ArticleScience advances2023
Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- N-terminal fusion length: The key to reliable and context-preserving regulatory sequence characterization.Synthetic and systems biotechnology · 2027Article
- The adaptive landscapes of three globaleLife · 2026Article
- Whole-cell and cell-free biosensor-driven metabolic engineering.Current opinion in biotechnology · 2025Review
- GLiDe: a web-based genome-scale CRISPRi sgRNA design tool for prokaryotes.BMC bioinformatics · 2025Article
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6 authors.
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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 transcription and translation to noise production in
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