tv.qiagenbioinformatics.com

The 37 trillion cells of the human body have a remarkable array of specialized functions and must cooperate in time and space to construct a functioning human.

In this webinar, Sarah Teichmann, professor of stem cell medicine at the University of Cambridge and co-founder and co-leader of the International Human Cell Atlas Consortium, discusses how her lab harnesses cutting-edge single-cell and spatial genomics to better understand this cellular diversity — specifically, how distinct microenvironments regulate cell identity.

The lab’s spatial atlas of the adult human heart includes the first comprehensive map of the conduction system, and it harnessed the unique molecular signature of the very rare pacemaker cells to make predictions about potential drug activities. Moving from the single organ to the systems level, Teichmann’s team revealed the context-specific and context-agnostic features of the vasculature across the body.

Finally, the team has taken cell atlases into 3D and 4D with its thymus atlases. Studying thymus development uncovered the rules of T-cell identity, which Teichmann’s team then harnessed in the dish to engineer T cells from thymic organoids. The lab also used advanced spatial and computational methods to map T-cell development to a continuous tissue axis, identifying the key spatial and temporal features of this crucial developmental trajectory.

QIAGEN Ingenuity Pathway Analysis (IPA) is designed to help you analyze and compare different types of 'omics data. In this webinar, we’ll compare bulk RNA-seq and single-cell RNA-seq data to identify common regulators/targets and see how those regulators/targets associate with your phenotype of interest. We will also use sample-level public data to validate gene expression of common genes in tissues and/or cell type of interest.

You'll learn how to:

• Generate a Comparison Analysis for bulk and single-cell RNA-seq

• Identify significant common genes with the Compare feature

• Build a custom network associating common genes to a phenotype

• Examine sample- and cell-level expression in OmicSoft content

Cancer care has been transformed by comprehensive molecular profiling and targeted therapies – but with improvements in sample analysis technologies, the volume of information and burden of interpretation has exploded. Physicians need guidance from the laboratories to translate molecular results into clear treatment paths. And with the pace of innovation only accelerating, the gap between discovery and actionable insight is widening fast. Laboratories need resources to help them to manage the information and communicate it to physicians in a reliable and efficient manner.

That’s why leading labs are turning to QCI Precision Insights (formerly N-of-One), a professional clinical variant interpretation service for molecular oncology:

• Trusted, reliable information: A dedicated team of PhD scientists and consulting oncologists translates complex biomarkers into clear, actionable guidance, including report-ready content for the physician to review.

• Real-world clinical impact: Interpretation is supported with FDA drug labels, practice guidelines, clinical trials, and peer-reviewed literature.

• Confidence for physicians, better care for patients: Delivers the insights oncologists need to make faster, more informed decisions – so patients get the right therapy at the right time.

Pathways and knowledge graphs have provided valuable insights into disease pathology, toxicology, target safety assessment, drug MOA and biological mechanisms in general. Though this is not possible without a high quality, reliable database. QIAGEN Knowledgebase is a comprehensive database with causal reasoning that is regularly updated from trusted peer-review publications and other sources using both high quality manual curation and utilizing AI/ML approaches.

In this webinar the trainer will

• Introduce the QIAGEN Knowledgebase

• Demonstrate how the user can take advantage of an easy-to-use graphical interface to study pathways and networks of interest

• Show how this database can be accessed programmatically for applications such as knowledge graph investigation

The Cancer Gene Census (CGC) is a widely used module within the Catalogue Of Somatic Mutations In Cancer (COSMIC) that comprises an ongoing effort to catalogue and describe all genes with causal impact in human cancer. In CGC, genes are characterized according to their roles in cancer – tumor suppressor, oncogene or fusion gene – and divided into two tiers based on the strength of supporting evidence. On

October 15, our COSMIC and QIAGEN field applications experts will walk you through the scientific and curation processes behind CGC. We will also explore another module, the Cancer Mutation Census (CMC), which classifies coding variants according to their computed pathogenicity. This helps prioritize variants by highlighting mutations with proven functional or clinical relevance, supporting faster interpretation and decision-making.

