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CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems.

Authors :
Alkhnbashi OS
Mitrofanov A
Bonidia R
Raden M
Tran VD
Eggenhofer F
Shah SA
Öztürk E
Padilha VA
Sanches DS
de Carvalho ACPLF
Backofen R
Source :
Nucleic acids research [Nucleic Acids Res] 2021 Jul 02; Vol. 49 (W1), pp. W125-W130.
Publication Year :
2021

Abstract

CRISPR-Cas systems are adaptive immune systems in prokaryotes, providing resistance against invading viruses and plasmids. The identification of CRISPR loci is currently a non-standardized, ambiguous process, requiring the manual combination of multiple tools, where existing tools detect only parts of the CRISPR-systems, and lack quality control, annotation and assessment capabilities of the detected CRISPR loci. Our CRISPRloci server provides the first resource for the prediction and assessment of all possible CRISPR loci. The server integrates a series of advanced Machine Learning tools within a seamless web interface featuring: (i) prediction of all CRISPR arrays in the correct orientation; (ii) definition of CRISPR leaders for each locus; and (iii) annotation of cas genes and their unambiguous classification. As a result, CRISPRloci is able to accurately determine the CRISPR array and associated information, such as: the Cas subtypes; cassette boundaries; accuracy of the repeat structure, orientation and leader sequence; virus-host interactions; self-targeting; as well as the annotation of cas genes, all of which have been missing from existing tools. This annotation is presented in an interactive interface, making it easy for scientists to gain an overview of the CRISPR system in their organism of interest. Predictions are also rendered in GFF format, enabling in-depth genome browser inspection. In summary, CRISPRloci constitutes a full suite for CRISPR-Cas system characterization that offers annotation quality previously available only after manual inspection.<br /> (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Details

Language :
English
ISSN :
1362-4962
Volume :
49
Issue :
W1
Database :
MEDLINE
Journal :
Nucleic acids research
Publication Type :
Academic Journal
Accession number :
34133710
Full Text :
https://doi.org/10.1093/nar/gkab456