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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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...
Transcription Factors02:16

Transcription Factors

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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
General Transcription Factors01:30

General Transcription Factors

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...
Transcriptional Regulation: Riboswitches01:23

Transcriptional Regulation: Riboswitches

Riboswitches are RNA elements that regulate gene expression by altering their secondary structures in response to specific effector molecules. These elements, located in the leader regions of certain mRNAs, act as transcriptional regulators by toggling between alternative conformations to control downstream gene expression. Riboswitch-mediated regulation is a precise mechanism for modulating biosynthetic pathways, as exemplified by the riboflavin biosynthesis pathway in Bacillus...

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Related Experiment Video

Updated: May 8, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

ROBNCA: robust network component analysis for recovering transcription factor activities.

Amina Noor1, Aitzaz Ahmad, Erchin Serpedin

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.

Bioinformatics (Oxford, England)
|August 14, 2013
PubMed
Summary
This summary is machine-generated.

RObust Network Component Analysis (ROBNCA) accurately reconstructs transcription factor activity and gene regulation, outperforming existing methods. This robust algorithm efficiently handles outliers and complex data, enabling practical applications in systems biology.

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Related Experiment Videos

Last Updated: May 8, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
11:32

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Published on: March 27, 2020

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Network Component Analysis (NCA) reconstructs transcription factor activity (TFA) using gene expression data and prior TF-gene regulation information.
  • Current NCA algorithms often suffer from inconsistency, poor reliability, or high computational complexity.
  • Existing methods lack robustness against outliers common in microarray data, necessitating improved algorithms for practical use.

Purpose of the Study:

  • To develop a robust and computationally efficient algorithm for Network Component Analysis (NCA).
  • To address the limitations of existing NCA methods, particularly their inability to handle outliers in microarray data.
  • To improve the accuracy and reliability of transcription factor activity and gene regulation reconstruction.

Main Methods:

  • Propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm designed to explicitly model outliers.
  • Derive a closed-form solution for estimating the connectivity matrix, a novel contribution.
  • Compare ROBNCA with existing methods like FastNCA and non-iterative NCA (NI-NCA) using synthetic and biological datasets.

Main Results:

  • ROBNCA demonstrates significantly higher accuracy in estimating TF activity profiles and TF-gene control strength compared to FastNCA and NI-NCA.
  • The algorithm performs robustly across varying levels of noise, correlation, and outliers in synthetic data.
  • ROBNCA outperforms existing algorithms on Saccharomyces cerevisiae and Escherichia coli datasets, showing comparable runtime to FastNCA and being much faster than NI-NCA.

Conclusions:

  • ROBNCA offers a robust, accurate, and computationally efficient solution for Network Component Analysis.
  • The algorithm effectively handles outliers in microarray data, improving the reliability of TFA and gene regulation reconstruction.
  • ROBNCA represents a significant advancement for practical applications in systems biology and gene network inference.