Model, analyze, and understand the complex biological and chemical systems at the core of life science research with IPA

Ingenuity IPA Interpret Biological Meaning Graphic

Peer Reviewed and Widely Adopted

QIAGEN’S Ingenuity Pathway Analysis (IPA) has been broadly adopted by the life science research community and is cited in thousands of peer-reviewed journal articles.

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Understand Complex ‘Omics Data

IPA helps you understand complex ‘omics data at multiple levels by integrating data from a variety of experimental platforms and providing insight into the molecular and chemical interactions, cellular phenotypes, and disease processes of your system.

Understand Complex 'Omics Data Graphic - Ingenuity IPA

Discover Causal Connections. Faster. NEW

Causal Network Analysis generates plausible regulatory networks which may explain the gene expression changes in your dataset. Discover novel regulators of the target molecules as these small hierarchical networks do not require all molecules have direct connection to the dataset.

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Advanced Features NEW

Powerful features in IPA, Causal Network Analysis, BioProfiler and My Findings will help you understand causal connections between diseases, genes networks of upstream regulators and leverage your own domain expertise and proprietary curation efforts.

Advanced Features- Ingenuity IPA

Unlock the Value of your Experiments

Analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays; metabolomics, proteomics, and RNA-Seq experiments; and small-scale experiments that generate gene and chemical lists with IPA.

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See What Customers Are Saying About IPA

"In our study, we coupled analysis with QIAGEN’S Ingenuity Pathway Analysis (IPA) and microarray data to evaluate the impact of exercise and muscle aging by measuring the variation in the products being made. The analysis determined that the genetic regulators of age-related genes were distinct and unrelated to the regulators of exercise-influenced genes. Using IPA, we were able to capture the relevant networks more easily and with far less cost than traditional methods."

Dr. James A. Timmons, Loughborough University, United Kingdom

Picture of David Crockett, Director, Bioinformatics, ARUP Laboratories
Causal Network
Market Leading Pathway Analysis
Unlock the insights buried in experimental data by quickly identifying relationships, mechanisms, functions, and pathways of relevance.
Causal Network
Predictive Causal Analytics
Powerful causal analytics at your fingertips help you to build a more complete regulatory picture and a better understanding of the biology underlying a given gene expression study.
RNA Seq Data Analysis
NGS/RNA-Seq Data Analysis
Get a better understanding of the isoform-specific biology resulting from RNA-Seq experiments.
Starting with FASTQ files? We’ve partnered with Maverix Biomics
Maverix icon

Quickly analyze your data to identify key insights with IPA

transcriptonomics
Transcriptomics
IPA can help with almost any transcriptomics-related question or application
biomarker
Biomarker Discovery
Identifies the most promising and relevant biomarker candidates within experimental datasets
microrna research
microRNA Research
Combines filtering tools and microRNA-mRNA content to provide insight into the biological effects of microRNAs
Toxigenomics
Toxicogenomics
Delivers a focused toxicity and safety assessment of candidate compounds, and provides a more complete understanding of pharmacological response, drug mechanism of action, and mechanism of toxicity
metabolomics
Metabolomics
Overcomes the metabolomics data analysis challenge by providing the critical context necessary to gain biological insight into cell physiology and metabolism from metabolite data
drug repositioning
Drug Repositioning
Expression profiling of approved drugs and comparison to profiles of diseased tissue can lead to discovery of new uses for these already approved entities
proteomics
Proteomics
Perform a comprehensive analysis of your proteomics for a deep understanding of proteins and related biological processes
target discovery
Target Discovery
Genes that are shown to be activated in a pathological condition may serve as promising targets for therapeutic development efforts

The Ingenuity Knowledge Base

knowledge base graphic

All of Ingenuity’s solutions leverage 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, or Findings, include rich details, links to the original article, and are reviewed for accuracy by PhD 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 to any other database.

IPA has a broad set of features that allow you to quickly understand and visualize your data

My Findings*

Leverage your domain expertise to customize QIAGEN's Ingenuity Pathway Analysis (IPA) content for a specific disease or therapeutic area of interest from your institution's internal curation efforts.

Regulator Effects new

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.

Comparison Analysis new

Quickly visualize trends and similarities across analyses using heat maps for Canonical Pathway, Downstream Effects, Upstream regulators and Causal Network Analyses. Prioritize by score, hierarchical cluster or trend.

Causal Network Analysis*

The new Causal Network Analysis provides a comprehensive approach to identifying upstream molecules that control the expression of the genes in your datasets. Expanding beyond "direct" or "single hop" relationships between the upstream regulator and the target molecules in the dataset, Causal Networks 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 or diseases/functions of interest.

