Optimal construction of a functional interaction network from pooled library CRISPR fitness screens.
Optimal construction of a functional interaction network from pooled library CRISPR fitness screens.
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DOI：
10.1186/s12859-022-05078-y
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发表时间：
2022-11-28
影响因子：
3
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文献类型：
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功能相互作用网络，其中边缘连接可能在相同生物过程或途径中操作的基因，可以从癌细胞系中的CRISPR敲除筛选中推断出来。在一组足够多样化的细胞系筛选中，具有相似敲除适应性特征的基因很可能是共功能的，这些“共本质”网络是基因功能和生物模块性越来越强大的预测因子。虽然已经发布了几个这样的网络，但大多数网络构建过程的每个步骤都使用不同的算法。在这项研究中，我们确定了功能相互作用的最佳衡量标准，并在每个步骤中测试了所有选项的组合-必要性评分，样本方差和协方差归一化以及相似性测量-以确定从CRISPR敲除数据生成功能相互作用网络的最佳实践。我们表明，贝叶斯因子和Ceres分数给出了最好的结果，Ceres优于较新的Chronos评分方案，并且协方差归一化是网络构建中的关键步骤。我们进一步表明，皮尔逊相关性，数学上相同的普通最小二乘协方差归一化后，可以通过使用偏相关检测和放大信号从“兼职”的蛋白质，显示上下文相关的相互作用与不同的合作伙伴。我们描述了一个系统的调查的方法，从癌症Dependency Map数据生成coessentiality网络，并提供了一个基于部分相关性的方法，探索上下文相关的相互作用。 在线版本包含补充材料，可在10.1186/s12859-022-05078-y获得。
Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these “coessentiality” networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process. In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step—essentiality scoring, sample variance and covariance normalization, and similarity measurement—to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from “moonlighting” proteins which show context-dependent interaction with different partners. We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions. The online version contains supplementary material available at 10.1186/s12859-022-05078-y.
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影响因子：
9.3
作者：
通讯作者：
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影响因子：
14.9
作者：
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
9.9
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
通讯作者：
Myers CL
影响因子：
4
作者：
Balderhaar, Henning J. Kleine;Ungermann, Christian
通讯作者：
Ungermann, Christian
DOI：
10.1126/science.1180823
发表时间：
2010-01-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Costanzo M;Baryshnikova A;Bellay J;Kim Y;Spear ED;Sevier CS;Ding H;Koh JL;Toufighi K;Mostafavi S;Prinz J;St Onge RP;VanderSluis B;Makhnevych T;Vizeacoumar FJ;Alizadeh S;Bahr S;Brost RL;Chen Y;Cokol M;Deshpande R;Li Z;Lin ZY;Liang W;Marback M;Paw J;San Luis BJ;Shuteriqi E;Tong AH;van Dyk N;Wallace IM;Whitney JA;Weirauch MT;Zhong G;Zhu H;Houry WA;Brudno M;Ragibizadeh S;Papp B;Pál C;Roth FP;Giaever G;Nislow C;Troyanskaya OG;Bussey H;Bader GD;Gingras AC;Morris QD;Kim PM;Kaiser CA;Myers CL;Andrews BJ;Boone C
通讯作者：
Boone C