In the rapidly evolving field of microRNA research, which relies heavily on a variety of measurement techniques and prediction algorithms for target identification, a common challenge is identifying the most biologically relevant targets. IPA makes that easy to do.
- If you have microRNA data (and optionally, have corresponding mRNA data) you can upload, analyze, prioritize, filter, and visualize microRNA-mRNA data and relationships using the microRNA Target Filter. You can leverage microRNA and mRNA data in combination with other types of ‘omics and high-throughput data for a fully integrated biological analysis. Using IPA in this way provides you with enhanced knowledge about subcellular location, functional gene family, association with drugs, pathways, disease and more.
- If you don’t have microRNA data but have mRNA or protein expression data and wish to predict upstream microRNAs that may control their expression, the Upstream Regulator Analysis in IPA will predict which microRNAs might have been up or down regulated in your experiment.
- In IPA’s pathway tools, you can easily connect microRNAs to their mRNA targets and explore the effects or up or down regulating the microRNAs in canonical pathways, or your own custom made pathways and networks
The microRNA capabilities in IPA provide insights into the biological effects of microRNAs, using experimentally validated interactions from TarBase, miRecords, and the peer-reviewed biomedical literature, as well as predicted microRNA-mRNA interactions from TargetScan. IPA reduces the number of steps it takes to confidently, quickly, and easily identify mRNA targets by letting you examine microRNA-mRNA pairings, explore the related biological context, and filter based on relevant biological information as well as the expression information. The ability to leverage biological context is key to overcoming the inherent complexity in current microRNA data analysis. Using IPA, you can easily answer questions like:
- Which microRNA is predicted to target a given mRNA, and how good is the prediction?
- Based on my expression data, which microRNAs have regulation that supports the target prediction?
- Which mRNAs participate in a relevant disease, subcellular location, or pathway?
- How do certain mRNAs and microRNAs interact, and what’s downstream?
- Which microRNAs might have been up or down regulated to account for the gene expression observed in an experimental dataset?
- What is the predicted impact of changes in microRNA expression on cellular processes, pathways, diseases, and phenotypes?