tv.qiagenbioinformatics.com

Join us to review exciting new features in IPA Interpret and OmicSoft Explorer.

We will walk though the latest enhancements in IPA Interpret like:

• Bubble charts for various analysis results

• Customizing charts

• More context for Graphical Summary results

• Explore matching and anti-matching datasets easily with more details

We will also walk through how to explore gene signatures in OmicSoft Explorer. You can readily find other experiments that match or anti-match your prescribed list of genes and expression patterns.

Are you new to QIAGEN Ingenuity Pathway Analysis (IPA) or interested in expanding your skill set? Join us as we learn more on large dataset analysis and knowledge base queries using QIAGEN IPA.

You’ll learn to:

• Upload multiple dataset types (e.g., RNA-seq, proteomics, metabolomics) and perform interactive core/pathway analysis in IPA

• Learn how to interpret different results, including pathways, key regulators, impact on biological functions/diseases and more

• Compare different experimental conditions (e.g., single-cell clusters, disease types) and identify similarities and contrasts

• Generate a network for hypothesis generation, even without a dataset or experimental design

Already have an IPA license? Install IPA and start using it now:

https://qiagen.showpad.com/share/CBv30blCPKFDUYHRWtAvI

Learn more about IPA or request a free trial: https://digitalinsights.qiagen.com/ipa

A core/expression analysis is performed for RNA-seq, scRNA-seq, proteomics and many other types of ‘omics data. Driven by high demand from new user training sessions, we will dive into the details around a core analysis and its results.

Answer questions such as:

• What are the different result types produced by an IPA core analysis?

• How can we identify key regulators and relevant regulatory networks?

• What are the biomarkers and genes associated with diseases and biological processes of interest?

• Can IPA help generate novel directional (regulator effects) and non-directional (interaction) networks from a user’s dataset?

Come with your questions, and we'll address them during the webinar.

If you haven’t done a core/expression analysis in IPA before, we recommend you review the slides below before (or after) this training. It’s not a requirement, but it will help.

https://qiagen.showpad.com/share/SQjinvdxIs1iGhL8H3eOd

Slide#4 = how to download the example dataset

Slide#20 -25 = review recommended dataset formats

Slide#53 onwards = step-by-step how to upload the dataset and perform analysis

o Install IPA: https://qiagen.showpad.com/share/A0mV7EWcejBtXPJUesbXd

o IPA beginner training recording:

https://qiagen.showpad.com/share/Dcdg28jxw0D2uRpgRqFK4

Identifying molecular signatures for rare diseases is challenging, mainly due to limited sample sizes and heterogenous data. Researchers must often piece together fragmented evidence to uncover shared biological mechanisms. Though inescapable, this process can be more efficient.

See how QIAGEN OmicSoft Lands and Ingenuity Pathway Analysis (IPA) streamlines biomarker signature discovery using curated real-world datasets and powerful causal analytics. With automation powered by SQL, Python and R APIs, OmicSoft and IPA enable scalable, reproducible analyses across multiple disease contexts, accelerating the path from data to discovery.

You will learn how to:

• Explore and query neurodegenerative disease datasets in QIAGEN OmicSoft Lands using SQL and Python

• Automate pathway and regulator analysis in IPA using Python and R APIs

• Scale and produce analyses across multiple cohorts

For your RNA-seq data, you will learn how to:

• Import FASTQ files, cell matrix files and metadata

• Download and/or import reference and annotations files

• Map reads to a reference genome

• Generate gene and transcript counts

• QC reports displaying % mapped reads etc.

• Generate visualizations of results, such as heatmaps, differential expression tables, PCA/PCOA plots, Venn diagrams, Sankey plot and more

• Easily customize RNA-seq workflows

• Export publication-quality graphics, tables and reports

Per audience request

• Send differential expression tables to QIAGEN Ingenuity Pathway Analysis directly from QIAGEN CLC Genomics Workbench to analyze and interpret relevant pathways

The Graphical Summary in IPA Interpret has been made more informative and easier to interpret. In Upstream Regulators, you can easily see the dataset details for genes in the summary. A tooltip now provides information about the Upstream Regulator’s score from your analysis and expression details (if the gene is in your dataset). This way, you can evaluate the expression of the underlying gene and view its prediction as a regulator at the same time.

New in IPA Interpret, bubble charts can help you find interesting patterns in your results, focus on the most important predictions and create compelling visualizations for presentation and publications.

IPA Interpret now offers extensive customization of bar charts when exporting them as images. You can easily change the bar spacing, thickness and color, fonts and more. After you have filtered out non-relevant bars from your chart (if desired), click the export button and choose “Custom styling before download” from the menu that appears.

Learn how to add users to your QIAGEN Digital Insights (QDI) license through the QDI Admin Tool (QDIAT).

The gene therapy space continues to grow with the discovery of new gene editing targets. Meanwhile, new indications are being identified for known targets by exploring vector biology and the biological mechanisms underlying disease pathology.

In this webinar, we will show how Biomedical KB-AI accelerates target discovery and indication expansion using generative AI. Biomedical KB-AI provides a more comprehensive view of the biological landscape, enabling the identification of novel relationships and patterns that may be missed by traditional methods.

You will learn:

• About Biomedical KB-AI and its structure

• How to easily search and filter results for the vector system of interest through a subgraph

• How to thoroughly interrogate the subgraph to look for similarity between diseases and targets that can be targeted by vectors of interest

• How to effectively use a knowledge graph for drug repurposing applications