IPA Fall Release 2015

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.


Fig 1. IsoProfiler results. The table displays all the isoforms that have a curated relationship to a biological function, phenotype, or disease. In this example, the table has been filtered to display the isoforms known to be involved in metastasis. This isoform of ADAM12 is upregulated in the dataset, providing an avenue of experimental inquiry – perhaps this short form is responsible for the aggressiveness of these breast cancer cells.

ADAM12 isoform view

Fig 2. ADAM12 isoform view shows that a shorter isoform, ADAM12S, is upregulated in the breast cancer cells, with a fold change of 66.3.

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.

Fig 3. Gain or Loss of function variants (green-colored nodes indicating loss of function variant) in genes on the ERK5 Signaling Pathway could lead to increased cell survival and decreased gene expression in this endometrioid endometrial carcinoma analysis.

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.

Fig 4. Upstream regulator analysis of an endometrioid endometrial cancer patient (tumor vs. normal adjacent tissue). The result shows that the NFKBIA protein is predicted to be an inhibited upstream regulator AND has a likely loss of function (see red box above), which corresponds with and may explain the predicted loss of its activity as an upstream regulator.