Workflows are great for large scale data processing operations, especially those involving multiple steps that take a long time, and must be run reproducibly on many samples. Terra’s powerful workflow management system empowers you to take full advantage of the cloud’s vaunted elasticity without having to manage any computational resources directly. The built-in Cromwell execution engine supports workflows written in the Workflow Description Language (WDL), a highly portable domain-specific language stewarded by the OpenWDL community and adopted as one of the standard workflow languages of the Global Alliance for Genomics and Health (GA4GH). Learn more about running WDL workflows in Terra
This collection of workspaces
preloaded with example data and analysis tools includes workflows that are fully configured for tutorial or demonstration purposes, such as the GATK Best Practices.
Visit the Showcase
This Github-connected tool repository offers a growing catalog of workflows contributed by 20+ organizations. Dockstore supports seamless import into Terra workspaces.
Terra’s internal repository allows you to develop and deploy your own workflows directly in Terra, as well as share them privately with collaborators or publicly with the Terra community.
When it comes to the critical step of extracting insights from your data, you need to be able to inspect, visualize and apply algorithms to the data interactively. Terra provides customizable cloud environments that support popular data science and bioinformatics applications. These environments are backed by virtual machines that you can use out of the box or customize through a user-friendly web form. Within a given environment, you can use the application as if it were on a regular workstation; install additional packages if needed, access cloud-hosted data, do work and save data to persistent storage. Learn more about Terra’s interactive cloud environments
This popular computational notebook application provides a user-friendly interface for running R and Python code; ideal for teaching, collaborating and publishing.
This much loved data science application offers arguably the most powerful development experience in R; ideal for seasoned analysts and algorithm developers.
This user-friendly bioinformatics framework offers access to a large collection of community-contributed tools and shareable histories; ideal for applying existing tools.