tv.qiagenbioinformatics.com

Which targets are you missing when you analyze genetics, 'omics or single-cell data alone?

Focusing on amyotrophic lateral sclerosis (ALS), a complex neuroinflammatory disease with limited therapeutic options and heterogeneous disease biology, we explore how pharma R&D teams can identify high-confidence, developable targets. Instead of focusing on known ALS genes, uncover new targets and disease mechanisms by combining knowledge graphs, multiomics and single-cell data.

See how integrated data analysis can reveal novel, evidence-backed targets – and determine which discoveries are worth pursuing as drug programs.

Advances in transcriptomics, proteomics and multiomics are transforming animal science research, but extracting actionable biological insights remains a challenge. How do you move from differential expression lists to understanding mechanisms of disease, growth and immune response?

Using an avian use case (chicken), we will show how QIAGEN Ingenuity Pathway Analysis (IPA) helps uncover critical pathways, upstream regulators and biological functions driving phenotypes of interest – and highlighting how these approaches can be applied across any animal species.

Join us to discover how IPA enables researchers to translate complex ‘omics datasets into mechanistic insights for animal health, development and infectious disease biology.

You will learn:

- To interpret gene expression data in the context of biological pathways and to identify key regulators that control biological processes

- To discover biological functions underlying the phenotype of interest

- To generate testable hypotheses for experimental validation using causal analytics

Companion animal 'omics and genetic discoveries often sit in silos, with no systematic way to connect them into a discovery strategy.Using canine obesity as an example, we will explore how QIAGEN Ingenuity Pathway Analysis (IPA) bridges that gap in two stages. First, we mine canine obesity RNA-seq data to surface candidate biomarkers and drug targets, extracting pathway activity, upstream regulators and downstream effects. Then, we take the most interesting candidates together with GWAS-derived obesity genes and build custom networks to reveal mechanisms, expose connections to existing drugs and diseases and turn scattered findings into a defensible program strategy.

You will learn:

To discover targets from animal transcriptomics

Extracting multiple insights from a single study and analyzing pathway activity, upstream regulators and downstream effects

To build networks from candidate genes

Combining your top candidates with GWAS hits to reveal mechanisms, links to known drugs and diseases and uncover the insights needed to guide projects forward

To turn connected biology into program strategy

Using a network-level view of converging targets, biomarkers and existing therapeutics to define scientific objectives, flag dead-ends and highlight gaps in the competitive landscape

Intestinal dilation syndrome (IDS) is an emerging disease of commercial laying hens that presents with severe intestinal shortening, segmental dilation of the small intestine, reduced egg production and high mortality. After over two decades of research, the underlying causes of IDS remain obscure, though an interesting pattern has emerged: its restriction to hens that lay brown eggs.

Join Professor Dana Stanley from Central Queensland University, Australia, as she shares what her team discovered about this connection. See how tertiary analysis of RNA-seq data using QIAGEN Ingenuity Pathway Analysis (IPA) has transformed complex transcriptomic datasets into biologically meaningful hypotheses, enabling a unifying explanation where conventional approaches have failed.

You will learn:

How they shed light on the first mechanistic framework linking brown eggshell pigmentation to IDS susceptibility

Including the roles of  intrinsic host factors and environmental triggers

How they uncovered the involvement of porphyrin-associated pathways

Particularly protoporphyrin IX, the pigment responsible for brown eggshell coloration

How they pinpointed activation of specific inflammatory pathways

Including HIF1α Signaling and Neutrophil Extracellular Trap Signaling

Drug discovery, clinical and translational teams have no shortage of promising therapeutic opportunities. But how do you efficiently choose the right one to develop, while avoiding potential biological, safety and clinical risks?

See how to systematically evaluate repurposing opportunities for a GLP-1 receptor agonist using causal biomedical knowledge graphs and evidence from over 6.8 million clinical trial records. We’ll use AI to help uncover new disease–target relationships, assess safety, learn from past clinical trials and find new indications that would otherwise be difficult to surface.

