$\mathrm{CROPSR}$: an automated platform for complex genome-wide $\mathrm{CRISPR}$ g$\mathrm{RNA}$ design and validation

Müller Paul, Hans; Istanto, Dave D.; Heldenbrand, Jacob; Hudson, Matthew E.

doi:10.1186/s12859-022-04593-2

$$\mathrm{CROPSR}$$: an automated platform for complex genome-wide $$\mathrm{CRISPR}$$ g$$\mathrm{RNA}$$ design and validation

Journal Article · Wed Feb 16 00:00:00 EST 2022 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-022-04593-2· OSTI ID:1980954

Müller Paul, Hans ^[1]; Istanto, Dave D. ^[2]; Heldenbrand, Jacob ^[2]; Hudson, Matthew E. ^[2]

University of Illinois at Urbana-Champaign, IL (United States); University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
University of Illinois at Urbana-Champaign, IL (United States)

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. 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. 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.

View Accepted Manuscript (DOE)

Research Organization:: Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States); University of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018420

OSTI ID:: 1980954

Alternate ID(s):: OSTI ID: 1996358

Journal Information:: BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 23; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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CROPSR: An Automated Platform for Complex Genome-Wide CRISPR gRNA Design and Validation

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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$$\mathrm{CROPSR}$$: an automated platform for complex genome-wide $$\mathrm{CRISPR}$$ g$$\mathrm{RNA}$$ design and validation

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References (23)

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