Disease View

Provides details associated with the disease or biological function such as molecules associated with that disease or function, known drug targets, drugs known to target those molecules, and more.

Interactive Disease and Functions Nodes

Interactive visual exploration of causality between molecules and disease, function, or phenotypes from a network or My Pathway.

BioProfiler*

Quickly profile a disease or phenotype 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

Predict upstream molecules, including microRNA and transcription factors, which may be causing the observed gene expression changes.

Mechanistic Networks

Automatically generate plausible signaling cascades describing potential mechanism of action leading to observed gene expression changes.

Downstream Effects Analysis

Identify whether significant downstream biological processes are increased or decreased based on gene expression results.

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

Pathway Analysis, Canonical Pathways, Overlapping Pathways, Pathway Import and scoring. Determine most significantly affected pathways.

Network Analysis

Build and explore transcriptional networks, microRNA-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.

MicroRNA Target Filter

Reduce the number of steps it takes to confidently, quickly, and easily identify mRNA targets by letting you examine microRNA-mRNA pairings, explore the related biological context, and filter based on relevant biological information as well as the expression information.  The microRNA Target Filter in IPA provides insights into the biological effects of microRNAs, using experimentally validated interactions from TarBase and miRecords, as well as predicted microRNA-mRNA interactions from TargetScan. Additionally, IPA includes a large number of microRNA-related findings from the peer-reviewed literature.

Tox Lists and Tox Functions

IPA-Tox uses Toxicity Functions in combination with Toxicity Lists to link experimental data to clinical pathology endpoints, understand pharmacological response, and support mechanism of action and mechanism of toxicity hypothesis generation.

Molecule Activity Predictor (MAP)

Interrogate sub-networks and Canonical Pathways and generate hypotheses 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

Quickly move beyond statistical analysis of high-throughput RNA-Seq data to understand the biological implications of your data.  Identify significantly regulated isoforms in your experiment and determine their potential impact using information about functional protein domains and isoform-specific literature

Gene and ChemView

IPA's Search & Explore capabilities offer researchers access to the most current Findings available on genes, drugs, chemicals, protein families, normal cellular and disease processes, and signaling and metabolic pathways.

Biomarker Filter

Rapidly identify the best biomarker candidates based on biological characteristics most relevant to the discovery study.

Path Designer

Transform your networks and pathways in IPA 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 IPA.

*Available for an additional cost.
 

Advanced Analytics for Scientists

Powerful features in recent releases of QIAGEN's Ingenuity Pathway Analysis (IPA), Causal Network Analysis, BioProfiler and My Findings will help you understand causal connections between diseases, genes networks of upstream regulators and leverage your own domain expertise and proprietary curation efforts.

BioProfiler*:

  • Quickly identify genes known to be causally relevant to a diseases or phenotype as potential drug targets or as targets of toxicity.
  • Find, filter and prioritize genes and compounds by their specific relationships with diseases, phenotypes, and biological functions (e.g. ‘apoptosis’).
  • Gain unprecedented access to the underlying Ingenuity disease and process ontology enabling precise search and filtering of the diseases and phenotypes of interest to you.

BioProfiler-screen


Causal Network Analysis*:

  • Generates plausible regulatory networks that may explain the gene expression changes in your dataset. These novel upstream regulators control small hierarchical networks that do not require all the molecules to have direct connection to the dataset.
  • Scoring criteria enable you to prioritize the most interesting and relevant hypothesis and quickly visualize the regulatory networks most closely associated with your disease or phenotype of interest.
  • Prioritize resulting hypothesis by molecule, disease, function and/or phenotype of interest. And now discover more distant connections between the causal network and the scoring criteria of interest.
  • Visualize network hypothesis predicted to be responsible for observed expression changes and how it connects to the scoring criteria.
  • See which other master regulators are upstream or downstream of the hypothesis of interest. In addition, export a data matrix of master regulators vs. their targets or master regulators vs. up to 20 analyses.

causal-network-diagram

My Findings*:

  • Leverage your domain expertise to customize Ingenuity content for a specific disease or therapeutic area of interest from your institution’s internal curation efforts.
  • Import your own proprietary molecule-to-molecule and molecule-to-(disease or function) relationships and use them throughout IPA including causal analytics.
  • Maintain these relationships in a secure proprietary database dedicated to your company or institution.

myfindings


*Available for an additional cost.

