IPA is more than just an analysis program; it can also help you write grants and publications. With IPA, you gain access to biological insights backed by expert-curated findings and thousands of pre-analyzed multi-omics datasets.

You will learn:

- To interrogate, predict and interpret

    > Interrogate critical pathways, predict regulators causing your observed expression changes and interpret your results in the context of internal and public data

- To search, build and generate

    > Search IPA’s vast collection of curated literature and ‘omics data, build your own networks and predict activity changes, and generate manuscript-ready tables and figures

- To use IPA Interpret

    > See how new features such as IPA Interpret supports collaboration and hypothesis generation

- To leverage public data

    > Explore a library of over 100,000 precomputed IPA analyses curated from peer-reviewed publications

AI excels at identifying patterns and generating insights across large datasets, but its output quality depends heavily on its training data. Meanwhile, projects involve making critical decisions – decisions that require a nuanced understanding of biology, subject-matter expertise and experience that AI lacks. That’s where humans come in.

Our fireside chat will explore a hybrid workflow that combines the best of both worlds: AI efficiency and human intelligence. Led by experts from Sygnature Discovery, we will discuss how this modern approach benefits drug discovery, shortening the path from target lists to confident decisions.

You will learn:

What “human-in-the-loop” and “lab-in-the-loop” means

The importance of expert intervention and wet-lab validation

The synergistic impact of well-trained AI and expert discretion

How they reduce project risk by flagging safety/IP/druggability issues and more

How cross-disciplinary input drives actionable results

Uncover insights into drug metabolism and pharmacokinetics (DMPK) with causation-driven interpretation and feasibility assays

Why explainability matters

How transparency and human expertise lead to defensible decisions

Neurodegenerative disorders linked to protein dysfunction are difficult to study because of the complexity of the underlying molecular interactions. A great example is TAR DNA-binding protein 43 (TDP-43), found in diseases like amyotrophic lateral sclerosis (ALS), Parkinson's disease and several types of dementia. Today, researchers are still trying to answer how TDP-43's activity translates into broader cellular effects.

In this webinar, we will explore how to combine CRISPR-based screening approaches and pathway-level analysis for biological interpretation that encompasses more than gene-level observations. Using QIAGEN Ingenuity Pathway Analysis (IPA), we will demonstrate strategies to contextualize screening results and assess the functional relevance of candidate modifiers within cellular networks.

You will learn: 

1. About the role of TDP-43 in the cell and its connection to neuropathies
   

2. How to investigate CRISPR screen hits using IPA

3. How to validate the rescue effect of genetic modifiers using pathway activation scores

Public neuroscience datasets are abundant, but they prove difficult to translate into targets due to complexities with brain regions, cell types and more.

Using Alzheimer’s disease as a case study, our webinar will explore how you can combine causal relationship evidence with deeply curated neurodegenerative and neuroinflammatory disease cohorts to prioritize targets relevant to the central nervous system (CNS).

You will learn

- To identify hub genes and validate human CNS cell-type expression

    > Which candidates align with CNS cell types (e.g., neurons, astrocytes, microglia, oligodendrocytes), supporting biological plausibility?

- To triage candidates based on known drug evidence

    > Which candidates intersect with known drug targets or pharmacology, enabling rapid triage?

- To extend insights into related neurological indications

    > How can network context and cohort evidence support indication expansion into related neurodegenerative or neuroinflammatory disorders?

Good manufacturing practices (GMP) environments, such as biologics production facilities, require transparent, reproducible and audit-ready workflows. Here, we introduce a standardized short- or long-reads NGS-based workflow for viral detection using QIAGEN CLC Genomics Workbench Software, highlighting best practices to ensure sensitivity and specificity – and support reliable viral safety testing.

