Abstract
Gene expression profiling has been extensively used in the past decades, resulting in an enormous amount of expression data available in public databases. These data sets are informative in elucidating transcriptional regulation of genes underlying various biological and clinical conditions. However, it is usually difficult to identify transcription factors (TFs) responsible for gene expression changes directly from their own expression, as TF activity is often regulated at the posttranscriptional level. In recent years, technical advances have made it possible to systematically determine the target genes of TFs by ChIP-seq experiments. To identify the regulatory programs underlying gene expression profiles, we constructed a database of phenotype-specific regulatory programs (DPRP, http://syslab.nchu.edu.tw/DPRP/) derived from the integrative analysis of TF binding data and gene expression data. DPRP provides three methods: the Fisher’s Exact Test, the Kolmogorov–Smirnov test and the BASE algorithm to facilitate the application of gene expression data for generating new hypotheses on transcriptional regulatory programs in biological and clinical studies.
Highlights
In the past decade, gene expression profiling by microarray and more recently by RNA-seq experiments has been extensively used to study transcriptional regulation, resulting in a plethora of expression data available in public databases such as the Gene Expression Omnibus [1]
To visualize the transcription factors (TFs) regulatory program, the web server draws a regulatory TF network consisting of all significant TFs, in which the TF!TF interaction indicates that one regulates the transcription of the other, which is identified by the Target identification from profiles (TIP) algorithm (P < 0.01) from ChIP-seq data
To apply the Fisher’s exact test, differentially expressed genes (DEGs) have to be defined based on the gene expression data
Summary
Gene expression profiling by microarray and more recently by RNA-seq experiments has been extensively used to study transcriptional regulation, resulting in a plethora of expression data available in public databases such as the Gene Expression Omnibus [1]. These data sets are informative in elucidating transcriptional regulation under various biological and clinical conditions. The ChEA databases collected large-scale ChIP-seq data and provided the integrative analysis of both ChIP-seq and gene expression data [13]; the ChIP-array web server can integrate ChIPseq data and gene expression profiles to construct regulatory networks [14].
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