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Related Concept Videos

Transcription Factors02:16

Transcription Factors

83.1K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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General Transcription Factors01:30

General Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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Co-activators and Co-repressors02:04

Co-activators and Co-repressors

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Co-activators and Co-repressors

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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Related Experiment Video

Updated: Feb 26, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Local network component analysis for quantifying transcription factor activities.

Qianqian Shi1, Chuanchao Zhang2, Weifeng Guo3

  • 1Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Shanghai, China.

Methods (San Diego, Calif.)
|July 16, 2017
PubMed
Summary

We developed a new computational method, local network component analysis (LNCA), to accurately measure transcription factor activity (TFA) by accounting for data heterogeneity. LNCA improves biological insights and cancer subtype classification compared to traditional methods.

Keywords:
Adaptive optimization strategyData heterogeneityIntegrative analysisNetwork component analysisTranscription factor activities

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Transcription factors (TFs) regulate key cellular processes, but accurately quantifying their activity remains challenging.
  • Traditional methods analyze TF regulatory signals at the population level, masking individual or subgroup variations.
  • This limitation leads to inaccurate analyses and limits understanding of complex biological mechanisms.

Purpose of the Study:

  • To develop a novel computational framework, local network component analysis (LNCA), for accurate quantification of transcription factor activity (TFA).
  • To address the limitations of traditional methods by exploiting data heterogeneity and integrating prior TF-gene regulatory knowledge.
  • To provide a more precise understanding of TF regulatory signals for biological experiments and molecular mechanism elucidation.

Main Methods:

  • LNCA integrates partitioned expression sets (local information) with prior TF-gene regulatory knowledge.
  • It employs an adaptive optimization strategy to evaluate local regulation control similarities and correct data integration biases.
  • The framework was validated using simulated datasets and two real biological datasets (yeast cell cycle and glioblastoma multiforme).

Main Results:

  • LNCA accurately quantifies TFA by exploiting data heterogeneity, outperforming traditional methods like FastNCA, ROBNCA, and NINCA.
  • The method successfully identified periodic modes in the yeast cell cycle.
  • In glioblastoma multiforme, LNCA-identified TFAs better distinguished tumor subtypes than TF expression values, offering insights into cancer heterogeneity.

Conclusions:

  • LNCA provides a robust computational framework for accurate TFA quantification, improving upon existing methods.
  • The approach enhances the understanding of cell cycle regulation and provides a novel way to classify cancer subtypes.
  • LNCA offers valuable insights into biological complexity and cancer heterogeneity.