SeqsLab on Azure#


SeqsLab is optimized for Azure and is deeply integrated with Azure Batch, Azure Data Lake Storage, Azure Web Apps, Azure Active Directory, and security to manage all your bioinformatics workflow life cycles and operations.

You can use SeqsLab as an Azure managed application that is deployed and transactable through Azure Marketplace. SeqsLab will be deployed with a subscription-based solution using an entire Azure infrastructure as a service (IaaS)-based solution, enabling Just-In-Time (JIT) access and allowing Atgenomix or our trusted service partners to request elevated access for management, troubleshooting, or maintenance.

For details, see Deploy SeqsLab on Azure.


Azure marketplace plans#

SeqsLab currently offers two plans on the Azure markeptlace. The Standard plan allows you to manage genomics data repository and containerized bioinformatics tools registry, and execute automated analysis pipelines described by Workflow Description Language (WDL). It is also suitable for biomedical analytics and data science workloads. The Premium plan provides additional support for more advanced features such as running and managing SQL and ML pipelines.

Pay for what you use#

Only pay for the computing resources you use per minute granularity. We use a simple, pay-per-use pricing by the number of Atgenomix Compute Units (ACU). The metrics consists of the cluster compute resources consumed and/or the amount of data processed.


The price excludes the Azure infrastructure or usage-based costs incurred by the resources deployed or used by SeqsLab.


Premium (available soon)

General-purpose pipeline compute
Run and manage analysis pipelines of command line tools at scale and speed

SQL pipeline compute
Run and manage unified analytics and structured data processing pipelines with SQL queries

ML pipeline compute
Run and manage large data science and machine learning pipelines.


- Run your Docker containerized WDL workflows scheduled from the sequencing Sample Sheet

- Automate cluster resource allocation, input localization, output delocalization, and data lake storage access


- Implicit and dynamic workload parallel processing

- Optimized Cromwell workflow execution engine

- GPU cluster computing acceleration


- Job workspaces (Azure resource groups)

- Managed computing clusters

- Management console and CLI interface

- Sequencing sample sheet integration

- Run status monitoring

- Audit trail logging


- Docker content trust

- Workflow tool version control

- GA4GH standards

- Data and execution integrity


- OAuth 2.0 authorization framework

- Azure AD credential single sign-on (SSO)

- Role-based access control (RBAC)