@Article{Padilha_Alkhnbashi_Shah-CRISP_Machi_learn-2020,
  author =	 {Padilha, Victor A. and Alkhnbashi, Omer S. and Shah, Shiraz 
                  A. and de Carvalho, Andre C. P. L. F. and Backofen, Rolf},
  title =	 {{CRISPRcasIdentifier}: {Machine} learning for accurate 
                  identification and classification of {CRISPR}-{Cas} systems},
  journal =	 {Gigascience},
  year =	 {2020},
  volume =	 {9},
  number =	 {6},
  pages =	 {},
  user =	 {alkhanbo},
  pmid =	 {32556168},
  doi = 	 {10.1093/gigascience/giaa062},
  issn = 	 {2047-217X},
  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.}
}