Literature DB >> 26048892

A Perspective on the Future of High-Throughput RNAi Screening: Will CRISPR Cut Out the Competition or Can RNAi Help Guide the Way?

Jessica Taylor1, Simon Woodcock2.   

Abstract

For more than a decade, RNA interference (RNAi) has brought about an entirely new approach to functional genomics screening. Enabling high-throughput loss-of-function (LOF) screens against the human genome, identifying new drug targets, and significantly advancing experimental biology, RNAi is a fast, flexible technology that is compatible with existing high-throughput systems and processes; however, the recent advent of clustered regularly interspaced palindromic repeats (CRISPR)-Cas, a powerful new precise genome-editing (PGE) technology, has opened up vast possibilities for functional genomics. CRISPR-Cas is novel in its simplicity: one piece of easily engineered guide RNA (gRNA) is used to target a gene sequence, and Cas9 expression is required in the cells. The targeted double-strand break introduced by the gRNA-Cas9 complex is highly effective at removing gene expression compared to RNAi. Together with the reduced cost and complexity of CRISPR-Cas, there is the realistic opportunity to use PGE to screen for phenotypic effects in a total gene knockout background. This review summarizes the exciting development of CRISPR-Cas as a high-throughput screening tool, comparing its future potential to that of well-established RNAi screening techniques, and highlighting future challenges and opportunities within these disciplines. We conclude that the two technologies actually complement rather than compete with each other, enabling greater understanding of the genome in relation to drug discovery.
© 2015 Society for Laboratory Automation and Screening.

Entities:  

Keywords:  CRISPR-Cas; RNAi; functional genomics; precise genome editing; screening

Mesh:

Year:  2015        PMID: 26048892     DOI: 10.1177/1087057115590069

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  13 in total

1.  Growth-restricting effects of siRNA transfections: a largely deterministic combination of off-target binding and hybridization-independent competition.

Authors:  Neha Daga; Simone Eicher; Abhilash Kannan; Alain Casanova; Shyan H Low; Saskia Kreibich; Daniel Andritschke; Mario Emmenlauer; Jeremy L Jenkins; Wolf-Dietrich Hardt; Urs F Greber; Christoph Dehio; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2018-10-12       Impact factor: 16.971

Review 2.  Gene editing tools: state-of-the-art and the road ahead for the model and non-model fishes.

Authors:  Hirak Kumar Barman; Kiran Dashrath Rasal; Vemulawada Chakrapani; A S Ninawe; Doyil T Vengayil; Syed Asrafuzzaman; Jitendra K Sundaray; Pallipuram Jayasankar
Journal:  Transgenic Res       Date:  2017-07-05       Impact factor: 2.788

Review 3.  Molecular phenotyping of infection-associated small non-coding RNAs.

Authors:  Lars Barquist; Alexander J Westermann; Jörg Vogel
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2016-11-05       Impact factor: 6.237

Review 4.  Considerations when choosing a genetic model organism for metabolomics studies.

Authors:  Laura K Reed; Charles F Baer; Arthur S Edison
Journal:  Curr Opin Chem Biol       Date:  2016-12-23       Impact factor: 8.822

5.  Orthogonal genome-wide screens of bat cells identify MTHFD1 as a target of broad antiviral therapy.

Authors:  Danielle E Anderson; Jin Cui; Qian Ye; Baoying Huang; Ya Tan; Chao Jiang; Wenhong Zu; Jing Gong; Weiqiang Liu; So Young Kim; Biao Guo Yan; Kristmundur Sigmundsson; Xiao Fang Lim; Fei Ye; Peihua Niu; Aaron T Irving; Haoyu Zhang; Yefeng Tang; Xuming Zhou; Yu Wang; Wenjie Tan; Lin-Fa Wang; Xu Tan
Journal:  Proc Natl Acad Sci U S A       Date:  2021-09-28       Impact factor: 11.205

6.  Guide RNA Design for CRISPR/Cas9-Mediated Potato Genome Editing.

Authors:  A V Khromov; V A Gushchin; V I Timerbaev; N O Kalinina; M E Taliansky; V V Makarov
Journal:  Dokl Biochem Biophys       Date:  2018-05-19       Impact factor: 0.788

Review 7.  Combine and conquer: challenges for targeted therapy combinations in early phase trials.

Authors:  Juanita S Lopez; Udai Banerji
Journal:  Nat Rev Clin Oncol       Date:  2016-07-05       Impact factor: 66.675

8.  Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia.

Authors:  Jennifer L Wilson; Simona Dalin; Sara Gosline; Michael Hemann; Ernest Fraenkel; Douglas A Lauffenburger
Journal:  Integr Biol (Camb)       Date:  2016-06-17       Impact factor: 2.192

Review 9.  A new age in functional genomics using CRISPR/Cas9 in arrayed library screening.

Authors:  Alexander Agrotis; Robin Ketteler
Journal:  Front Genet       Date:  2015-09-24       Impact factor: 4.599

10.  Target discovery screens using pooled shRNA libraries and next-generation sequencing: A model workflow and analytical algorithm.

Authors:  Christiane Schaefer; Nikhil Mallela; Jochen Seggewiß; Birgit Lechtape; Heymut Omran; Uta Dirksen; Eberhard Korsching; Jenny Potratz
Journal:  PLoS One       Date:  2018-01-31       Impact factor: 3.240

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