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Related Experiment Video

Updated: Jul 11, 2025

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq
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Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high

Huibao Feng1, Fan Li1, Tianmin Wang2,3

  • 1MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.

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|November 8, 2023
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Summary

We developed a deep-learning Sort-Seq method for high-throughput analysis of gene expression. This approach precisely measures expression noise and strength, revealing insights into cellular behavior and noise control strategies in bacteria.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Bioengineering

Background:

  • Intracellular protein abundance in clonal populations follows a distribution due to gene expression's inherent variability.
  • Understanding expression strength and noise is crucial for cellular behavior but traditional methods are slow and laborious.

Purpose of the Study:

  • To introduce a high-throughput, precise method for profiling gene expression properties.
  • To investigate the sources of noise in gene expression in Escherichia coli.
  • To explore potential noise control strategies in bacterial systems.

Main Methods:

  • Development of a deep-learning-assisted Sort-Seq (dSort-Seq) approach for high-throughput gene expression analysis.
  • Application of dSort-Seq for large-scale dose-response assays of biosensors.
  • Comprehensive investigation of transcriptional and translational contributions to gene expression noise in E. coli.

Main Results:

  • dSort-Seq enables precise, high-throughput profiling of gene expression characteristics.
  • Expression noise in E. coli is strongly correlated with the mean expression level.
  • Transcriptional interference from overlapping RpoD-binding sites contributes significantly to noise production.

Conclusions:

  • The dSort-Seq method offers a powerful tool for quantitative analysis of gene expression.
  • Findings elucidate the relationship between expression noise and mean expression in bacteria.
  • Identification of transcriptional interference suggests a feasible strategy for noise control in E. coli.