Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 남진우 | - |
dc.date.accessioned | 2021-05-14T02:06:44Z | - |
dc.date.available | 2021-05-14T02:06:44Z | - |
dc.date.issued | 2020-03 | - |
dc.identifier.citation | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, v. 18, page. 814-820 | en_US |
dc.identifier.issn | 2001-0370 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S2001037019304039?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/162038 | - |
dc.description.abstract | The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas systems, including dead Cas9 (dCas9), Cas9, and Cas12a, have revolutionized genome engineering in mammalian somatic cells. Although computational tools that assess the target sites of CRISPR-Cas systems are inevitably important for designing efficient guide RNAs (gRNAs), they exhibit generalization issues in selecting features and do not provide optimal results in a comprehensive manner. Here, we introduce a Comprehensive Guide Designer (CGD) for four different CRISPR systems, which utilizes the machine learning algorithm, Elastic Net Logistic Regression (ENLOR), to autonomously generalize the models. CGD contains specific models trained with public datasets generated by CRISPRi, CRISPRa, CRISPR-Cas9, and CRISPR-Cas12a (designated as CGDi, CGDa, CGD9, and CGD12a, respectively) in an unbiased manner. The trained CGD models were benchmarked to other regression-based machine learning models, such as ElasticNet Linear Regression (ENLR), Random Forest and Boruta (RFB), and Extreme Gradient Boosting (Xgboost) with inbuilt feature selection. Evaluation with independent test datasets showed that CGD models outperformed the pre-existing methods in predicting the efficacy of gRNAs. All CGD source codes and datasets are available at GitHub (https://gitub.com/vipinmenon1989/CGD), and the CGD webserver can be accessed at http://big.hanyang.ac.kr:2195/CGD. | en_US |
dc.description.sponsorship | This work was supported by the Bio and Medical Technology Development Program and the Basic Science Research Program through the National Research Foundation (NRF), funded by the Ministry of Science and ICT, South Korea (grant numbers 2014M3C9A3063541 and 2018R1A2B2003782). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER | en_US |
dc.subject | CRISPR system | en_US |
dc.subject | Cas9 | en_US |
dc.subject | Cas12a | en_US |
dc.subject | dCas9 | en_US |
dc.subject | gRNA design | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Logistic regression | en_US |
dc.title | CGD: Comprehensive guide designer for CRISPR-Cas systems | en_US |
dc.type | Article | en_US |
dc.relation.volume | 18 | - |
dc.identifier.doi | 10.1016/j.csbj.2020.03.020 | - |
dc.relation.page | 814-820 | - |
dc.relation.journal | COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | - |
dc.contributor.googleauthor | Menon, A Vipin | - |
dc.contributor.googleauthor | Sohn, Jang-il | - |
dc.contributor.googleauthor | Nam, Jin-Wu | - |
dc.relation.code | 2020049734 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF NATURAL SCIENCES[S] | - |
dc.sector.department | DEPARTMENT OF LIFE SCIENCE | - |
dc.identifier.pid | jwnam | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.