'Omics research has made it easy to gather large volumes of data on differentially expressed genes in various conditions. Changes in gene expression levels give an unprecedented birds-eye view on what functional changes are going on in a disease or biological process. However, this advantage comes with its own set of challenges. These extensive sets of important up- and down-regulated genes come without biological context and are impossible to interpret manually. This is where causal pathway analysis is an invaluable approach to identify and group interconnected genes in a network or pathway, and annotate functional changes brought about by the differences in gene expression. The combination of human-curated biological knowledge from databases, statistical analysis and computational modeling allow us to make these connections easily, with the right software tools.
The most essential factor in determining the suitability of the tool is the quality of the knowledge it holds on molecular connections and the specific kind of interactions that form relationships among biological molecules. This is where most freely-available interaction/knowledge databases fail. They are not updated on a regular basis and therefore lack the most recently discovered molecular interactions. More importantly, they also lack causality – whether a gene or chemical has an activating or inhibitory effect on another gene or pathway or disease.
This is where QIAGEN Ingenuity Pathway Analysis (IPA) reigns supreme. The database which supports IPA, the QIAGEN Knowledge Base, currently contains over 7 million findings and is continually updated, with an average of over 1000 new findings added daily. Our scientists read the biomedical literature and capture the granular context and causality in each curated paper which they enter into the knowledge base.
Use these top five tips on performing pathway analysis in QIAGEN IPA, and you can master pathway analysis in no time:
1. Pathway Activation Prediction: QIAGEN IPA goes beyond basic pathway analysis. Other applications, such as DAVID, simply tell you which signaling pathways are enriched from a limited collection of such pathways, yet don't tell you whether the overlapping genes are up- or down-regulated in your dataset – information which is crucial to achieving deeper insights into your data. IPA analyzes your data in the context of the most extensively curated set of signaling and metabolic pathways. Not only will IPA identify the most significant pathways, but it also tells you which pathways are predicted to be activated or inhibited based on your data. This is a key feature that helps you understand the biological mechanisms underlying your data.
2. Regulatory Network Analysis: After identifying significant pathways and systems based on your data, one of the next steps is to identify key regulators that are likely responsible for the changes observed in your data. IPA's Regulator Effects analysis enables you to understand regulatory networks by identifying key regulators upstream and how they drive the predicted downstream effects on biological and disease processes from your data. This provides additional insights by integrating Upstream Regulator results with Downstream Effects results. By connecting cause and effect, you can develop actionable hypotheses that explain what is occurring upstream – resulting in particular downstream phenotypic or functional outcomes.
3. Molecule Activity Predictor (MAP): Identifying networks or pathways that contain a gene or molecule of interest can be challenging, and digging deeper by identifying which of those networks are activated or inhibited is nearly impossible using many applications. QIAGEN IPA's MAP function allows you to interrogate sub-networks and Canonical Pathways and generate hypotheses by selecting a molecule of interest and indicate up- or down-regulation, simulating directional consequences in downstream molecules and the inferred activity upstream in the network or pathway.
4. IPA Analysis Match: Once you have a working hypothesis, one of the most time consuming and resource-intensive next steps is to validate your hypothesis experimentally. Fortunately, IPA's Analysis Match allows you to automatically identify datasets with significant similarities and differences from a library of over 65,000 curated analyses. This enables you to locate and compare results and better understand causal connections among diseases, genes and networks of upstream regulators in other biological settings. This unique feature allows you to strengthen and verify your existing hypothesis and discover new biological insights by comparing your data to published, peer-reviewed results.
Download our brochure to learn more about QIAGEN IPA with Analysis Match
5. IPA with Land Explorer: Searching for information about a gene of interest in the public domain can take hours, and requires filtering and evaluating a large amount of data. Now with the click of a button, you can explore expertly-curated 'omics data on over 500,000 biological samples across tens of thousands of genes, with robust visualizations. Jump from a gene of interest in QIAGEN IPA to discover its expression in various tissues and cell types with Land Explorer. Explore the diseases and treatment contexts in which it is up-or-down-regulated. Visualize how mutations correlate with changes in expression, the effect of mutations on clinical outcomes and survival and much more. All this information with customizable visualizations is instantly at your fingertips, from a single software interface.
