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

Victor Alexandre Padilha | Zendy; Omer S. Alkhnbashi | Zendy; Shiraz A. Shah | Zendy; André C. P. L. F. de Carvalho | Zendy; Rolf Backofen | Zendy

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

Author(s) -

Victor Alexandre Padilha,

Omer S. Alkhnbashi,

Shiraz A. Shah,

André C. P. L. F. de Carvalho,

Rolf Backofen

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa062

Subject(s) - crispr , identification (biology) , computer science , computational biology , benchmark (surveying) , annotation , genome , machine learning , artificial intelligence , gene , biology , genetics , botany , geodesy , geography

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.

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