Enable greater efficiency from your tertiary analysis workflows with explainable AI (XAI) and automation supporting genomes, exomes, virtual panels, and targeted panels.
Unify your laboratory and NGS instrumentation with your IT systems to simplify and secure your complete workflow.
Confidently keep pace with evolving science, technology, and demand with up-to-date knowledge graph options, curation capabilities, and a team of experts to support your journey.
Increase throughput without increasing headcount using explainable AI (XAI) and automated workflows.
Broaden your analysis to WGS or WES or standardize panels on a backbone assay. Analyze various variant types—SNVs, indels, short tandem repeats (STRs), copy number variants (CNVs), other structural variants, and mtDNA.
Implement a high throughput WGS, WES, virtual panel, or targeted panel workflow that is integrated into your lab's digital ecosystem.
Leverage the power of collaboration to share knowledge across a private network of labs.
Never a black box. XAI prioritizes insights backed by evidence to increase workflow efficiency and confidence.
Maximize efficiency and scale by optimizing workflows for your standard operating procedures (SOPs) across test types, locking in your automated flow.
Integrate workflows with application programming interfaces (APIs), linking your tertiary analysis with laboratory information management systems (LIMS), storage, pipelines, and more.
97% accuracy in prioritizing relevant insights, AI can suggest variants in complex data sets that typically require hours of manual review.
Transparent logic. Every AI hypothesis is backed by literature and database sources.
Time-saving automated ACMG classifications for SNV, indel, CNV, and SV deletions/duplication variants.
Maximize use and reuse of your organization’s curated knowledge. Share across a private network of connected labs.
Understand how automating insights can help you confidently scale your data operations.
Overview of the automated insights solution with AI-prioritization that can streamline dry lab workflows for WGS, WES, virtual panels, and targeted panels.
Dr. Linyan Meng, Division Director Clinical Interpretation, Baylor Genetics, presents the results of a research study demonstrating the utility of machine learning for interpretation in a 180-sample cohort. By automating their variant prioritization and classification processes, machine learning technologies support eliminating the bottleneck in genomic data interpretation.Watch Webinar
WGS is the most comprehensive method for genetic disease testing and is increasingly applied to rare disease and other hereditary disease research.Learn More
Virtual panels or “slices” can be created from a more comprehensive “backbone” assay that is standardized in the lab, such as WGS or WES.Learn More