Yuanyuan Yang
Yan Qiu
Md Reyad-ul-ferdous
Qiurong Ding
Yi Wang
aCAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
bInstitute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
cMinistry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200438, China
dHuman Phenome Institute, Fudan University, Shanghai 200438, China
More InformationCorresponding author: E-mail address: qrding@sibs.ac.cn (Qiurong Ding);E-mail address: wangyi_fudan@fudan.edu.cn (Yi Wang)
Publish Date:2020-11-25
Abstract
Abstract
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.Keywords: SeqCor,
CRISPR/Cas9-based screening,
Machine learning
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