从组合CRISPR筛选中鉴定合成致死性的基因相互作用评分方法的基准测试。

IF 2.8 Q1 GENETICS & HEREDITY
NAR Genomics and Bioinformatics Pub Date : 2025-09-26 eCollection Date: 2025-09-01 DOI:10.1093/nargab/lqaf129
Hamda Ajmal, Sutanu Nandi, Narod Kebabci, Colm J Ryan
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引用次数: 0

摘要

合成致死(SL)是一种极端形式的负性基因相互作用,其中两个非必需基因同时破坏导致细胞死亡。SL可以用于开发针对具有特定突变的肿瘤细胞的癌症疗法,从而潜在地限制毒性。汇集组合CRISPR筛选,其中两个基因同时受到干扰并估计其对适应度的影响,现在广泛用于鉴定癌症中的SL靶点。已经开发了各种评分方法来从这些筛选中推断SL遗传相互作用,但没有对这些方法进行系统比较。在这里,我们使用5个组合CRISPR数据集对SL检测的5种评分方法进行了全面分析。我们使用平行SL的两种不同基准评估了每种算法在每个屏幕数据集上的性能。我们发现没有一种方法在所有屏幕上表现最好,但确定了两种方法在大多数数据集上表现良好。在这两个分数中,Gemini-Sensitive有一个可用的R包,可以应用于大多数屏幕设计,使其成为合理的首选。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Benchmarking genetic interaction scoring methods for identifying synthetic lethality from combinatorial CRISPR screens.

Synthetic lethality (SL) is an extreme form of negative genetic interaction, where simultaneous disruption of two non-essential genes causes cell death. SL can be exploited to develop cancer therapies that target tumour cells with specific mutations, potentially limiting toxicity. Pooled combinatorial CRISPR screens, where two genes are simultaneously perturbed and the resulting impacts on fitness estimated, are now widely used for the identification of SL targets in cancer. Various scoring methods have been developed to infer SL genetic interactions from these screens, but there has been no systematic comparison of these approaches. Here, we performed a comprehensive analysis of five scoring methods for SL detection using five combinatorial CRISPR datasets. We assessed the performance of each algorithm on each screen dataset using two different benchmarks of paralog SL. We find that no single method performs best across all screens but identify two methods that perform well across most datasets. Of these two scores, Gemini-Sensitive has an available R package that can be applied to most screen designs, making it a reasonable first choice.

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来源期刊
CiteScore
8.00
自引率
2.20%
发文量
95
审稿时长
15 weeks
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