CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems

  1. Victor A Padilha
  2. Omer S Alkhnbashi
  3. Shiraz A Shah
  4. André C P L F de Carvalho
  5. Rolf Backofen

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Abstract

Background

CRISPR-Cas genes are extraordinarily diverse and evolve rapidly when compared to other prokaryotic genes. With the rapid increase in newly sequenced archaeal and bacterial genomes, manual identification of CRISPR-Cas systems is no longer viable. Thus, an automated approach is required for advancing our understanding of the evolution and diversity of these systems and for finding new candidates for genome engineering in eukaryotic models.

Results

We introduce CRISPRcasIdentifier, a new machine learning–based tool that combines regression and classification models for the prediction of potentially missing proteins in instances of CRISPR-Cas systems and the prediction of their respective subtypes. In contrast to other available tools, CRISPRcasIdentifier can both detect cas genes and extract potential association rules that reveal functional modules for CRISPR-Cas systems. In our experimental benchmark on the most recently published and comprehensive CRISPR-Cas system dataset, CRISPRcasIdentifier was compared with recent and state-of-the-art tools. According to the experimental results, CRISPRcasIdentifier presented the best Cas protein identification and subtype classification performance.

Conclusions

Overall, our tool greatly extends the classification of CRISPR cassettes and, for the first time, predicts missing Cas proteins and association rules between Cas proteins. Additionally, we investigated the properties of CRISPR subtypes. The proposed tool relies not only on the knowledge of manual CRISPR annotation but also on models trained using machine learning.

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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giaa062

    Victor A. Padilha 1Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense 400, São Carlos, SP, 13564-002, BrazilFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteOmer S. Alkhnbashi 2Chair of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 101, Freiburg, 79110, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteShiraz A. Shah 3Danish Archaea Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen, DK-2200, DenmarkFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteAndré C. P. L. F. de Carvalho 1Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trabalhador São Carlense 400, São Carlos, SP, 13564-002, BrazilFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteRolf Backofen 2Chair of Bioinformatics, University of Freiburg, Georges-Köhler-Allee 101, Freiburg, 79110, Germany4Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Georges-Köhler-Allee 101, Freiburg, 79110, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: backofen@informatik.uni-freiburg.de

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa062 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102294 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102295

  2. Version published to 10.1093/gigascience/giaa062
  3. Version published to 10.1101/817619v1 on bioRxiv