Explore new Canonical Pathways related to two important research topics Enhance your research and discovery of the mechanisms driving the development of lupus and cancer immunotherapy with these new pathway maps:
Now from the Overlay tool, you can search for analyses and datasets to overlay onto networks and pathways, rather than by manually browsing in the Project Manager tree. Furthermore, Analysis Match analyses and datasets from OmicSoft are included in the search results for users with an Analysis Match QIAGEN IPA license.
This workflow enables rapid visual assessment of any analysis or dataset on the pathway or network of interest. The figure below shows the Interferon Signaling Pathway overlain with expression data from mouse lung infected with an Influenza A virus vs. uninfected lung (analysis from GEO dataset GSE36328 as processed by OmicSoft for Analysis Match).
In this release, many more columns in the Core Analysis tabs are filterable, which will help you narrow down and focus your results. Furthermore, now you can use the less than (<) or greater than (>) symbols to tailor the results.
The new filters can be found in the following tabs: Upstream Analysis, Diseases & Functions, Regulator Effects, Networks, and Molecules. Some of the filters also appear in Comparison Analyses.
Metadata values that differ between case and control are now displayed in a table at the top of the metadata panel in the Project Search results. An example is shown in the figure below.
Also, specific fields that are important in understanding the dataset (such as the organism, tissue and platform) have been extracted into a small section called “Comparison Context” that follows the case/control table.
If you have chosen to use the standard QIAGEN IPA case and control metadata keys for your datasets, they will also be automatically displayed in a table and placed into the Comparison Context section.
We are excited to introduce brand new features in the QIAGEN IPA Spring 2019 Release:
QIAGEN IPA can now improve your success of mapping identifiers in your datasets by evaluating more than one column of gene or chemical IDs. Assign up to five columns of IDs, and QIAGEN IPA will scan from left to right across the columns of identifiers and stop (for that row) when it successfully maps an ID.
Mapping across multiple columns of IDs is especially valuable in the case of metabolite (chemical) IDs. Figure 1 shows a dataset during the upload process with four columns of metabolite IDs, which resulted in more rows being mapped than when using any one identifier column alone.Figure 1: Assigning multiple columns of IDs during upload to increase mapping coverage. This dataset has four columns of IDs that are assigned for mapping. The dataset summary tab is automatically updated each time a new ID column is assigned and its source(s) chosen. In this example, 344 chemicals (rows) were mapped using only one column (HMDB), but when all of the ID columns (HMDB, PubChem, CAS, and KEGG) were used together, QIAGEN IPA scanned from left to right and was able to map 379 of the rows.
QIAGEN IPA now calculates a Benjamini-Hochberg (B-H) corrected p-value for Upstream Regulators and for Causal Networks, increasing the statistical stringency of these results in Core Analyses. The B-H p-value corrects for multiple testing-- the fact that the more statistical tests you run, the greater the chance that you will observe a false positive result. Figure 2 shows the Upstream Regulator tab in a Core Analysis with the new B-H column. Note that these new p-values won’t be present for any analysis that you have run prior to this release. Please re-run previous analyses to calculate the values.Figure 2: Upstream Regulator tab now has an optional “B-H corrected p-value column”. The column is not shown by default, and you must click the Customize Table button, then tick the B-H corrected p-value checkbox to display the column. In this example, note that the B-H p-values for these regulators are at a B-H statistical significance of ~ 0.01, whereas the standard p-value are approximately three orders of magnitude more significant.
B-H p-values have been available in QIAGEN IPA for Canonical Pathways and for Diseases and Functions for several years, however, the values were not easily accessible for the latter. An optional B-H column is now available in the Diseases & Functions tab as shown below:Figure 3: The Diseases & Functions tab now has an optional “B-H corrected p-value column” in the table. The column is not shown by default, and you must click the Customize Table button then tick the B-H p-value checkbox to display the column.
