{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:14:59Z","timestamp":1782746099770,"version":"3.54.5"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australia Research Council","doi-asserted-by":"crossref","award":["DP180100120"],"award-info":[{"award-number":["DP180100120"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000923","name":"Australia Research Council","doi-asserted-by":"crossref","award":["FT130101457"],"award-info":[{"award-number":["FT130101457"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000923","name":"Australia Research Council","doi-asserted-by":"crossref","award":["DP140102164"],"award-info":[{"award-number":["DP140102164"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Health and Medical Research Council and Cancer Council NSW"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>CRISPR\/Cas9 is driving a broad range of innovative applications from basic biology to biotechnology and medicine. One of its current issues is the effect of off-target editing that should be critically resolved and should be completely avoided in the ideal use of this system.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed an ensemble learning method to detect the off-target sites of a single guide RNA (sgRNA) from its thousands of genome-wide candidates. Nucleotide mismatches between on-target and off-target sites have been studied recently. We confirm that there exists strong mismatch enrichment and preferences at the 5\u2032-end close regions of the off-target sequences. Comparing with the on-target sites, sequences of no-editing sites can be also characterized by GC composition changes and position-specific mismatch binary features. Under this novel space of features, an ensemble strategy was applied to train a prediction model. The model achieved a mean score 0.99 of Aera Under Receiver Operating Characteristic curve and a mean score 0.45 of Aera Under Precision-Recall curve in cross-validations on big datasets, outperforming state-of-the-art methods in various test scenarios. Our predicted off-target sites also correspond very well to those detected by high-throughput sequencing techniques. Especially, two case studies for selecting sgRNAs to cure hearing loss and retinal degeneration partly prove the effectiveness of our method.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The python and matlab version of source codes for detecting off-target sites of a given sgRNA and the supplementary files are freely available on the web at https:\/\/github.com\/penn-hui\/OfftargetPredict.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty558","type":"journal-article","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T06:00:37Z","timestamp":1530943237000},"page":"i757-i765","source":"Crossref","is-referenced-by-count":54,"title":["Recognition of CRISPR\/Cas9 off-target sites through ensemble learning of uneven mismatch distributions"],"prefix":"10.1093","volume":"34","author":[{"given":"Hui","family":"Peng","sequence":"first","affiliation":[{"name":"Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Childhood Cancer Research, University of New South Wales, Kensington, Australia"},{"name":"Children\u2019s Cancer Institute, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyan","family":"Li","sequence":"additional","affiliation":[{"name":"Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061313510098000_bty558-B1","doi-asserted-by":"crossref","first-page":"e1005807.","DOI":"10.1371\/journal.pcbi.1005807","article-title":"A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action","volume":"13","author":"Abadi","year":"2017","journal-title":"PLoS Comput. 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