tv.qiagenbioinformatics.com

In gene therapy space, there is great interest in discovering new gene editing targets as well as identifying new indications for known targets though exploration of vector biology and studying biological mechanism underlying disease pathology. In this webinar, we will focus on how recently introduced Biomedical KB-AI can enable and accelerate such investigation. QIAGEN Biomedical KB-AI uses generative AI to identify novel relationships and patterns that may be missed by traditional methods and can provide a more comprehensive view of the biological landscape.

Participants will learn about

• Biomedical KB-AI and its structure

• How to easily search and filter results for vector system of interest through subgraph

• How to thoroughly interrogate the subgraph to look for similarity between diseases and targets that can be targeted by vectors of interest

• How to effectively use knowledge graph for drug repurposing applications

In the rapidly evolving landscape of drug discovery, the ability to integrate high-quality research findings into knowledge graphs is paramount. For over twenty years, our scientists have curated the relationships between genes, drugs, diseases, and pathways to power Ingenuity Pathway Analysis. Now, these data are available via our QIAGEN Biomedical KB-HD, which provides direct access to flat files, SQL APIs in Python and R, and the ability to export knowledge graph objects for analysis in Neo4j. In this talk, we will explore how to use this rich data resource to:

• aggregate relevant findings across our comprehensive disease and gene ontologies

• cross-reference clinical trial results to focus your research on genes upstream or downstream of known drug targets

• filter causal relationships by the directionality of observed effects

• combine the above methods to accelerate the drug discovery process

By demonstrating the underlying database live, we will show how the high-quality curated biomedical knowledge bases can be rapidly deployed, as well as how the underlying schema and ontologies could serve as a scaffold for integrating your own research. Overall, this demonstration will show the critical role of knowledge graphs in finding viable drug targets while avoiding potential adverse outcomes and toxicity.

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

• Import FASTQ files, cell matrix files and metadata and how to download references

• Map reads to a reference genome and generate gene and transcript counts and QC reports displaying % mapped reads, knee plots, etc.

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

• Easily customize RNA-seq workflows

• Export publication-quality graphics, tables and reports

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

Are you new to QIAGEN Ingenuity Pathway Analysis (IPA) or interested in expanding your skill set? Join us as we learn more on large dataset analysis and knowledge base queries using QIAGEN IPA.

You’ll learn to:

• Upload multiple dataset types (e.g., RNA-seq, proteomics, metabolomics) and perform interactive core/pathway analysis in IPA

• Learn how to interpret different results, including pathways, key regulators, impact on biological functions/diseases and more

• Compare different experimental conditions (e.g., single-cell clusters, disease types) and identify similarities and contrasts

• Generate a network for hypothesis generation, even without a dataset or experimental design

QIAGEN Ingenuity Pathway Analysis (IPA) is designed to help you analyze and compare different types of 'omics data. In this webinar, we’ll compare bulk RNA-seq and single-cell RNA-seq data to identify common regulators/targets and see how those regulators/targets associate with your phenotype of interest. We will also use sample-level public data to validate gene expression of common genes in tissues and/or cell type of interest.

You'll learn how to:

• Generate a Comparison Analysis for bulk and single-cell RNA-seq

• Identify significant common genes with the Compare feature

• Build a custom network associating common genes to a phenotype

• Examine sample- and cell-level expression in OmicSoft content

Neoantigens are among the newest promising targets for cancer treatment. Unlike tumor-associated antigens, neoantigens are produced only by tumor cells. These unique molecular markers are highly visible to the immune system, making them ideal for personalized immunotherapies. They may also be able to predict both tumor survival prognosis and immune checkpoint blockade responses. Neoantigens are presented by human leukocyte antigen class I molecules (HLA-I) to T cells, providing an ideal pathway for detection by shotgun proteomics. To identify these elusive targets, scientists often rely on whole exome sequencing (WES)-derived data. However, this approach can miss rare or low-frequency mutations, especially in cancers with a low tumor mutational burden (TMB).

The Catalogue of Somatic Mutations in Cancer (COSMIC) can bridge this gap by providing researchers with detailed information about somatic mutations in human cancer. Unlock the full potential of neoantigen-targeted therapies and enhance traditional approaches like WES with our comprehensive, expert-curated knowledge base.

In this webinar, you’ll learn to:

• Uncover and validate potential neoantigens with a recent peer-reviewed study that uses COSMIC mutation data from human hepatocellular carcinoma (HCC), a low-TMB cancer

This OmicSoft DiseaseLand training is designed to empower researchers to take advantage of data on non-oncological diseases (e.g., inflammatory, infectious, metabolic) as well as normal tissues (e.g., for toxicology or target safety assessment).

OmicSoft DiseaseLand is a repository that contains high-quality curated ‘omics data (e.g., RNA-seq, scRNA-seq, microarray) from diverse data sources (e.g., GEO, SRA, GTEx). This training will focus on leveraging public data, but users can also apply this knowledge towards their institutions' internal Lands ('omic data repositories). 

You learn to answer questions such as:

• How is a gene (or a set of genes) expressed across different conditions (e.g., diseases, treatments, tissues, cell types)

• How is the expression of a gene correlated with the expression of other genes?  

• For a list of genes, can we generate a heatmap studying their expression across different conditions? 

• How do you get a list of biomarkers specific to a condition from public studies? 

Are you interested in exploring how you can evaluate phenotypic differences between custom cohorts? You can learn more about drug response, survival, co-expressing biomarkers, mutations and more based on gene expression.

In this webinar, you'll learn how to investigate patient cohorts using high-quality curated genomic repositories like the QIAGEN OmicSoft Lands database. You'll be able to to perform in-depth investigations across diverse data-sources, including GEO datasets, TCGA and more.

In this webinar, attendees will learn how to:

• Easily locate studies of interest using OmicSoft Lands APIs

• Generate custom cohorts based on expression levels of biomarker(s) of interest

• Thoroughly investigate drug response and patient survival for these custom cohorts

• Study variants/mutations present in these patient population and their association with disease pathology