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Chaos game representation and its applications in bioinformatics
Hannah Franziska Löchel1, Dominik Heider1
1Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany.
Computational and Structural Biotechnology Journal
|December 13, 2021
Summary
Chaos game representation (CGR) and its extension, frequency matrix representation (FCGR), offer powerful alignment-free bioinformatics tools. These methods uniquely encode sequences for machine learning, phylogenetic analysis, and sequence comparison.
Area of Science:
- Bioinformatics
- Computational Biology
- Machine Learning
Background:
- Chaos game representation (CGR) is a graphical bioinformatics tool for sequence analysis.
- Alignment-free methods are crucial for comparing biological sequences.
- Machine learning requires effective feature encoding for biological data.
Purpose of the Study:
- To review the construction of Chaos Game Representation (CGR) and Frequency Matrix Representation (FCGR).
- To explore the applications of CGR and FCGR in DNA and protein analysis.
- To provide an overview of recent advancements and applications in bioinformatics.
Main Methods:
- The study reviews the Chaos Game Representation (CGR) algorithm.
- It introduces the Frequency Matrix Representation (FCGR) as an extension of CGR.
- The generalized Markov chain properties of CGR are discussed.
Main Results:
- CGR and FCGR enable alignment-free sequence comparison.
- These methods provide unique sequence representations for machine learning.
- Applications span phylogenetic analysis and sequence encoding for DNA and proteins.
Conclusions:
- CGR and FCGR are versatile tools in bioinformatics.
- Their ability to encode sequences makes them valuable for machine learning.
- The review highlights their broad applicability and ongoing progress in the field.
Keywords:
Alignment-free sequence comparisonBioinformaticsChaos game representationDNA and protein encodingMachine learningSequence analysisMore Related Videos
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