As hereditary NGS testing grows more complex, clinical labs must interpret variants with greater speed and accuracy. This webinar introduces Franklin by QIAGEN, now integrated with HGMD Professional, combining AI-powered workflows and automated ACMG annotation with the leading expert-curated germline database. Together, they enable faster, evidence-backed interpretation, reduce manual review, and boost reporting confidence by minimizing missed clinically relevant findings.

In this webinar, you’ll see how Franklin + HGMD accelerates variant matching, streamlines VUS assessment, supports long-read sequencing, and delivers instant access to curated clinical and functional evidence—all through a unified interpretation workflow.

Key highlights include:

• Integrated ACMG-based annotation + automatic literature curation for evidence-backed interpretation

• Instant variant matching to quickly identify known, reported, or novel variant

• Faster VUS resolution with direct access to relevant publications and functional data

• Minimal manual literature search with direct links to HGMD Professional in-platform

• Long-read sequencing support for interpreting complex alleles and structural variants

Accurate clinical interpretation of somatic cancer variants is critical for diagnosis and guidance of precision oncology treatment. Without shared standards, laboratories and clinical decision-support (CDS) systems have implemented independent approaches, leading to significant variability and highlighting a need for standardization. 

Through a collaborative effort, ClinGen, the Cancer Genomics Consortium (CGC) and the Variant Interpretation for Cancer Consortium (VICC) have developed a framework for standardized classification. In this webinar, we will evaluate these standards and compare their classifications with results from the QIAGEN Clinical Insight (QCI) Interpret CDS system.

You will learn: 

• Understand ClinGen/CGC/VICC standards for variant classification
  Learn how these expert-developed guidelines define and standardize somatic cancer variant classification.

• Compare software classifications with guideline-based human assessments
  See how classifications from the ClinGen/CGC/VICC standards compare with those generated by QCI Interpret.

• Best practices to use guidelines and tools together
  Discover how to combine expert guidelines with clinical decision support tools for more reliable variant interpretation.

This hands-on workshop will cover a highly requested use case using OmicSoft Explorer and IPA Interpret, two new features that QIAGEN IPA* and OmicSoft customers can now access.

Specifically, the trainer will go over how to:

• Easily search for deeply curated high-quality 'omics datasets (RNA-seq, scRNA-seq, etc.) based on user-defined criteria (disease name, drug treatment, etc.) curated from sources such as GEO, SRA, TCGA and more

• Explore project details to gain a better understanding of the experimental setup and sample metadata

• Study volcano plots to identify biomarkers associated with conditions of interest

• Generate sharable pathway analysis reports that provide insights into disease pathology, drug mechanism of action, cellular processes and more

*with a premium license.

IPA Interpret and OmicSoft Explorer are now available through both IPA and OmicSoft. Learn more.

This webinar will introduce you to the Long Read Support plugin for Oxford Nanopore and PacBio sequencing reads in QIAGEN CLC Genomics Workbench.

Learn how to:

• - Download and install needed plugins

• - Import data required for the analysis

• - Perform long read de novo assembly

• - Map reads to a reference and visualize an assembly

• - Use BLAST to investigate the contigs

• - Use additional long read tools: polish with short reads, structural variant calling

• - Use genome finishing tools: analyze and assemble contigs

• - Use analytical tools: RNA-seq analysis for long reads, classify long read amplicons for metagenomics

This 90-minute training session will show you how QIAGEN Ingenuity Pathway Analysis (IPA) helps visualize molecular intricacies and variations at multiple levels, such as transcriptome, proteome and metabolome. Through a series of brief technical vignettes, we will demonstrate how to:

• - Generate associations among molecular signatures obtained via integrating multi-omics data

• - Extract mechanisms from multi-omics data for precision medicine

• - Disease stratification based on multi-omics profiles

• - Map disease networks among targets and indications

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

In this video, we demonstrate the import and association of metadata with sample files.

  https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Metadata.html

In this video, we demonstrate how to view details of specific data points in plot views using the synchronized table view. See how you can easily access the list of genes that overlap in a Venn diagram for RNA-seq. This functionality is also available for other types of plots like track lists, expression browsers and volcano plots, to name a few.

Starting with CLC version 26, workflow control can be simplified using fork collections.

https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Fork.html#SECTION001425230000000000000

In this 90-minute training using Inflammatory Bowel Disease (IBD) public data from NCBI GEO through Omicsoft DiseaseLand, attendees will learn how to: