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What’s New in the IPA Summer Release (July 2022)

Visualize your data in new ways with Canonical Pathway bubble charts
You can now generate bubble charts in QIAGEN Ingenuity Pathway Analysis (IPA) for your Core Analysis Canonical Pathway scores. For example, Figure 1 shows a bubble chart which plots the effect of an NRF2 activator on Canonical Pathway activity. In this chart, the scores are organized by pathway category and colored according to predicted activation. The bubble size is related to the number of genes that overlap each pathway. The figure shows that this NRF2 activator turns on multiple pathways related to xenobiotic metabolism, toxicity and cellular stress (see bottom right area of Figure 1). These pathways also exhibit a higher number of overlapping genes relative to other pathways in the chart. 
Figure 1. NRF2 activator Canonical Pathway scores arranged by category. The colors indicate the z-score, and bubble size corresponds to the number of overlapping genes. The large orange bubbles represent pathways that are statistically significant, predicted to be activated, and have many overlapping genes from the dataset. Gene expression data from Shelton, L.M. et al. (2015) Kidney Int. 88-1261. PMC4676083.
Bubble charts can be used to spotlight the highly significant and activated pathways with many overlapping genes (see top right of chart in Figure 2).
Figure 2. NRF2 activator pathway data plotted to highlight the most significant pathways that are activated or inhibited. Pathway bubbles near the top of the chart are the most significant. The blue bubbles towards the left are inhibited, and the orange bubbles towards the right are activated. 
Visualize OmicSoft single-cell data in Land Explorer 
Now you can easily explore single-cell expression for any gene in the public data curated by OmicSoft. These single-cell views are available via new links in IPA Gene Views (Figure 3).
Figure 3. New links for single-cell views in Gene View (highlighted in the red box).
Improve readability of your networks with new node-label placement
The labels of nodes (e.g., gene names, disease names) in IPA networks can now be positioned below their node shapes to make them easier to read, especially when they otherwise would be superimposed over dark fill colors. Figure 4 below shows two examples of IPA networks in which the node labels are positioned below their corresponding shapes. 
Figure 4. Interpret your networks more readily with the node labels placed below the node shapes. Examples are shown for Graphical Summary (left) and for an interaction network in which the data values also displayed (right).
Expand your research with support for the upload of new species datasets 
Now you can analyze datasets from a wider range of species in IPA, including crab-eating macaque, pig and Chinese hamster ovary (CHO) cells. IPA now supports an additional 11 species for a total of 25 supported species.
See this help article for the full list of 25 species.
Take advantage of inferred disease and phenotype networks with Search
A large library of Disease and Phenotype Networks has been created by leveraging an unsupervised machine learning (ML) model of the literature-derived QIAGEN Knowledge Graph (QKG). 

Each network in the collection focuses on a single disease or phenotype and contains key genes and impacted biological functions, as well as relationships between them that drive the condition. In addition, a colored pattern of predicted activation is overlaid to show how the activation or inhibition of genes leads to the disease. The ML algorithm prioritizes the most important genes and functions and generates networks of reasonable sizes (~50 nodes on average) that provide a good overview in a comprehensible manner. As shown in Figure 5, you can search for these networks in the Pathways and Lists tab using terms, such as diseases, phenotypes or gene names. 
Figure 5. Discover IPA’s recently developed Disease and Phenotype networks via Search. In the top panel, the disease term "cholestasis" has been used to search, whereas, in the bottom panel, the gene name "ABCB4" has been used. Each row in the results is a specific network. Clicking a blue hyperlink in the result will open the corresponding network.
Speed up your work by opening Canonical Pathways directly from links in Gene Views
Clicking a Canonical Pathway name in a Gene View will now open the pathway in the IPA client, as shown in Figure 6. The gene of interest will be highlighted in the opened pathway.
Figure 6. Canonical Pathway links on Gene Views. Clicking a link opens the pathway diagram and highlights the corresponding gene of interest (i.e., the gene represented by the clicked Gene View).
Reduced runtimes for Core Analysis
With this update, Core Analyses complete more quickly than before. Improving the performance of IPA is an important issue to the IPA team at QIAGEN, and this is the first of several performance improvements in upcoming releases.
Option to turn Molecule Activity Predictor (MAP) off by default
Now you can turn off MAP prediction by default (Preferences > Application Preferences > Graph Appearance). MAP is normally turned on by default, so that, if you open a pathway from an analysis, the MAP color overlay is automatically enabled. Now you can turn off MAP prediction globally and still have the option to use it in an “on demand” fashion with the MAP option in the Overlay menu.
Single sign on availability
The latest version of the IPA client launcher (https://analysis.ingenuity.com/pa/installer/select) enables signing on using your institution’s single sign on (SSO) service. Using SSO means that you no longer need to maintain a separate password for IPA or perform multi-factor authentication (e.g., requesting an emailed code) — you simply use the same institutional password that you are already using at work for other systems. This is a free (no cost) service. For more information, visit https://apps.ingenuity.com/ingsso/ssoInstructions.
Content updates
Explore new areas with 10 new Canonical Pathways

  • CDX Gastrointestinal Cancer Signaling Pathway
  • Immunogenic Cell Death Signaling Pathway
  • Macrophage Classical Activation Signaling Pathway
  • MicroRNA Biogenesis Signaling Pathway
  • Multiple Sclerosis Signaling Pathway
  • Pathogen-Induced Cytokine Storm Signaling Pathway
  • Ribonucleotide Reductase Signaling Pathway
  • Role of Chondrocytes in Rheumatoid Arthritis Signaling Pathway
  • Role of Osteoblasts in Rheumatoid Arthritis Signaling Pathway
  • Role of Osteoclasts in Rheumatoid Arthritis Signaling Pathway
>450,000 new findings (bringing the total in IPA to over 10.3 million)

  • ~45,000 Expert findings
  • ~400,000 cancer mutation findings from ClinVar
  • ~18,000 protein–protein interaction findings from BioGrid
  • ~2100 target-to-disease findings from ClinicalTrials.gov
  • ~1900 drug-to-disease findings from ClinicalTrials.gov
  • ~800 Gene Ontology findings
  • ~300 protein–protein interaction findings from IntAct
  • ~180 gene to disease or phenotype associations from the Mouse Genome Database (MGD or "Jax”)
  • ~40 chemical to cancer findings from the Chemical Carcinogenesis Research Information System (CCRIS)
  • ~175 newly mappable chemicals
118,293 expression datasets are now available (6,858 added)
This release offers a new source of data, ENCODE RNA binding, which contains RNA-seq experiments of 1122 samples for two popular cell lines (K562 and HEPG2) after shRNA knockdown targeting various proteins (Van Nostrand, E.L., et al. (2020) Nature 583:711; https://www.nature.com/articles/s41586-020-2077-3):
  • RNA-binding proteins
  • Transcription factors
  • Cofactors
  • DNA repair proteins
  • Chromatin remodeler proteins
  • RNA-polymerase complex
  • DNA replication proteins
Read more here about the latest improvements to QIAGEN IPA.

 

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