Seven Bridges To Support Execution Of Nextflow And WDL Workflows

Seven-Bridges-Expands-Platform-to-Support-Execution-of-Nextflow-and-WDL-Workflows

Seven Bridges, the bioinformatics ecosystem provider, announced the expansion of its platform to enable execution of workflows written in Nextflow and Workflow Description Language (WDL).

In addition, the Seven Bridges platform will continue to support Common Workflow Language (CWL). This expansion will now empower more scientists to use the Seven Bridges interface in a secure, compliant, collaborative environment, to run tools described in the workflow language of their choice.

“CWL, WDL and Nextflow offer unique characteristics that make them attractive to researchers with varied expertise and at different phases of the research lifecycle,” said Jeff Gentry co-founder of WDL and CWL leadership member. “By supporting multiple workflow languages, Seven Bridges makes it easier for researchers to realise the benefit of reproducible analysis at scale.”

In addition to the more than 600 optimised CWL workflows available from Seven Bridges, this release enables researchers to leverage large content repositories of WDL and Nextflow workflows, in addition to workflows developed in-house, on the Seven Bridges platform.

Seven Bridges is the leading provider of interoperable data ecosystems and this release underscores the company’s commitment to adopting standards that improve and accelerate researcher’s ability to extract meaningful insights from large, multi-dimensional data.

“The development and adoption of standards for secure data exchange and portable, reproducible analysis is critical to realising the promise of precision medicine, particularly when data are housed in geographically diverse regions or with varied access requirements,” said Brandi Davis-Dusenbery, Chief Scientific Officer at Seven Bridges. “This expansion, when coupled with our industry leading multi-cloud capabilities, enables more researchers across the world to effectively generate insights from large biomedical datasets.”