You will learn:

To implement this new end-to-end NGS analysis workflow for viral detection using any sequencing technology

Process raw reads to final viral detection results using reproducible and traceable analysis steps, including quality control (QC), reference selection (e.g., RVDB) and contamination control

How this new workflow aligns with GMP

See how CLC supports audit-ready analysis through full parameter tracking, versioned references and run documentation suitable for regulated environments

To perform sensitive and robust viral detection

Detect low-level viral contamination using curated databases, optimized alignment strategies and result interpretation frameworks

The Spring 2026 release of IPA introduces the Comparison Analysis bubble map to IPA Interpret. This feature lets users compare multiple treatments, diseases, cell types and other experimental conditions in a single, customizable and interactive graphic.

With support for up to 50 IPA Core Analyses, this bubble map also shows all scored entities – Upstream Regulators, Canonical Pathways and more – in one matrix, a huge step from IPA Desktop, which caps at 20 analyses and shows only one entity type per heat map.

You will learn:

• > To set up comparison analysis in IPA Interpret

• > To use bubble colors, size, filters, sorting and other features to drive discoveries

• > To open pathways and networks represented by bubbles in comparison analysis to generate strong hypotheses

QIAGEN Ingenuity Pathway Analysis (IPA) is a tertiary analysis platform that:

- Predicts the activity of pathways, diseases, genes and more

- Validates your results against existing research

- Interprets your ‘omics data into clear, action-oriented insights

Ready to turn your ‘omics data into discoveries? See how you can leverage expert-curated, AI-augmented knowledge for actionable ‘omics insights → https://digitalinsights.qiagen.com/ipa

QIAGEN Ingenuity Pathway Analysis (IPA) is a tertiary analysis platform that:

- Predicts the activity of pathways, diseases, genes and more

- Validates your results against existing research

- Interprets your ‘omics data into clear, action-oriented insights

Ready to turn your ‘omics data into discoveries? See how you can leverage expert-curated, AI-augmented knowledge for actionable ‘omics insights → https://digitalinsights.qiagen.com/ipa

Accurate variant interpretation is critical in hereditary cancer genetics, but it’s often complex due to conflicting data and evolving clinical context. In this webinar, Molecular Geneticist Dr. Ana Krivokuca will lead a live, case-based demonstration using Franklin by QIAGEN, showing how the platform's structured workflows, ACMG criteria and integrated data can support confident, transparent interpretations while addressing uncertainty and real-world clinical decision-making.

You will learn:

- How to evaluate complex variants

    > Using real-world cases examples of how Franklin helps users classify variants of unknown significance (VUS) and structural findings

- How to balance evidence and recognize when a finding is relevant

    > Applying automated ACMG classification, a vast repository of community-shared data and integrated Human Gene Mutation Database (HGMD) content to determine significance and avoid overinterpretation

- How to develop a practical framework for hereditary cancer variant interpretation

    > Leveraging a platform that acts as a hybrid system that automates technical processes while providing tools for human experts to make final, informed decisions

Comorbidity data can reveal unexpected and insightful connections between diseases. Understanding shared risks and underlying mechanisms are critical for informing patient care, yet these analyses often present significant challenges for researchers.

Using a case study on the correlation between asthma and inflammatory bowel disease (IBD) in women, this webinar will demonstrate how comorbidity data can be integrated with harmonized omics datasets and knowledge graph–based analytical approaches.

Discover how you can combine large-scale, preprocessed omics resources with network-driven analytics to uncover shared molecular mechanisms across diseases. Leveraging extensive collections of preprocessed data, the approach identifies gender-specific differentially expressed genes within each disease condition. Findings are then contextualized within a molecular interaction network to clarify associated biological functions and support hypothesis generation.

This webinar is based on a 2022 NHS population study on multimorbidity and comorbidity1, which demonstrates how large-scale datasets can be used to propose mechanistic hypotheses that help explain observed patterns in patient health.

Reference:

[1] Kuan, Valerie, et al. “Identifying and visualizing multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study.” The Lancet Digital Health 5.1 (2023): e16-e27.