You will learn about:

• The criteria used to evaluate genes for inclusion into the CGC

• The structured information captured within the CGC and CMC modules

• Their real-world applications across research and clinical contexts

Speaker Info: 

• Ellen Wiedemann is an Application Scientist at the Welcome Sanger Institute with over a decade of experience in next-generation sequencing (NGS) and bioinformatics.

• Kyle Nilson, PhD is a sequencing-focused molecular biologist with a background in biochemistry and technical support. As a field software trainer at QIAGEN Digital Insights, Dr. Nilson works closely with our global bioinformatics team to provide direct customer support and assist with software training, implementation and optimization.

In this training, you will learn how to analyze and interpret your own single cell RNA-seq data using QIAGEN CLC Genomics Workbench starting with either FASTQ or matrix files.

Using CLC Genomics Workbench, you will learn how to perform secondary analysis on your single cell RNA-seq data. Specifically, you will learn how to:

• Import your raw FASTQ or processed cell-matrix files.

• Use pre-configured but customizable pipelines/workflows for single cell RNA-seq data.

• Generate high resolution visuals and other files from your analysis for publications and biopharmaceutical discoveries.

  • Dimension reduction (UMAP, t-SNE) plots

  • Differential expression table for clusters, cell types, or combination of both

  • Heat map

  • Dot plots

  • Violin plots

• Learn how to use “Create Cell Annotations from Hashtags” for cell hashing (i.e., CITE-seq).

• Dive into spatial transcriptomic analysis, the latest feature in the single cell RNA-seq module.

QIAGEN CLC Microbial Genomics Module provides tools and workflows for a broad range of bioinformatics applications, including microbiome analysis, isolate characterization, functional metagenomics and antimicrobial resistance characterization. The module supports the analysis of bacterial, viral and eukaryotic (fungal) genomes and metagenomes.

This training will be focused on amplicon-based taxonomic profiling (16S/18S/ITS sequencing OTU clustering). The trainer will cover:

• Overview of different tools within QIAGEN CLC Microbial Genomics Module and supported research areas

• For taxonomic profiling:

  • Importing data 

  • Utilization of metadata 

  • Downloading and managing references 

  • Walk through of OTU clustering workflow (analytical pipeline) 

  • Downstream processing of abundance tables 

  • Creating and exporting high-quality graphics

QIAGEN OmicSoft Lands are high-quality curated repositories of genomics and proteomics data sourced from public, published studies (GEO, EBI, SRA) and consortia (TCGA, CPTAC, GTEx, and more). Because of our expert curation and stringent quality checks, OmicSoft users can deeply investigate across diverse oncology and normal tissue/cell datasets to discover and validate candidate drug targets and biomarkers.

In this training, attendees will use OmicSoft Studio, our graphical user interface, to access public data from The Cancer Genome Atlas (TCGA). Using this data, you will learn how to:

• View gene expression and somatic mutation frequency across different tumor types and conditions

• Identify genes whose expression correlates or anti-correlates with mutation of your target

• Prioritize your candidate biomarkers using expression data across comparisons (tumor vs. normal, MUT vs. WT, etc.)

• Establish survival consequences from changes in your candidate biomarker’s expression or mutation status

Discover actionable drug targets in your spatial transcriptomics data with systems biology and network analysis.

In this 60-minute session, we’ll start with differential expression data and explore ways to derive biological insights across experimental observations. Then, we’ll investigate and compare these findings across other harmonized and curated data.

We’ll show you how to:

• Identify regions of interest in oral squamous cell carcinoma biopsies

• Generate new networks by connecting genes to functional hallmarks of cancer

• Predict upstream regulators and activity in a custom network with new causal analysis on the fly features

• Test potential drug targets with in-silico experimentation

• Overlay data and save custom network patterns of interest for causal scoring in future experiments

• Explore other indications and potential treatments with curated, harmonized data

Find out what happens when spatial ‘omics data meets network biology.

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