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Related Concept Videos

The Eukaryotic Promoter Region02:40

The Eukaryotic Promoter Region

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The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences.  The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...
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Initiation is the first step of transcription in eukaryotes. Prokaryotic RNA Polymerase (RNAP) can bind to the template DNA and start transcribing. On the other hand, transcription in eukaryotes requires additional proteins, called transcription factors, to first bind to the promoter region in the DNA template. This binding helps recruit the specific RNAP that can assemble on the DNA and start transcription.
The promoters and enhancers and their accessory proteins allow tight regulation of...
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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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Master Transcription Regulators02:23

Master Transcription Regulators

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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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RNA polymerase (RNAP) carries out DNA-dependent RNA synthesis in both bacteria and eukaryotes. Bacteria do not have a membrane-bound nucleus. So, transcription and translation occur simultaneously, on the same DNA template.
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Related Experiment Video

Updated: Apr 28, 2026

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
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Promoter recognition based on the maximum entropy hidden Markov model.

Xiao-yu Zhao1, Jin Zhang1, Yuan-yuan Chen1

  • 1College of Science, Nanjing Agricultural University, China.

Computers in Biology and Medicine
|June 2, 2014
PubMed
Summary

Bioinformatics methods identify gene regions. Maximum entropy Markov models (MEMM) and maximum entropy hidden Markov models (ME-HMM) were developed to improve promoter recognition accuracy and reduce false positives in large-scale genomic data.

Keywords:
Forward algorithmIIS algorithmME-HMMMEMMPromoter

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Genome sequencing generates vast datasets requiring efficient analysis.
  • Identifying gene regions like promoters is crucial for understanding gene regulation.
  • Existing methods may have limitations in accuracy and false positive rates.

Purpose of the Study:

  • To develop novel bioinformatics methods for accurate gene region recognition, specifically promoter identification.
  • To address the high false positive rate (FPR) associated with existing models.
  • To leverage biological features for improved promoter detection.

Main Methods:

  • Introduction of a Maximum Entropy Markov Model (MEMM) utilizing promoter biological features.
  • Development of a Maximum Entropy Hidden Markov Model (ME-HMM) that relaxes the independence assumption.
  • Implementation of MEMM and ME-HMM using R language for comparison with Hidden Markov Models (HMM).

Main Results:

  • MEMM demonstrates excellence in identifying conserved biological signals within gene regions.
  • ME-HMM effectively reduces the false positive rate (FPR) and improves the true positive rate.
  • Both novel methods show advantages over traditional HMM, overcoming its shortcomings.

Conclusions:

  • MEMM and ME-HMM offer improved accuracy for gene region identification, particularly promoter recognition.
  • The ME-HMM approach significantly enhances precision by mitigating false positives.
  • These advanced models provide valuable tools for analyzing large-scale genomic data in gene regulation studies.