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ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system.
Kaur K
,
Gupta AK
,
Rajput A
,
Kumar M
.
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Genome editing by sgRNA a component of CRISPR/Cas system emerged as a preferred technology for genome editing in recent years. However, activity and stability of sgRNA in genome targeting is greatly influenced by its sequence features. In this endeavor, a few prediction tools have been developed to design effective sgRNAs but these methods have their own limitations. Therefore, we have developed "ge-CRISPR" using high throughput data for the prediction and analysis of sgRNAs genome editing efficiency. Predictive models were employed using SVM for developing pipeline-1 (classification) and pipeline-2 (regression) using 2090 and 4139 experimentally verified sgRNAs respectively from Homo sapiens, Mus musculus, Danio rerio and Xenopus tropicalis. During 10-fold cross validation we have achieved accuracy and Matthew's correlation coefficient of 87.70% and 0.75 for pipeline-1 on training dataset (T(1840)) while it performed equally well on independent dataset (V(250)). In pipeline-2 we attained Pearson correlation coefficient of 0.68 and 0.69 using best models on training (T(3169)) and independent dataset (V(520)) correspondingly. ge-CRISPR (http://bioinfo.imtech.res.in/manojk/gecrispr/) for a given genomic region will identify potent sgRNAs, their qualitative as well as quantitative efficiencies along with potential off-targets. It will be useful to scientific community engaged in CRISPR research and therapeutics development.
Figure 1. ROC representing Area under the curve between different hybrids features.In composition profile hybrid of mono-di-trinucleotide have AUC of 0.88 (blue), binary profile of dinucleotide have AUC of 0.92 (red) and hybrid of mono-di-trinucleotide composition and dinucleotide binary display AUC of 0.93 (green).
Figure 2. Two sample sequence logo depicting preference of A,T,G,C at 20 positions of highly effective and least effective sgRNAs.
Figure 3. Workflow of pipeline-2.(a) sgRNA scanner for extracting sgRNAs in user provided genome/gene (b) output of pilpeline-2 depicting efficiency of each sgRNA (c) sgRNA profile, displaying secondary structure and off-targets associated with individual sgRNA.
Figure 4. Diagrammatic representation of ge-CRISPR pipeline development.
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