Illumina Expands BioInsight Platform with New AI-Powered Software to Accelerate Genomic Discovery
New Capabilities Enhance Sequencing Workflows, Variant Prioritization, and Large-Scale Genomic Research
a global leader in DNA sequencing and genomic technologies, has announced three new early-access capabilities for its BioInsight Platform designed to help researchers transform sequencing data into meaningful scientific discoveries more efficiently. The new software capabilities focus on automating laboratory workflows, improving the prioritization of genetic variants, and enabling researchers to investigate patterns across large genomic, multiomic, and phenotypic datasets.
The newly introduced capabilities include BioInsight Platform Workflow Orchestrator, DRAGEN Ultra, and BioInsight Platform Learning Cohorts. Together with the previously announced BioInsight Data offering, these tools are designed to help research organizations manage increasingly complex datasets, identify promising research targets, and understand individual findings within broader biological and population contexts.
As genomic research expands, scientists face growing volumes of sequencing information that require advanced computational tools and consistent analytical processes. Illumina’s latest developments aim to address these challenges by connecting laboratory operations, artificial intelligence (AI)-powered analysis, and large-scale cohort research within a flexible software environment.
“The promise of genomics depends on generating vast and complex data, then distilling that data into insights that advance discovery,” said Rami Mehio, senior vice president and general manager of BioInsight at Illumina. He emphasized the importance of scalable and reproducible workflows, AI capabilities that help researchers identify significant findings, and tools that enable scientists to study samples across increasingly large and diverse populations.
The BioInsight Platform was introduced the previous week as a suite of genomic and multiomic software tools and data resources. Its modular approach allows researchers to select the capabilities most relevant to their scientific objectives, connect different tools, and expand their workflows as research programs develop.
Workflow Orchestrator Streamlines Large-Scale Genomic Operations
BioInsight Platform Workflow Orchestrator is designed to help laboratories establish consistent, traceable, and scalable sequencing and informatics workflows. As research organizations process more samples and manage increasingly complicated analytical procedures, maintaining quality, reproducibility, and operational oversight becomes essential.
The new orchestration software connects multiple stages of genomic analysis into a coordinated process. These stages include sample tracking, sequencing-run planning, secondary analysis, quality control, and tertiary software applications used to generate scientific insights.
Through a visual workflow builder, laboratory teams can begin with templates developed by Illumina and customize parameters to reflect their specific research requirements and laboratory procedures. Scientists define the desired workflow, while the software executes the configured processes consistently across large sample collections.
The system is designed to support operations involving tens of thousands of samples, helping research organizations expand their analytical capacity without introducing unnecessary workflow complexity.
Another important feature is the ability to maintain a single identity for each sample across connected software tools. This creates a traceable record of the inputs, software versions, configuration settings, and quality metrics associated with each result.
Such detailed result lineage can help laboratories reproduce analyses, investigate discrepancies, and document the processes used to generate scientific findings. It also provides an audit-ready operational foundation for organizations that require consistent documentation and quality oversight.
By coordinating sequencing and computational workflows, Workflow Orchestrator aims to reduce the operational burden on research teams and allow scientists to devote more time to interpreting results and advancing their research objectives.
DRAGEN Ultra Extends Genomic Analysis Beyond Variant Calling
Illumina is also expanding its DRAGEN technology portfolio with DRAGEN Ultra, a capability designed to extend genomic analysis beyond identifying genetic variants.
For more than a decade, DRAGEN has supported genomic secondary analysis with an emphasis on performance, accuracy, and analytical capabilities. DRAGEN Ultra builds on this foundation by combining Illumina’s genomic AI models with proprietary knowledge resources to help researchers prioritize variants for further investigation.
Variant calling identifies genetic differences in sequencing data. However, identifying a variant does not necessarily establish whether it contributes to a particular biological characteristic or observed phenotype. Researchers often need additional evidence to determine which variants deserve closer examination.
DRAGEN Ultra is designed to help bridge this gap by integrating annotation and prioritization into the analytical process. The capability aims to help scientists focus on variants that may be more relevant to their research questions, potentially accelerating the transition from variant identification to biological interpretation.
The technology is intended to support research applications including rare disease studies, population genomics, and multiomic investigations. It will be available through the Illumina BioInsight Platform and can also be deployed on customer premises, in private cloud environments, and in air-gapped settings.
These deployment options are intended to accommodate different research infrastructure requirements and data-management preferences.
