A method for benchmarking genetic screens reveals a predominant mitochondrial bias.
A method for benchmarking genetic screens reveals a predominant mitochondrial bias.
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DOI：
10.15252/msb.202010013
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发表时间：
2021-05
影响因子：
9.9
通讯作者：
Myers CL
中科院分区：
文献类型：
--
作者：
Rahman M;Billmann M;Costanzo M;Aregger M;Tong AHY;Chan K;Ward HN;Brown KR;Andrews BJ;Boone C;Moffat J;Myers CL
我们提出了FLEX（实验扰动的功能评估），这是一个利用几种功能注释资源建立参考标准的管道，用于对人类全基因组CRISPR筛选数据和分析方法进行基准测试。FLEX提供了给定基因对数据集捕获的功能信息的定量测量，以及探索输入数据集捕获的功能多样性的方法。我们应用FLEX分析DepMap项目产生的不同细胞系筛选的数据。我们在来自这些数据的共必要性网络中确定了一个主要的线粒体相关信号，并探索了该信号的基础。我们在单个细胞系中的分析和时间分辨CRISPR筛选表明，与线粒体基因相关的可变表型可能反映了筛选动态和蛋白质稳定性效应，而不是遗传依赖性。我们描述了这种功能性偏倚，并证明了它与解释任何CRISPR筛选背景下的差异命中的相关性。更一般地说，我们展示了FLEX管道的实用性，用于对CRISPR筛选或处理它们的方法进行稳健的比较评估。FLEX是一种对大规模遗传数据集进行系统评估和基准测试的方法，可测量基因对数据中功能信号的数量和组成。
We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome‐wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene‐pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria‐associated signal within co‐essentiality networks derived from these data and explore the basis of this signal. Our analysis and time‐resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them. FLEX is a method for systematic evaluation and benchmarking of large‐scale genetic datasets that measures both the quantity and the composition of functional signals in gene‐pair data.
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影响因子：
9.3
作者：
Pan J;Meyers RM;Michel BC;Mashtalir N;Sizemore AE;Wells JN;Cassel SH;Vazquez F;Weir BA;Hahn WC;Marsh JA;Tsherniak A;Kadoch C
通讯作者：
Kadoch C
影响因子：
5.8
作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
影响因子：
8.8
作者：
Tzelepis K;Koike-Yusa H;De Braekeleer E;Li Y;Metzakopian E;Dovey OM;Mupo A;Grinkevich V;Li M;Mazan M;Gozdecka M;Ohnishi S;Cooper J;Patel M;McKerrell T;Chen B;Domingues AF;Gallipoli P;Teichmann S;Ponstingl H;McDermott U;Saez-Rodriguez J;Huntly BJP;Iorio F;Pina C;Vassiliou GS;Yusa K
通讯作者：
Yusa K
影响因子：
9.9
作者：
Drew K;Lee C;Huizar RL;Tu F;Borgeson B;McWhite CD;Ma Y;Wallingford JB;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
1.2
作者：
Chan, Katherine;Tong, Amy Hin Yan;Moffat, Jason
通讯作者：
Moffat, Jason