Transcription factors orchestrate complex regulatory networks of gene expression. A better understanding of the common transcription factors, and their shared interactions, among a set of coregulated or differentially expressed genes can provide powerful insights into the key pathways governing such expression patterns. Critically, such information must also be considered in the context of the frequency in which a transcription factor is present in a properly selected background, and in the context of existing evidence of gene and transcription factor interaction. Given the vast amount of publicly available gene expression data that can be further scrutinized by the user-friendly analysis tools described here, many useful insights are assuredly to be revealed. The proceeding methods for application of the analysis tool CiiiDER for transcription factor-binding site identification, enrichment analysis, and coregulatory factor identification should be applicable to any dataset comparing differential gene expression in response to various stimuli and gene coexpression datasets. These methods should assist the researcher in identifying the most relevant regulators within a gene set, and refining the list of targets for future study to those which may share biologically important regulatory networks.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/978-1-0716-1162-3_20 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!