You will learn

- How to surface new causal relationships and reduce risk with strategic AI usage

- To combine knowledge graph analytics with predictive modeling

- How to evaluate therapeutic opportunities with the strongest supporting evidence

QIAGEN OmicSoft APIs enable rapid access to harmonized multiomics datasets, including transcriptomics, genomics, single cell, proteomics and functional genomics data, complemented by deeply curated clinical, treatment, demographic and outcome metadata where available.

Explore how researchers can use OmicSoft APIs to efficiently identify biomarkers, characterize disease biology, investigate treatment response and resistance mechanisms and build predictive molecular signatures across large-scale datasets.

We will also highlight the new OmicSoft API Skills framework, which provides reusable analytical workflows and AI-assisted templates that help researchers accelerate common multiomics discovery tasks and generate insights more efficiently.

You will learn:

• - Multiomics data coverage, sources and harmonization; API-driven cohort selection and data retrieval

• - Biomarker and molecular signature discovery; Treatment response and resistance analyses

• - Cell-type-specific investigations; Integration of transcriptomic, genomic, proteomic and functional datasets

• - OmicSoft API Skills for faster, reproducible analyses

Modern ‘omics research generates vast amounts of complex data, creating a need for streamlined workflows that transform raw sequencing results into meaningful biological insights. In this webinar, we will show how you can leverage QIAGEN Digital Insights platforms for secondary and tertiary analysis to accelerate development and discoveries.

Our robust workflow enables you to analyze next-generation sequencing (NGS) data, uncover biological mechanisms and potential biomarkers and explore and contextualize findings using curated multiomics datasets. Using RNA-seq as an example, you will see how our integrated workflow can simplify data analysis and support impactful research outcomes.

Our integrated bioinformatics workflow includes:

- Analyzing raw RNA-seq data using CLC Genomic Workbench to generate differential expression data

• - Interpreting differential expression data using Ingenuity Pathway Analysis (IPA) to uncover pathways, regulatory molecules and disease associations

• - Exploring deeply curated data within OmicSoft Lands to identify expression trends and validate findings

Join this webinar to discover how Franklin by QIAGEN streamlines the entire rapid genome analysis workflow, helping laboratories move from raw sequencing data to clinically actionable reports in record time. Through a live demonstration, you'll see how Franklin combines AI-assisted variant prioritization, automated ACMG classification, phenotype-driven analysis and integrated clinical evidence to reduce millions of variants to a concise list of the most relevant findings.

Reduce turnaround time with an end-to-end, automated rapid genome workflow

Prioritize clinically relevant variants using AI-assistance and phenotype-driven analysis

Standardize interpretation with automated ACMG classification and reusable internal knowledge

Leverage more than 100 integrated databases alongside Franklin's unique community knowledge base

Scale confidently across sequencing platforms, variant types and laboratory workflows with a vendor-agnostic solution

Simplify variant filtering, collaborative review and report generation with highly customizable workflows

Biomedical knowledge graphs are powerful tools for organizing complex biological information and uncovering relationships that are difficult to see in traditional data formats. For part 2 of this series, we will show how researchers new to knowledge graphs can use them to support hypothesis generation, target discovery and data exploration.

We will explore:

- Knowledge graph fundamentals

- 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.

As the adoption of relational and deeply curated ‘omics databases increases, creating SQL queries has become a mandatory skill rather than a desirable one. For part 1 of this series, we will equip you with the know-how and best practices around designing SQL queries – no prior experience required.

While we will use QIAGEN biomedical databases, the knowledge you acquire can be easily applied to other databases. We will cover:

- SQL basics

- A brief overview of the knowledge databases to be used

- How to design queries to help you answer scientific questions, e.g. related to disease pathology, MOA, drug response, gene signatures and more

- Some applications and frequently asked questions

- Resources