tv.qiagenbioinformatics.com

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/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/

Many QIAGEN Ingenuity Pathway Analysis (IPA) users have expressed strong interest in taking advantage of public single-cell expression data. Such data is readily available and easily accessible in IPA. We are hosting a 1hr training focused on some common use cases related to this topic.

You'll learn to:

• Easily locate single-cell studies, samples or cell types of interest

In this 90-minute training session, you will learn how to identify target mRNAs for your miRNAs of interest and associate them with pathways, diseases, biological functions, tissues and cell types.

Topics that will be covered include:

• How to analyze miRNA-seq datasets alone or both miRNA and corresponding mRNA datasets together

Are you interested in biomedical knowledge graphs, but don’t know where to start? This webinar will show you how to repurpose drugs and uncover hidden insights with knowledge graphs. Start learning the fundamentals of training embeddings and how to predict links in knowledge graphs. You’ll be able to apply that knowledge towards your biomedical discoveries with advanced machine learning techniques.

In this webinar, you will:

• Meet QIAGEN Biomedical KB-HD, the manually curated knowledge base of over 24 million relationships that powers QIAGEN Ingenuity Pathway Analysis

In this 90-minute training, attendees will learn how to use basic expression analysis (example RNA-seq) functionalities in QIAGEN OmicSoft studio and how that integrates with OncoLand, DiseaseLand and Single Cell Land. This webinar will focus on expression data analysis using QIAGEN OmicSoft Studio’s powerful analysis and visualization capabilities to explore your ‘omics data: 

To make most out of this training, please consider reviewing below ahead of time (not a requirement to attend the training though).

A slide illustrating some of the many result types/graphics you will be able to generate after this training. https://qiagen.showpad.com/share/jYTCDPNfz1SdIMPQPSPYS

Analyze and interpret your own single-cell RNA-seq data using QIAGEN CLC Genomics Workbench, starting with either FASTQ or matrix files.

Learn how to perform the different steps of secondary analysis on your single-cell RNA-seq data, such as:

• Importing your raw FASTQ or processed cell-matrix files

IPA is more than just an analysis program; it can also help you write grants and publications. IPA provides biological insights backed by expert-curated findings and thousands of pre-analyzed multi-omics datasets. You can search for existing knowledge on molecules of interest, build networks and review results from public studies. Or you can identify key pathways, regulatory molecules and functional changes in your results and see how your data relates to published studies.

By connecting diverse entities and relationships, biomedical knowledge graphs hold the potential to uncover new insights from existing data. This webinar introduces a machine learning-based approach that utilizes causal interactions from the QIAGEN Knowledge Graph to predict novel drug-disease relationships and construct networks that capture relevant supporting evidence. We will present examples that showcase the application of this approach in the context of drug repurposing.