IPA is useful in a wide range of applications at gaining valuable insight into your genomics data

biomarker
Biomarker Discovery
Identify the most promising and relevant biomarker candidates most relevant to the study within the experimental data.
metabolomics
Metabolomics
Contextualize your metabolomics data with rich pathway and process information from the Ingenuity Knowledge Base.
microrna research
MicroRNA Research
Identify the most biologically relevant targets in your microRNA research.
proteomics
Proteomics
Answer the biological questions the researchers you had in mind when you designed your proteomic experiment.
Toxigenomics
Toxicogenomics
Generate a focused toxicity and safety assessment of candidate compounds that are being examined using toxicogenomics approaches.
transcriptonomics
Transcriptomics
Get help with almost any transcriptomics-related question or application.
Causal Network
Causal Network Analysis
Build a more complete regulatory picture and a better understanding of the biology underlying a given gene expression study.

Explore Our Webinars and see how the scientific community has used IPA to create novel discoveries

John A. Martignetti Headshot
Garry Nolan Ph.D. Headshot

Realizing the promise of genomics through rapid innovation in biological analysis and interpretation


Presenter: John A. Martignetti, M.D., Ph.D. and Garry Nolan Ph.D.
Original Broadcast Date:Thursday, March 13, 2014

Discover how intuitive web-based applications can help scientists quickly analyze and accurately interpret the biological meaning in their genomic data.

In the last few years, researchers and clinicians have been faced with the challenge of how to effectively sort through and accurately understand the biological meaning from their genomic data. Now, web-based applications such as QIAGEN's Ingenuity Platform can be used to better comprehend complex biological systems, answer questions, analyze and interpret data.

In this webinar, the audience will discover how Ingenuity Pathway Analysis (IPA) and Ingenuity Variant Analysis softwares can be used to provide new insight into and quick interpretation of their scientific findings.

The speakers, John Martignetti and Garry Nolan, will discuss how these software solutions were applied to their respective research areas to more effectively search, explore, visualize, analyze and interpret biological findings related to genes, proteins and small molecules.

