BAGEL: a computational framework for identifying essential genes from pooled library screens.
BAGEL: a computational framework for identifying essential genes from pooled library screens.
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DOI：
10.1186/s12859-016-1015-8
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发表时间：
2016-04-16
影响因子：
3
通讯作者：
Moffat J
中科院分区：
文献类型：
--
作者：
Hart T;Moffat J
CRISPR-Cas9系统对哺乳动物细胞中汇集文库基因敲除筛选的适应，代表了对RNA干扰的重大技术飞跃，RNA干扰是目前的技术水平。需要新的分析数据和评价结果的方法。我们提供BAGEL（基因本质贝叶斯分析），一种用于分析基因敲除筛选的监督学习方法。结合基本和非必要基因的金标准参考集，BAGEL提供了比当前方法更高的灵敏度，而计算优化减少了运行时间的数量级。使用BAGEL，我们在5% FDR的情况下，在人类细胞系的汇集文库敲除筛选中鉴定了约2000个适合基因，这是竞争平台的一大进步。即使在不同实验室使用不同文库和试剂进行的筛选中，BAGEL也显示出高灵敏度和特异性。
The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude. Using BAGEL, we identify ~2000 fitness genes in pooled library knockout screens in human cell lines at 5 % FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens performed by different labs using different libraries and reagents.