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 comput-erizes 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.