ArticleScientific reports2016
ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools.RNA biology · 2020Pooled it
- A simple and versatile fluorescence-based method to enhance prime editing in human pluripotent stem cells.BMC methods · 2026Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Codon usage and expression-based features significantly improve prediction of CRISPR efficiency.NPJ systems biology and applications · 2024Article
- Strong association between genomic 3D structure and CRISPR cleavage efficiency.PLoS computational biology · 2024Article
- gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement.Biomolecules · 2023Review
- Prospective Advances in Genome Editing Investigation.Advances in experimental medicine and biology · 2023Article
- Evaluation of efficiency prediction algorithms and development of ensemble model for CRISPR/Cas9 gRNA selection.Bioinformatics (Oxford, England) · 2022Article
- BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models.BMC bioinformatics · 2022Article
- A Machine Learning Approach to Identify the Importance of Novel Features for CRISPR/Cas9 Activity Prediction.Biomolecules · 2022Article
- Uncertainty-aware and interpretable evaluation of Cas9-gRNA and Cas12a-gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning.Nucleic acids research · 2022Article
- CRISPR genome editing using computational approaches: A survey.Frontiers in bioinformatics · 2022Review
- MMV-db: vaccinomics and RNA-based therapeutics database for infectious hemorrhagic fever-causing mammarenaviruses.Database : the journal of biological databases and curation · 2021Article
- sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.Plant molecular biology · 2021Article
- Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.Briefings in bioinformatics · 2021Review
- Article
- sgRNA-PSM: Predict sgRNAs On-Target Activity Based on Position-Specific Mismatch.Molecular therapy. Nucleic acids · 2020Article
- Genome editing enables reverse genetics of multicellular development in the choanoflagellateeLife · 2020Article
- CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2.Frontiers in microbiology · 2020Article
- NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus.Database : the journal of biological databases and curation · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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