Veritas Genetics acquires Curoverse to enable AI push

By Jonah Comstock
Share

Veritas Genetics,a leader in whole-genome sequencing, has acquired computing and bio-informatics firm Curoverse for an undisclosed amount. Curoverse provides infrastructure for life sciences companies to manage large datasets, including an open source platform called Arvados.

The acquisition isn't totally unexpected, since the two companies have a strong existing relationship. Not only were both companies cofounded by Harvard professor Dr. George Church, but Veritas and Curoverse have worked together on Harvard's Personal Genome Project. 

"There are very few companies in the world that have the expertise and experience of more than a decade in aggregating genomic data and enabling machine learning," Church said in a statement. "I am pleased to see these two teams work even closer together. They not only share a common technological goal but also a commitment to making this invaluable information actionable and accessible."

Veritas offers whole genome sequencing for $999 and delivers results to customers' smartphones. The goal of the acquisition is to use Curoverse's big data expertise to enable Veritas to more easily use artificial intelligence and machine learning to extract insight from that genomic data. 

"At Veritas, we are building a platform to sequence, and more importantly, interpret hundreds of thousands, and eventually millions, of human genomes per year," Veritas CEO Mirza Cifric said in a statement. "This will only be possible by deploying AI and machine learning at scale, which requires data that is produced, stored and managed in a standardized way. Curoverse excels at this capability. Working closely together will not only benefit Veritas, but the industry as a whole."

Through initiatives like Arvados and the Common Workflow Language project, Curoverse has been involved in efforts to standardize the way genomic data is produced and aggregated. Veritas intends to continue supporting the goal of open standards for genomic data.