We are pleased to announce that the 2023 Release of QCI Interpret, QIAGEN’s decision support software platform for the annotation, classification, and reporting of somatic and germline variants, is now available.
Expanding on the software’s current capabilities, the QCI Interpret 2023 Release extends its market-leading, unrivalled content with further advancements in Artificial Intelligence (AI) to enable clinical exome completeness, enhanced phenotype driven ranking, and improved literature searches.
The latest release of QCI Interpret for Hereditary includes AI enhanced coverage of thousands of rare disease genes. The below graphic illustrates the improved curation process for QCI Interpret, showing how all content is initially extracted using AI and machine learning, the most prevalent disease genes undergo human-certified curation, and all content undergoes rigorous quality control to ensure consistency and accuracy.
QCI Interpret is clinical decision support software that combines the unmatched accuracy and consistency of QIAGEN’s proprietary expert (MD/PhD) curation with the superior efficiency of machine curation (AI-powered curation) to enable high-confidence variant interpretation and reporting. Advanced features enable clinical diagnostic labs to rapidly identify pathogenic variants, improve diagnostic yields, and reduce turnaround times. Panel- and sequencer-agnostic, QCI Interpret can be fully customized to accommodate gene panels, exomes, and genomes.
QCI Interpret for Hereditary
QCI Interpret for Oncology
Part 1: October 12 | Part 2: November 9
A virtual event to help diagnostic labs learn how to safely apply AI to exome and genome sequencing workflows,
REGISTER NOW
Clinical exome sequencing (CES) is increasingly being adopted by small and mid-sized laboratories to diagnose genetic diseases, aid treatment decisions, and provide prognostic information. However, the exponential increase in genetic data generated from exome and genome panels poses significant workflow challenges. The ability to prioritize potentially pathogenic variants from large datasets and identify the few candidate variants becomes more difficult. This issue is further amplified in cases where labs must use deep phenotyping of patients and compare that to reference genotype-phenotype knowledge associated with each candidate variant. To overcome these challenges, labs are beginning to implement Artificial Intelligence (AI) in their variant analysis, interpretation and reporting workflows.
Join us for our 2023 Clinical Hereditary Disease Diagnostics Summit, a free-to-attend, two-part event exploring the opportunities and limitations of AI in hereditary disease diagnostics. Designed to help clinical diagnostic labs learn how to safely apply AI to exome and genome sequencing workflows, the content-rich event will feature invited lectures from lab directors and clinical geneticists, thought-provoking discussions on the future of hereditary disease diagnostics, as well as educational presentations on the latest databases and AI-powered software for germline secondary and tertiary analysis.
Part II: Roundtable discussion with genomics experts – November 9, 2023
A panel discussion featuring experts in the field of clinical genomics that will explore the challenges and opportunities in the future of inherited disease diagnostic testing.
For the list of speakers and session information, visit our event page here.
Clinical exome sequencing (CES) is increasingly being adopted by small and mid-sized laboratories to diagnose genetic diseases, aid treatment decisions, and provide prognostic information. However, the exponential increase in genetic data generated from exome and genome panels poses significant workflow challenges. The ability to prioritize potentially pathogenic variants from large datasets and identify the few candidate variants becomes more difficult. This issue is further amplified in cases where labs must use deep phenotyping of patients and compare that to reference genotype-phenotype knowledge associated with each candidate variant. To overcome these challenges, labs are beginning to implement Artificial Intelligence (AI) in their variant analysis, interpretation and reporting workflows.
Join us for our 2023 Clinical Hereditary Disease Diagnostics Summit, a free-to-attend, two-part event exploring the opportunities and limitations of AI in hereditary disease diagnostics. Designed to help clinical diagnostic labs learn how to safely apply AI to exome and genome sequencing workflows, the content-rich event will feature invited lectures from lab directors and clinical geneticists, thought-provoking discussions on the future of hereditary disease diagnostics, as well as educational presentations on the latest databases and AI-powered software for germline secondary and tertiary analysis.
Part I: Educational talks – October 12, 2023
An education session exploring the latest databases, software, and services for germline secondary and tertiary NGS analysis. Topics will include:
In its latest release, QCI Interpret for Hereditary extends its market-leading content with further advancements in Artificial Intelligence (AI) for enhanced capabilities in clinical exome NGS testing. Now, with the addition of AI-derived literature references for rare disease genes, QCI Interpret provides complete exome coverage, on top of the existing unrivalled manually curated content and bibliography.
In this webinar, we will demonstrate how QCI Interpret is expanding its literature coverage enabling easy and efficient variant filtering workflow, based on bibliography and on patient’s phenotype. This new feature provides greater control over bibliography context, allowing users to only look at publications where the causative variant is associated with a specific disease. In addition, the release provides AI-enhanced phenotype-driven ranking. Using this approach that has been trained using thousands of solved cases, QCI Interpret for Hereditary Diseases provides superior overall candidate ranking for causative variants in rare diseases. The new variant-ranking approach is enhanced by taking into account additional variant related variables supported also by AI, as well as the patient’s symptoms, and all of the manually curated literature in the QIAGEN Knowledge Base to give the best possible chance of reaching an accurate diagnosis.
Learning objectives:
