Back to Search Start Over

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation

Authors :
Hans Müller Paul
Dave D. Istanto
Jacob Heldenbrand
Matthew E. Hudson
Source :
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-19 (2022)
Publication Year :
2022
Publisher :
BMC, 2022.

Abstract

Abstract Background CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. Results We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. Conclusions CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

Details

Language :
English
ISSN :
14712105
Volume :
23
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.1b9c77657ea74b22acdc137992e7b09a
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-022-04593-2