The Diseases & Functions TreeMap can be visualized using the B-H corrected p-value. The rectangles can be colored by and/or sized by the -log of the B-H p-value, as shown below in Figure 4.Figure 4: The Diseases & Functions TreeMap can be visualized using the B-H corrected p-value. Use the menus (highlighted above) to color and/or size the heatmap by the -log of the B-H p-value.
The B-H statistics are also available in Comparison Analysis for your analyses that are run (or re-run) after this release, and are calculated for all Analysis Match analyses as well.
The Help menu in QIAGEN IPA now has a quick link to a set of video tutorials to help you get started with how to use QIAGEN IPA. The topics range from how to format and upload your data, how to analyze your data, and how the p-values in QIAGEN IPA are calculated:
We are excited to introduce brand new features in the QIAGEN IPA Winter 2018 Release:
Now you can examine detailed expression patterns across human tissues directly from QIAGEN IPA’s Isoform Views. QIAGEN 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.
QIAGEN 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 QIAGEN 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 QIAGEN 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.
Create and open QIAGEN 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.
• FAT10 Cancer Signaling Pathway
• T Cell Exhaustion Signaling Pathway
~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 ClinicalTrials.gov
~1500 new drug-to-disease findings from ClinicalTrials.gov
~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
The Analysis Match repositories will be updated in QIAGEN 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 QIAGEN IPA analyses that have been processed from data from public sources using Array Suite.
Powered by QIAGEN 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:>52,000datasets will be available in QIAGEN IPA Analysis Match in this release (on Jan 4th, 2019).
QIAGEN IPA Core Analysis now opens much more quickly! Just double click the analysis icon as usual and the analysis will open into a ready-state much faster than in prior releases.
Note: A change has been made in the information that is displayed in the molecules tab. The tab now lists all of the molecules in the original dataset and indicates (in bold in the Symbol column) those that are "analysis ready", meaning they passed filters and cut-offs and were therefore submitted for analysis.
There are nearly 1,800 new Analysis Match datasets in this release (see below). Analysis Match enhances interpretation and drives discovery by placing your dataset in the context of thousands of QIAGEN IPA analyses that have been processed from public sources using Array Suite. Powered by QIAGEN 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.
QIAGEN IPA can now predict metabolic activities in a dataset using its entire collection of more than 300 metabolic pathways. The prediction is based on the set of up and down regulated molecules in your datasets and the directionality of the metabolic pathway itself. See Figure 1 below which shows the Canonical Pathways tab in a Core Analysis, with metabolic pathways marked with red arrows. The orange color of the bars indicated they are predicted to have increased activity in this dataset.
The methodology QIAGEN IPA uses to predict the metabolic activity from a dataset in Core Analysis is described here. QIAGEN IPA can predict metabolic activity from your differential gene expression dataset, differential metabolomics dataset, or a dataset where you have concatenated both differential gene expression and differential metabolite concentrations into one “observation”.
The metabolic pathway activity scores contribute to Canonical Pathway signatures in Analysis Match*, as shown below in Figure 2.
*Analysis Match requires additional licensing. Please contact us at AdvancedGenomicsSupport@qiagen.com for info.
There are 1,100+ new analyses for Analysis Match in this release, bringing the total available in QIAGEN IPA to >8,000. This includes two new repositories, RatDisease (under DiseaseLand) and Pediatrics (under OncoLand). Table 1 compares the repositories and their respective sizes in this release versus the prior one.
Table 1: Comparison of the number of datasets and repositories in this release (green color) to the prior release (red color). There are over 1,100 new datasets and their corresponding analyses in the current release.
Analysis Match* automatically discovers other QIAGEN IPA Core Analyses with similar (or opposite) biological results as compared to yours, to help confirm your interpretation of the results or to provide unexpected insights into underlying shared biological mechanisms across experimental situations. QIAGEN IPA matches your analysis against other analyses you have created (in your Project Manager) as well as thousands of other human and mouse expression analyses curated from public sources. This “analysis-to-analysis” matching is based on shared patterns of Canonical Pathways, Upstream Regulators, Causal Networks, and Diseases and Functions.
