Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier.
Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier.
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DOI：
10.1186/s13073-020-00809-3
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发表时间：
2021-01-06
期刊：
影响因子：
12.3
通讯作者：
Hart T
中科院分区：
文献类型：
--
作者：
Kim E;Hart T
在全基因组功能丧失筛查中识别必需基因是功能基因组学和癌症靶点发现的关键一步。我们之前描述了从短发夹RNA和CRISPR/Cas9全基因组遗传筛选中准确分类基因必要性的贝叶斯基因必要性分析（BAGEL）算法。我们推出了一个更新版本BAGEL2，它采用了一个改进的模型，提供了更大的贝叶斯因子动态范围，从而能够检测肿瘤抑制基因；多靶点校正，减少脱靶CRISPR引导RNA的假阳性；并实现了一种交叉验证策略，其性能比先前的自举重采样方法提高了10倍。我们还描述了复制级别的屏幕质量度量，并演示了不同的算法如何以完全不同的方式处理低质量数据。与BAGEL相比，BAGEL2大大提高了灵敏度、特异性和性能，并在CRISPR基因敲除适应度筛选分析中建立了新的技术水平。BAGEL2是用Python 3编写的，其源代码以及所有支持文件都可以在github （https://github.com/hart-lab/bagel）上获得。
Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).
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影响因子：
46.9
作者：
Evers, Bastiaan;Jastrzebski, Katarzyna;Bernards, Rene
通讯作者：
Bernards, Rene
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
17.1
作者：
Lin A;Giuliano CJ;Palladino A;John KM;Abramowicz C;Yuan ML;Sausville EL;Lukow DA;Liu L;Chait AR;Galluzzo ZC;Tucker C;Sheltzer JM
通讯作者：
Sheltzer JM
影响因子：
12.3
作者：
Fortin, Jean-Philippe;Tan, Jenille;Martin, Scott E.
通讯作者：
Martin, Scott E.
DOI：
10.1534/g3.117.041277
发表时间：
2017-08-07
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Hart T;Tong AHY;Chan K;Van Leeuwen J;Seetharaman A;Aregger M;Chandrashekhar M;Hustedt N;Seth S;Noonan A;Habsid A;Sizova O;Nedyalkova L;Climie R;Tworzyanski L;Lawson K;Sartori MA;Alibeh S;Tieu D;Masud S;Mero P;Weiss A;Brown KR;Usaj M;Billmann M;Rahman M;Constanzo M;Myers CL;Andrews BJ;Boone C;Durocher D;Moffat J
通讯作者：
Moffat J