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Neural network method to analyze data compression in DNA and RNA sequences

T Alvager1, G Graham, D Hutchison

  • 1Department of Physics, Indiana State University, Terre Haute 47809, USA.

Journal of Chemical Information and Computer Sciences
|March 1, 1997
PubMed
Summary

Neural network analysis reveals data compression in RNA sequences, enabling discrimination between structured and random regions. This method quanties information content in DNA, including noncoding DNA.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequences possess complex structures influencing their function.
  • Understanding sequence information content is crucial for biological insights.
  • Distinguishing structured from random DNA/RNA regions remains a challenge.

Purpose of the Study:

  • To demonstrate data compression in RNA sequences using neural networks.
  • To establish a method for discriminating between structured and random sequence regions.
  • To assess the information content of DNA, including noncoding regions.

Main Methods:

  • Application of neural network computations to RNA sequences.
  • Calculation of sequence compressibility.
  • Illustration using transfer RNA (tRNA) sequences.

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Main Results:

  • Data compression is achievable in RNA sequences.
  • A method for discriminating structured from random regions was developed.
  • The technique provides a measure of information content.

Conclusions:

  • Neural network-based compressibility analysis is a viable tool for RNA and DNA.
  • This approach can quantify information content in both coding and noncoding DNA.
  • The findings support the analogy between natural language and DNA attributes.