Backed by an extensive, human-curated knowledge base
Analyze and compare vast amounts of data from a single platform
24/5 support from a team of PhD-level scientists ready to help
Your pathway analysis program should empower you to extract the insights you need for publishing.
IPA has all the tools you need, so you don't have to look for anything.
See and understand your data with just a few clicks.
Explore new biological themes and connections in your data
Reveal which pathways are enriched, activated or inhibited and find affected pathways, patterns of gene expression and changes in pathway activity in your data
Visualize trends and similarities across your IPA analyses as heatmaps
Discover the major biological themes entities in your IPA Core Analysis, creating an easily understood synopsis built with ML techniques. It also infers new connections between entities in our QIAGEN Knowledge Graph. Visualize the relationships between canonical pathways, upstream regulators, diseases and biological functions.
Find significantly affected pathways, patterns of gene expression and changes in pathway activity in your biological data. Analyze your data in the context of established signaling and metabolic pathways. Determine which pathways are predicted to be activated or inhibited.
Creates visualizations across multiple analyses that can help you identify trends, similarities and differences. Examine your Canonical Pathway, Upstream Regulators, Causal Networks and Downstream Effects results to find changes in transcription factors or clusters of biological functions. Prioritize by score, trend and more.
Don't just look at correlation: find what's causing the changes you see.
Predict which upstream regulators are activated or inhibited to explain changes in gene expression and their causes
Explore how changes in gene expression may increase or decrease diseases and biological processes
Predict the toxicological effects in your data
Create causal hypotheses that explain biological outcomes
Identify key regulators and novel regulatory mechanisms
Predict the effects of molecules based on changes in activity of neighboring molecules
Identify upstream regulators that can explain observed gene expression changes. IPA takes these results even further by showing which regulators may be working together in Mechanistic Networks. Unlike other tools, IPA predicts which regulators are activated or inhibited. Visualize networks of regulators and targets and explain how they interact, providing testable hypotheses for gene regulatory networks.
Tox Lists and Tox Functions (IPA-Tox) help you link experimental data with clinical pathology endpoints to understand pharmacological response and support hypothesis generation.
Our hand-curated Tox Lists include molecules that are known to be involved in renal, hepatic and cardiac toxicity. They represent critical biological processes and key toxicological responses such as adaptive, defensive or reparative responses to xenobiotic insult, including molecules associated with response to mitochondrial toxicants.
Tox Functions catalog the molecules and causes associated with toxicity endpoints and phenotypes. They include clinical chemistry and hematology assays, associations with organ injuries, and pathway-related endpoints such as peroxisomal proliferation, phospholipidosis, mitochondrial function and hyperbilirubinemia.
Identify upstream regulators that can explain observed gene expression changes. IPA takes these results even further by showing which regulators may be working together in Mechanistic Networks. Unlike other tools, IPA predicts which regulators are activated or inhibited. Visualize networks of regulators and targets and explain how they interact, providing testable hypotheses for gene regulatory networks.
Discover novel regulatory mechanisms by expanding upstream analysis to include regulators that are not directly connected to targets in the dataset. Examine multi-level regulatory networks that can explain the expression changes in a dataset and regulatory networks associated with your gene, chemical, disease or biological function of interest.
Interrogate sub-networks and Canonical Pathways and generate hypotheses by applying expression data or manually indicating up- or down-regulation and simulating directional consequences of downstream molecules and inferred upstream activity.
