Optimizing sgRNA Design

Kuan, P.F. et. al. (2017) BMC Bioinformatics, 18:297 https://www.ncbi.nlm.nih.gov/pubmed/28587596

Design of sgRNAs can prove challenging due to the tendency of RNA to form complex secondary structures and different sequences imparting different thermodynamic properties.  By using a machine learning approach and previous oligonucleotide design and models, Kuan et al. analyzed CRISPR datasets to create an R package, predictSGRNA, to help with sgRNA design.

Author: Advanced Analytical

Advanced Analytical Technologies, Inc. (AATI) simplifies complex genomics workflows to accelerate research and discovery in pharmaceuticals, life science, biofuels, biotechnology and healthcare.

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