In this release, improvements to Analysis Match enable you to more easily control which of the Lands are used in the matching, and the detailed results in the heat map are more easily interpreted and available for follow up. You can now manually add experiment metadata to your own datasets to label them more clearly in the Analysis Match table and to find them using Project Search.
Now you can annotate your uploaded datasets with information that will help you quickly find those datasets (or analyses created from them) using project search, or help you to remember details about them when interpreting the results of their analysis. This is especially useful in the context of Analysis Match, where metadata from the dataset can be displayed in columns in the Analysis Match tab.
When you upload your dataset, you can enter relevant metadata about it in the QIAGEN IPA user interface. For example, you could annotate them by leveraging existing OmicSoft fields such as “case.disease” or “case.tissue” by typing in values such as “asthma” or “lung”, or create your own custom fields to annotate. For example, you could create a new field called “eNotebook record” and enter a clickable hyperlink that points to an internal online record about the experiment that led to that dataset, or create a field called “Collaborators” and put in names of colleagues involved with that dataset. The metadata you add to a dataset is automatically propagated to any Core Analysis created from it. Keep in mind that the metadata you enter is for your purposes only, and is not used by QIAGEN IPA to influence the analysis results. Figure 6 shows how you can enter metadata for a dataset.
Metadata can be added or edited either before or after saving the dataset file. It is also possible to insert metadata at the top of the dataset text or Excel file itself before you upload it, by following instructions here. This is especially useful when you wish to enter a large amount of metadata or if you have many similarly derived datasets that have mostly the same metadata. In this release, you can edit that uploaded metadata in the metadata tab (during upload), or after saving and re-opening it.
*Analysis Match requires additional licensing. Please contact us for info.
QIAGEN IPA now gives you more flexibility to use your creativity to build and modify networks and pathways. You can globally select nodes on pathways by additional criteria to take further actions on the nodes. Specifically, you can highlight or select nodes by their overlay and by their connectivity. For example, if you have overlaid expression fold change values, you can first select only the up-regulated genes and move them all at once to a different place on the network canvas, and do the same for the down-regulated nodes. Or you can select all the unconnected nodes and delete them. Or you could highlight the most highly connected nodes in the network.
Separate up and down cutoffs must now be entered (rather than a single absolute value) for directional measurement types such as fold change or log ratio. This gives you more control over the makeup of the set of molecules that QIAGEN IPA analyzes from your dataset, as compared to using a single absolute cutoff. Figure 11 below shows an example of this.
September 30, 2017
Analysis Match* automatically discovers other QIAGEN IPA Core Analyses with similar (or opposite) biological results as compared to yours, to help confirm your interpretation of the results or to provide unexpected insights into underlying shared biological mechanisms. It matches your analysis against other analyses you have created (in your Project Manager) as well as thousands of other human and mouse expression analyses curated from public sources. This “analysis-to-analysis” matching is based on shared patterns of Canonical Pathways, Upstream Regulators, Causal Networks, and Diseases and Functions.
With this new capability, you can:
The analyses included in Analysis Match were generated in QIAGEN IPA from more than 6,000 highly curated and quality-controlled human and mouse disease and oncology datasets re-processed from SRA, GEO, Array Express, TCGA and more. These datasets were generated by QIAGEN’s recently acquired company, OmicSoft, and are the “comparisons” found in DiseaseLand and OncoLand representing various contrasts between disease and normal, treatment vs. non-treatment and much more.
Figure 1 shows the new Analysis Match tab from one of QIAGEN IPA’s Example Analyses based on the expression data derived from mouse lung exposed to welding fumes. The results in the figure have been filtered to show only the highest scoring results against all the analyses in the OmicSoft repository within QIAGEN IPA. Of the more than 6,000 in the repository, 125 analyses had an overall score of >60% or <-60%, corresponding to strongly similar or dissimilar patterns, respectively. You can further filter the results in a number of ways, for example by type of comparison, by disease state, tissue, and much more. The keyword filtering is possible because each analysis has been extensively annotated by OmicSoft using a controlled vocabulary which can be displayed in columns as shown in figure 1. Only a few columns are shown in QIAGEN IPA by default due to screen space limitations.
