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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

12.2K
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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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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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Cooperative Binding of Transcription Regulators02:13

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Co-activators and Co-repressors02:04

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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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Master Transcription Regulators02:23

Master Transcription Regulators

8.0K
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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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Identifying Cis-Regulatory Elements and Modules Using Conditional Random Fields.

Yanglan Gan, Jihong Guan, Shuigeng Zhou

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a novel computational method, CRFEM, for accurately identifying cis-regulatory elements and modules. This unsupervised approach enhances gene regulation studies by integrating genomic and epigenetic data without needing labeled data.

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    Area of Science:

    • Computational biology
    • Genomics
    • Bioinformatics

    Background:

    • Accurate identification of cis-regulatory elements and modules is crucial for understanding transcriptional regulation.
    • Computational identification of these elements is challenging due to data limitations.

    Purpose of the Study:

    • To develop a computational method for simultaneous identification of cis-regulatory elements and their correlated modules.
    • To leverage unsupervised learning to overcome limitations of annotated data in identifying regulatory elements.

    Main Methods:

    • Introduced a Conditional Random Fields (CRFEM) model.
    • Integrated sequence features and long-range genomic dependencies, including epigenetic features.
    • Employed an unsupervised learning approach to automatically learn model parameters.

    Main Results:

    • The CRFEM model accurately identifies cis-regulatory elements and modules simultaneously.
    • The method optimizes predictive probability without requiring labeled data.
    • Demonstrated superior accuracy compared to existing methods for genome-wide analysis.

    Conclusions:

    • The CRFEM model offers a more accurate and efficient approach for identifying cis-regulatory elements and modules.
    • This method is suitable for large-scale, genome-wide studies of gene regulation.
    • Unsupervised learning effectively addresses challenges in computational identification of regulatory elements.