Disease pathogenesis is often context-dependent, specifically on tissue expression profiles within a given disease context. However, tissue expression profile of drug targets or potential targets is often difficult to ascertain. In this webinar, we will dive deep into the wealth of available deeply curated high quality ‘omics data (RNA-seq, scRNA-seq and more) in the OmicSoft Lands database using APIs to achieve the following:
In the drug discovery pipeline, evaluating multiple pathways between a drug target and disease is crucial for identifying potential therapeutic approaches. By examining overlapping pathways between competing drugs, researchers can uncover novel drug targets, while isoform-specific findings may elucidate unexpected clinical trial outcomes. Filtering these pathways with real-world expression and proteomics data is essential to validate new hypotheses and avoid pursuing non-viable leads.
Leveraging tools such as Neo4j, Python, and R, powered by curated databases like the QIAGEN Biomedical Knowledge Base and OmicSoft Lands, enables scientists to efficiently explore potential mechanisms of action during both target discovery and later stages of drug development.
Attendees will learn to:
In this training, you will learn how to analyze and interpret your own single cell RNA-seq data using QIAGEN CLC Genomics Workbench starting with either FASTQ or matrix files.
Using CLC Genomics Workbench, you will learn how to perform secondary analysis on your single cell RNA-seq data. Specifically, you will learn how to:
While there is great interest in the scientific community to investigate drug targets and biomarkers from public immune-oncology data sources such as The Cancer Genome Atlas (TCGA), such investigation is hindered by difficulties in finding and combining related datasets to perform large-scale meta-analyses. This webinar will focus on how high-quality curated genomic repositories such as QIAGEN OmicSoft Lands immediately allows in-depth investigations across diverse data sources (GEO, CPTAC, TCGA, GTEx and more) to discover and validate candidate checkpoint inhibitor drug targets and biomarkers.
You will discover how to:
This training will be relevant to both QIAGEN Main and Genomics workbench users and prospects who are interested in below analytics.
Join us for a 90-minute LIVE group session designed to address different technical questions regarding IPA certification. QIAGEN IPA scientists will answer different IPA related technical questions and clarify various topics per requests of IPA certification participants.
Please note:
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:
Already have an IPA license? Install IPA and start using it now.
Learn more about IPA or request a free trial.
Each year, more than 134 million patients experience adverse drug events, causing 2.4 million deaths worldwide. What if there was a better way to develop and prescribe safer, more effective drugs based on the unique genetic information of every individual?
In this webinar, learn about a game-changing solution for labs and pharmaceutical companies to rapidly and confidently translate complex pharmacogenomics (PGx) data into evidence-backed insights to inform therapeutic outcomes. QIAGEN Pharmacogenomic Insights (PGXI) is a new expert-curated knowledgebase solution that provides comprehensive access to verified and up-to-date data from multiple PGx sources, including the FDA, CPIC, DPWG, PharmVar, and PubMed. Built upon a legacy platform used to deliver more than 1.6 million PGx annotations for more than 250 clinical research laboratories, PGXI seamlessly integrates into existing or new workflows to automate the annotation of PGx data from all platforms, including NGS and arrays. Within minutes, users can retrieve a custom output of relevant information, including relevant conditions associated with queried genes of interest, relevant drug-gene associations, and curated literature evidence for alleles in question.
In this webinar, you will learn:
Plus, if eligible, webinar attendees can try PGXI for free with an exclusive trial offer from QIAGEN. There’s an easier and faster way to translate PGx data into insights. Don’t miss this opportunity to elevate and transform your PGx program.
Learn more and register here.
Learn how QCI Interpret, clinical decision support software for variant interpretation and reporting, can help your lab rapidly identify pathogenic variants, improve diagnostic yields, and significantly reduce test turnaround time for hereditary diseases. Attendees will receive a demonstration of QCI Interpret’s unique capabilities and advanced features for germline variant interpretation with example workflows for carrier screening and whole-exome sequencing panels. Key highlights will include how your lab can expedite variant interpretation by leveraging the most extensive, manually curated knowledge base, dynamically compute pathogenicity based on ACMG guidelines for every variant with full transparency, and leverage QCI Interpret’s proprietary augmented molecular intelligence approach to literature curation and variant classification to streamline your interpretation workflow.
In this webinar, attendees will:
Speaker:
Elias Hage, PhD, is the Associate Director of Global Product Management for Hereditary Disease and Oncology Applications at QIAGEN Digital Insights (QDI). In this role, he oversees the development, management, and optimization of QDI software, databases and services for genomic analysis and interpretation of hereditary disease and oncology cases. Prior to joining QDI, Dr. Hage served as a Global Product Manager at Agilent Technologies, where he managed the Genomics division flagship SaaS application (Alissa Interpret) and supported its usage globally in both research and IVD settings. Dr. Hage obtained his Ph.D. in virology and genomics from Hannover Medical School in Belgium.
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:
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.