The analyses are matched based on a set of signatures that are created for each analysis, namely one signature for the Canonical Pathways, one for Upstream Regulators, one for Causal Networks, and one for Diseases and Functions. Each signature is used independently to match against other analyses, and an overall average is computed.
*Analysis Match requires additional licensing. Please contact us for info.
As shown in Figure 1, the analysis with the best overall match from the repository is an expression analysis from mouse lung exposed to heat killed influenza virus (from GSE41684), which has strong similarity across all 4 signature types. The next step is to explore the signatures themselves across all or a subset of matching analyses, to understand in more detail which “entities” (the set of upstream regulators, canonical pathways, etc.) drove the similarity scoring. In this example, the matching analyses were further filtered to limit to the repository folder called “MouseDisease” which retained 75 of the analyses, and a heatmap was created by clicking the View as Heatmap button. Figure 2 shows this heatmap, where the rows are the entities from the four signatures with columns for the 75 similar (and dissimilar) analyses. The z-score for each entity from each analysis is represented in the cells with an orange or blue color (for positive and negative z-score respectively).
The heatmap is filterable to enable you to focus on the types of entities of interest to you. Figure 3 shows the heatmap filtered for upstream regulators which are classified as transcription regulators. The clustering of the rows reveals which transcription regulators have similar patterns across the analyses, whereas the clustering of the columns shows which analyses are most closely correlated to one another based on the underlying transcriptional regulator pattern.
The clustering of the entities (the rows) can reveal interesting similarities among the entities. For example, after removing the prior filter in order to show all the entities, Figure 4 shows that the drug bexarotene clusters closely with the “PPAR/RXR activation” canonical pathway in a larger cluster containing CR1L, ALDH1A2, SUMO1, and ABCB4. Bexarotene is an RXRA and RXRB agonist, providing a rationale why it tightly correlates with this pathway in the heatmap. SUMO1 is a regulator of PPAR activity, whereas it is not as clear why the other entities appear in this cluster, an observation which could provide interesting avenues of investigation.
You can select and send entities (except Canonical pathways) to a My Pathway for further analysis, for example to connect nodes together or to discover drugs that target them.
Another valuable way to use the OmicSoft analysis repository is to start by finding analyses of interest by using QIAGEN IPA’s Dataset and Analysis Search by entering keywords such as disease name or tissue. Figure 5 below shows a search for human asthma analyses but excluding those involving albuterol. From search results like these, you can double click to open an analysis, or select up to 20 to visualize in a full comparison analysis.
The repository of datasets and analyses are stored in QIAGEN IPA’s Libraries folder in the project manager as shown in Figure 6. Note that these are read-only and cannot be exported out of QIAGEN IPA.
Analysis Match combines literature-powered causal analytics from QIAGEN IPA with a massive dataset collection provided by OmicSoft, creating a unique opportunity for you to make biological discoveries.
Changes in the phosphorylation states of proteins provide an important regulatory mechanism in mammalian cells. Now you can get more from your phosphoproteomics datasets in QIAGEN IPA with a new Phosphorylation Core Analysis*.
Discover upstream regulators and causal network master regulators that may be driving the changes in phosphorylation levels of the proteins in your phosphoproteomics dataset. These results provide testable hypotheses by identifying potential upstream signaling cascades from the phosphorylation patterns in your dataset.
To illustrate this new feature, we analyzed a phosphoproteomics experiment obtained from the literature, in which insulin was applied to starved mouse adipocytes that had been differentiated from 3T3-L1 cells in vitro (PMC3690479). Phosphorylated proteins were isolated from the cells by the authors during a time course of 15 seconds to 1 hour.
As shown below in Figure 1, after 15 seconds of insulin exposure, a characteristic phosphorylation pattern is established in these adipocytes highlighted by the fact QIAGEN IPA predicts insulin (gene symbol Ins1 below) as one of the top predicted upstream regulators which is activated.Fig 1. Upstream Regulator Analysis. The pattern of differentially phosphorylated proteins in the dataset of insulin- treated cells was used to predict the responsible upstream molecules.
