[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121873-en":3,"doc-seo-121873-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121873,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning","Genome editing powered by CRISPR–Cas technology has transformed many biological applications. Beyond Cas9 nucleases, Cas12a (Cpf1) offers an alternative for editing AT-rich genomes, yet computational prediction of guide RNA efficiency remains insufficiently accurate. Through a computational meta-analysis, the study identifies Cas12a target and off-target cleavage behavior as driven by nucleotide bias combined with nucleotide mismatches relative to the PAM site. A Random Forest model trained with these features improves guide efficiency prediction accuracy by at least 15% over existing algorithms. The work also highlights the need for more representative datasets and further benchmarking to ensure reliable prediction of Cas12a guide efficiency and off-target effects.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nPredicting CRISPR-Cas12a guide efficiency for targeting using machine learning.  \nPermalink  \n[https://escholarship.org/uc/item/7kv6x6k2](https://escholarship.org/uc/item/7kv6x6k2)  \nJournal  \nPLoS ONE, 18(10)  \nAuthors  \nBurgio, Gaetan  \nOBrien, Aidan Bauer, Douglas  \nPublication Date  \n2023  \nDOI  \n10.1371/journal.pone.0292924  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPLOS ONE  \nOPEN ACCESS  \nCitation: O’Brien A, Bauer DC, Burgio G (2023) Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning. PLoS ONE 18(10): e0292924 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0292924  \nEditor: Zhiming Li, Columbia University Irving Medical Center, UNITED STATES  \nReceived: June 22, 2023  \nAccepted: October 2, 2023  \nPublished: October 17, 2023  \nCopyright: © 2023 O’Brien et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: All relevant data are within the paper and its Supporting Information files.  \nFunding: The authors would like to thank the National Computational Infrastructure (NCI), which is supported by the Australian Government. A. O. Bis supported from a John Curtin School of Medical Research Scholarship and the Technology Opportunities [Program from the CSIRO. G.B. work](Program from the CSIRO. G.B. work)[ ](Program from the CSIRO. G.B. work)is supported from the National Health and Medical Research Council and the Australian Research Council. The funders have no role in study, design,  \nRESEARCH ARTICLE  \nPredicting CRISPR-Cas12a guide efficiency for targeting using machine learning  \nAidan O’Brien1,2, Denis C. Bauer2,3,4 *, Gaetan Burgio1 *  \n1 Division of Genome Science and Cancer and The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, College of Health and Medicine, The Australian National University, Canberra, ACT, Australia, 2 Commonwealth Scientific and Industrial Research (CSIRO) Health and Biosecurity, Adelaide, SA, Australia, 3 Faculty of Medicine and Health Science, Department of Biomedical Sciences, Macquarie University, Macquarie Park, Australia, 4 Faculty of Science and Engineering, Applied BioSciences, Macquarie University, Macquarie Park, Australia  \n* denis.bauer@csiro.au (DCB); [Gaetan.burgio@anu.edu.au](Gaetan.burgio@anu.edu.au) (GB)  \nAbstract  \nGenome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)–Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational metaanalysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.  \nIntroduction  \nCRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)–Cas technology is arguably now widely used for the generation of genetically modified organisms, synthetic biology and biotechnology applications [ 1] . A class 2 CRISPR prototype system is comprised of a programmable single e","cbCainXQTpSfSiW5","https://ap.wps.com/l/cbCainXQTpSfSiW5","pdf",1962365,1,23,"English","en",105,"# Abstract\n# Introduction\n## CRISPR–Cas systems and Cas12a relevance\n## Guide RNA recognition and mismatch considerations\n## Off-target and on-target effects in genome engineering\n# Methods and analysis (as described in the paper)","[{\"question\":\"What problem does the study address in CRISPR-Cas12a guide design?\",\"answer\":\"The study addresses the limited accuracy of computational tools for predicting CRISPR-Cas12a guide RNA efficiency.\"},{\"question\":\"Which features are most important for predicting Cas12a cleavage behavior?\",\"answer\":\"Cas12a target and off-target cleavage behavior is associated with nucleotide bias together with nucleotide mismatches relative to the PAM site.\"},{\"question\":\"How much improvement does the proposed machine learning model achieve?\",\"answer\":\"The Random Forest model improves guide RNA efficiency prediction accuracy by at least 15% compared with existing algorithms.\"}]","Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning | 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