CRISPRstrand
Subtitle: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci
Contributors: Costa, Fabrizio Verfasser; Shah, Shiraz A. Verfasser; Saunders, Sita Johanna 1983- Verfasser; Backofen, Rolf 1963- Verfasser; Albert-Ludwigs-Universität Freiburg Institut für Informatik; Albert-Ludwigs-Universität Freiburg Centre for Biological Signalling Studies; Albert-Ludwigs-Universität Freiburg Fakultät für Angewandte Wissenschaften; Albert-Ludwigs-Universität Freiburg Fakultät für Biologie; Albert-Ludwigs-Universität Freiburg
Series: Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria
Publisher: Universität
Published: 2014
Place of publication: Freiburg
Language: eng
Subjects: CRISPR/Cas-Methode
Description: Abstract: Motivation: The 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.<br><br>Results: We 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.<br><br>Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap
Notes: Bioinformatics. 30, 17 (2014), i489-i496, DOI 10.1093/bioinformatics/btu459, issn: 1460-2059

IN COPYRIGHT http://rightsstatements.org/page/InC/1.0 rs

Archivierung/Langzeitarchivierung gewährleistet