QIAGEN Ingenuity Pathway Analysis (IPA)

For modeling, analyzing, and understanding complex 'omics data


The QIAGEN Ingenuity Pathway Analysis (IPA) is intended for molecular biology applications. This product is not intended for the diagnosis, prevention, or treatment of a disease.
IPA Analysis Match NUL

Cat. No. / ID:  830018

IPA Analysis Match NUL
IPA Analysis MatchExplorer NUL

Cat. No. / ID:  836508

IPA Analysis MatchExplorer NUL


  • Analysis of gene expression/miRNA/SNP microarray data
  • Deeper understanding of metabolomics, proteomics, and RNAseq data
  • Identification of upstream regulators
  • Insight into molecular and chemical interactions and cellular phenotypes
  • Discoveries about disease processes

Product Details

IPA is an all-in-one, web-based software application that enables analysis, integration, and understanding of data from gene expression, miRNA, and SNP microarrays, as well as metabolomics, proteomics, and RNAseq experiments. IPA can also be used for analysis of small-scale experiments that generate gene and chemical lists. IPA allows searches for targeted information on genes, proteins, chemicals, and drugs, and building of interactive models of experimental systems. Data analysis and search capabilities help in understanding the significance of data, specific targets, or candidate biomarkers in the context of larger biological or chemical systems. The software is backed by the Ingenuity Knowledge Base of highly structured, detail-rich biological and chemical findings. Learn more about QIAGEN Ingenuity Pathway Analysis (IPA).


A wealth of analysis capabilities in IPA
Analysis Match

Broaden the scope of your discovery when you evaluate your dataset in the context of more than 50,000 IPA analyses. Analysis Match automatically helps you discover other IPA Core Analyses with similar (or opposite) biological results as compared to yours. These matches can help confirm your interpretation of the results or provide unexpected insights into underlying shared biological mechanisms among ostensibly unrelated datasets.>

Causal Network Analysis

Causal Network Analysis comprehensively identifies upstream molecules that control the expression of the genes in datasets. Expanding beyond direct or single-hop relationships between the upstream regulator and the target molecules in the dataset, Causal Network Analysis uncovers networks of regulators that connect to the dataset targets. Focus on the networks that are of highest relevance by scoring the resulting causal networks against molecules, diseases, or functions of interest.>

Comparison Analysis

Comparison Analysis provides quick visualization of canonical pathway score trends across dose, time, or other factors using the Comparison Analysis heat map. Prioritize by score, hierarchical cluster, or trend.>


A disease or phenotype can be rapidly profiled by understanding its associated genes and compounds. Identify genes known to be causally relevant as potential targets or identify targets of toxicity, associated known drugs, biomarkers, and pathways.>

Upstream Regulator Analysis

This analysis predicts upstream molecules, including miRNA and transcription factors, that may be causing observed gene expression changes.>

Mechanistic Networks

Mechanistic Networks automatically generate plausible signaling cascades, describing potential mechanisms of action that lead to observed gene expression changes.>

Downstream Effects Analysis

Gene expression results are used to identify whether significant downstream biological processes are increased or decreased.>

Pathway Analysis, Canonical Pathways, Overlapping Pathways, Pathway Import, and scoring

These analyses are used to determine the most significantly affected pathways.>

Comparison Analysis

Comparison Analysis determines the most significant pathways, upstream regulators, diseases, biological functions, and more, across time points, dose, or other conditions.>

Network Analysis

Build and explore transcriptional networks, miRNA–mRNA target networks, phosphorylation cascades, and protein–protein or protein–DNA interaction networks. Identify regulatory events that lead from signaling events to transcriptional effects. Understand toxicity responses by exploring connections between drugs or targets and related genes or chemicals. Edit and expand networks based on the molecular relationships most relevant to the project.>

Regulator Effects

Provides insights into your data by integrating Upstream Regulator results with Downstream Effects results to create causal hypotheses that explain what may be occurring upstream to cause particular phenotypic or functional outcomes downstream.>

Phosphorylation Analysis

Changes in the phosphorylation states of proteins provide an important regulatory mechanism in mammalian cells. Discover upstream regulators and causal network master regulators that may be driving the changes in phosphorylation levels of the proteins in your phosphoproteomics dataset. Visualize how the phosphorylated proteins affect Canonical Signaling pathways. These results provide testable hypotheses by identifying potential signaling cascades from the phosphorylation patterns in your dataset.>


IsoProfiler helps you to identify and prioritize isoforms having interesting biological properties relevant to your research. Find genes with RNA transcripts having unusual pattern(s) of expression such as isoform switching or with known diseases or functions. Or use the fully integrated human GTEx expression data to identify transcripts with known tissue-specific expression. Visualize isoform-level expression in one or across multiple datasets.>

microRNA Target Filter

This filter reduces the number of steps it takes to confidently, quickly, and easily identify mRNA targets by allowing examination of miRNA–mRNA pairings, exploration of related biological context, and filtering based on relevant biological information as well as the expression information. The microRNA Target Filter provides insights into the biological effects of miRNAs, using experimentally validated interactions from TarBase and miRecords, as well as predicted miRNA–mRNA interactions from TargetScan. Additionally, Ingenuity IPA includes a large number of miRNA-related findings from the peer-reviewed literature.>

Toxicity Lists and Toxicity Functions

Toxicity Functions and Toxicity Lists link experimental data to clinical pathology endpoints, enable understanding of pharmacological response, and support mechanism-of-action and mechanism-of-toxicity hypothesis generation.>

Molecule Activity Predictor (MAP)

MAP enables interrogation of sub-networks and canonical pathways and hypothesis generation by selecting a molecule of interest, indicating up or down regulation, and simulating directional consequences of downstream molecules and the inferred activity upstream in the network or pathway.>

Isoform View

Using Isoform view, the biological implications of high-throughput RNAseq data become clear. Significantly regulated isoforms in your experiment can be identified and their potential impact determined using information about functional protein domains and isoform-specific literature.>

Gene and ChemView

Search and explore capabilities in Ingenuity IPA provide access to the most current findings on genes, drugs, chemicals, protein families, normal cellular and disease processes, and signaling and metabolic pathways.>

Biomarker Filter

This filter rapidly identifies the best biomarker candidates based on biological characteristics most relevant to the discovery study.>

Path Designer

Path Designer transforms networks and pathways into publication-quality pathway graphics rich with color, customized text and fonts, biological icons, organelles, and custom backgrounds. Expand and explore pathways using the high-quality content stored in Ingenuity IPA.


Unparalled database knowledge
IPA leverages the Ingenuity Knowledge Base, a repository of expertly curated biological interactions and functional annotations created from millions of individually modeled relationships between proteins, genes, complexes, cells, tissues, drugs, and diseases. These modeled relationships include rich details, links to the original article, and are reviewed for accuracy by Ph.D. scientists. The curated content in the Knowledge Base is structured into an ontology that allows for contextual information, computation by the applications, and synonym resolution to ensure consistency across concepts. These features make the Ingenuity Knowledge Base distinctive and unparalleled by any other database.


IPA helps to uncover the discovery behind the data in:
  • Transcriptomics
  • Biomarker discovery
  • miRNA research
  • Toxicogenomics
  • Metabolomics
  • Drug repositioning
  • Proteomics
  • Causal network analysis


Safety Data Sheets (1)
Certificates of Analysis (1)