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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Related Experiment Video

Updated: Jun 30, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell

Yuchen Wang1, Xingjian Chen1,2, Zetian Zheng1

  • 1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.

Iscience
|March 21, 2024
PubMed
Summary

We developed scGREAT, a novel framework for inferring gene regulatory networks (GRNs) from single-cell transcriptomics. This method uses gene embeddings and transformers to overcome computational limitations of existing approaches.

Keywords:
BioinformaticsComputational bioinformaticsHuman genetics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular functions.
  • Single-cell sequencing enables GRN inference at a granular level.
  • Existing methods face computational costs and simplistic assumptions.

Purpose of the Study:

  • To introduce scGREAT, a novel framework for inferring GRNs from single-cell transcriptomics.
  • To address computational and assumption-based limitations of current GRN inference tools.

Main Methods:

  • scGREAT constructs gene expression and biotext dictionaries from scRNA-seq data.
  • It employs a transformer-based engine to learn gene pair representations via optimized embedding spaces.
  • The framework integrates gene expression and textual information for robust GRN inference.

Main Results:

  • scGREAT demonstrated superior performance compared to contemporary methods on benchmark datasets.
  • Gene representations generated by scGREAT offer significant insights into gene regulation.
  • External validation using spatial transcriptomics confirmed scGREAT's mechanistic annotations.

Conclusions:

  • scGREAT provides an efficient and accurate framework for inferring gene regulatory networks.
  • The method offers valuable gene regulatory insights and identifies novel TF-target interactions.
  • scGREAT advances the field of single-cell GRN inference.