{"id":292757,"date":"2016-09-14T09:10:39","date_gmt":"2016-09-14T16:10:39","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=292757"},"modified":"2023-10-24T09:52:00","modified_gmt":"2023-10-24T16:52:00","slug":"crispr","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/crispr\/","title":{"rendered":"CRISPR ML"},"content":{"rendered":"

End-to-end guide design for CRISPR\/Cas9 with machine learning<\/h1>\n

Azimuth and Elevation: On-target and off-target guide prediction<\/h2>\n

The CRISPR\/Cas9 system provides state-of-the art genome editing capabilities. However, several facets of this system are under investigation for further characterization and optimization. One in particular is the choice of guide RNA that directs Cas9 to target DNA: given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the constraints of the CRISPR\/Cas9 PAM sequence. Only some of these guides efficiently target DNA to generate gene knockouts. Also, some guides may accidentally have activity in unintended places. We have developed two predictive modelling approaches, Azimuth [1] and Elevation [2], for these respective problems of on-target and off-target activity prediction.<\/p>\n

Publications<\/h2>\n

Please cite these papers if using our on-target or off-target model:<\/p>\n

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  1. John G. Doench<\/u>*<\/sup>, Nicolo Fusi<\/u>*<\/sup>, Meagan Sullender*<\/sup>, Mudra Hegde*<\/sup>, Emma W. Vaimberg*<\/sup>, Katherine F. Donovan, Ian Smith, Zuzana Tothova, Craig Wilen , Robert Orchard , Herbert W. Virgin, Jennifer Listgarten<\/u>*<\/sup>, David E. Root<\/u>. Optimized sgRNA design to maximize activity and minimize off-target effects for genetic screens with CRISPR-Cas9 .\u00a0Nature Biotechnology<\/em> Jan 2016, doi:10.1038\/nbt.3437 (opens in new tab)<\/span><\/a>. (*equal contributions, corresponding author<\/u>)<\/li>\n
  2. Jennifer Listgarten<\/span>*, Michael Weinstein<\/span>*, Benjamin P. Kleinstiver, Alexander A. Sousa, J. Keith Joung, Jake Crawford, Kevin Gao, Luong Hoang, Melih Elibol, John G. Doench<\/span>*, Nicolo Fusi<\/span>*. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs.\u00a0Nature Biomedical Engineering<\/em> Jan 2018, doi:10.1038\/s41551-017-0178-6 (opens in new tab)<\/span><\/a>. (*equal contributions, corresponding author<\/u>)<\/li>\n<\/ol>\n

    How to use our software<\/h2>\n

    On-target prediction (Azimuth):<\/h3>\n

    On-target predictions are available through our web service at\u00a0https:\/\/crispr.ml (opens in new tab)<\/span><\/a>\u00a0(using the \u201cInput Sequence\u201d toggle), or through the\u00a0GPP sgRNA Designer (opens in new tab)<\/span><\/a>\u00a0tool maintained by the Broad Institute of MIT and Harvard.<\/p>\n

    In addition, a\u00a0Python implementation of our model (and other competing methods)\u00a0is\u00a0available from\u00a0GitHub (opens in new tab)<\/span><\/a>.<\/p>\n

    Note:\u00a0<\/strong>We are no longer supporting our Azure ML prediction server, previously accessed through Excel or the web API, as it is running an outdated version of our code. See the\u00a0release notes on GitHub (opens in new tab)<\/span><\/a>\u00a0for further information.<\/p>\n

    Off-target prediction (Elevation):<\/h3>\n

    We have precomputed all on-target and off-target scores for the human exome (GRCh38), and made the results available through the web service at https:\/\/crispr.ml (opens in new tab)<\/span><\/a>.<\/p>\n

    A Python implementation of the Elevation-score and Elevation-aggregate off-target models is available from GitHub. (opens in new tab)<\/span><\/a> Software for running Elevation-search (the genome search for potential off-targets) is also available on GitHub here (opens in new tab)<\/span><\/a>. See the documentation in the GitHub repositories for installation and usage instructions.<\/p>\n

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    Press<\/h2>\n