New dnaAI Model Supports Variant Prioritization
DRAGEN Ultra will include dnaAI, a new variant-prioritization model developed by Illumina’s BioInsight AI Lab. The model is designed to predict which genetic variants are most likely to explain an observed phenotype, helping researchers narrow the field of potential candidates.
Illumina presented early research findings for dnaAI during a plenary session at the annual meeting of the American Society of Human Genetics (ASHG).
According to the company, applying dnaAI to large-scale datasets produced an 8% increase in disease associations across previously unsolved cases. Illumina also reported that the model ranked previously established disease-associated variants among its top four candidates in 99.4% of the genomes analyzed.
These preliminary findings highlight the potential role of AI in prioritizing genetic evidence and supporting research into unresolved disease cases. However, the reported results should be understood within the context of the datasets and evaluation methods used, and further research will help establish the model’s performance across additional settings.
In addition to dnaAI, DRAGEN Ultra incorporates Illumina models designed to address different categories of biological variation. These include PrimateAI-3D, PromoterAI, and the newly announced SpliceAI2.
By combining these resources, the capability aims to provide researchers with a broader analytical framework for investigating genetic variation and identifying candidates for follow-up studies.
Learning Cohorts Helps Researchers Discover Patterns Across Populations
The third new capability, BioInsight Platform Learning Cohorts, is designed to help researchers move beyond the analysis of individual samples and investigate relationships across larger collections of genomic, multiomic, and phenotypic information.
Traditionally, researchers may evaluate samples individually or work with predefined groups of participants. Although these approaches can generate valuable findings, they may make it more difficult to identify patterns that emerge only when larger and more diverse datasets are examined together.
Learning Cohorts is intended to help researchers explore private sample collections, compare individual observations with broader populations, and investigate associations that may not be apparent in isolated samples.
The tool makes population-level research capabilities more accessible to private research organizations that may not have access to the large public cohorts commonly used in genomic studies.
Researchers can begin with a scientific question, identify relevant populations within available datasets, and dynamically create cohorts for further analysis. This approach allows research groups to investigate populations based on emerging scientific questions rather than relying exclusively on groups established before analysis begins.
For example, a scientist studying a rare genetic variant could identify individuals with similar genomic characteristics or phenotypes, compare findings across different groups, and explore associations that may warrant additional investigation.
By facilitating comparisons across samples and populations, Learning Cohorts is designed to support hypothesis generation and help researchers place individual findings within a wider biological context.
BioInsight Data Adds Broader Biological Context
Learning Cohorts works alongside BioInsight Data, which Illumina previously announced as a source of curated reference datasets.
These reference resources can help researchers enrich their institutional datasets and compare findings against broader biological and population information. Combining the two capabilities enables research teams to investigate patterns across datasets, generate new hypotheses, and develop research resources that become more informative as additional genomic, multiomic, and phenotypic data are incorporated.
The combination is intended to strengthen the connection between individual genomic observations and broader patterns of biological variation.
Rather than treating each analysis as an isolated activity, researchers can use accumulated data to inform subsequent investigations and identify new questions for further study.
Integrated Software Supports a More Connected Research Environment
The three new capabilities address different stages of the genomic research process. Workflow Orchestrator focuses on operational consistency and traceability, DRAGEN Ultra extends analysis through AI-based variant prioritization, and Learning Cohorts supports population-level exploration and hypothesis generation.
Together with BioInsight Data, the tools are intended to provide research organizations with a more connected environment for managing sequencing workflows and extracting value from large datasets.
The platform’s modular structure allows organizations to adopt individual capabilities according to their needs and connect them as their research programs expand. This flexibility may be particularly useful for institutions working across multiple research areas or handling growing volumes of genomic and multiomic information.
All three newly announced applications are available to early-access customers through the Illumina BioInsight Platform.
Illumina Showcases New Capabilities at ASHG 2026
Illumina introduced the new software capabilities during the annual meeting of the American Society of Human Genetics in Montréal.
Attendees can learn more about DRAGEN Ultra, BioInsight Platform Workflow Orchestrator, and Learning Cohorts at Illumina’s booth number 701. The company also scheduled an Industry Education session
The announcements reflect Illumina’s continued focus on integrating sequencing technologies, computational analysis, AI, and data resources to support genomic research.
As scientists work with increasingly complex biological datasets, tools that improve workflow management, prioritize promising findings, and enable broader population comparisons are becoming important components of research infrastructure.
With the introduction of these three early-access capabilities, Illumina aims to help research organizations scale their analytical operations, investigate genetic variation more efficiently, and generate insights that support future scientific discoveries.
