tv.qiagenbioinformatics.com

The autoimmune drug market is dominated by blockbuster biologics, including Janssen’s Stelara® (ustekinumab), a dual IL-23 and IL-12 inhibitor approved for plaque psoriasis, psoriatic arthritis, Crohn’s disease and ulcerative colitis. These biologics generate billions in annual revenue, but when exclusivity ends, competition from biosimilars quickly becomes a concern.

Using the real-world example of Stelara, which faced multiple new biosimilars within a year of its patent expiry, we explore how to sustain value with smarter lifecycle strategy. We’ll combine data curation, large-scale ‘omics evidence and pathway analytics with QIAGEN Discovery Platform to uncover new opportunities for Stelara.

In this session, you’ll learn how to:

• Support discovery decisions in immunology research

• Contextualize targets with curated biological knowledge and pathway analytics

• Identify opportunities for indication expansion and combination strategies

• Generate AI-ready insights from integrated multimodal data and knowledge graphs

Speakers:

Tim Hou, PhD

Senior Field Application Scientist, QIAGEN Digital Insights

Tim Hou, PhD, is a Senior Field Application Scientist at QIAGEN Digital Insights, where he leverages extensive expertise in molecular biology, genomics and bioinformatics to support researchers with biological data analysis and interpretation platforms from QIAGEN.

Ethan Strattan, PhD

Immunology Consulting Scientist, QIAGEN Digital Insights

Ethan Strattan, PhD, helps clients understand the biological underpinnings of ‘omics-level findings. He works as an immunology and oncology subject matter expert in the QDI Services team, bridging the gap between data scientists and bench researchers. He works with diverse datasets to generate use cases and proof-of-concepts for findings generated from cutting-edge informatics and AI pipelines. 

The early stages of drug development are inherently high-risk; molecule screening, target evaluation and lead refinement can take years and cost millions of dollars – often have little to show for the effort. Indication expansion and drug repurposing can open new avenues of possibility, after careful evaluation of the relationships between the drug, its targets and the new disease context. These connections can be uncovered by mining curated, causal knowledge graphs, which inform smarter target identification.

Learn how to evaluate targets and drugs in this oncology-focused webinar, which examines BRAF as a target in multiple myeloma. Also, we’ll evaluate different drugs in clinical trials with an analysis that covers the GOT-IT assessment blocks for drug evaluation. These blocks, developed by the GOT-IT (Guidelines On Target Assessment for Innovative Therapeutics) working group, are part of a target assessment framework that supports robust, reproducible data.

We’ll cover how to:

• Apply the GOT-IT framework to target evaluation and indication expansion

• Uncover causal relationships between existing drugs and new diseases with our curated knowledge graphs

• Make informed decisions based on concrete data, including toxicity, adverse events and competing drugs in clinical trials

Speakers:

Sathiya Manivannan, PhD

Scientific Consulting Expert (Multiomics), QIAGEN Digital Insights

Sathiya Manivannan, PhD, delivers tailored solutions for pharmaceutical and biotech companies. His work includes large-scale single-cell and spatial transcriptomics analyses, multi-omics database generation, and the development of graph knowledge bases and AI/ML-driven solutions that accelerate therapeutic target discovery and optimize drug development.

Ruth Stoney, PhD

Senior Field Application Scientist, QIAGEN Digital Insights

Ruth Stoney, PhD, designs data science projects for customers using ‘omics datasets and insights mined from the QDI knowledge graph. She collaborates with academics and biopharma data scientists on biological discovery, AI usage and more.

Viral constructs are essential tools for gene therapy, vaccine development and advanced cell engineering workflows. QIAGEN CLC Genomics Workbench has the tools to streamline each stage of construct development, from in silico design to resequencing analysis and adventitious agent detection.

In this webinar, you will learn how to build custom viral variants, verify construct integrity using NGS data and ensure manufacturing safety through sensitive, reproducible contaminant screening.

• Design and build custom viral constructs using intuitive sequence editing, annotation and assembly tools, supporting rapid prototyping and iterative engineering.

• Analyze resequencing data for construct verification, including reference-based mapping, mutation profiling, structural variant detection and quality control (QC) reporting to confirm construct fidelity.

• Detect and characterize adventitious agents during manufacturing using metagenomic pipelines, taxonomic classification tools and cross‑contamination analysis within CLC Genomics Workbench.

Biomedical knowledge graphs are powerful tools for organizing complex biological information and uncovering relationships that are difficult to see in traditional data formats. This introductory webinar is for researchers new to knowledge graphs who want to understand how they can support hypothesis generation, target discovery and data exploration.

You will explore:

• - The fundamental concepts behind knowledge graphs

• - How biological entities and relationships are represented

• - How to query, navigate and interpret the interconnected biomedical data within the QIAGEN Biomedical Knowledge Base

No prior experience with graph databases or graph analytics is required – just curiosity about new ways to work with biomedical knowledge.

In this era of precision medicine, do you have what it takes to leverage next-generation sequencing (NGS) data towards better cancer research, diagnostics and patient care?

