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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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...
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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...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Related Experiment Video

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search

Rahul Karnik1, Michael A Beer2

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.

Plos One
|October 15, 2015
PubMed
Summary

MotifSpec, a new algorithm, identifies predictive DNA binding motifs from sequencing data more accurately than existing methods. It uses a dynamic search and position weight matrices for improved motif discovery in genomics.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Genomic binding and accessibility data generation is rapidly advancing.
  • Current computational methods for DNA motif identification often produce motifs with limited predictive capability.

Purpose of the Study:

  • To introduce MotifSpec, a novel algorithm for identifying predictive DNA binding motifs.
  • To improve upon existing methods for motif discovery in large-scale sequencing datasets.

Main Methods:

  • Developed MotifSpec, a computational algorithm employing a dynamic search space and a learned threshold.
  • Modeled motifs using full position weight matrices (PWMs) instead of k-mers or regular expressions.
  • Applied the algorithm to ChIP-seq and gene expression datasets.

Main Results:

  • MotifSpec successfully identified known binding specificities in mammalian ChIP-seq data.
  • The algorithm's PWMs achieved classification accuracy comparable to or better than existing methods.
  • MotifSpec discovered novel motifs in datasets where other algorithms failed.
  • Applied to C. elegans expression data, it identified new predictive motifs using dynamic expression similarity.

Conclusions:

  • MotifSpec offers a more effective approach for discovering predictive DNA binding motifs.
  • The algorithm demonstrates superior performance in identifying known and novel motifs across different genomic datasets.
  • This method advances motif discovery by utilizing dynamic search and PWM modeling.