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Updated: Jul 25, 2026
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Analyzing and Building Nucleic Acid Structures with 3DNA
Published on: April 26, 2013
A general procedure for locating and analyzing protein-binding sequence motifs in nucleic acids
1Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.
Summary
This study combines information theory and artificial neural networks to create a powerful search engine for nucleic acid sequence analysis. The tool identifies sequence families and defines binding motifs by ranking base contributions.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Information theory and artificial neural networks are valuable tools in sequence analysis.
- Integrating these approaches can enhance analytical capabilities.
Purpose of the Study:
- To develop a powerful search engine by combining information theory and artificial neural networks.
- To enable robust identification of nucleic acid sequence families.
- To define and rank sequence motifs based on base contribution.
Main Methods:
- Integration of information theory and artificial neural network methodologies.
- Development of a novel search engine for sequence analysis.
- Implementation of motif definition and base contribution ranking.
Main Results:
- The combined approach yields a powerful search engine for sequence analysis.
- The engine effectively locates members of nucleic acid sequence families in local and global searches.
- The program can define motifs and rank base contributions within them.
Conclusions:
- The integration of information theory and artificial neural networks offers a powerful approach to sequence analysis.
- This method is applicable to DNA and RNA sequence families for identifying binding motifs.
- The tool provides insights into motif structure by ranking base contributions.
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