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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...
Master Transcription Regulators02:23

Master Transcription Regulators

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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Related Experiment Video

Updated: Jun 27, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A predictive model for identifying mini-regulatory modules in the mouse genome.

Mahesh Yaragatti1, Ted Sandler, Lyle Ungar

  • 1Biotechnology Program, CIS, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104, USA. myar@seas.upenn.edu

Bioinformatics (Oxford, England)
|December 5, 2008
PubMed
Summary

Researchers developed a machine learning model to identify active genomic regions. This model accurately predicts regulatory elements using nucleosome-free regions and conserved sequences, aiding genome-wide analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome technology advancements provide vast data for analysis.
  • Integrating diverse genomic data (assembly, comparative genomics, gene predictions, expression) is crucial.
  • Machine learning offers a powerful approach to decipher complex genomic information.

Purpose of the Study:

  • To develop a systems model integrating multiple genomic data types.
  • To engineer a machine learning approach for identifying active genomic regions.
  • To predict regulatory elements within a genome.

Main Methods:

  • Defined a systems model integrating assembly, comparative genomics, gene predictions, and expression data.
  • Employed a machine learning approach using predictive parameters from the systems model.
  • Utilized nucleosome-free region (NFR) modules and Vista Enhancer Browser (VEB) elements for prediction.

Main Results:

  • Nucleosome-free region modules showed higher conservation, RNA-encoding sequences, CpG islands, and GC-rich areas.
  • Machine learning model achieved >95% prediction accuracy using NFR modules.
  • Model demonstrated >85% prediction accuracy using VEB elements for regulatory regions.
  • Identified higher percentages of DNA repeats and lower conservation in random in silico fragments compared to NFR modules.

Conclusions:

  • The developed systems model effectively identifies putative active genomic regions.
  • The model demonstrates high accuracy in predicting small regulatory elements.
  • This approach is applicable across various organisms for genome-scale identification of transcriptional modules.