Canonical pathways
Compares your data with expert-curated signalling and metabolic pathways and predicts which of them are activated or inhibited.
Pathway analysis · curated knowledge base
QIAGEN IPA — the biology behind your omics data
QIAGEN IPA is a platform for the analysis and biological interpretation of omics data, using an expert-curated database of genes, biological pathways, regulators and molecular interactions. It helps explain which biological processes and regulatory mechanisms lie behind the changes observed in the analysed data.
- 25+ years Of expert literature curation
- 21 Analysis types
- 250K+ Public datasets for comparison
- 40K+ Users worldwide
What IPA is
From a gene list to biological meaning and explanation
IPA is a platform for the downstream biological interpretation of omics experiment results. It works with lists of genes, proteins or metabolites and connects them to biological pathways, regulators, diseases and drugs. In this way, it helps answer the question of what may lie behind the changes observed in the analysed data.
QIAGEN has been building the IPA database and software for more than 25 years. Clinical and scientific experts continuously review the published literature and record experimentally supported relationships between molecules. Computational models of biological pathways and their interactions then run on top of this database.
IPA in numbers
The scale of the knowledge base and analyses
- 25+ years Of expert literature curation
- 21 Analysis types
- 250K+ Public omics datasets for comparison
- 10K+ Biological pathways, regulators and functions
Analysis types
How IPA works with data
IPA works with RNA-seq, proteomics, phosphoproteomics, metabolomics and microarray data. Even a simple list of genes or molecules is enough for an analysis.
Upstream regulators
Identifies transcription factors, kinases, drugs and other regulators that may explain the observed changes in the data.
Causal relationships
Builds hierarchical regulatory networks and identifies master regulators acting through downstream regulatory cascades.
Diseases and biological functions
Shows which biological functions, diseases and toxicological processes may be affected in the analysed data.
Dataset comparison
Compares several experiments at once and shows the differences in predicted pathways and biological processes.
Analysis Match
Compares your result with more than 250,000 publicly available omics datasets and finds similar profiles.
Why IPA
What IPA brings to the lab
Experimentally supported relationships
The content is based on expert review of original publications, not just on automated text mining. Individual relationships are supported by experimental data.
Activity prediction, not just a list of pathways
IPA does not just determine whether a given pathway or function is affected. It also predicts whether it is activated or inhibited.
No coding required
Analyses can be run in a graphical interface without writing code. The outputs can be used directly in presentations or publications.
Multiple omics data types
RNA-seq, proteomics, phosphoproteomics, metabolomics and microarray data in a single analysis environment.
Linked to public data
Your own results can be compared with a large collection of public datasets to see whether they match known biological patterns or differ from them.
A widely cited tool
IPA has been cited in tens of thousands of scientific publications, including papers in Nature, Cell, Science and PNAS.
What is new
Artificial intelligence in IPA
IPA Investigator (beta)
An assistant built directly into IPA Desktop. It answers biological questions, explains results and carries out further analysis steps on request.
IPA Interpret
A web interface with AI-generated summaries. Results can be shared, and visualisations can be customised for presentations or publications.
Single Cell Experiment Viewer
The predicted activity of biological pathways, regulators and functions can be displayed directly at the level of individual cells.
Applications
Main areas where IPA is used
Drug discovery
Identification of therapeutic targets, elucidation of mechanisms of action and prediction of adverse effects from transcriptomic data.
Cancer research
Analysis of biological pathways and regulators associated with a tumour phenotype, and identification of candidate biomarkers.
Translational research
Linking results from model systems to patient samples and comparing biological patterns across datasets.
Toxicology
Analysis of toxicological functions and biological pathways in safety studies, including phosphoproteomic data.
Single-cell analysis
Biological interpretation of single-cell data at the level of individual cells and cell populations.
MicroRNA and epigenetics
Analysis of microRNAs, their target genes and their links to measured gene expression.
Let's get started
Would you like to try IPA?
We would be happy to demonstrate IPA on your own data, walk you through the analysis types and arrange trial access and training.