MicroRNA Target Filter
In the rapidly evolving field of microRNA research, which still 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’s new microRNA Target Filter functionality enables prioritization of experimentally validated and predicted mRNA targets.
microRNA workflow in IPA
Upload, analyze, prioritize, filter, and visualize microRNA-mRNA data and relationships, all within a single tool. You can also 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 your microRNA research provides you with enhanced knowledge about subcellular location, functional gene family, association with drugs and pathways, and more.
Highlights
The microRNA Target Filter in IPA provides insights into the biological effects of microRNAs, using experimentally validated interactions from TarBase and miRecords, as well as predicted microRNA-mRNA interactions from TargetScan. Additionally, IPA includes a large number of microRNA-related findings from the peer-reviewed literature.
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 and consistently 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?
- What is the predicted impact of changes in microRNA expression on cellular processes, pathways, diseases, and phenotypes?
Capabilities
MicroRNA content:
- TargetScan content: Identify predicted mRNA targets for microRNAs using predicted microRNA–mRNA binding relationships from TargetScan that are binned into high and moderate confidence. (IPA uses predicted targets of mammalian microRNAs. Targets are predicted using TargetScan algorithm by searching for the presence of conserved 8mer and 7mer sites that match the seed region of each microRNA.)
- TarBase content: Identify experimentally demonstrated microRNA/mRNA using content from TarBase with miRBase identifiers
- miRecords: Experimentally validated human, rat, and mouse microRNA-mRNA interactions from ˜550 published articles
- Ingenuity® Knowledge Base: Thousands of microRNA-related Findings manually curated from published literature by Ingenuity scientific experts
Search for mRNA targets, disease states, synonyms, regulators, biomarkers, expression and more in IPA using microRNA content in the Ingenuity Knowledge Base. (Example above is a Gene View for Entrez Gene miR-106, which is within family miR-17. MicroRNA IDs are from miRBase and predicted relationships are from TargetScan.)
MicroRNA Target Filter
- Prioritize and filter lists of microRNA-mRNA relationships based on source, confidence level, and role or presence in species, diseases, tissues, pathways, cell lines, molecules and more. Utilize the microRNA Target Filter to overlay microRNA data onto networks and pathways, grow out to add molecules to networks, and compare molecules from different experimental observations. Quickly see how your microRNA affects a signaling or metabolic pathway from the Ingenuity Knowledge Base, or see how genes in your experiment support a hypothesis using IPA’s search and explore functionality.
- Examine microRNA-mRNA pairings and explore the related biological context of the mRNAs.
No other tool lets you identify, filter, and prioritize microRNA-mRNA relationships in one easy step.
Above, the MicroRNA Target Filter ready to prioritize relationships based upon molecular type or pathway.
Once key relationships are identified, they can be exported to a pathway for visualization and exploration. In this case, from over 13,000 potential targets, IPA helped identify that just two are the top proteins – KIT and MC1R – driving melanocyte development and pigmentation signaling. They are targeted by about 10 different microRNAs, 5 of which have inverse expression patterns in metastatic melanoma.