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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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 addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...

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Related Experiment Video

Updated: Jun 16, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Quantifying the effect of sequence variation on regulatory interactions.

Thomas Manke1, Matthias Heinig, Martin Vingron

  • 1Max Planck Institute for Molecular Genetics, Computational Biology, Ihnestrasse 73, Berlin, Germany. manke@molgen.mpg.de

Human Mutation
|February 4, 2010
PubMed
Summary
This summary is machine-generated.

We developed sTRAP, a computational framework that predicts how DNA sequence variations impact transcription factor binding and regulatory networks. This tool aids in understanding sequence-driven phenotypic diversity and disease associations.

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Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster
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Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster

Published on: December 19, 2011

Related Experiment Videos

Last Updated: Jun 16, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster
08:19

Quantitative Comparison of cis-Regulatory Element (CRE) Activities in Transgenic Drosophila melanogaster

Published on: December 19, 2011

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Increasing sequence data presents challenges in linking genetic variations to phenotypic diversity.
  • Mechanistic models are needed to understand the regulatory consequences of DNA sequence variations.

Purpose of the Study:

  • To introduce a computational framework, sTRAP, for predicting the effects of sequence variations on regulatory networks.
  • To provide a tool for analyzing DNA sequence variations and their impact on transcription factor binding.

Main Methods:

  • sTRAP analyzes DNA sequence variations.
  • It predicts quantitative changes in transcription factor binding strength using existing binding models.
  • The method was validated against known SNP-regulatory consequence associations.

Main Results:

  • sTRAP predictions demonstrated robustness across various parameters and model assumptions.
  • The framework establishes a quantifiable benchmark for evaluating future improvements in predicting regulatory consequences.
  • The tool showed good performance in predicting the impact of sequence variations.

Conclusions:

  • sTRAP offers a robust computational framework for assessing the regulatory impact of sequence variations.
  • A publicly available tool based on sTRAP can facilitate routine analysis of disease-associated DNA regions.
  • This work provides a starting point for understanding sequence-phenotype links in regulatory networks.