AIMC Topic: RNA, Guide, CRISPR-Cas Systems

Clear Filters Showing 11 to 20 of 42 articles

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting.

Cell systems Dec 12, 2023
Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplet...
CRISPR-Cas Systems RNA RNA, Guide, CRISPR-Cas Systems Humans Transcriptome Deep Learning
View on PubMed DOI

A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency.

Computers in biology and medicine Sep 6, 2023
CRISPR/Cas9 system is a powerful tool for genome editing. Numerous studies have shown that sgRNAs can strongly affect the efficiency of editing. However, it is still not clear what rules should be followed for designing sgRNA with high cleavage effic...
Machine Learning Deep Learning RNA, Guide, CRISPR-Cas Systems Neural Networks, Computer
View on PubMed DOI

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.

Nature biotechnology Jul 3, 2023
Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes i...
Deep Learning CRISPR-Cas Systems Clustered Regularly Interspaced Short Palindromic Repeats Gene Editing RNA, Guide, CRISPR-Cas Systems RNA Humans
View on PubMed DOI

Deep learning models to predict the editing efficiencies and outcomes of diverse base editors.

Nature biotechnology May 15, 2023
Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and ...
Deep Learning CRISPR-Cas Systems RNA, Guide, CRISPR-Cas Systems Alkanesulfonic Acids CRISPR-Associated Protein 9 Gene Editing
View on PubMed DOI

AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity.

BMC bioinformatics Dec 13, 2021
BACKGROUND: More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods b...
RNA, Guide, CRISPR-Cas Systems Machine Learning CRISPR-Cas Systems
View on PubMed DOI

Easy-Prime: a machine learning-based prime editor design tool.

Genome biology Aug 19, 2021
Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning-based program traine...
HEK293 Cells Gene Editing RNA, Guide, CRISPR-Cas Systems DNA CRISPR-Cas Systems Humans Machine Learning
View on PubMed DOI

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.

Nature communications May 28, 2021
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. ...
Lentivirus Genetic Vectors RNA, Guide, CRISPR-Cas Systems HEK293 Cells Plasmids Gene Editing Computational Biology CRISPR-Cas Systems Software Deep Learning Humans
View on PubMed DOI

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

PLoS computational biology Jan 8, 2021
Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments ...
RNA, Guide, CRISPR-Cas Systems Humans Animals Machine Learning Gene Library Exons Mice Cells, Cultured Embryonic Stem Cells Gene Editing Genetic Therapy CRISPR-Cas Systems
View on PubMed DOI

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology Jan 1, 2021
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (s...
Zea mays CRISPR-Cas Systems Internet HeLa Cells Triticum Neural Networks, Computer Algorithms Glycine max Computational Biology RNA, Guide, CRISPR-Cas Systems HEK293 Cells Humans Crops, Agricultural Sorghum Gene Editing HCT116 Cells
View on PubMed DOI

Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning.

Cell Jun 12, 2020
Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and AB...
Gene Library Animals RNA, Guide, CRISPR-Cas Systems Humans Mouse Embryonic Stem Cells Mice Point Mutation Machine Learning Gene Editing
View on PubMed DOI
Popular Topics
Recent Journals