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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...
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...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

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Related Experiment Video

Updated: Jul 3, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs.

Jianfei Hu1, Haiyan Hu, Xiaoman Li

  • 1Division of Biostatistics, School of Informatics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA.

Nucleic Acids Research
|July 9, 2008
PubMed
Summary

A new method, MOPAT (motif pair tree), identifies cis-regulatory modules (CRMs) by finding groups of co-occurring motifs. This approach improves CRM discovery without multiple alignments and handles numerous known motifs effectively.

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Last Updated: Jul 3, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying cis-regulatory modules (CRMs) is crucial for understanding eukaryotic gene regulation.
  • Existing CRM prediction methods have limitations, including reliance on multiple alignments and scalability issues with large motif sets.

Purpose of the Study:

  • To develop a novel method for identifying CRMs that overcomes the limitations of current approaches.
  • To introduce MOPAT (motif pair tree) for efficient and accurate CRM prediction.

Main Methods:

  • Developed MOPAT, a method that identifies CRMs by detecting motif modules (groups of co-occurring motifs).
  • MOPAT does not require multiple sequence alignments and can process a large number of known motifs.
  • Applied MOPAT to mouse developmental genes and evaluated predictions using gene expression data and known interacting motif pairs.

Main Results:

  • MOPAT successfully identified CRMs and motif modules without relying on multiple alignments.
  • Genes containing CRMs from the same motif module showed significantly correlated expression profiles.
  • Known interacting motif pairs were significantly enriched in the predicted CRMs.
  • MOPAT demonstrated superior performance compared to several existing CRM identification methods.

Conclusions:

  • MOPAT provides an effective approach for identifying cis-regulatory modules, particularly in scenarios where traditional methods struggle.
  • The method enhances the understanding of gene regulatory mechanisms by accurately predicting functional CRMs and motif modules.
  • MOPAT offers a scalable and versatile tool for CRM discovery in genomic research.