CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats

By C. Bland, T. L. Ramsey, Fareedah Sabree, M. Lowe, Kyndall Brown, Nikos Kyrpides, Philip Hugenholtz. BMC Bioinformatics, 2007

955 citations recorded by the source corpus.

BackgroundClustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes.ResultsCRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT proved to be a huge improvement over Patscan. Both CRT and Pilercr were comparable in speed, however CRT was faster for genomes containing large numbers of repeats.ConclusionIn this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, showed some important improvements over current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(n) in space and O(nm/l) in time.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/f8c682febf0b44c943927a1a4c3cc30ad6eea005

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Citations in the corpus

IMG: the integrated microbial genomes database and comparative analysis system (2011, methodology): “IMG’s data integration pipeline associates every genome with metadata from GOLD (2), and fills in additional information potentially missing from the RefSeq files such as CRISPR repeats (3), signal peptides computed using SignalP (4) and transmembrane helices computed using TMHMM (5).”
Transcriptome engineering with RNA-targeting Type VI-D CRISPR effectors (2018, positive): “…CRISPR array as a search anchor, we first obtained all prokaryotic genome assemblies and scaffolds from the NCBIWGSdatabase and adapted algorithms for de novo CRISPR array detection (Bland et al., 2007; Edgar, 2007; Grissa et al., 2007) to identify 21,175 putative CRISPR repeat arrays (Figure S1A).”
MicrobesOnline: an integrated portal for comparative and functional genomics (2009, methodology): “CRISPR and RNA loci potentially missing from the RefSeq annotations are computed using PILER-CR (8), CRT (9), tRNAScan (10), and BLASTn.”
IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes (2018, methodology): “Briefly, CRISPR elements are detected using a modified CRT (5); tRNAs, ribosomal RNAs, non-coding RNAs and RNA regulatory features are predicted using Rfam covariance models and Infernal tools (6–8); and proteincoding genes are called by Prodigal v2.”
Metagenomics - a guide from sampling to data analysis (2012, background): “There exists also a number of tools for the prediction of non-protein coding genes such as tRNAs [66,67], signal peptides [68] or CRISPRs [69,70], however they might require significant computational resources or long contiguous sequences.”
CRISPR/Cas system and its role in phage-bacteria interactions. (2010, background): “Fortunately, specific CRISPR programs are now available (10, 28, 31, 32).”
The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4) (2015, methodology): “The predictions from PILER-CR and CRT are concatenated and, in case of overlapping predictions, the CRT prediction is retained.”
NCBI prokaryotic genome annotation pipeline (2016, methodology): “For CRISPRs prediction and annotation PGAP uses a combination of the CRISPR recognition tool (CRT) (26) and PILER-CR (27).”
MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems (2014, background): “We searched for CRISPR-arrays with [17] as described in [27] and found that 88% of the detected Cas systems are close ( , 1kb, same result for , 5kb) to a CRISPR-array and that 98% are present in a replicon containing at least one CRISPR-array.”
IMG/M 4 version of the integrated metagenome comparative analysis system (2013, methodology): “Metagenome gene prediction starts with the detection of CRISPR elements using CRT (5) and PILERCR (6).”
Computational approaches to predict bacteriophage–host relationships (2015, methodology): “Several bioinformatics tools have been developed to identify CRISPR spacers in bacterial genomes (Edgar and Myers 2005; Bland et al. 2007;Grissa,VergnaudandPourcel2007a),andspacersequences have also been collected in publicly accessible databases (Grissa, Vergnaud and Pourcel 2007b; Rousseau et al.…”
DRAM for distilling microbial metabolism to automate the curation of microbiome function (2020, methodology): “For the single binned HMP sample (SRS019068), viral sequences were matched to host MAGs using the CRISPR Recognition Tool (68) plugin (version 1.”