Title |
CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins
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Published in |
BMC Bioinformatics, April 2023
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DOI | 10.1186/s12859-023-05296-y |
Pubmed ID | |
Authors |
Ho-min Park, Jongbum Won, Yunseol Park, Esla Timothy Anzaku, Joris Vankerschaver, Arnout Van Messem, Wesley De Neve, Hyunjin Shim |
Abstract |
CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA-protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool. |
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Country | Count | As % |
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Belgium | 1 | 13% |
France | 1 | 13% |
Saudi Arabia | 1 | 13% |
India | 1 | 13% |
United Kingdom | 1 | 13% |
Unknown | 3 | 38% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 50% |
Scientists | 4 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 1 | 11% |
Student > Bachelor | 1 | 11% |
Unknown | 7 | 78% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 1 | 11% |
Computer Science | 1 | 11% |
Medicine and Dentistry | 1 | 11% |
Unknown | 6 | 67% |