2013 IPA Fall Release Product Introduction Webinar [34:11 minutes]
Presented by Dr. Stuart Tugendreich, Scientific Director, IPA
See the whole picture! Powerful functionality enables you to understand causal connections between molecules and diseases. This Fall 2103 release of IPA® from Ingenuity® Systems has exciting new capabilities which enable interactive visual exploration of causality between molecules and disease, functions, or phenotypes.
Life Long Changes in DNA Methylation & ncRNAs in Fetal Alcohol Syndrome (FAS) [54:54 minutes]
Presented By: Ben Laufer, PhD Candidate, Western University, Ontario, Canada
Fetal alcohol spectrum disorders (FASDs) are characterized by life-long changes in gene expression, neurodevelopment and behavior. What mechanisms initiate and maintain these changes are not known, but current research suggests a role for alcohol-induced epigenetic changes. We assessed alterations to adult mouse brain tissue by assaying DNA cytosine methylation and small noncoding RNA (ncRNA) expression, specifically the microRNA (miRNA) and small nucleolar RNA (snoRNA) subtypes. We found long-lasting alterations in DNA methylation as a result of fetal alcohol exposure, specifically in the imprinted regions of the genome harboring ncRNAs and sequences interacting with regulatory proteins. The findings of this study help to expand on the mechanisms behind the long-lasting changes in the brain transcriptome of FASD individuals.
Leveraging Ingenuity for Predictive Systems Biology: An Approach To Broad-Spectrum, Host-Directed Drug Target Discovery In Infectious Diseases [50:33 minutes]
Presented By: Dr. Ramon Felciano, Co-founder and Senior Vice President, Applied Research and Partnering, QIAGEN Redwood City
Knowledge of immune system and host-pathogen pathways can inform development of targeted therapies and molecular diagnostics based on a mechanistic understanding of disease pathogenesis and the host response. We used the Ingenuity plaform to investigate the feasibility of rapid target discovery for novel broad-spectrum molecular therapeutics was investigated through comprehensive systems biology modeling and analysis of pathogen and host-response pathways and mechanisms. We used this approach to identify and prioritize candidate host targets based on strength of mechanistic evidence characterizing the role of the target in pathogenesis and tractability desiderata that include optimal delivery of new indications through potential repurposing of existing compounds or therapeutics. We will describe our approach, experimental results, and the key technology innovations now publically available in IPA.
IPA the Fast Path to Toxicity Targets of Interest [36:55 minutes]
Presented by Dr. Aaron Erdely, Health Effects Laboratory Division, NIOSH
and Kaushal Parekh, Associate Staff Ontology Engineer, QIAGEN Redwood City
Metal-rich particulate matter inhalation exposure results in target organ toxicity but also adverse systemic effects including cardiovascular dysfunction and immunosuppression. As a preliminary search for induction of systemic mechanisms, generation of comprehensive transcriptome datasets by DNA microarray, along with gene network analysis, was performed from circulating whole blood cells, aorta, and lung then compared to determine related biological signaling following inhalation exposure.
Also demonstrated are some exciting new features in the new 2013 IPA Spring Release. Learn how IPA can help you quickly filter down to specific toxicity targets of interest
IPA 2013 Spring Release [48:24 minutes]
Presented by Dr. Stuart Tugendreich, Scientific Director, IPA
The 2013 IPA Spring Release is here! Powerful new functionality enables you to upload, find, and compare datasets, and understand causal connections between diseases, genes, and networks of upstream regulators. Stuart Tugendriech, PhD, Scientific Director, IPA from QIAGEN Redwood City gives an overview of the new IPA capabilities in the release, as well as a use case utilizing the new features and how IPA helps to Discover Causal Connections. Faster.
Ingenuity Knowledge-Based Tools for Comprehensive Interpretation of Variant & Gene Expression Data [56:16 minutes]
Presented by Jean-Noel Billaud and Megan Laurance
Ingenuity Staff Scientists Jean-Noel Billaud and Megan Laurance present strategies for integrated analysis and interpretation of variant and gene expression data generated from cell lines representative of 2 breast cancer subtypes: Claudin-Low and Luminal. These subtypes represent different disease entities associated with specific molecular alterations and histo-clinical features. Interrogating these samples at both the variant and transcript level with Ingenuity's Variant Analysis and IPA software presents a powerful approach to drawing clear molecular paths from variants and gene expression changes to phenotypes relevant to these disease subtypes including Epithelial-to-Mesenchymal Transition and Metastasis.
A Bioinformatician's Guide to Lung Cancer: Wnt7a Signaling and Beyond [40:13 minutes]
Presented by Dr. Michael Edwards, Assistant Professor at the University of Colorado Health Sciences, Denver.
This webinar discusses an important antitumor pathway in lung cancer (Wnt7a signaling) as a framework to explain the bioinformatic analysis process using IPA. Topics covered include biological function and pathway analysis, network construction, and identifying and interpreting upstream regulators.
IPA and Coronary Artery Disease: A Case Study from Harvard [36:23 minutes]
Presented by Dr. Jochen Danny Muehlschlegel, M.D., Harvard Medical School
See how IPA was used for the discovery of novel pathways of affected genes in coronary artery disease. Cardiopulmonary bypass (CPB) with cardioplegic arrest is associated with ischemia leading to metabolic substrate depletion, reperfusion injury, apoptosis and necrosis. The study hypothesized that human left ventricular (LV) myocardium responds differently to the stress of (CPB) depending on the presence or absence of coronary artery disease (CAD). Therefore, they assessed differences in gene expression in patients undergoing aortic valve replacement (AVR) with (CPB) prior to and after cardioplegic arrest using whole-genome transcriptional profiling.
The Role of microRNAs in Kidneys of Hypertensive Patients [23:09 minutes]
Presented by Aimee Jackson, Ph.D.
MicroRNAs are small, non-coding RNAs that function as central regulators of gene expression and development. These regulatory molecules have been implicated in a wide range of normal and pathological activities, including embryonic development, cancer, inflammation, cardiovascular disease and viral infections. We explored the possibility that microRNA dysfunction in the kidney might contribute to hypertension, a significant health issue. We analyzed mRNA and microRNA expression profiling data from kidneys of untreated hypertensive patients and normotensive patients to identify microRNAs, microRNA targets, and gene networks that are dysregulated in hypertension. The results of these analyses identify microRNAs and their targets that could be biomarkers or therapeutic targets for hypertension.
Differential Expression of Focus Genes Associated Feed Efficiency [41:26 minutes]
Presented by Dr. Walter Bottje, Professor, Dept. of Poultry Science, University of Arkansas
Global RNA expression in breast muscle obtained from a male broiler line phenotyped for high or low feed efficiency (FE) was investigated using microarray analysis. By using an overlay function of IPA in which canonical pathways can be projected onto a set of genes, differentially expressed focus genes were identified. We selected 130 out of 260 possible canonical pathways in the IPA program that would likely be associated with normal metabolic activities and did not select those that were obviously tissue or disease specific. The results of this study provide additional insight into gene expression in muscle associated with the phenotypic expression of feed efficiency in broilers.
Scientific Webinar What You Should Know About Your 'Omics Data [58:11 minutes]
Presented by Tim Bonnert, PhD, QIAGEN Redwood City
Learn how you can now predict the cause and effect of changes in gene expression and predict the activation or inhibition of upstream molecules, such as cytokines, kinases, microRNA, receptors and many more! Included in the webinar will be an example of the biological analysis and interpretation of a gene expression data set from a study of Docetaxel Resistance in the Breast Cancer Cell Line MCF-7.
Distinct Gene Expression Profile of Regulatory T Cells in Prostate Cancer [45:00 minutes]
Presented by Simo Arredouani, Assistant Professor of Surgery at Harvard Medical School
The inhibitory role of regulatory T-cells (Tregs) in cancer is now well established. Furthermore, inhibition of Treg function has been shown both experimentally and clinically to improve the immune response towards a variety of cancers. Developing new and more effective strategies interfering with the function of Tregs in cancer requires a deep understanding of Treg suppressor machinery, and a thorough dissection of the molecular elements that orchestrate their differentiation from T cells.
A Combined Biological and Bioinformatic Analysis of Primary and Metastatic Tissues from NGS Ewing's Sarcoma Patients [58:00 minutes]
Presented by Jean-Noel Billaud, Ph.D., QIAGEN Redwood City and and Sylvain Foissac, Ph.D., Integromics
The Ewing's Sarcoma family of tumors is a category of cancers that predominantly affects teenagers between the ages of 10 to 20. Learn how QIAGEN Redwood City' IPA software and Integromics' SeqSolve software were used to investigate Ewing's Sarcoma patient samples generated from Helicos' NGS technology. We will present a combined bioinformatic and biological analysis of Ewing's Sarcoma patient samples, focusing on the differences between primary and metastatic tissues. IPA was used to analyze the significantly regulated genes and Integromics' NGS SeqSolve software was used to prepare the RNA-Seq data. IPA's new transcription factor analysis tool and downstream effects map were used to help narrow down targets and visualize the biological networks.
Using IPA to Analyze Illumina RNA-Seq Data Reveals Abundance-Specific Biological Signatures in Alzheimer's Disease [33:44 minutes]
Presented by Darryl Gietzen, Ph.D., QIAGEN Redwood City
This webinar discusses how IPA was used to interpret Alzheimer's disease biology by comparing Illumina RNA-Seq data from Alzheimer's disease (AD) and normal brain samples. This analysis revealed very specific biological changes in certain classes of transcript expression, demonstrating how the unique benefits of RNA-Seq can help characterize disease changes.

