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Updated: May 31, 2025

Analyzing and Building Nucleic Acid Structures with 3DNA
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Overview and Prospects of DNA Sequence Visualization.

Yan Wu1, Xiaojun Xie1, Jihong Zhu1

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.

International Journal of Molecular Sciences
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

Biological sequence visualization methods are reviewed, covering 2D, 3D, 4D, and dynamic approaches. Future directions include knowledge graphs and cross-modal visualization for big biological data.

Keywords:
DNA sequencesbiological sequenceknowledge graphmachine learningvisualization

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomic Computing

Background:

  • Biological sequence visualization is crucial in the post-genome era for representing complex data.
  • Current visualization methods lack universality, despite advances in big data and deep learning.
  • Exponential growth of biological sequence data necessitates advanced knowledge acquisition and inference techniques.

Purpose of the Study:

  • To provide a comprehensive overview of existing biological sequence visualization methods.
  • To analyze the strengths and limitations of 2D, 3D, 4D, and dynamic visualization techniques.
  • To propose future research directions for enhanced biological sequence visualization.

Main Methods:

  • Review of existing literature on biological sequence visualization techniques.
  • Categorization of methods into 2D, 3D, 4D, and dynamic approaches.
  • Analysis of strengths and limitations for each visualization category.

Main Results:

  • Detailed discussion of 2D, 3D, 4D, and dynamic visualization methods for DNA sequences.
  • Identification of challenges in graphical feature extraction and knowledge association network generation.
  • Proposal of knowledge graphs and cross-modal visualization as future research avenues.

Conclusions:

  • Existing visualization methods have limitations, particularly with the increasing volume of biological data.
  • Future research should focus on knowledge graph construction for big biological data.
  • Cross-modal visualization using machine learning offers promising avenues for biological sequence analysis.