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Information theory applications for biological sequence analysis.

Susana Vinga1

  • 1IDMEC, Instituto Superior Técnico - Universidade de Lisboa (IST-UL), Av. Rovisco Pais, 1049-001 Lisboa, Portugal. Tel.: +351-218419504; Fax: +351-218498097; svinga@dem.ist.utl.pt.

Briefings in Bioinformatics
|September 24, 2013
PubMed
Summary
This summary is machine-generated.

Information theory (IT) provides powerful tools for molecular biology, enhancing sequence analysis and comparison. This review explores IT applications from genome-wide studies to cellular communication models.

Keywords:
Rényi entropyalignment-freechaos game representationgenomic signatureinformation theorysequence analysis

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Information Theory

Background:

  • Information theory (IT) offers analytical frameworks for communication systems.
  • IT concepts like entropy and mutual information are crucial for alignment-free sequence analysis in biology.
  • The application of IT in molecular biology is expanding across various domains.

Purpose of the Study:

  • To review and categorize the diverse applications of information theory in molecular biology.
  • To highlight the utility of IT in sequence analysis, genome comparison, and biological system modeling.
  • To connect IT methods with broader concepts like genomic signatures and phylogenetic classification.

Main Methods:

  • Review of existing literature on information theory applications in molecular biology.
  • Categorization of IT methods for global and local sequence analysis.
  • Exploration of IT in high-level biological correlations and cellular communication models.

Main Results:

  • IT is applied to global genome analysis (e.g., block entropy, iterative maps) and local sequence analysis (e.g., motif classification, binding site prediction).
  • IT facilitates sequence characterization using linguistic complexity and entropic profiles.
  • IT models describe information transmission in cellular processes and evolution, linking DNA, RNA, and protein features with sequence-independent properties.

Conclusions:

  • Information theory provides a versatile framework for understanding complex biological systems and sequences.
  • IT applications range from detailed sequence analysis to high-level biological correlations and evolutionary processes.
  • This review serves as a resource for leveraging IT in genomics, bioinformatics, and systems biology.