What’s New in the IPA Fall Release (September 2015)
Find the biology hidden in your RNA-seq dataset with IsoProfiler
Quickly see which diseases, functions, and phenotypes are associated with differentially expressed isoforms in your RNA-seq experiment using IPA’s new IsoProfilerBETA. Get early access to IsoProfiler as part of Advanced Analytics.
Simply filter to determine if certain isoforms (splice variants and their products) are known to drive a disease or process. For example, Figure 1 shows isoforms driving metastatic processes in a human breast cancer RNA-seq dataset.
Understand the biological impact of prioritized variants from DNA or RNA-sequencing experiments
Import genetic gain/loss information for a set of genes and predict the variant effect on diseases, functions, phenotypes and canonical pathways. IPA now supports a new data type for gain or loss of function variants that result from genome or transcriptome sequencing data.
Overlay Gain or Loss of function variant values onto genes on networks and pathways to display their effects on genes and use MAP (Molecule Activity Predictor) to compute the impact on neighboring connected genes.
Discover mechanisms of upstream activation or inhibition by combining variant gain or loss of function results with expression data
Combining Gain or Loss of Function variant data with expression data unlocks the ability to investigate whether upstream regulator predictions based on expression data may in fact derive from variants that activate or inactivate the regulator itself.
Using Upstream Regulator Analysis, if there are cases where an upstream molecule has been predicted to be activated or inhibited, you can quickly discover if the gene for that regulator has a corresponding gain or loss of function variant.