Jan 10, 2018   |  
John Roach A collaboration between computer scientists and biologists from research institutions across the United States is yielding a set of computational tools that increase efficiency and accuracy when deploying CRISPR, a gene-editing technology that is transforming industries from healthcare to agriculture. CRISPR is a nano-sized sewing kit that can be designed to cut and alter DNA at a specific point in a specific gene.

The technology, for example, may lead to breakthrough applications such as modifying cells to combat cancer or produce high-yielding drought-tolerant crops such as wheat and corn. Elevation, the newest tool released by the team, uses a branch of artificial intelligence known as machine learning to predict so-called off-target effects when editing genes with the CRISPR system.

Although CRISPR shows great promise in a number of fields, one challenge is that lots of genomic regions are similar, which means the nano-sized sewing kit can accidentally go to work on the wrong gene and cause unintended consequences – the so-called off-target effects. “Off-target effects are something that you really want to avoid,” said Nicolo Fusi, a researcher at Microsoft’s research lab in Cambridge, Massachusetts.

“You want to make sure that your experiment doesn’t mess up something else.” Fusi and former Microsoft colleague Jennifer Listgarten, together with collaborators at the Broad Institute of MIT and Harvard, University of California Los Angeles, Massachusetts General Hospital and Harvard Medical School, describe Elevation in a paper published Jan. 10 in the journal Nature Biomedical Engineering.

Elevation and a complementary tool for predicting on-target effects called Azimuth are publicly available for free as a cloud-based end-to-end guide-design service running on Microsoft Azure as well as via open-source code. Using the computational tools, researchers can input the name of the gene they want to modify and the cloud-based search engine will return a list of guides that researchers can sort by predicted on-target or off-target effects. Read more from blogs.microsoft.com…

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