With all the expression datasets that are available to the public, wouldn’t it be great to compare them to your own expression data, so you could get a better understanding of the underlying biology of your data?
With QIAGEN IPA Analysis Match, you can compare your expression data with well over 100,000 publicly available datasets that have been processed and analyzed in IPA, as well as all the IPA analyses that you have performed to date.
In this webinar, we will cover how Analysis Match works and demonstrate this feature with a live demonstration. Feel free to follow along with your analysis and ask questions along the way!
With all the expression datasets that are available to the public, wouldn’t it be great to compare them to your own expression data, so you could get a better understanding of the underlying biology of your data?
With QIAGEN IPA Analysis Match, you can compare your expression data with well over 100,000 publicly available datasets that have been processed and analyzed in IPA, as well as all the IPA analyses that you have performed to date.
In this webinar, we will cover how Analysis Match works and demonstrate this feature with a live demonstration. Feel free to follow along with your analysis and ask questions along the way!
Do you ever struggle to formulate hypotheses based on your experimental expression data? You may be comparing results from healthy versus tumor cell lines or treated versus untreated samples. What do the differences between expression patterns in the data mean? Many of us struggle to make biological sense of our RNA-seq or microarray data. The massive amount of expression data generated from experiments leaves us with thousands of data points but often no understanding of their biological meaning.
Advanced pathway analysis is an excellent way to gain a deeper understanding of expression data and experimental results. Here we offer three easy ways to go from expression data to pathway analysis so you can give your experimental data biological context to start gathering meaningful insights.
QIAGEN Ingenuity Pathway Analysis (IPA) is a popular tool for analyzing, comparing and contextualizing differential gene expression results from experiments in human, mouse or rat, among other organisms. QIAGEN CLC Genomics Workbench has convenient tools for processing raw data from RNA-seq or microarray experiments and performing differential gene expression analysis. In addition, with an IPA license and the Ingenuity Pathway Analysis Pathway plugin installed, you can upload results to IPA directly. By combining QIAGEN CLC Genomics Workbench with QIAGEN IPA, we offer a versatile platform for linking various instrument readout formats to biological insights.
These are the three most common use cases we see among our customers:
Use case 1: FASTQ data to IPA
Typical experiments you may be running involve sending RNA (mRNA, miRNA, lncRNA, etc.) from treatment and control samples to an NGS sequencing facility. After sequencing, you perform bioinformatics analysis using QIAGEN CLC Genomics Workbench on the FASTQ file(s) returned by the facility. First, you can do QC and trimming using the Prepare Raw Data workflow. Samples that meet QC criteria are then associated with metadata describing the experimental setup. You can identify differentially expressed genes (DEGs) using the RNA-Seq and Differential Gene Expression Analysis workflow. Differential gene expression analysis is based on the fit of a Generalized Linear Model with a negative binomial distribution, like the approaches taken by the popular tools EdgeR (Robinson et al., 2010) and DESeq2 (Love et al., 2014). Paired designs are supported, and it is possible to control for batch effects. You can then upload DEGs directly to IPA for additional analysis, comparison and contextualization. Analyze Expression Data and Upload Comparisons to IPA provides a convenient Sample to Insight workflow.
See also our manual on RNA-seq and small RNA analysis.
Use case 2: RNA-seq expression data to IPA
In this use case, the sequencing facility processes the raw FASTQ files and returns an expression matrix file, which takes up much less space than FASTQ files do. You can import expression matrices using the Import Expression Matrix tool in QIAGEN CLC Genomics Workbench. Then you can apply QC criteria, associate metadata and compare the experimental groups as described for use case 1. This use case is also supported by a Sample to Insight workflow: Analyze Count Matrix and Upload Comparisons to IPA.
Use case 3: Microarray expression data to IPA
In this third scenario, the samples have been processed on microarrays, not using NGS. Various generic and vendor-specific formats are supported in QIAGEN CLC Genomics Workbench. Steps include setting up a microarray experiment to group the samples, followed by transforming and normalizing the expression data and running a statistical test to identify differential expression. Several tests are available, including proportion-based tests, t-tests and ANOVA. Filtered DEGs can be uploaded to IPA for pathway analysis as described for use case 1.
Ready to give it a try?
QIAGEN Digital Insights bioinformatics tools for transcriptomics support microarray and RNA-seq data analysis with a variety of specialized tools. They enable you quickly go from raw instrument output to biological insights, as well as compare to over 100,000 curated public datasets. Learn more and request a consultation about our portfolio of tools for biomarker and target discovery that support expression data analysis. Ready to try these applications? Request a trial of QIGEN CLC Genomics Workbench and QIAGEN IPA to see how these tools can work together to streamline your insights from expression data.
References:
Resources:
Plugin:
Blogs:
Best practices for RNA-seq data analysis
Compare biological expression results with QIAGEN IPA Analysis Match
Webinars:
Training on RNA-seq and pathway analysis
Example analysis performed using QIAGEN CLC Genomics Workbench:
Tutorials:
Expression analysis using RNA-Seq data
Advanced RNA-seq analysis with upload to IPA
Microarray-based expression analysis
Building workflows in QIAGEN CLC Genomics Workbench video tutorials
How to include external applications to QIAGEN CLC Genomics Server video tutorials
Basic pathway analysis has several limitations that can slow down research progress and hinder insights from biological data. One of those limitations includes the inability to compare and contrast your experimental data to public data. Such a capability helps you better understand pathogenesis, disease and biological processes, find key targets or biomarkers and help reaffirm your results. Analyzing how your dataset compares to other similar datasets provides reassurance and evidence that your hypotheses and conclusions are on the right track and lets you make unexpected connections to other research areas.
Analysis Match from QIAGEN Ingenuity Pathway Analysis (IPA) was built to help you compare and contrast your biological data to re-curated public data from GEO, ArrayExpress, SRA, TCGA and many more sources, so you can more quickly and accurately strengthen hypotheses and discover new biological insights. Analysis Match automatically identifies curated datasets with significant similarities and differences to your results, enabling you to compare results, validate interpretation and better understand causal connections between diseases, genes, and networks of upstream regulators.
One hundred thousand analyses are now available in Analysis Match!
Analysis Match now lets you instantly scan your analyses against over 100,000 curated publicly available datasets and any of your own previous experiments you wish to include. Strengthen hypotheses and discover new biological insights by combining an enormous compendium of knowledge from the literature with a massive collection of gene expression measurements. Analysis Match debuted with just over 6000 datasets four years ago and has rapidly expanded to more than 100,000 today, covering vastly more research areas than ever before, including a large representation of single-cell clusters from human and mouse.
You might be tempted to curate datasets yourself, but this dataset collection represents over 460,000 samples analyzed with RNA-seq and microarrays and required over 30 person-years and more than 100 CPU-years to create. An extensive and unbiased data collection for comparison enables serendipitous discovery of closely matching or even “anti-matching” datasets that inform your research.
What's more, IPA provides other ways of looking at this massive data collection. For example, it enables you to answer questions like “under what conditions is the Coronavirus Pathogenesis Pathway inhibited?”, enabling in silico research that leverages the enormous amount of initial work done by other scientists to generate the data.
Don’t miss a research breakthrough. Discover why advanced pathway analysis tools such as Analysis Match are something your research can’t do without.
Sound like something you’d be interested in? Learn more about QIAGEN IPA Analysis Match and request a trial today.
'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.
