Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/149062
Title: | Designing custom CRISPR libraries for hypothesis-driven drug target discovery | Authors: | Iyer, Vaishnavi Srinivasan Jiang, Long Shen, Yunbing Boddul, Sanjaykumar V. Panda, Sudeepta Kumar Kasza, Zsolt Schmierer, Bernhard Wermeling, Fredrik |
Keywords: | Science::Chemistry | Issue Date: | 2020 | Source: | Iyer, V. S., Jiang, L., Shen, Y., Boddul, S. V., Panda, S. K., Kasza, Z., Schmierer, B. & Wermeling, F. (2020). Designing custom CRISPR libraries for hypothesis-driven drug target discovery. Computational and Structural Biotechnology Journal, 18, 2237-2246. https://dx.doi.org/10.1016/j.csbj.2020.08.009 | Journal: | Computational and Structural Biotechnology Journal | Abstract: | Over the last decade Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) has been developed into a potent molecular biology tool used to rapidly modify genes or their expression in a multitude of ways. In parallel, CRISPR-based screening approaches have been developed as powerful discovery platforms for dissecting the genetic basis of cellular behavior, as well as for drug target discovery. CRISPR screens can be designed in numerous ways. Here, we give a brief background to CRISPR screens and discuss the pros and cons of different design approaches, including unbiased genome-wide screens that target all known genes, as well as hypothesis-driven custom screens in which selected subsets of genes are targeted (Fig. 1). We provide several suggestions for how a custom screen can be designed, which could broadly serve as inspiration for any experiment that includes candidate gene selection. Finally, we discuss how results from CRISPR screens could be translated into drug development, as well as future trends we foresee in the rapidly evolving CRISPR screen field. | URI: | https://hdl.handle.net/10356/149062 | ISSN: | 2001-0370 | DOI: | 10.1016/j.csbj.2020.08.009 | Schools: | School of Physical and Mathematical Sciences | Rights: | © 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SPMS Journal Articles |
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1-s2.0-S2001037020303627-main.pdf | 1.41 MB | Adobe PDF | View/Open |
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