Download our brochure to learn more about QIAGEN IPA with Land Explorer
With these unique features, you can perform your pathway analysis like a pro and be part of the QIAGEN IPA expert community. With more than 7 million findings, 40 integrated databases, over 4500 publications citing IPA each year and regular product feature updates, QIAGEN IPA is the best-suited pathway analysis tool to quickly and easily get you deeper insights into your biological data.
Don’t have QIAGEN IPA, or perhaps you are missing the latest features? Request a 14-day trial now.
Neurofibromatosis (NF) is a genetic disorder that causes tumors to form on nerve tissue. These tumors can develop anywhere in the nervous system, including the brain, spinal cord and nerves. NF is usually diagnosed in childhood or early adulthood.
Every year, scientists and clinicians who focus on advancing basic, translational and clinical research in NF gather at the Children's Tumor Foundation's NF conference to foster important discussion and collaboration within the NF community. As a prelude to the NF conference, this year the 2nd NF Hackathon took place from September 13–15, and experts from QIAGEN participated, helping to broaden NF awareness and introduce new perspectives to NF research.
During the three-day event, participants explored and analyzed data from the NF Data Portal, the leading open source collection of genomic and clinical data dedicated to this genetic disorder. This year, the NF Hackathon focused on analyzing diverse datasets including genomic, drug screening, drug-target association, imaging and other data for all the three types of NF (NF1, NF2, Schwannomatosis).
Team QIAGEN was among the winners of the hackathon, with their submission ‘Inferring regulators and pathways involved in NF1 and NF2 tumors originating from Schwann cells using gene expression data’. They used several QIAGEN bioinformatics tools in their analyses, including OmicSoft Array Studio, Ingenuity Pathway Analysis and the QIAGEN Knowledge Base. The team concluded that NF1 and NF2 are clearly differentiated, and that their approach is able to discriminate between tumor types by identifying drivers and signaling cascades. The team also identified potential therapeutic targets, including SMARCA4. As a result of their findings, Team QIAGEN was selected to present their work at the Children's Tumor Foundation's NF conference, held recently from Sept. 21–24 in San Francisco.
Congratulations to all the winners!
The 17th annual Advances in Genome Biology and Technology meeting was a whirlwind of stellar talks, great posters, and - of course - fabulous parties.
While all the talks were terrific, some really resonated with us. In the opening session, David Haussler made the case for sharing genomic data and rare variant information as much as possible, a message that we loved hearing as co-founders of the Allele Frequency Community. We believe that data sharing is essential for genomic medicine to have the kind of impact we all anticipate. In another talk, Stephan Schuster announced the launch of the GenomeAsia 100K, a new project to sequence 100,000 people in Asia for a better understanding of the genetic diversity in that population. We’re excited to see what this new trove of data contributes to the global genomics community.
Our team was busy during the conference and one of the highlights of our efforts was the lunch workshop featuring Chris Mason from Weill Cornell Medicine. He presented updates on some of his more unique projects, including efforts to sequence pathogens across New York City and more extreme environments and to unravel the biological effects of space travel through a study of twin astronauts. Mason has been beta testing the QIAseq Targeted RNA Panels, and during his presentation, he highlighted the benefits of the panels in delivering digital RNA sequencing for gene expression analysis, and he shared results from that metagenomics work as well. It was a great workshop thanks to Mason and all the attendees who took the time to join us!
We also participated in the software demo session, showcasing our QIAGEN Ingenuity® Variant Analysis™ application for sequencing data interpretation. With the connections to the Ingenuity Knowledge Base and HGMD, it relies on meticulously curated genomic and biological data to help scientists quickly home in on variants of interest to get at the root cause of disease or other phenotypes. We provided software demos at our hospitality suite as well, and we’d like to thank all the people who stopped by.
We had a great time in Orlando and we’re for sure looking forward to AGBT meeting next year – hope to see you there!
Learn more about QIAGEN Ingenuity Variant Analysis
Read more about the new QIAseq Targeted RNA Panels
