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
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
收藏
页码:137 / 162
页数:26
相关论文
共 50 条
  • [21] An analysis and validation pipeline for large-scale RNAi-based screens
    Michael Plank
    Guang Hu
    A. Sofia Silva
    Shona H. Wood
    Emily E. Hesketh
    Georges Janssens
    André Macedo
    João Pedro de Magalhães
    George M. Church
    Scientific Reports, 3
  • [22] Capitalizing on large-scale mouse mutagenesis screens
    Justice, MJ
    NATURE REVIEWS GENETICS, 2000, 1 (02) : 109 - 115
  • [23] Computational solutions to large-scale data management and analysis
    Schadt, Eric E.
    Linderman, Michael D.
    Sorenson, Jon
    Lee, Lawrence
    Nolan, Garry P.
    NATURE REVIEWS GENETICS, 2010, 11 (09) : 647 - 657
  • [24] Computational solutions to large-scale data management and analysis
    Eric E. Schadt
    Michael D. Linderman
    Jon Sorenson
    Lawrence Lee
    Garry P. Nolan
    Nature Reviews Genetics, 2010, 11 : 647 - 657
  • [25] Analysis methods for large-scale neuronal recordings
    Stringer, Carsen
    Pachitariu, Marius
    SCIENCE, 2024, 386 (6722)
  • [26] Advanced Computational Methods Drive Large-Scale Data Analysis in 4D-Proteomics
    Cox J.
    Kruppa G.
    Genetic Engineering and Biotechnology News, 2020, 40 (12): : 22 - 23
  • [27] Fast computational methods for large-scale eddy-current computation
    Rubinacci, G
    Tamburrino, A
    Ventre, S
    Villone, F
    IEEE TRANSACTIONS ON MAGNETICS, 2002, 38 (02) : 529 - 532
  • [28] Computational methods for a large-scale inverse problem arising in atmospheric optics
    Gilles, L
    Vogel, C
    Bardsley, J
    INVERSE PROBLEMS, 2002, 18 (01) : 237 - 252
  • [29] Computational Methods for Large-Scale Inverse Problems: A Survey on Hybrid Projection Methods\ast
    Chung, Julianne
    Gazzola, Silvia
    SIAM REVIEW, 2024, 66 (02) : 205 - 284
  • [30] Large-scale compound screens and pharmacogenomic interactions in cancer
    McDermott, Ultan
    CURRENT OPINION IN GENETICS & DEVELOPMENT, 2019, 54 : 12 - 16