tv.qiagenbioinformatics.com

Direct outputs through a workflow based on sequence count, coverage, and sample quality using branching elements https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Branching_elements.html

Per attendees’ request from a recent training, we are hosting this in-depth training focused on how to effectively construct a network and easily modify it for both no dataset or dataset (RNA-seq, proteomics, etc.) in QIAGEN Ingenuity Pathway Analysis.

In this interactive training, attendees will learn:

• How to construct a network from scratch or open a network/pathway of interest from their own analysis or Ingenuity database

• How to effectively use tools present in build and overlay menu to add molecules (genes/proteins), chemicals, metabolites, biological processes/diseases and more of their interest • How to modify the network (ex. keep only specific type of relationship like activation, phosphorylation, protein-protein binding and more)

• Perform in silico prediction (ex. if a drug, siRNA or CRISPR were to reduce activity of a gene/protein, how will impact remaining genes/proteins/diseases on that network)

• Export high resolution graphics or tabular relationships and/or make the created network usable in future pathway analysis

Additional QIAGEN Digital Insights (QDI) scientists will be on the call to answer your questions and help with other inquiries such as how to install the software.

QIAGEN’s CLC bioinformatics software portfolio provides user-friendly and intuitive solutions that run on any platform. This helps scientists to focus on the biology of their research without requiring them to write code, or compile and run software from the command line.

At QIAGEN we understand, however, that no single piece of software can meet the needs of every bioinformatics challenge. Sometimes, you need to supplement standard pipelines with your own scripts, open-source tools or third party applications from the command-line.

Accordingly, this training will go over:

• A quick introduction to CLC Genomics Workbench

• Wrapping an external application into the CLC environment

• Running external application alongside CLC tools and exporting results.

In this on-demand recording from ACMG 2024, hear from Dr. David Bick, Principal Clinician of the Newborn Genomes Program at Genomics England, as he discusses the first-of-its-kind initiative aiming to sequence the genomes of 100,000 newborns in England to screen for over 200 selected conditions.

In his talk, you will learn about:

• The development and deployment of The Generation Study

• How Genomics England selected the genes and conditions to include in the point-of-care test

• How QIAGEN provided expert-curated content for 69,844 pathogenic or likely pathogenic variants across all 209 conditions

• The variant prioritization strategy used by Genomics England to enable efficient and actionable reporting

• The anticipated research outcomes and future plans

Learn more about how QIAGEN is supporting The Generation Study here.

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

Per audience request

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

In this webinar, the trainer will cover a highly requested usecase using OmicSoft Explorer and IPA Interpret, two of the new features recently introduced to both QIAGEN IPA and OmicSoft customers.

Specifically, the trainer will go over how to

• Easily search for deep curated high-quality omics (RNA-seq, scRNA-seq etc.) datasets of interest based on user defined criteria (Disease name, drug treatment and more) curated from sources such as GEO, SRA, TCGA and more

• Explore project details to gain better understanding of experimental setup and sample metadata

• Study volcano plots to identify biomarkers associated with conditions of interest

• Generate sharable pathway analysis reports that provide insights into disease pathology, drug mechanism of action, cellular processes and more

IPA Interpret and OmicSoft Explorer are now available through both IPA and OmicSoft. Learn more: https://qiagen.my.salesforce-sites.com/KnowledgeBase/KnowledgeNavigatorPage?id=kA4Sc00000023mbKAA&categoryName=BioX

Geomicrobiology and petroleum microbiology are important fields that study how microbes interact with geological, geochemical, and petroleum processes. Understanding the composition of microbial communities can advance our knowledge of biotransformation, biodegradation, and bioremediation in industries such as environmental engineering, mining, and oil and gas.

QIAGEN CLC Genomics Workbench and the Microbial Genomics Module provide tools and workflows for a broad range of bioinformatics applications, including microbiome analysis, isolate characterization, and functional metagenomics. This webinar will focus on amplicon-based taxonomic profiling (16S/18S/ITS sequencing OTU clustering) using data collected from oil reservoirs. Specifically, the trainer will be:

• Highlighting different tools within the MGM application and research areas supported

• For taxonomic profiling:

o Importing data

o Utilizing metadata

o Downloading and managing references

o Launching the OTU clustering workflow (analytical pipeline)

• Downstream processing of abundance tables

• Creating and exporting high-quality graphics

Cancer cell line models have been a cornerstone of cancer research for decades. Profiling cancer cell lines can be a powerful tool to identify gene alterations or cancer-related pathways and aid in discovering putative drug targets. In this webinar, we'll use QIAGEN OmicSoft Lands and QIAGEN Ingenuity Pathway Analysis (IPA) to help you select cell lines and translate insights from your cell line experiments for drug target discovery.

During this 90-minute discussion, we'll explore how you can use these software tools to:

• Select appropriate cancer cell lines for a variety of applications such as drug discovery, precision disease modeling, understanding gene function in cancer, immune-oncology research and more

• Examine various 'omics data for genes of interest for expression, mutation, hotspots and gene dependency data

• Generate networks for hypotheses and test them in silico to improve the translation of insights derived from cell line models to the drug target identification

• Integrate analyses of public 'omics data and drug response phenotypes using cell line model systems by exploring data from the Library of Integrated Network-Based Cellular Signatures (LINCS)

• Prioritize drug targets and profile phenotypic/downstream effects of drug action by overlaying public data on user-generated networks Our system uses millions of curated literature findings from QIAGEN Knowledge Base and the OmicSoft digital warehouse. This discussion is intended for both those familiar with QIAGEN IPA and newcomers interested in learning more.

This webinar shows how we enable identification and prioritization of a novel drug target—MUC16—using data from The Cancer Genome Atlas (TCGA), all without requiring deep bioinformatics expertise. The analyst shows how to mine frequently mutated genes across solid tumors, filter for druggable targets based on subcellular localization and extracellular domain structure and evaluate mutation prevalence and protein context. MUC16 emerged as a compelling candidate, particularly in pancreatic adenocarcinoma, where mutations and high expression levels correlated with poorer patient survival. Despite its relevance, few clinical trials currently target MUC16 in this disease, revealing a potentially untapped therapeutic niche. Network analysis and in silico perturbation simulations further suggested that modulating MUC16 expression could meaningfully influence oncogenic pathways. The session concluded by encouraging attendees to explore digital services and underscoring the speed and accessibility of tools for hypothesis generation and target validation.

Description: In this 90-minute training session, you will learn how to identify target mRNAs for your miRNAs of interest and associate them with pathways, diseases, biological functions, tissues and cell types.

Topics that will be covered include:

• How to analyze miRNA-seq datasets alone or both miRNA and corresponding mRNA datasets together

• How to use QIAGEN IPA without a dataset, using miRNA IDs

• Introduction to databases and curated content specific to miRNA

• How to effectively apply various filters and functionalities to identify biomarkers, key targets and biological mechanisms

To learn more about IPA or request for a trial: https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/?cmpid=CM_QDI_DISC_042024webinars-miRNA