Regulation expression pathway analysis (REPA): a novel method to facilitate biological interpretation of high throughput expression profiling data

摘要

In the past decade there have been great advances and emergence of new techniquesudin the field of gene expression profiling. As the popularity of these techniques grew,udthe amount of data that gets generated has also grown. The task of analyzing thisuddata to create a global picture to identify the biological pathways that are relevantudto the study has been addressed by many. These approaches (collectively termed asudenrichment analysis) have also grown in sophistication and accuracy making themudthe default step following a gene profiling experiment. However, enrichment analysisudapproaches do not provide pointers to likely regulators in their results.udIn this project we built a system called Regulation Expression Pathway Analysisudor REPA to facilitate the biological interpretation of results from high throughputudgene expression profiling experiments. In particular, we provide researchers with geneudsets that were most active in the biological phenomenon under study and their likelyudregulators. Users can input the gene expression profile data from their expressionudprofiling experiments in REPA and get a list of disturbed gene sets and inferredudtranscription factors that possibly regulate these gene sets.udTo build this system first we processed the transcription factor binding data fromudthe ENCODE project to quantify the strength of regulation that each transcriptionudfactor has on each gene set. Then we build a gene expression enrichment analysisudsystem that can analyze the gene expression profiling data and list the most active gene sets. Finally we combine the results from the previous two steps to arrive at audmore complete picture that gives users information about not only the most activeudgene sets, but also about the most likely regulators of these gene sets.