IPA Fall Release 2016

What’s New in the IPA Fall Release (September 2016)

Discover significant isoforms in RNA sequencing data with enhanced IsoProfiler
RNA sequencing technologies can generate datasets with thousands of differentially spliced transcripts. IsoProfiler helps you determine which isoforms have interesting biological properties relevant to your research project.

What’s new

  • Results are now expanded to include gene-level disease and function annotations to enable you to focus on potentially biologically interesting (but not yet well-understood) isoforms
  • Quickly narrow down to the transcripts of interest by searching on specific gene names or disease or function terms
  • Save time by visualizing isoform schematics inside IsoProfiler to understand the basic structure of the isoforms of interest
  • Focus on protein-coding transcripts with the new transcript type column for RefSeq datasets
IsoProfiler


Fig 1. Overview of IsoProfiler, with highlights indicating the new features. IsoProfiler can visualize one or more transcript-level RNA sequencing datasets in a single view and enables you to filter and sort to focus on isoforms that have biologically relevant attributes. The top right table shows each gene in your dataset with its associated transcripts and expression data. When a gene is selected, the bottom right table shows the specific isoform-level details for that gene. 1) A new column displaying diseases and functions known to be associated at the gene-level (as well as at the isoform level) has been added to the top table. This may help you identify the specific isoforms in your experiment that drive the known gene level associations. 2) New filters have been added to search for specific gene name or specific disease and function terms that are pertinent to your dataset(s). See Figure 2 for additional details. 3) New dynamically re-sizable schematics of the isoforms are now displayed in the lower table for the gene selected enabling you to see the overall splicing pattern of each transcript.

IsoProfiler


Fig 2. Gene-level Disease or Function filter in IsoProfiler. Simply start typing in the text box to focus the list down to relevant filters. In this example, “epith” has been typed, which instantly limits the list of filters to terms like “chemotaxis of epithelial cells”, etc. The same type of filter is now also provided for isoform-level diseases and functions.

IsoProfiler is available in IPA with Advanced Analytics.

Visualize phosphoproteomics data on networks and pathways

Enhance your multi-omics research approaches by uploading simplified phosphoproteomics datasets to IPA for overlay onto networks and pathways. In a first step to better support the understanding of phosphorylation state and the associated biology, a new “phospho” measurement type is being introduced with this release of IPA. Overlay phosphorylation and expression profiles on networks and pathways to identify key areas where phosphorylation is impacting the biological activity of the encoded proteins.

Multi-omics overlay

If you have performed both gene expression and phosphoproteomics profiling, you can visualize both of these data types simultaneously as bar charts on networks and pathways. Figure 3 below shows the upstream regulator MAPK1 which IPA predicted to be activated by alpha-toxin (hemolysin) treatment of S9 cells. This prediction was based on a Core Analysis of the gene expression data after exposure to the toxin. The expression data shows that MAPK1 is not itself differentially expressed, but overlaying the accompanying phosphoproteomics dataset on the MAPK1 network provides a possible mechanism for its activation—MAPK1’s phosphorylation level is increased which is likely to activate it and lead to the observed expression changes downstream. In Figure 3, you can see in contrast that JUN is both upregulated and exhibits higher protein phosphorylation after the treatment.

IsoProfiler


Fig 3. Upstream Regulator Network for MAPK1 with expression and phosphorylation data overlaid. MAPK1 is differentially phosphorylated, which may explain its predicted activation as a regulator of the expression of the genes connected to it in the network. In contrast, JUN is both phosphorylated and differentially expressed. The microarray and phosphoproteomics data used in this figure was obtained from http://dx.doi.org/10.1371/journal.pone.012208