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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

10.9K
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...
10.9K
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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Related Experiment Video

Updated: Oct 18, 2025

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

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Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Shuangquan Zhang1, Anjun Ma2, Jing Zhao2

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Briefings in Bioinformatics
|October 4, 2021
PubMed
Summary

Deep learning methods accurately identify transcription factor binding sites. This study systematically assesses 20 deep learning tools for motif prediction, guiding users to select the best method based on data type and size.

Keywords:
CLIP-seqChIP-seqTF binding sites identificationdeep learning method assessmentmotif prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying cis-regulatory motifs is essential for understanding gene regulation and transcription factor (TF) binding sites.
  • Deep learning (DL) methods have emerged as powerful tools for TF binding site identification and motif prediction since 2015.
  • A systematic evaluation of existing DL methods is needed to guide researchers in tool selection.

Purpose of the Study:

  • To systematically evaluate 20 deep learning methods for cis-regulatory motif prediction.
  • To assess the performance of these methods across various genomic and transcriptomic datasets.
  • To provide guidance on selecting the most appropriate DL tool based on specific research needs.

Main Methods:

  • Evaluation of 20 DL methods using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq, and 55 RNA CLIP-seq datasets.
  • Assessment metrics included motif finding accuracy, DNA/RNA sequence classification performance, algorithm scalability, and tool usability.
  • Comparative analysis of the strengths and weaknesses of each DL method.

Main Results:

  • The study found significant complementarity among the evaluated DL methods.
  • Performance varied based on data size and type, highlighting the need for tailored tool selection.
  • Deep learning methods demonstrate high accuracy in predicting TF binding sites and motif patterns.

Conclusions:

  • The choice of the most suitable DL method for cis-regulatory motif prediction depends heavily on the characteristics of the input data (size and type) and the desired output.
  • This systematic assessment aids researchers in navigating the landscape of DL tools for motif discovery.
  • Complementarity among DL methods suggests potential for integrated approaches in future research.