tv.qiagenbioinformatics.com

Identifying molecular signatures for rare diseases is challenging, mainly due to limited sample sizes and heterogenous data. Researchers must often piece together fragmented evidence to uncover shared biological mechanisms. Though inescapable, this process can be more efficient.

See how QIAGEN OmicSoft Lands and Ingenuity Pathway Analysis (IPA) streamlines biomarker signature discovery using curated real-world datasets and powerful causal analytics. With automation powered by SQL, Python and R APIs, OmicSoft and IPA enable scalable, reproducible analyses across multiple disease contexts, accelerating the path from data to discovery.

You will learn to:

• Explore and query neurodegenerative disease datasets in QIAGEN OmicSoft Lands using SQL and Python

• Automate pathway and regulator analysis in IPA using Python and R APIs

• Scale and produce analyses across multiple cohorts

For your RNA-seq data, you will learn how to:

• Import FASTQ files, cell matrix files and metadata

• Download and/or import reference and annotations files

• Map reads to a reference genome

• Generate gene and transcript counts

• QC reports displaying % mapped reads etc.

• Generate visualizations of results, such as heatmaps, differential expression tables, PCA/PCOA plots, Venn diagrams, Sankey plot and more

• Easily customize RNA-seq workflows

• Export publication-quality graphics, tables and reports

Per audience request:

• Send differential expression tables to QIAGEN Ingenuity Pathway Analysis directly from QIAGEN CLC Genomics Workbench to analyze and interpret relevant pathways

Cyclospora cayetanensis remains an important focus of food safety, public health and parasitology research. As genomic resources and molecular surveillance efforts continue to grow, researchers gain new opportunities to investigate parasite diversity, genetic variation, transmission patterns and the biological mechanisms underlying infection.

Our webinar explores how QIAGEN CLC Genomics Workbench can streamline the analysis of parasite sequencing data through intuitive, reproducible and accessible bioinformatics workflows, using Cyclospora as an example.

You will learn to:

• > Analyze next-generation sequencing (NGS) data using the integrated workflows in QIAGEN CLC Genomics Workbench

• > Perform read mapping, variant detection and comparative genomics analyses on Cyclospora datasets

• > Use additional tools for investigating genetic diversity and sequence variation in parasite populations

Did you know that IPA can build networks from scratch? Using the QIAGEN Biomedical Knowledgebase, the database that powers IPA,  you can find connections between genes or proteins of interest, diseases, or relevant functions right within the application itself. Whether you are in your initial planning stages of hypothesis generation, trying to elucidate mechanism of actions, or simply looking for related biomarkers or new targets, IPA can jumpstart your research.

You will learn:

- How to build networks from scratch

- Explore the evidence behind relationships between genes, proteins, diseases, or functions

- Find biomarkers or drugs that target nodes on the custom network

- Test out hypotheses in silico on your custom network

- Overlay curated and harmonized data on top of your custom network

• - Predict activity and enrichment of pathways and upstream regulators within your network

Join us for an exclusive preview of the Summer 2026 release of QCI Interpret, QCI Interpret One and QCI Interpret Translational. Designed for current QCI Interpret users, this webinar will highlight new features, workflow enhancements and key updates arriving in July 2026. Learn how these changes may impact your laboratory, how to prepare for deployment, and best practices for making the most of the new capabilities. You'll also have the opportunity to ask questions during a live Q&A with the QCI team.

You will learn:

- Enhanced variant interpretation workflows

    > Including updated ACMG/AMP classification logic, refined HGVS nomenclature and improved distinction between structural duplications and amplifications

- Expanded reporting capabilities

    >The ability to consolidate NGS and orthogonal testing results into a single report and built-in comment translation for multilingual reporting

- New Sample Quality Control (QC) integration

    > Bringing quality metrics directly into the interpretation workflow to support streamlined review, standardized QC assessment and inclusion of QC data in final reports

Per user request, we are hosting a training specifically focused on metabolomics data. In this training, the attendees will learn how to

• - Format, upload and start a pathway analysis for a metabolomics dataset

• - Identify and investigate critical pathways and regulatory networks

• - Export critical tabular outputs and publication quality high resolution images

In addition, per registrant feedback, the trainer may cover additional topics (example comparison analysis, pathway search and more)

The COSMIC Knowledgebase, the world’s leading expert-curated resource for somatic mutations in cancer, combines rigorous manual review with scalable computational approaches to keep pace with rapidly expanding scientific literature. In this webinar, we explore how artificial intelligence (AI) is integrated into the COSMIC curation workflow to support – rather than replace – expert decision-making.

