Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

ABSTRACT: Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of t

SUBMITTER: Feng H 

PROVIDER: S-EPMC10631719 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature