SeqCor: correct the effect of guide RNA sequences in clustered regularly interspaced short palindromic repeats/Cas9 screening by machine learning algorithm

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based screening using various guide RNA (gRNA) libraries has been executed to identify functional components for a wide range of phenotypes with regard to numerous cell types and organisms. Using data from public CRISPR/Cas9-based screening experiments, we found that the sequences of gRNAs in the library influence CRISPR/ Cas9-based screening. As building a standard strategy for correcting results of all gRNA libraries is impractical, we developed SeqCor, an open-source programming bundle that enables researchers to address the result bias potentially triggered by the composition of gRNA sequences via the organization of gRNA in the library used in CRISPR/Cas9-based screening. Furthermore, SeqCor completely computerizes the extraction of sequence features that may influence single-guide RNA knockout efficiency using a machine learning approach. Taken together, we have developed a software program bundle that ought to be beneficial to the CRISPR/Cas9-based screening platform. Copyright (C) 2020, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights reserved.