PAVOOC: designing CRISPR sgRNAs using 3D protein structures and functional domain annotations
Author(s) -
Moritz Schaefer,
Djork-Arné Clevert,
B Weiss,
Andreas Steffen
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty935
Subject(s) - computer science , mit license , python (programming language) , visualization , crispr , source code , guide rna , debugging , cas9 , computational biology , subgenomic mrna , software , programming language , data mining , biology , gene , genetics
Single-guide RNAs (sgRNAs) targeting the same gene can significantly vary in terms of efficacy and specificity. PAVOOC (Prediction And Visualization of On- and Off-targets for CRISPR) is a web-based CRISPR sgRNA design tool that employs state of the art machine learning models to prioritize most effective candidate sgRNAs. In contrast to other tools, it maps sgRNAs to functional domains and protein structures and visualizes cut sites on corresponding protein crystal structures. Furthermore, PAVOOC supports homology-directed repair template generation for genome editing experiments and the visualization of the mutated amino acids in 3D.
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