Computational Methods for Analysis of Large-Scale CRISPR Screens

被引：4

作者：

Lin, Xueqiu ^{[1
]}

Chemparathy, Augustine ^{[1
]}

La Russa, Marie ^{[1
]}

Daley, Timothy ^{[1
,2
]}

Qi, Lei S. ^{[1
,3
,4
]}

机构：

[1] Stanford Univ, Dept Bioengn, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Chem & Syst Biol, Stanford, CA 94305 USA

[4] Stanford Univ, ChEMH Chem Engn & Med Human Hlth, Stanford, CA 94305 USA

来源：

ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 3, 2020 | 2020年 / 3卷

关键词：

CRISPR screen; high-throughput; computational method; genetic interaction; single-cell; gene editing; noncoding element; genotype; phenotype; FUNCTIONAL GENOMICS; GENETIC SCREENS; TRANSCRIPTIONAL ACTIVATION; REGULATORY ELEMENTS; DNA; KNOCKOUT; DESIGN; BASE; IDENTIFICATION; SPECIFICITY;

D O I：

10.1146/annurev-biodatasci-020520-113523

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Large-scale CRISPR-C as pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.

引用

页码：137 / 162

页数：26

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