ArticleNucleic acids research2021
CRISPRidentify: identification of CRISPR arrays using machine learning approach.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Exploring the emerging role of CRISPR-Cas systems in probiotic development.Engineering microbiology · 2026Review
- Harnessing induced pluripotent stem cells and organoids for disease modeling and precision medicine.Stem cell research & therapy · 2026Review
- The complexity of multiple CRISPR arrays in strains with (co-occurring) CRISPR systems.microLife · 2026Article
- Comprehensive analysis of CRISPR array repeat mutations reveals subtype-specific patterns and links to spacer dynamics.microLife · 2026Article
- Age-dependent patterns of the gut microbiome, antibiotic resistome, and pathogenicity in captive koalas (Phascolarctos cinereus).Communications biology · 2025Article
- A genomic atlas of the human gut virome elucidates genetic factors shaping host interactions.bioRxiv : the preprint server for biology · 2025Article
- An evolutionary approach to predict the orientation of CRISPR arrays.PLoS computational biology · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Identification and characterization ofMicrobiology spectrum · 2025Article
- Article
- Metagenomic CRISPR Array Analysis Tool: a novel graph-based approach to finding CRISPR arrays in metagenomic datasets.microLife · 2025Article
- Unlocking the potential of CRISPR tools and databases for precision genome editing.Frontiers in plant science · 2025Review
- Characterization, diversity, and biogeochemical potential of soil viruses inhabiting in Yuncheng Salt Lake.Frontiers in microbiology · 2025Article
- Disparate mechanisms counteract extraneous CRISPR RNA production in type II-C CRISPR-Cas systems.microLife · 2025Article
- Article
- Defence systems and horizontal gene transfer in bacteria.Environmental microbiology · 2024Article
- Machine Learning Meets Cancer.Cancers · 2024Review
- Defense systems and horizontal gene transfer in bacteria.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.