Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.

ABSTRACT: CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) desig

SUBMITTER: Ozden F 

PROVIDER: S-EPMC11472043 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature