifying the transcription factor interactions that are responsible for cell-specific gene expression programs is key to understanding the regulation of cell behaviors, such as self-renewal, proliferation, differentiation, and death. The rapidly increasing availability of microarray-derived global gene expression data sets, coupled with genome sequence information from multiple species, has driven the development of computational methods to reverse engineer and dynamically model genetic regulatory networks. An understanding of the architecture and behavior of transcriptional networks should lend insight into how the huge number of potential gene expression programs is constrained and facilitates efforts to direct or redirect cell fate.

TFBScluster: a resource for the characterization of transcriptional regulatory networks.

SUMMARY: One major challenge of the post-sequencing era of the human genome project will be the functional annotation of the non-coding portion of the genome, in particular gene regulatory sequences. We have developed a new web-based tool, TFBScluster, which performs genome-wide identification of transcription factor binding site clusters that are conserved in multiple mammalian genomes. Clusters representing candidate gene regulatory elements can be filtered further, based on the presence or absence of additional user-defined DNA sequence motifs or by constraining the orientation or order of binding sites. Comprehensive results files, returned by email, are designed to facilitate experimental validation of computationally identified candidate gene regulatory sequences. TFBScluster, therefore, has the potential to contribute to deciphering transcriptional networks that regulate a wide range of mammalian developmental processes.