4.7 Article

CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection

Journal

BIOINFORMATICS
Volume 38, Issue -, Pages ii42-ii48

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac466

Keywords

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Funding

  1. ECCB2022
  2. German Research Foundation (DFG) [BA 2168/23-1/2, HE 2544/14-2 SPP 2141]
  3. Much more than Defence: the Multiple Functions and Facets of CRISPR-Cas
  4. BadenWuerttemberg Ministry of Science, Research and Art
  5. University of Freiburg. Funding for open access charge. Probabilistic Structures in Evolution [BA 2168/13-1 SPP 1590]
  6. Freiburg Galaxy Team: Rolf Backofen and Bjorn Gruning, Bioinformatics, University of Freiburg (Germany) - Collaborative Research Centre 992 Medical Epigenetics [SFB 992/1 2012]
  7. German Federal Ministry of Education and Research BMBF [031 A538A]

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This study introduces a new pipeline, CRISPRtracrRNA, for screening and evaluating tracrRNA candidates in genomes. The pipeline combines evidence from different components of the Cas9-sgRNA complex. It also utilizes a newly developed structural model to simulate the structure of tracrRNA. Additionally, evidence is provided through the detection of repeat sequences, terminator signals, and RNA-RNA interactions.
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.

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