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CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection

MOTIVATION: The CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRN...

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Autores principales: Mitrofanov, Alexander, Ziemann, Marcus, Alkhnbashi, Omer S, Hess, Wolfgang R, Backofen, Rolf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486595/
https://www.ncbi.nlm.nih.gov/pubmed/36124799
http://dx.doi.org/10.1093/bioinformatics/btac466
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author Mitrofanov, Alexander
Ziemann, Marcus
Alkhnbashi, Omer S
Hess, Wolfgang R
Backofen, Rolf
author_facet Mitrofanov, Alexander
Ziemann, Marcus
Alkhnbashi, Omer S
Hess, Wolfgang R
Backofen, Rolf
author_sort Mitrofanov, Alexander
collection PubMed
description MOTIVATION: The CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems. RESULTS: We introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA–RNA interaction between the CRISPR array repeat and the 5′-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems. AVAILABILITY AND IMPLEMENTATION: The implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen (backofen@informatik.uni-freiburg.de). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-94865952022-09-20 CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection Mitrofanov, Alexander Ziemann, Marcus Alkhnbashi, Omer S Hess, Wolfgang R Backofen, Rolf Bioinformatics Genomes Track MOTIVATION: The CRISPR-Cas9 system is a Type II CRISPR system that has rapidly become the most versatile and widespread tool for genome engineering. It consists of two components, the Cas9 effector protein, and a single guide RNA that combines the spacer (for identifying the target) with the tracrRNA, a trans-activating small RNA required for both crRNA maturation and interference. While there are well-established methods for screening Cas effector proteins and CRISPR arrays, the detection of tracrRNA remains the bottleneck in detecting Class 2 CRISPR systems. RESULTS: We introduce a new pipeline CRISPRtracrRNA for screening and evaluation of tracrRNA candidates in genomes. This pipeline combines evidence from different components of the Cas9-sgRNA complex. The core is a newly developed structural model via covariance models from a sequence-structure alignment of experimentally validated tracrRNAs. As additional evidence, we determine the terminator signal (required for the tracrRNA transcription) and the RNA–RNA interaction between the CRISPR array repeat and the 5′-part of the tracrRNA. Repeats are detected via an ML-based approach (CRISPRidenify). Providing further evidence, we detect the cassette containing the Cas9 (Type II CRISPR systems) and Cas12 (Type V CRISPR systems) effector protein. Our tool is the first for detecting tracrRNA for Type V systems. AVAILABILITY AND IMPLEMENTATION: The implementation of the CRISPRtracrRNA is available on GitHub upon requesting the access permission, (https://github.com/BackofenLab/CRISPRtracrRNA). Data generated in this study can be obtained upon request to the corresponding person: Rolf Backofen (backofen@informatik.uni-freiburg.de). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-09-18 /pmc/articles/PMC9486595/ /pubmed/36124799 http://dx.doi.org/10.1093/bioinformatics/btac466 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Genomes Track
Mitrofanov, Alexander
Ziemann, Marcus
Alkhnbashi, Omer S
Hess, Wolfgang R
Backofen, Rolf
CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title_full CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title_fullStr CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title_full_unstemmed CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title_short CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection
title_sort crisprtracrrna: robust approach for crispr tracrrna detection
topic Genomes Track
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486595/
https://www.ncbi.nlm.nih.gov/pubmed/36124799
http://dx.doi.org/10.1093/bioinformatics/btac466
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