IPA Spring Release 2016

What’s New in the IPA Spring Release (March 2016)

Quickly compare results across ‘omics datasets on networks and pathways

Identify significant trends in genes involved in a pathway or network across conditions such as time or dose and elucidate possible mechanisms driving gene expression results with both variant gain or loss of function and expression results. Visualize multiple ‘omics datasets simultaneously on IPA networks and pathways.

  • Overlay multiple gene expression datasets/analyses on a canonical pathway (or on any collection of genes) simultaneously to see how genes are regulated across various conditions. Visualize multiple measurements at once—for example both Fold Change and the Intensity of the expression (e.g. RPKM in the case of RNA-seq data) as shown in Figure 1.
Three RNA-seq time points taken during in vitro mouse cardiomyocyte development overlaid on the Integrin Signaling Pathway

Fig 1. Three RNA-seq time points taken during in vitro mouse cardiomyocyte development overlaid on the Integrin Signaling Pathway (zoomed in). As the cells differentiate from embryonic stem cells into beating cardiomyocytes in vitro, a number of genes on this pathway are progressively upregulated. Several genes in the myosin subunit regulatory light chain family are upregulated over the time course. The new bar charts can show multiple measurements and datasets at one time to give you more insight into the details of the differential expression. In this example both the RNA-seq fold change and the intensity (RPKM) across the three analyses are shown. From this visualization, one can deduce that Myl7 becomes much more highly expressed than Myl2 (RPKM ~3800 vs ~115), even though Myl7 has a lower fold change than Myl2 (~955 vs. ~19,149). The fold changes alone don’t reveal this level of detail across the time points.

IPA also presents the multi-dataset / multi-measurement results in a table view that can be exported. Figure 2 shows an example of a portion of that table:

Clearly identify trends across genes, conditions, and datasets with the exportable table view

Fig 2. Clearly identify trends across genes, conditions, and datasets with the exportable table view. The same genes shown in Figure 1 above are shown here in the new table view within the Overlay Datasets, Analyses & Lists tool, though in this table a line is drawn to connect the bars when possible to help visualize patterns.

  • Elucidate possible mechanisms driving gene expression results by simultaneously overlaying both gene expression analysis and variant loss/gain datasets on a pathway or network. In this way you can see which genes are differentially expressed and harbor potentially deleterious variants.
Uncover possible mechanisms driving gene expression results.

Fig 3. Uncover possible mechanisms driving gene expression results. RNA-seq gene expression data from three hepatocellular carcinoma (HCC) patients was used to predict that the NONO protein is inhibited. Expression from the three patients was processed in Biomedical Genomics Workbench (BxWB) and then analyzed in IPA, which led to the prediction of NONO inhibition using Causal Network Analysis. Variants were also called on the transcript sequences from these patients using BxWB and analyzed using Ingenuity Variant Analysis. All three patients were found to have potentially deleterious frameshift and missense variants in the NONO gene. Data from both BxWB and Variant Analysis were exported directly to IPA. The three green bars in Figure 3 correspond to predicted loss of function variants for each of the patients, and the red bar indicates that the expression was upregulated in the patients, perhaps as a compensatory mechanism for loss of function. NONO has been found to be mutated in a number of cancer types.