CRISPulator: a discrete simulation tool for pooled genetic screens

Abstract Background The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and s...

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Main Authors: Tamas Nagy, Martin Kampmann
Format: Article
Language:English
Published: BMC 2017-07-01
Series:BMC Bioinformatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12859-017-1759-9
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spelling doaj-14916f21d3214d72896d4032c14f97072020-11-25T00:35:17ZengBMCBMC Bioinformatics1471-21052017-07-0118111210.1186/s12859-017-1759-9CRISPulator: a discrete simulation tool for pooled genetic screensTamas Nagy0Martin Kampmann1Graduate program in Bioinformatics, University of CaliforniaDepartment of Biochemistry and Biophysics, Institute for Neurodegenerative Diseases and California Institute for Quantitative Biomedical Research, University of CaliforniaAbstract Background The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters. Results We present CRISPulator, a Monte Carlo method-based computational tool that simulates the impact of screen parameters on the robustness of screen results, thereby enabling users to build intuition and insights that will inform their experimental strategy. CRISPulator enables the simulation of screens relying on either CRISPR interference (CRISPRi) or CRISPR nuclease (CRISPRn). Pooled screens based on cell growth/survival, as well as fluorescence-activated cell sorting according to fluorescent reporter phenotypes are supported. CRISPulator is freely available online ( http://crispulator.ucsf.edu ). Conclusions CRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. We illustrate its power by deriving non-obvious rules for optimal screen design.http://link.springer.com/article/10.1186/s12859-017-1759-9CRISPRCRISPRiFunctional genomicsGenome-wide screensSimulationMonte Carlo
collection DOAJ
language English
format Article
sources DOAJ
author Tamas Nagy
Martin Kampmann
spellingShingle Tamas Nagy
Martin Kampmann
CRISPulator: a discrete simulation tool for pooled genetic screens
BMC Bioinformatics
CRISPR
CRISPRi
Functional genomics
Genome-wide screens
Simulation
Monte Carlo
author_facet Tamas Nagy
Martin Kampmann
author_sort Tamas Nagy
title CRISPulator: a discrete simulation tool for pooled genetic screens
title_short CRISPulator: a discrete simulation tool for pooled genetic screens
title_full CRISPulator: a discrete simulation tool for pooled genetic screens
title_fullStr CRISPulator: a discrete simulation tool for pooled genetic screens
title_full_unstemmed CRISPulator: a discrete simulation tool for pooled genetic screens
title_sort crispulator: a discrete simulation tool for pooled genetic screens
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2017-07-01
description Abstract Background The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters. Results We present CRISPulator, a Monte Carlo method-based computational tool that simulates the impact of screen parameters on the robustness of screen results, thereby enabling users to build intuition and insights that will inform their experimental strategy. CRISPulator enables the simulation of screens relying on either CRISPR interference (CRISPRi) or CRISPR nuclease (CRISPRn). Pooled screens based on cell growth/survival, as well as fluorescence-activated cell sorting according to fluorescent reporter phenotypes are supported. CRISPulator is freely available online ( http://crispulator.ucsf.edu ). Conclusions CRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. We illustrate its power by deriving non-obvious rules for optimal screen design.
topic CRISPR
CRISPRi
Functional genomics
Genome-wide screens
Simulation
Monte Carlo
url http://link.springer.com/article/10.1186/s12859-017-1759-9
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