2013
DOI: 10.1093/nar/gkt183
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Crass: identification and reconstruction of CRISPR from unassembled metagenomic data

Abstract: Clustered regularly interspaced short palindromic repeats (CRISPR) constitute a bacterial and archaeal adaptive immune system that protect against bacteriophage (phage). Analysis of CRISPR loci reveals the history of phage infections and provides a direct link between phage and their hosts. All current tools for CRISPR identification have been developed to analyse completed genomes and are not well suited to the analysis of metagenomic data sets, where CRISPR loci are difficult to assemble owing to their repet… Show more

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Cited by 152 publications
(135 citation statements)
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“…Crass was used to identify CRISPR repeat and spacer sequences 49 , and was run both on individual sample reads and combined reads from all samples. To identify the microbial hosts of viruses and determine if viral predation was ongoing in the deep shale, we used BLASTn with an E-value cutoff of 1e-8 to identify contigs with repeat and spacers.…”
Section: Methodsmentioning
confidence: 99%
“…Crass was used to identify CRISPR repeat and spacer sequences 49 , and was run both on individual sample reads and combined reads from all samples. To identify the microbial hosts of viruses and determine if viral predation was ongoing in the deep shale, we used BLASTn with an E-value cutoff of 1e-8 to identify contigs with repeat and spacers.…”
Section: Methodsmentioning
confidence: 99%
“…Because CRISPR arrays’ repetitive nature makes them highly difficult to assemble, CRISPR arrays were detected purely from unassembled metagenomic reads. De novo detection was performed with Crass (Skennerton, Imelfort & Tyson, 2013) with default parameters, which identifies repeat-spacer-repeat patterns in individual reads, uses these as “seeds” to recruit other CRISPR-containing reads, and then assembles these reads into CRISPR arrays. We further clustered Crass spacers using CD-HIT (Li & Godzik, 2006) with a similarity threshold of 0.9 and aligned them against known direct repeats from CRISPRdb (Grissa, Vergnaud & Pourcel, 2007) to determine the associated host  organism.…”
Section: Methodsmentioning
confidence: 99%
“…3), with alternating spacer and repeat units, results in a computationally identifiable sequence signature. Several bioinformatics tools have been developed to identify CRISPR spacers in bacterial genomes (Biswas et al 2013;Bland et al 2007;Edgar and Myers 2005;Grissa et al 2007;Skennerton et al 2013), and spacer sequences have also been collected in publicly accessible databases (Grissa et al 2007;Rousseau et al 2009).…”
Section: Crisprmentioning
confidence: 99%