AIMC Topic: Gene Editing

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CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

International journal of molecular sciences
CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-ta...

CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network.

PLoS computational biology
Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to d...

Effector translocation and rational design of disease resistance.

Trends in microbiology
The effector repertoire of a pathogen is dynamically evolving. However, the effector translocation mechanism, partly elucidated recently, may be conserved. By targeting the effector translocation machinery, rather than the individual evolving effecto...

Prediction of base editor off-targets by deep learning.

Nature communications
Due to the tolerance of mismatches between gRNA and targeting sequence, base editors frequently induce unwanted Cas9-dependent off-target mutations. Here, to develop models to predict such off-targets, we design gRNA-off- target pairs for adenine bas...

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning.

The CRISPR journal
Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect ma...

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

Nature biotechnology
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 models to predict the editing efficiencies and outcomes of diverse base editors.

Nature biotechnology
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 ...

Enabling technology and core theory of synthetic biology.

Science China. Life sciences
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling ...

Predicting prime editing efficiency and product purity by deep learning.

Nature biotechnology
Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423...