Ingenuity Pathway Analysis (IPA)

QIAGEN Ingenuity Pathway Analysis (IPA) is an all-in-one, web-based software application designed for molecular biologists and bioinformaticians who need to analyze, integrate, and interpret complex multi-omics data. IPA supports data from gene expression, miRNA, and SNP microarrays, as well as metabolomics, proteomics, and RNAseq experiments, and can also handle smaller-scale experiments that generate gene and chemical lists. The platform is intended for research use in molecular biology applications and is not intended for diagnostic, preventive, or therapeutic purposes.

At the core of IPA is the Ingenuity Knowledge Base — a repository of expertly curated biological interactions and functional annotations built from millions of individually modeled relationships between proteins, genes, complexes, cells, tissues, drugs, and diseases. Each relationship is reviewed for accuracy by Ph.D. scientists, includes links to the original literature, and is structured within an ontology that enables contextual computation and synonym resolution, making it a uniquely comprehensive and reliable resource for biological discovery.

Key Features and Capabilities

• Analysis of gene expression, miRNA, and SNP microarray data
• Deeper understanding of metabolomics, proteomics, and RNAseq datasets
• Identification of upstream regulators, including miRNA and transcription factors
• Insight into molecular and chemical interactions and cellular phenotypes
• Discovery of disease processes and biological mechanisms
• Searches for targeted information on genes, proteins, chemicals, and drugs
• Building of interactive models of experimental systems

Analysis Tools and Modules

• Analysis Match: Evaluates your dataset against more than 50,000 IPA analyses to automatically discover other Core Analyses with similar or opposite biological results, helping confirm interpretations or reveal unexpected shared biological mechanisms.
• Causal Network Analysis: Comprehensively identifies upstream molecules controlling gene expression, expanding beyond direct relationships to uncover networks of regulators connecting to dataset targets, with scoring against molecules, diseases, or functions of interest.
• Upstream Regulator Analysis: Predicts upstream molecules, including miRNA and transcription factors, that may be causing observed gene expression changes.
• Mechanistic Networks: Automatically generates plausible signaling cascades describing potential mechanisms of action leading to observed gene expression changes.
• Downstream Effects Analysis: Uses gene expression results to identify whether significant downstream biological processes are increased or decreased.
• Comparison Analysis: Provides quick visualization of canonical pathway score trends across dose, time, or other experimental factors using heat maps, with prioritization by score, hierarchical cluster, or trend.
• Pathway Analysis: Includes Canonical Pathways, Overlapping Pathways, Pathway Import, and scoring to determine the most significantly affected pathways.
• Network Analysis: Builds and explores transcriptional networks, miRNA–mRNA target networks, phosphorylation cascades, and protein–protein or protein–DNA interaction networks, with editing and expansion capabilities.
• Regulator Effects: Integrates Upstream Regulator results with Downstream Effects results to create causal hypotheses explaining upstream causes of particular phenotypic or functional downstream outcomes.
• Phosphorylation Analysis: Discovers upstream regulators and causal network master regulators driving changes in phosphorylation levels, and visualizes how phosphorylated proteins affect canonical signaling pathways.
• BioProfiler: Rapidly profiles a disease or phenotype by identifying associated genes, compounds, potential targets, toxicity targets, known drugs, biomarkers, and pathways.
• IsoProfiler: Identifies and prioritizes isoforms with interesting biological properties, including isoform switching, disease or function associations, and tissue-specific expression using integrated human GTEx data.
• microRNA Target Filter: Identifies mRNA targets by examining miRNA–mRNA pairings using experimentally validated interactions from TarBase and miRecords, as well as predicted interactions from TargetScan, supplemented by miRNA-related findings from peer-reviewed literature.
• Toxicity Lists and Toxicity Functions: Links experimental data to clinical pathology endpoints, supports understanding of pharmacological response, and aids mechanism-of-action and mechanism-of-toxicity hypothesis generation.
• Molecule Activity Predictor (MAP): Enables interrogation of sub-networks and canonical pathways by selecting a molecule of interest, indicating up or down regulation, and simulating directional consequences throughout the network.
• Isoform View: Clarifies the biological implications of high-throughput RNAseq data by identifying significantly regulated isoforms and determining their potential impact using functional protein domain information and isoform-specific literature.
• Gene and ChemView: Provides search and exploration access to current findings on genes, drugs, chemicals, protein families, cellular and disease processes, and signaling and metabolic pathways.
• Biomarker Filter: Rapidly identifies the best biomarker candidates based on biological characteristics most relevant to the discovery study.
• Path Designer: Transforms networks and pathways into publication-quality graphics with customizable color, text, fonts, biological icons, organelles, and custom backgrounds.

Application Areas

• Transcriptomics
• Biomarker discovery
• miRNA research
• Toxicogenomics
• Metabolomics
• Drug repositioning
• Proteomics
• Causal network analysis

IPA is delivered as a web-based platform, making it accessible without local installation. It is available in multiple configurations, including IPA Analysis Match and IPA Analysis MatchExplorer, to suit different research needs. The software is backed by the continuously updated Ingenuity Knowledge Base, ensuring researchers have access to the most current and rigorously curated biological and chemical findings to support their discovery workflows.