tv.qiagenbioinformatics.com

tv.qiagenbioinformatics.com

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tv.qiagenbioinformatics.com episodes

  • How to accelerate your clinical reporting workflow for high-throughput...

    As the demand for personalized cancer treatment grows, clinical diagnostic labs are facing unprecedented pressure to scale-up, increase test throughput, reduce turnaround times, and deliver comprehensive, oncologist-ready reports that match patients to the most appropriate therapies and clinical trials, in a time-efficient manner.




    In this webinar, learn how QIAGEN Clinical Insight (QCI) Interpret for Oncology, the world’s leading variant interpretation and reporting software, can help your lab accelerate your clinical reporting workflow for high-throughput precision oncology NGS testing. The webinar will demonstrate the capabilities and features of QCI Interpret for Oncology through an example workflow for comprehensive genomic profiling. Key highlights include the software’s capability to expedite variant interpretation, dynamically compute pathogenicity and actionability based on the ACMG and AMP/ASCO/CAP guidelines for every variant in over 30,000 cancer types and subtypes with full transparency. Additionally learn how to leverage over 500,000 pre-formulated, oncologist-reviewed variant interpretation summaries to build customizable, oncologist-ready reports with up-to-date gene, variant, diagnostic, prognostic and therapeutic information.



    Topics covered in this webinar:
    - QCI Interpret for Oncology’s new performance and scalability enhancements, including bulk variant assessment, flagging of co-occurring variants, and a somatic reporting policy that allows users to set pathogenicity in a phenotype-agnostic manner.
    - Learn how the panel- and sequencer-agnostic software can facilitate true scalability by rendering efficient test and process management capabilities, quick identification and comparison of tests from a patient for follow-up.
    - How the software’s underlying knowledge base combines the unmatched accuracy and consistency of QIAGEN’s proprietary expert (MD/PhD) curation with the superior efficiency of machine curation (AI-powered) to identify all the literature, treatments, and clinical trials relevant to a patient’s tumor type with just one-click.

    51 min
  • Accelerating drug discovery with comprehensive curated knowledge

    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, the Ingenuity team with QIAGEN has curated these nodes and relationships. Our QIAGEN Biomedical KB-HD make these data available for consumption outside of Ingenuity Pathway Analysis.

    In this talk, we will:

    • Explore how to query and leverage this curated data resource to accelerate the drug discovery process.

    • Provide live demonstration of the underlying database Biomedical-HD
    • Show how the Biomedical KB-HD can be rapidly deployed
    • Show 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 predicting adverse outcomes and toxicity, highlighting their transformative potential in steering pharmaceutical research and development.

    Learn more about Biomedical KB: https://qiagen.showpad.com/share/GmpIJaN5kRfsHRD5WPwgc

    56 min
  • Leveraging high-quality public proteomics data for biomarker and drug target...

    Per feedback from recent trainings, we are hosting a training focused on biomarkers and drug target investigation through effectively utilizing high quality curated public proteomics expression data. OmicSoft Lands database is a high-quality curated repository containing gene and protein expression data derived from a variety of sources such as CPTAC, TCGA, HPA, GTEx and more.

    Using OmicSoft Lands APIs, you will learn how to:



    • Easily locate proteomics samples and projects (studies) of interest (example can we locate proteomics data related to the breast cancer or specific drug treatment?)
    • Check expression of a protein of interest (drug target or biomarkers) across different experimental groups or conditions (diseases, treatments, cell types and more) as well as across samples coming from different collections (example is a protein upregulated in breast cancer samples from TCGA and CPTAC but downregulated in normal samples from TCGA, GTEx and HPA)
    • Correlate protein levels with RNA-seq expression data for the same samples, and compare differential expression results from proteomic and RNA-seq results
    • Investigate whether there is correlation in expression between two (or a group/list of proteins) across different experimental groups/conditions (example do two proteins of interest show correlation in expression in TNBC samples? Is protein and gene expression correlated as well?)
    • Derive a protein signature from conditions of interest (example are a group of protein upregulated in disease condition but downregulated upon drug treatment)
    • Perform Geneset Enrichment for a group of proteins

    58 min
  • The Implementation Journey of Comprehensive Cancer Genome Profiling and...

    This session traces the evolution of comprehensive cancer genome profiling (CGP) and liquid biopsy techniques in South East Asia, discussing the initial hurdles and ultimate triumphs. It details how interventions like international collaborations, technological enhancements, and policy support have enabled these advanced diagnostic tools' successful adoption. Highlighting the critical role of liquid biopsy in early cancer detection and monitoring treatment efficacy, the study underscores the importance of continued genomics research and investment for improved cancer care globally. It illustrates that, despite significant challenges, integrating advanced genomic profiling into oncology practice is attainable and transformative.

    1 hr 3 min
  • Sharing your analysis results with others in QIAGEN IPA

    QIAGEN Ingenuity Pathway Analysis (IPA) is a powerful tool that allows you to upload and analyze your 'omics data to identify key biological pathways and mechanisms and regulatory molecules. But did you know that you can share your analysis results with others? In this 60-minute webinar, we’ll explore the Share Manager and the Collaboration Workspace in IPA and go over the many ways you can share your analysis results with others or how they can share their results with you.

    Attendees will learn how to:

    • Share datasets and analysis with other IPA users

    • Use the Share with me feature
    • Email interactive pathways to other IPA users
    • Use a Collaboration Workspace (CWS)

    52 min
  • QIAGEN Ingenuity Pathway Analysis (IPA) new user training

    Are you new to QIAGEN 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 how 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

    2 hr 2 min
  • Discovery from sample-level public data (GEO, SRA and more) using IPA Land...

    Within QIAGEN Ingenuity Pathway Analysis (IPA), public data sourced from OmicSoft is already processed and standardized, making it easy to jump to the actual research.

    In this training, we'll show how to effectively use sample-level public data and metadata from sources like GEO, SRA, TCGA, GTEx, Blueprint, CCLE and other sources using the IPA Analysis Match and Land Explorer features. We will walk through various use cases, such as biomarker discovery, drug target investigation, studying survival in custom patient cohorts, multi-gene correlation and more.

    You will learn to answer questions such as:

    • How is a gene of interest expressed across different conditions (e.g. diseases, treatments, cell lines)?
    • Is there correlation in the expression for two genes or biomarkers of user interest for a given condition?
    • For a given condition of interest, can we derive a list of genes (e.g., genes specific to a disease, treatment or cell type)?
    • Can we generate custom cohorts of patients (e.g., example TP53 wt vs mutant or PDCD1 high vs low expression) and then, generate survival curves representing those cohorts?
    Can we generate p-values to see if there is significant difference?
    • Can we detect expression of a gene in different cell types from single cell data?

    56 min

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