特异性 CRISPR 筛选的再现性指标。

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Maximilian Billmann, Henry N Ward, Michael Aregger, Michael Costanzo, Brenda J Andrews, Charles Boone, Jason Moffat, Chad L Myers
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引用次数: 0

摘要

CRISPR筛选被广泛用于系统地研究表型到基因型的问题。早期的 CRISPR 筛选确定了细胞健康的核心基因,与此不同的是,目前的大多数研究工作都旨在确定细胞系、遗传背景或药物治疗等相关条件的特异性表型。虽然CRISPR相关技术前景广阔,创新速度很快,但更好地理解CRISPR筛选结果质量评估的标准和方法对于指导技术开发和应用至关重要。具体来说,许多常用的筛选质量量化指标并不能准确衡量特定上下文命中的可重复性。我们强调了报告与筛选目的直接相关的可重复性统计数据的重要性,并建议使用对特定上下文信号敏感的指标。补充信息中包含了本文透明的同行评审过程记录。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reproducibility metrics for context-specific CRISPR screens.

Reproducibility metrics for context-specific CRISPR screens.

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background, or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal. A record of this paper's transparent peer review process is included in the supplemental information.

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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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