Join our interactive workshop and learn how Human Gene Mutation Database (HGMD) Professional can help you uncover germline variant insights using expert-curated data. Explore almost 50 years of curated variant knowledge, spanning over 17,600 genes and over 38,000 phenotypes.

This workshop will cover how you can use HGMD Pro to:

• Distinguish clinically significant variants from VUS  

• Use batch search vs. advanced search  

• Curate genes using HGMD Pro’s accurate, up-to-date information  

• Identify the predominant genetic variant in a specific gene to understand the genetic mechanisms underlying associated diseases  

• Elevated experience to access HGMD content in Franklin 

• Answer other variant-related questions 

The trainer may add or remove agenda items based on registrant request. While the workshop is scheduled for 90min, it may go longer (or end earlier) depending on audience questions.

This is the last part of a series that will show how you can easily turn your ‘omics data into actionable insights using QIAGEN Ingenuity Pathway Analysis (IPA). Here, we take you deeper into the expert-curated content in the QIAGEN Knowledge Base.

You will learn:

• How findings from publications are curated into the knowledge base

• Which tools you can use – with or without a dataset – to investigate genes or diseases of interest

• How to uncover potentially novel connections and formulate promising hypotheses

About this series

From ‘Omics to Insights: Data Analysis Fundamentals with IPA

Do you find ‘omics data analysis overwhelming? Good news: it doesn’t have to be.

Discover how you can easily turn your ‘omics data into actionable insights with QIAGEN Ingenuity Pathway Analysis (IPA), an advanced tertiary analysis program for your RNA-seq, proteomics or metabolomics dataset.

This webinar series will show you how to set up your dataset for upload into IPA, run Core Analyses and interpret the different sets of results – from pathways, upstream regulators and diseases/toxicity to matching published datasets. Learn how to search the QIAGEN Knowledge Base, the rich, expert-curated content that powers IPA. Explore the biological landscape around a gene of interest, formulate promising hypotheses and uncover potentially novel connections – without worrying about programming or paywalls.

In an era of near-limitless public experimental data but little standardization, meaningful insights are lost to noise. Large collections of quality experimental data are essential for big-picture discoveries that stand up to scrutiny.

In this webinar, you will learn how to feed your drug discovery programs by integrating connections mined from QIAGEN Biomedical Knowledge Base with deeply-curated disease datasets from QIAGEN OmicSoft Lands.

Combining unified 'omics datasets with contextual relationship evidence from our knowledge graph, we will address complex questions such as:

- Which genes aren't expressed in normal tissue, yet are expressed in diseases of interest, based on experimental evidence?

- Which of these proteins are cell surface proteins, with evidence for extracellular localization?

- How are these proteins related directly or indirectly to disease pathways, and can these be connected to known drug targets?

- Can we identify correlated biomarkers, mutation targets, clinical factors or other means of cohort selection?

Accurate interpretation of cancer genomics depends on standardized disease classification, tumor characterization and variant annotation. In this joint COSMIC and QIAGEN webinar, we show how these robust frameworks enable reliable cohort building and reproducible research.
 
The webinar covers three themes: 

• Disease classification: COSMIC’s tumor classification and mapping to NCI Thesaurus and EFO. 

• Tumor site and histology subtyping: COSMIC’s highly detailed classification system, conversion of histologies into controlled terms, and how these support construction of focused cohorts and studying rare cancer types.

• Variant annotation: COSMIC gene identifiers, cross-mapping strategies and annotation conversion. 

The session explains COSMIC’s approach, its benefits, common challenges, and practical use cases, combining high-level overviews with technical detail and reproducible demonstrations.
 
By attending this webinar, you will gain:

• A clear understanding of COSMIC disease classification

• Guidance on mapping to external ontologies (e.g. NCI Thesaurus)

• Insight into how detailed tumor subtyping supports precise cohort building

• An understanding of variant cross-mapping

• Practical strategies for converting COSMIC annotations for downstream analysis

• Demonstrations of reproducible analyses and COSMIC data integration

Advances in genomic medicine hold enormous promise, but that promise cannot be fully realized without greater diversity in research and clinical application.

In this webinar, Eimear Kenny will discuss how population genetics can drive more accurate, equitable genomic health by expanding representation in genomic studies and databases. Drawing on work from her group at the Icahn School of Medicine at Mount Sinai, she will highlight efforts in expanding the understanding of human genomic diversity by prioritizing populations underrepresented in genomics, developing and sharing computational tools that improve precision across diverse populations, and uncovering disease-related genetic architectures shaped by demographic history.

In this webinar, attendees will:

• Learn how her research group is expanding diversity in large-scale genomic databases and integrating new models of genomic medicine into routine clinical care to ensure insights translate into real-world practice

• Explore why inclusivity, methodological innovation and diverse populations are essential to advancing genomic health, improving patient care and promoting health equity

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.

o Dimension reduction (UMAP, t-SNE) plots

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

o Heat map

o Dot plots

o 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.