The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats
Open accessAbstract
Abstract Background Clustered 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. Results CRT 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. Conclusion In 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,940
CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes
Barrangou, R., Fremaux, C., Deveau, H., Richards, M., Boyaval, P., Moineau, S., Romero, D. A., & Horvath, P. (2007) · Science · doi:10.1126/science.1138140 · Cited by 5,947
Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology
Gusfield, D. (1997) · Cambridge University Press eBooks · doi:10.1017/cbo9780511574931 · Cited by 3,078
REPuter: the manifold applications of repeat analysis on a genomic scale
Kurtz, S. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.22.4633 · Cited by 2,618
A fast string searching algorithm
Boyer, R. S., & Moore, J. S. (1977) · Communications of the ACM · doi:10.1145/359842.359859 · Cited by 2,290
Intervening Sequences of Regularly Spaced Prokaryotic Repeats Derive from Foreign Genetic Elements
Mojica, F. J. M., D�ez-Villase�or, C., Garc�a-Mart�nez, J., & Soria, E. (2005) · Journal of Molecular Evolution · doi:10.1007/s00239-004-0046-3 · Cited by 2,042
Identification of genes that are associated with DNA repeats in prokaryotes
Jansen, R., Embden, J. D. A. V., Gaastra, W., & Schouls, L. M. (2002) · Molecular Microbiology · doi:10.1046/j.1365-2958.2002.02839.x · Cited by 1,802
Algorithms on strings, trees, and sequences
Gusfield, D. (1997) · Cited by 1,697
Bolotin, A., Quinquis, B., Sorokin, A., & Ehrlich, S. D. (2005) · Microbiology · doi:10.1099/mic.0.28048-0 · Cited by 1,558
Cited by
An updated evolutionary classification of CRISPR–Cas systems
Makarova, K. S., Wolf, Y. I., Alkhnbashi, O. S., Costa, F., Shah, S. A., Saunders, S. J., Barrangou, R., Brouns, S. J. J., Charpentier, E., Haft, D. H., et al. (2015) · Nature Reviews Microbiology · doi:10.1038/nrmicro3569 · Cited by 2,480
RNA-guided genetic silencing systems in bacteria and archaea
Wiedenheft, B., Sternberg, S. H., & Doudna, J. A. (2012) · Nature · doi:10.1038/nature10886 · Cited by 1,851
MetaHIT Consortium, Nielsen, H. B., Almeida, M., Juncker, A. S., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D. R., Gautier, L., Pedersen, A. G., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2939 · Cited by 1,184
IMG: the integrated microbial genomes database and comparative analysis system
Markowitz, V. M., Chen, I.-M. A., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., Ratner, A., Jacob, B., Huang, J., Williams, P., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1044 · Cited by 1,311
Self-citationTranscriptome Engineering with RNA-Targeting Type VI-D CRISPR Effectors
Konermann, S., Lotfy, P., Brideau, N. J., Oki, J., Shokhirev, M. N., & Hsu, P. D. (2018) · Cell · doi:10.1016/j.cell.2018.02.033 · Cited by 1,164
Metadata
- Authors
- Bland, C., Ramsey, T. L., Sabree, F., Lowe, M., Brown, K., Kyrpides, N. C., & Hugenholtz, P.
- Venue
- BMC Bioinformatics
- Published
- 2007-06-18
- Volume
- 8
- Issue
- 1
- ISSN
- 1471-2105
- OpenAlex
- W2127391962
- Cited by
- 1,049
- References
- 30
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. https://app.cochat.ai/papers/10.1186/1471-2105-8-209
- The paper
- Bland, C., Ramsey, T. L., Sabree, F., Lowe, M., Brown, K., Kyrpides, N. C., & Hugenholtz, P. (2007). CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-8-209
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
