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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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Heterochromatin02:38

Heterochromatin

The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
Constitutive heterochromatin: It is a highly compact region of chromatin that is mostly concentrated in the centromere and telomere. Unlike euchromatin, the amino acid at 9th...

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Related Experiment Video

Updated: Jul 14, 2026

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

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Published on: December 19, 2011

Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models.

Wendy S W Wong1, Rasmus Nielsen

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. sww8@cornell.edu

Bioinformatics (Oxford, England)
|June 7, 2007
PubMed
Summary

This study introduces EvoPromoter, a novel computational method that enhances the accuracy of identifying transcription factor binding sites by utilizing comparative genomic data. The phylogenetic hidden Markov model approach improves predictions compared to methods ignoring multi-species information.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Identifying regulatory modules for transcription factor binding is crucial for understanding gene expression regulation.
  • Existing methods often do not leverage comparative genomic data, limiting their predictive power.

Purpose of the Study:

  • To develop a novel computational method for identifying transcription factor binding regulatory modules in eukaryotic species.
  • To improve the accuracy of regulatory module prediction by incorporating phylogenetic data.

Main Methods:

  • Development of a new method utilizing phylogenetic data for regulatory module identification.
  • Application of a phylogenetic hidden Markov model to analyze comparative genomic data.
  • Validation through computer simulations and analysis of real biological data.

Main Results:

  • The developed method demonstrates increased accuracy in predicting regulatory modules.
  • Phylogenetic hidden Markov models outperform methods that do not utilize multi-species data.
  • The EvoPromoter software package implements this novel approach.

Conclusions:

  • Integrating comparative genomic data significantly enhances the accuracy of regulatory module prediction.
  • The EvoPromoter tool provides a valuable resource for researchers studying gene regulation.
  • This approach advances the field of computational genomics for regulatory element discovery.