AIMC Topic: RNA, Guide, CRISPR-Cas Systems

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CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BMC bioinformatics Jun 1, 2020
BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using ...
Base Sequence Gene Editing RNA, Guide, CRISPR-Cas Systems Machine Learning Area Under Curve Algorithms CRISPR-Cas Systems Humans Reproducibility of Results HEK293 Cells Deep Learning ROC Curve Databases, Genetic
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SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

Science advances Nov 6, 2019
We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-indu...
Reproducibility of Results Deep Learning CRISPR-Cas Systems Humans RNA, Guide, CRISPR-Cas Systems CRISPR-Associated Protein 9 Mutation Internet Gene Editing
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Prediction of sgRNA on-target activity in bacteria by deep learning.

BMC bioinformatics Oct 24, 2019
BACKGROUND: One of the main challenges for the CRISPR-Cas9 system is selecting optimal single-guide RNAs (sgRNAs). Recently, deep learning has enhanced sgRNA prediction in eukaryotes. However, the prokaryotic chromatin structure is different from euk...
Bacteria CRISPR-Cas Systems Gene Editing Neural Networks, Computer Deep Learning RNA, Guide, CRISPR-Cas Systems Sequence Deletion
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An overview and metanalysis of machine and deep learning-based CRISPR gRNA design tools.

RNA biology Sep 27, 2019
The CRISPR-Cas9 system has become the most promising and versatile tool for genetic manipulation applications. Albeit the technology has been broadly adopted by both academic and pharmaceutic societies, the activity (on-target) and specificity (off-t...
RNA, Guide, CRISPR-Cas Systems Algorithms Sensitivity and Specificity Deep Learning CRISPR-Cas Systems Gene Editing Gene Targeting Humans Machine Learning
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Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning.

Nature communications Sep 19, 2019
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA acti...
RNA, Guide, CRISPR-Cas Systems Endonucleases INDEL Mutation Humans HeLa Cells HEK293 Cells Base Sequence Clustered Regularly Interspaced Short Palindromic Repeats Mice Gene Editing Animals CRISPR-Cas Systems Promoter Regions, Genetic Deep Learning Internet Cell Line, Tumor
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Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks.

BMC bioinformatics Jun 13, 2019
BACKGROUND: CRISPR-Cpf1 has recently been reported as another RNA-guided endonuclease of class 2 CRISPR-Cas system, which expands the molecular biology toolkit for genome editing. However, most of the online tools and applications to date have been d...
Deep Learning CRISPR-Cas Systems Base Sequence Endonucleases RNA, Guide, CRISPR-Cas Systems Neural Networks, Computer Algorithms
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Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Scientific reports Feb 26, 2019
Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window ar...
Machine Learning Animals Oligodeoxyribonucleotides Mutation Mice Gene Editing DNA Repair Mice, Inbred C57BL RNA, Guide, CRISPR-Cas Systems DNA Breaks, Double-Stranded CRISPR-Cas Systems
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Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network.

Journal of chemical information and modeling Dec 7, 2018
The CRISPR-Cas9 system derived from adaptive immunity in bacteria and archaea has been developed into a powerful tool for genome engineering with wide-ranging applications. Optimizing single-guide RNA (sgRNA) design to improve efficiency of target cl...
RNA, Guide, CRISPR-Cas Systems CRISPR-Cas Systems Gene Editing Nucleic Acid Conformation Deep Learning Computational Biology Models, Molecular
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A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action.

PLoS computational biology Oct 16, 2017
The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA)...
CRISPR-Cas Systems Gene Editing RNA, Guide, CRISPR-Cas Systems Algorithms ROC Curve Computational Biology Humans Machine Learning
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Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR-Cas9 off-target activity.

NAR genomics and bioinformatics Jun 1, 2025
Despite advances in determining the factors influencing cleavage activity of a CRISPR-Cas9 single guide RNA (sgRNA) at an (off-)target DNA sequence, a comprehensive assessment of pertinent physico-chemical/structural descriptors is missing. In partic...
Gene Editing RNA, Guide, CRISPR-Cas Systems CRISPR-Associated Protein 9 Machine Learning CRISPR-Cas Systems Mutation
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