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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
Published on: May 7, 2010
Analysis of genomic sequences by Chaos Game Representation
J S Almeida1, J A Carriço, A Maretzek
1ITQB/Universidade Nova Lisboa, PO Box 127, 2780 Oeiras, Portugal. almeidaj@musc.edu
Bioinformatics (Oxford, England)
|May 2, 2001
Summary
Chaos Game Representation (CGR) can model sequence succession schemes, generalizing Markov models. This DNA sequence analysis tool offers computational efficiency and scale independence.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Chaos Game Representation (CGR) maps biological sequences to continuous space coordinates.
- CGR provides unique sequence representation and allows similarity measurement via coordinate distances.
- Previous studies overlooked CGR's potential for sequence modeling and succession scheme identification.
Purpose of the Study:
- To explore Chaos Game Representation (CGR) as a sequence modeling tool.
- To investigate the potential of CGR for identifying sequence succession schemes.
- To upgrade CGR from a representation technique to a powerful sequence modeling tool.
Main Methods:
- Iterative mapping of biological sequences (DNA, proteins) to continuous space coordinates.
- Analysis of the properties of CGR-generated position distributions.
- Application of CGR to specific genes (thrA, thrB, thrC) in Escherichia coli K-12.
Main Results:
- CGR position distributions generalize Markov chain probability tables, accommodating non-integer orders.
- Markov models are identified as specific cases of CGR models.
- CGR offers practical advantages like computational efficiency and fundamental benefits like scale independence.
Conclusions:
- Chaos Game Representation (CGR) is a powerful sequence modeling tool, not just a representation technique.
- CGR provides a more general framework than traditional Markov models for sequence analysis.
- The findings have implications for understanding sequence similarity and succession patterns in biological data.
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