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ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system

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...

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Detalles Bibliográficos
Autores principales: Kaur, Karambir, Gupta, Amit Kumar, Rajput, Akanksha, Kumar, Manoj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007494/
https://www.ncbi.nlm.nih.gov/pubmed/27581337
http://dx.doi.org/10.1038/srep30870
Descripción
Sumario: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.