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RNA, Guide, CRISPR-Cas Systems

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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 ...

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...

Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities.

Briefings in bioinformatics
In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient o...

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

Computers in biology and medicine
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...

Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions.

Nature computational science
Finely tuned enzymatic pathways control cellular processes, and their dysregulation can lead to disease. Developing predictive and interpretable models for these pathways is challenging because of the complexity of the pathways and of the cellular an...

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

Cell systems
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-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.

Briefings in bioinformatics
CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, ...

SLKB: synthetic lethality knowledge base.

Nucleic acids research
Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases li...

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...

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...