Figure 2 indicates there is a positive phosphorylation relationship (orange line) between Ins1 and GAB1. This is supported by a paper that showed that in differentiated 3T3-L1 cells, insulin can increase the phosphorylation of GAB1. For the relationship between Ins1 and STAT3, a different paper showed that insulin can increase the phosphorylation of Stat3 in RAW 264.7 cells (see Figure 3 below).Fig 3. Examples of phosphorylation findings curated from the literature in the QIAGEN Knowledge Base. Both indicate that insulin can increase a target protein’s phosphorylation (indirectly through unspecified mediators).
Causal Network Analysis predicts regulatory networks to explain phosphorylation changes exhibited in a dataset. Causal Network Analysis enables the discovery of novel regulatory mechanisms by expanding upstream analysis to include regulators that do not yet have known “direct” connections to the targets in your dataset.
For example, stimulating adipocytes with insulin is predicted to activate the master regulator FLT1 (also known as the vascular endothelial growth factor receptor 1) after 15 seconds of exposure. In this causal hypothesis, FLT1 is predicted to drive the activity of nine other regulators which in turn drive changes in the phosphorylation of a larger number of dataset proteins as shown below in Figure 4.Fig 4. Causal Network Analysis. FLT1 is predicted to activate or inhibit several intermediate regulators leading to the changes in phosphorylation in dataset proteins.
If you’re an existing customer, launch QIAGEN IPA from your desktop and check out the new features. If you need to install QIAGEN IPA, click here.
Changes in the phosphorylation states of proteins is an important regulatory mechanism in cells. Now you can get more from your phosphoproteomics datasets in QIAGEN IPA with improvements to phosphorylation data upload and visualization.
Last September the QIAGEN IPA Fall Release added a new data type to QIAGEN IPA to support the upload of protein or gene IDs along with corresponding phosphorylation increases or decreases represented as fold change (or log ratio). With this December release you can now upload the corresponding individual phospho sites for display on networks and pathways. These can be represented with any text you wish; such as the actual phosphorylated peptide, e.g. _FSSS(ph)QPEPR_ as shown in Figure 1 below, just a residue number (e.g. Y347), or any combination of text and numbers.
1) Visualize multiple differentially phosphorylated sites (phospho peptides) on networks and pathways.
2) Easily identify the proteins on networks and pathways where QIAGEN IPA predicts that increases in phosphorylation inhibits their activity or where decreases in phosphorylation increases their activity. The activity of certain proteins is more likely to be inhibited by phosphorylation than activated by it. In the example below the Molecular Activity Predictor, with overlaid phospho data, indicates this by using blue or orange halos to indicate the predicted activity.
Get more from your phosphoproteomics datasets in QIAGEN IPA. If you’re an existing customer, launch QIAGEN IPA from your desktop and check out the new features. If you need to install QIAGEN IPA, click here.
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.
IsoProfiler is available in QIAGEN IPA with Advanced Analytics.
Enhance your multi-omics research approaches by uploading simplified phosphoproteomics datasets to QIAGEN 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 QIAGEN 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.
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 QIAGEN 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.
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.
Drill-down into the “IsoProfiler Findings” view to explore the details about the isoforms that have disease or biological function findings captured from the literature. This is done by selecting rows (or all rows) in the top table and clicking the IsoProfiler Findings button at the top of the table. This will open a special window as shown in Figure 3. Only isoforms with disease or function associations will appear in this window. This table enables filtering on findings-level details using the funnels, or filters, above each column.
IsoProfiler is part of Advanced Analytics.
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 QIAGEN IPA networks and pathways.
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.
QIAGEN 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.
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.
Quickly see which diseases, functions, and phenotypes are associated with differentially expressed isoforms in your RNA-seq experiment using QIAGEN 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. 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.
Import genetic gain/loss information for a set of genes and predict the variant effect on diseases, functions, phenotypes and canonical pathways. QIAGEN 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.
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.