 
Using a large, long-established curated dataset, COSMIC Knowledgebase has trained and evaluated AI models to pre-screen PubMed search results and identify papers likely to contain curatable mutation data. This enables efficient triage of hundreds of publications weekly and reduces manual workload while maintaining high data quality.
 
We also showcase an AI-assisted workflow currently being developed by the COSMIC Actionability team to identify potentially relevant clinical trials from ClinicalTrials.gov. AI helps detect biomarkers in the appropriate clinical context and supports automated filtering, while final inclusion decisions are made by expert manual review.
 
In this webinar, we will explore the development, performance and application of COSMIC’s AI curation pipeline, including its limitations, ongoing refinement and how human expertise remains essential to validation, prioritization and interpretation.
 
What you will learn:

• - How COSMIC integrates AI into a human-led curation workflow to pre-screen and prioritize scientific literature

• - How AI models are trained, evaluated and applied to identify papers likely to contain curatable mutation data

• - The strengths, limitations and performance of current AI approaches, and where expert curators remain essential

• Future directions for scaling high-quality, AI-assisted cancer mutation curation

This webinar walks participants through a complete de novo transcript discovery workflow using QIAGEN CLC Genomics Workbench and the Transcript Discovery tools. Using a real-world dataset from the wild strawberry Fragaria vesca — combining PacBio long reads and Illumina short reads, attendees will learn how to move from raw RNA-seq data to a newly annotated reference genome ready for downstream expression analysis.

You will learn to:

• - Run the Large Gap Read Mapper to align RNA-seq reads to a genomic reference while accounting for introns

• - Use the Transcript Discovery tool to predict novel genes, transcripts, splice variants, and coding regions from read mapping data

• - Incorporate prior annotation knowledge to update and improve an existing reference annotation

• - Generate a new annotated reference genome from your discovered transcripts

• - Run RNA-seq expression analysis against your new reference to quantify gene and transcript expression

• - Interpret output tracks including predicted genes, rejected events, and splice junctions

Large-scale single-cell datasets are creating new opportunities to understand biology, model drug response, and accelerate discovery. As these datasets grow, two needs are becoming increasingly important: generating clean, high-quality single-cell data at unprecedented scale to train AI models and applying analytical frameworks that can turn complex results into biological insight.

This webinar will feature two complementary presentations focused on different aspects of these challenges.

First, Johnny Yu, CSO and cofounder of Tahoe Therapeutics, will discuss the generation of Tahoe-100 Million, the largest publicly available single-cell dataset to date. Spanning 50 cancer cell lines, 379 drugs at three concentrations and more than 56,000 conditions, Tahoe-100M comprises over 100 million single-cell transcriptomes from co-cultured, drug-treated cells. It provides a training dataset capable of powering predictive models of drug action, shifting discovery from one-perturbation-at-a-time studies toward AI-driven prediction of efficacy, resistance and combination effects.

Following, Britt Flaherty, director at QIAGEN Digital Insights, will focus on the critical analytical challenge of extracting mechanistic insight from large and complex single-cell datasets. While single-cell workflows often rely on clustering, marker gene identification and descriptive annotation, these approaches can limit the ability to translate high-dimensional cellular states into biologically meaningful and experimentally actionable hypotheses.

The speakers will discuss a case study in neuroblastoma to demonstrate how a dataset of this scale can uncover cellular response mechanisms, prioritize therapeutic hypotheses, and generate actionable insights into disease biology.

Attendees will learn:

• - About the Tahoe-100 Million dataset and Tahoe’s plans to build an even larger dataset consisting of 300 million single cells

• - How Parse Biosciences Evercode single-cell RNA sequencing technology enables the generation of high-resolution, single-cell data at scale

• - How large-scale single-cell datasets can support AI models for predicting drug MoA, efficacy, resistance, and combination effects

• - How to harmonize, curate, contextualize, and rapidly interrogate large-scale single-cell datasets to support cross-study comparisons, perturbation-driven analyses, and hypothesis generation

• - How to use integrated pathway- and network-driven interpretation to move analyses beyond clustering to prioritize targets, uncover signaling programs, and accelerate translational insight

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.