Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Genome Med. 2021 Jan 6;13(1):2. doi: 10.1186/s13073-020-00809-3.
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 ).
在全基因组功能丧失筛选中鉴定必需基因是功能基因组学和癌症靶标发现的关键步骤。我们之前描述了贝叶斯基因必需性分析（BAGEL）算法，用于从短发夹 RNA 和 CRISPR/Cas9 全基因组遗传筛选中准确分类基因必需性。
我们引入了一个更新的版本 BAGEL2，它采用了改进的模型，提供了更大的贝叶斯因子动态范围，能够检测肿瘤抑制基因；多靶点校正可减少来自非靶向 CRISPR 向导 RNA 的假阳性；并实施了交叉验证策略，将性能提高了约 10 倍，优于之前的自举重采样方法。我们还描述了一种在复制水平上的筛选质量指标，并展示了不同算法如何以截然不同的方式处理较低质量的数据。
BAGEL2 在灵敏度、特异性和性能方面都大大优于 BAGEL，并确立了 CRISPR 敲除适应筛选分析的新的最新技术水平。BAGEL2 是用 Python 3 编写的，源代码以及所有支持文件都可在 github 上获得（ https://github.com/hart-lab/bagel ）。
