Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification

AUTOR(ES)

Haverty, Peter M.

FONTE

Oxford University Press

RESUMO

We have developed a computational method for transcriptional regulatory network inference, CARRIE (Computational Ascertainment of Regu latory Relationships Inferred from Expression), which combines microarray and promoter sequence analysis. CARRIE uses sources of data to identify the transcription factors (TFs) that regulate gene expression changes in response to a stimulus and generates testable hypotheses about the regulatory network connecting these TFs to the genes they regulate. The promoter analysis component of CARRIE, ROVER (Relative OVER-abundance of cis-elements), is highly accurate at detecting the TFs that regulate the response to a stimulus. ROVER also predicts which genes are regulated by each of these TFs. CARRIE uses these transcriptional interactions to infer a regulatory network. To demonstrate our method, we applied CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae. The predicted networks were validated with comparisons to literature sources, experimental TF binding data, and gene ontology biological process information.

Documentos Relacionados

• Computational prediction of transcription-factor binding site locations
• Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data
• Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura
• DNA binding site preferences and transcriptional activation properties of the Arabidopsis transcription factor GBF1.
• Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrep-resented upstream motifs