{"id":330407,"date":"2016-12-01T18:51:37","date_gmt":"2016-12-02T02:51:37","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=330407"},"modified":"2018-10-16T20:31:02","modified_gmt":"2018-10-17T03:31:02","slug":"predicting-off-target-effects-end-end-crispr-guide-design","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/predicting-off-target-effects-end-end-crispr-guide-design\/","title":{"rendered":"Predicting off-target effects for end-to-end CRISPR guide design"},"content":{"rendered":"<p><span style=\"color: #555555;font-family: 'Droid Sans', Arial, sans-serif;font-size: 13px;font-style: normal;font-weight: normal;letter-spacing: normal;text-align: left;text-indent: 0px;background-color: #f9f9f9;float: none\"><span style=\"color: #191919;font-family: 'Lucida Sans', Helvetica, Arial, sans-serif;font-size: 14.6667px;font-style: normal;font-weight: normal;letter-spacing: normal;text-align: start;text-indent: 0px;background-color: #ffffff;float: none\">To enable more effective guide design we have developed the first machine learning-based approach to assess CRISPR\/Cas9 off-target effects. Our approach consistently and substantially outperformed the state-of the-art over multiple, independent data sets, yielding up to a 6-fold improvement in accuracy. Because of the large computational demands of the task, we also developed a cloud-based service for end-to-end guide design which incorporates our previously reported on-target model, Azimuth, as well as our new off-target model, Elevation (https:\/\/www.microsoft.com\/en-us\/research\/project\/crispr)<\/span><\/span><\/p>\n<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span>","protected":false},"excerpt":{"rendered":"<p>To enable more effective guide design we have developed the first machine learning-based approach to assess CRISPR\/Cas9 off-target effects. Our approach consistently and substantially outperformed the state-of the-art over multiple, independent data sets, yielding up to a 6-fold improvement in accuracy. Because of the large computational demands of the task, we also developed a cloud-based 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