AIMC Topic: Gene Editing

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Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework.

Cells
As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its ...

Somatic Genome Editing with the Use of AI: Big Promises but Doubled Legal Issues.

European journal of health law
Both Artificial Intelligence ('AI') and genome editing are technologies that on their own promise to revolutionise healthcare. But their common application can facilitate progress in the field even more. Multiplied benefits go along with increased ri...

Deep Learning-Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells.

Small (Weinheim an der Bergstrasse, Germany)
Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing ma...

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Nature communications
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great ...

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

Genome biology
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...

Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...

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

Nature communications
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. ...

Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform.

SLAS technology
Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capabl...

Machine learning based CRISPR gRNA design for therapeutic exon skipping.

PLoS computational biology
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 ...