CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.

ABSTRACT:

Motivation

CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, existing prediction methods are dependent on both feature and model engineering, which restricts their performance to existing knowledge about CRISPR/Cas9 editing.

Results

Herein, deep multi-task convolutional neural networks (CNNs) and neural architecture search (NAS) were used to automate both feature and model engineering and create an end-to-end deep-learning framework, CROTON (CRISPR Outcomes Through cONvolutional neural networks). The CROTON model architecture was tuned automatically with NAS on a synthetic large-scale const

SUBMITTER: Li VR 

PROVIDER: S-EPMC8275342 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature