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.
['Liu|Xiaojian|X|', 'Yang|Yuanyuan|Y|', 'Qiu|Yan|Y|', 'Reyad-Ul-Ferdous|Md|M|', 'Ding|Qiurong|Q|', 'Wang|Yi|Y|']
D000465:Algorithms D001483:Base Sequence D064113:CRISPR-Cas Systems D064112:Clustered Regularly Interspaced Short Palindromic Repeats D000072669:Gene Editing D015723:Gene Library D006801:Humans D000069550:Machine Learning D017394:RNA, Guide D012984:Software
SeqCor Machine learning CRISPR/Cas9-based screening