Dataset Information

CRISPRclassify: Repeat-Based Classification of CRISPR Loci.

ABSTRACT: Detection and classification of CRISPR-Cas systems in metagenomic data have become increasingly prevalent in recent years due to their potential for diverse applications in genome editing. Traditionally, CRISPR-Cas systems are classified through reference-based identification of proximate cas genes. Here, we present a machine learning approach for the detection and classification of CRISPR loci using repeat sequences in a cas-independent context, enabling identification of unclassified loci missed by traditional cas-based approaches. Using biological attributes of the CRISPR repeat, the core element in CRISPR arrays, and leveraging methods from natural language processing, we developed a machine learning model capable of accurate classification of CRISPR loci in an extensive set of metagenomes, resulting in an F1 measure of 0.82 across all predictions and an F1 measure of 0.97 when limiting to classifications with probabilities >0.85. Furthermore, assessing performance on novel repeats yielded an F1 measure of 0.96. Although the performance of cas-based identification will exceed that of a repeat-based approach in many cases, CRISPRclassify provides an efficient approach to classification of CRISPR loci for cases in which cas gene information is unavailable, such as metagenomes and fragmented genome assemblies.

SUBMITTER: Nethery MA

PROVIDER: S-EPMC8392126 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

CRISPRclassify: Repeat-Based Classification of CRISPR Loci.

Nethery Matthew A MA Korvink Michael M Makarova Kira S KS Wolf Yuri I YI Koonin Eugene V EV Barrangou Rodolphe R

The CRISPR journal 20210801 4

Detection and classification of CRISPR-Cas systems in metagenomic data have become increasingly prevalent in recent years due to their potential for diverse applications in genome editing. Traditionally, CRISPR-Cas systems are classified through reference-based identification of proximate <i>cas</i> genes. Here, we present a machine learning approach for the detection and classification of CRISPR loci using repeat sequences in a <i>cas</i>-independent context, enabling identification of unclassi ...[more]

PMID: 34406047

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Project description:MotivationThe discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs.ResultsWe present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0.AvailabilityCRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

CRISPRclassify: Repeat-Based Classification of CRISPR Loci.

Publications

CRISPRclassify: Repeat-Based Classification of CRISPR Loci.

Similar Datasets

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