Latest improvements for IPA

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What’s New in the IPA Winter 2018 Release

With Ingenuity Pathway Analysis (IPA), you can now plot gene expression in 51 different human tissues from the GTEx project via a newly-integrated, lite version of OmicSoft Land Explorer.

We are excited to introduce brand new features in the IPA Winter 2018 Release:

Explore sample-level human tissue expression through OmicSoft Land Explorer 

Now you can examine detailed expression patterns across human tissues directly from IPA’s Isoform Views. IPA now offers access to a lite version of OmicSoft Land Explorer. With this new feature, you can provide interactive plots of gene expression in 51 different human tissues from the GTEx project, for both gene level and individual splice variants. You can filter the view for a particular tissue, or filter on metadata, such as tissue donor age or gender. You can also download the detailed sample-level expression data for the gene.

IPA users can access the lite version of Land Explorer at no extra cost and does not require registration or manual sign-in. For broader access to hundreds of thousands of samples from healthy and disease tissue, please take a tour of the full OmicSoft Land Explorer (launching soon!).

Figures 1-3 demonstrate how you can access a lite version of Land Explorer via IPA for no extra cost. The figures show how the FABP4-201 isoform of FABP4 (the longest protein-coding isoform of the Fatty Acid Binding Protein 4 gene) is expressed at higher levels in adipose and breast tissues than in other tissues.

Figure 1. Navigate to sample-level human tissue expression for human genes via links in isoform view. Click the link (shown in the red box) to view  Land Explorer via the IPA web page that plots the expression of the isoforms (splice variants) of a human gene in 51 different human tissues. Gene-level expression is also available in Land Explorer.

Figure 2. View of human isoform-level expression in human tissue samples for FABP4. The underlying RNA-seq data were reprocessed by OmicSoft (a QIAGEN company) from raw fastq files obtained from the GTEx consortium, and represents the expression of the isoforms of a particular gene in >8000 samples harvested from one of 51 different human tissues. Each chart displays the expression for one human transcript ID (either RefSeq, or Ensembl as shown above) where each circle represents the quantity of RNA (in FPKM) in one particular tissue sample. The pink bars show a box plot that summarizes the distribution of FPKM in that tissue or set of tissues.

The plot can be switched to show gene-level expression as well, as shown below in Figure 3.

Figure 3: Land Explorer Views can be switched to show gene-level rather than isoform-level expression. (1) The menu at the top middle of the screen can be used to switch to “Gene FPKM” as shown. (2) There are a number of filters available as well in the Add Filter menu. (3) Note that by default the tissues are grouped into similar types. For example, there is initially just one “row” for brain as shown above. Use the Grouping menu to choose “Tissue Detail Type” to expand to show all the individual tissues.

Faster and Improved Comparison Analyses

Create and open IPA Comparison Analyses much more quickly and add statistical stringency to your Comparison Analyses with the Benjamini–Hochberg correction. B-H corrected p-values are now available for display and filtering in Canonical Pathways and Diseases and Function tabs, as shown below in Figure 5.

Figure 4: Comparison Analyses can now be created and reopened more quickly than in prior releases.

Figure 5: Benjamini-Hochberg corrected p-values are now available in Comparison Analysis for display and filtering. In both the Canonical Pathways tab and the Diseases & Functions tab, you can color the heatmap squares by B-H p-value and can use the filter as shown to hide rows that don’t meet a particular cutoff that you enter.

Content Updates

Two New Canonical Signaling Pathways

• FAT10 Cancer Signaling Pathway

• T Cell Exhaustion Signaling Pathway


~104,000 new findings (bringing the total to greater than 6.6 million findings), including:

~38,500 new Expert findings

~400 new ExpertAssist findings

~50,800 new cancer mutation disease association findings from COSMIC

~1300 new ontology findings from GO

~2100 new disease-to-target findings from

~1500 new drug-to-disease findings from

~9000 new protein-protein interactions from the BioGRID database

~700 new protein-protein interactions from the IntAct database

~160 new mouse knockout-to-phenotype findings from MGD (JAX Labs)

~150 newly mappable chemicals

Analysis Match updates

The Analysis Match repositories will be updated in IPA on Jan 4th, 2019. There will be over 3,500 new Analysis Match datasets in this release, as outlined in Table 1.

Analysis Match enhances interpretation and drives discovery by placing your dataset in the context of thousands of IPA analyses that have been processed from data from public sources using Array Suite.

Powered by IPA Advanced Analytics, Analysis Match automatically identifies the analyses of curated datasets that have significant similarities and differences, enabling you to compare results, validate interpretation and better understand causal connections between diseases, genes, and networks of upstream regulators.

Table 1: >52,000 datasets will be available in IPA Analysis Match in this release (on Jan 4th, 2019).

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