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Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity

DC Field Value Language
dc.contributor.authorHui Kwon Kim-
dc.contributor.authorSeonwoo Min-
dc.contributor.authorMyungjae Song-
dc.contributor.authorSoobin Jung-
dc.contributor.authorJae Woo Choi-
dc.contributor.authorYounggwang Kim-
dc.contributor.authorSangeun Lee-
dc.contributor.authorSungroh Yoon-
dc.contributor.authorHyongbum Kim-
dc.date.available2018-07-18T02:04:48Z-
dc.date.created2018-05-16-
dc.date.issued2018-03-
dc.identifier.issn1087-0156-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/4612-
dc.description.abstractWe present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets-
dc.description.uri1-
dc.language영어-
dc.publisherNATURE PUBLISHING GROUP-
dc.titleDeep learning improves prediction of CRISPR-Cpf1 guide RNA activity-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000426698700017-
dc.identifier.scopusid2-s2.0-85042934649-
dc.identifier.rimsid63282-
dc.date.tcdate2018-10-01-
dc.contributor.affiliatedAuthorHyongbum Kim-
dc.identifier.doi10.1038/nbt.4061-
dc.identifier.bibliographicCitationNATURE BIOTECHNOLOGY, v.36, no.3, pp.239 - +-
dc.citation.titleNATURE BIOTECHNOLOGY-
dc.citation.volume36-
dc.citation.number3-
dc.citation.startPage239-
dc.citation.endPage+-
dc.date.scptcdate2018-10-01-
dc.description.wostc5-
dc.description.scptc5-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusHUMAN-CELLS-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusSGRNA DESIGN-
dc.subject.keywordPlusOFF-TARGET-
dc.subject.keywordPlusIN-VIVO-
dc.subject.keywordPlusGENOME-
dc.subject.keywordPlusCPF1-
dc.subject.keywordPlusSPECIFICITIES-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordPlusMICE-
Appears in Collections:
Center for Nanomedicine (나노의학 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
31.Nature Biotechnology 36, 239-241, (2018).pdfDownload

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