Evaluation of off-targets predicted by sgRNA design tools

Jaspreet Kaur Dhanjal; Samvit Dammalapati; Shreya Pal; Durai Sundar

doi:10.1016/j.ygeno.2020.04.024

Evaluation of off-targets predicted by sgRNA design tools

Genomics. 2020 Sep;112(5):3609-3614. doi: 10.1016/j.ygeno.2020.04.024. Epub 2020 Apr 27.

Authors

Jaspreet Kaur Dhanjal¹, Samvit Dammalapati², Shreya Pal³, Durai Sundar⁴

Affiliations

¹ Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. Electronic address: bez138509@iitd.ac.in.
² Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. Electronic address: mt1150613@maths.iitd.ac.in.
³ Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. Electronic address: bb1350038@iitd.ac.in.
⁴ Department of Biochemical Engineering and Biotechnology, DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. Electronic address: sundar@dbeb.iitd.ac.in.

PMID: 32353475
DOI: 10.1016/j.ygeno.2020.04.024

Abstract

The ease of programming CRISPR/Cas9 system for targeting a specific location within the genome has paved way for many clinical and industrial applications. However, its widespread use is still limited owing to its off-target effects. Though this off-target activity has been reported to be dependent on both sgRNA sequence and experimental conditions, a clear understanding of the factors imparting specificity to CRISPR/Cas9 system is important. A machine learning-based computational model has been developed for prediction of off-targets with more likelihood to be cleaved in vivo with an accuracy of 91.49%. The sequence features important for the prediction of positive off-targets were found to be accessibility, mismatches, GC-content and position-specific conservation of nucleotides. The instructions and code to generate the dataset and reproduce the analysis has been made available at http://web.iitd.ac.in/crispcut/off-targets/.

Keywords: CRISPR; Cas9; Gradient boosted regression tree; Machine learning; sgRNA.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
CRISPR-Cas Systems*
Machine Learning*
RNA / genetics*
RNA Editing

Substances

RNA