@article{Mitrofanov_Alkhnbashi_Shmakov-CRISP_ident_CRISP-NAR2020,
author = {Mitrofanov, Alexander and Alkhnbashi, Omer S. and Shmakov, 
          Sergey A. and Makarova, Kira S. and Koonin, Eugene V. and 
          Backofen, Rolf},
title = {{CRISPRidentify}: identification of {CRISPR} arrays using 
         machine learning approach},
journal = {NAR},
year = {2020},
doi = {10.1093/nar/gkaa1158},
volume = {},
user = {alkhanbo},
pmid = {33290505},
pages = {},
number = {},
issn = {1362-4962},
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.}
}