Ingenuity offers online training with our product technicians and web semimars presented by notable third parties, sign up below!

Data Upload and Analysis

These sessions dive deeply into data upload and analysis in QIAGEN’s Ingenuity Pathway Analysis (IPA). You’ll see how to rapidly understand biological processes most perturbed in your dataset, or across multiple timepoints or doses using the IPA Core Analysis. You’ll also get an introduction to functional analysis, significance calculations, and how IPA can help you understand the cause and effect of gene expression changes in your experiment. For all our other pre-recorded and written training materials please consult our Customer Portal.

Data Analysis: Part 1 Data Upload (1 hour)

This session is recommended for all new IPA users.

This session focus on formatting your own data and the best practices to analyze them in IPA. Topics include:

  • Format the incoming data to be analyzed by IPA
  • Upload the data to be analyzed
  • Set filter parameters and other core setting options
  • Run analysis

To Register:

Data Analysis: Part 2 Result Interpretation (1 hour)

This session is recommended for users that have a good understanding of the file format, upload and running a Core analysis or attended Part 1.

This session focus on the Core Analysis and the multiple ways of relating the molecules in your dataset to the body of information in the Knowledge Base:

  • Biological functions and diseases that are over-represented in your data, and the predicted directional effects on these functions and diseases.
  • Signaling and metabolic canonical pathways enriched in your data.
  • Predicted upstream regulators that might explain the changes observed in your data.
  • Molecular networks (algorithmically generated pathways describing potential molecular interactions in your experimental system)

To Register:

Search and Explore (1 hour)

This session is recommended for all new IPA users.

Explore how IPA’s knowledge and discovery tools allow you to relate the most recent literature findings to your experimental data, create interactive and customized pathways using tools such as species/tissue highlights and complex searches, and help in hypothesis generation. For all our other pre-recorded and written training materials please consult our  Customer Portal.

To Register:

documents icon QIAGEN's Ingenuity Pathway Analysis (IPA) Support Documents

The Basics


IPA White Papers – Explore IPA's capabilities 


Applying IPA