tv.qiagenbioinformatics.com

In this webinar, the speaker will go over how to:• Identify critical pathways and networks in your single cell data, giving you insight into biological mechanisms• Discover novel regulators, master regulators and biomarkers associated with different cell types• Compare different cell clusters through pathways/networks activity heatmap to understand differences between your cell types or experimental groups• Generate interactive IPA Interpret reports for sharing and collaboration• Export high resolution images and tabular results for posters, publications and biopharma reports

Pre-clinical toxicology studies generate complex datasets that require robust interpretation to identify potential safety liabilities early in drug development. QIAGEN Ingenuity Pathway Analysis (IPA) can help uncover mechanistic insights, predict toxicity outcomes and support decision-making in pre-clinical safety assessments. Explore how you can apply IPA to ‘omics data to identify toxicity signatures, understand mode of action (MoA) and prioritize biomarkers.· Overview of QIAGEN Ingenuity Pathway Analysis· Find transcriptomic and proteomic data from pre-clinical and clinical studies and gain mechanistic insights using OmicSoft Explorer and Ingenuity Pathway Analysis· Visualize and interpret complex biological relationships that may be behind toxicity mechanisms using network construction· Prioritize candidate biomarkers for safety monitoring using biomarker tools and OmicSoft Land Explorer

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 designAlready have an IPA license? Install IPA and start using it now:https://qiagen.showpad.com/share/CBv30blCPKFDUYHRWtAvILearn more about IPA or request a free trial: https://digitalinsights.qiagen.com/ipa

In this webinar, the speaker will cover how to simplify your single cell RNA-seq analysis with biologist-friendly point and click tools.• Generate high-resolution visuals and other files from your analysis for publications and biopharmaceutical discoveries• Generate dimension reduction (UMAP, t-SNE) plots to understand differences between cell clusters/experimental conditions• Identify and study clusters and cell types specific biomarkers using differential expression tables, gene expression heat maps, dot plots and violin plots• Generate desired cell annotations using hashtags• Visualize and investigate spatial transcriptomics plot using your Cell Ranger output to better understand cellular organization and generate hypothesis• Use preconfigured but customizable pipelines/workflows for single-cell RNA-seq data

Precision medicine is rapidly transforming healthcare, and one of the most significant accelerators of its progress has been the dramatic reduction in cost. In this webinar, we explore how innovations in genomics and biotherapeutics are making advanced, personalized healthcare more accessible and affordable than ever before. Dr. George M. Church will examine how the cost of whole genome sequencing has been reduced from $3 billion per haploid genome—previously unusable in clinical settings—to just $300 per phased diploid genome today. We’ll also look at the economics behind cell therapy, such as blood transfusions (now as low as $200), and gene therapy technologies like mRNA vaccines (available for as little as $30).

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-seqIdentify significant common genes with the Compare featureBuild a custom network associating common genes to a phenotypeExamine 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 willIntroduce the QIAGEN KnowledgebaseDemonstrate how the user can take advantage of an easy-to-use graphical interface to study pathways and networks of interestShow 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) plotsDifferential expression table for clusters, cell types, or combination of bothHeat mapDot plotsViolin plotsLearn 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 areasFor 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

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 designAlready have an IPA license? Install IPA and start using it now: https://qiagen.showpad.com/share/CBv30blCPKFDUYHRWtAvILearn more about IPA or request a free trial: https://digitalinsights.qiagen.com/ipa

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 biopsiesGenerate new networks by connecting genes to functional hallmarks of cancerPredict upstream regulators and activity in a custom network with new causal analysis on the fly featuresTest potential drug targets with in-silico experimentationOverlay data and save custom network patterns of interest for causal scoring in future experimentsExplore other indications and potential treatments with curated, harmonized dataFind out what happens when spatial ‘omics data meets network biology.

See how highly sensitive somatic detection of classic tumor reference samples is made possible through the Twist Oncology - DNA CGP Panel™ combined with downstream analysis using QIAGEN® CLC Genomics Workbench.What is the Twist Oncology - DNA CGP Panel?It is a panel from Twist Biosciences® that is designed for the comprehensive genomic profiling (CGP) of solid tumors to find pathogenic variants. It also supports the scoring of microsatellite instability (MSI) and tumor mutational burden (TMB). What is QIAGEN CLC Genomics Workbench?A powerful all-in-one analysis and visualization toolkit, it is software filled with various tools and workflows tailored to next-generation sequencing (NGS) data, including data from a wide range of resequencing panel designs.

In this video we introduce sample batching using CLC tools. This can be helpful when you need to perform the same analysis on a large number of files. Also see this manual page  for more details:https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Batch_processing.html

This video demonstrates how to analyze multiple input files per sample using batch mode. Please also see this FAQ page for more details:How can I run a batch job with multiple libraries for each sample?   

The history view keeps track of what tools and parameters were used to generate results in CLC. This video shows how the history view can be used to compare two different analyses when the results are unexpected.Details about the history view can be found on this manual page:https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=History_Element_Info_views.html

This video provides a quick introduction to myCLC. If you do not already have access to myCLC Please contact bioinformaticslicense@qiagen.comAlso, check out these FAQ pages:What is myCLC?  How can I get access to myCLC?  How can I find information about my CLC licenses?   How can I add someone as a technical contact to myCLC?  How can I view information for another myCLC account?