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Statistical modelling of CG interdistance across multiple organisms
Merlotti A1, Faria do Valle I2, Castellani G3
1Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
BMC Bioinformatics
|October 28, 2018
Summary
Statistical analysis of CG dinucleotide positioning reveals the Gamma distribution as optimal for characterizing genomic features across diverse organisms. This method aids in understanding biological complexity and potential classification applications.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Statistical methods and information theory enhance understanding of genetic sequences.
- CG dinucleotide distribution analysis reveals its epigenetic role in DNA methylation.
- Previous studies highlighted distinct CG dinucleotide patterns compared to other sequences.
Purpose of the Study:
- Extend CG dinucleotide distance distribution analysis to higher-order organisms.
- Apply optimal probability density functions to a large, diverse set of organisms.
- Characterize emerging global genomic features across different biological complexities.
Main Methods:
- Comparative analysis of various probability density functions for CG dinucleotide distribution.
- Selection of the Gamma distribution as the optimal fit for a subset of organisms.
- Application of the Gamma distribution parameters to over 4400 organisms from bacteria to mammals.
Main Results:
- The Gamma distribution was found to be optimal for CG dinucleotide positioning in the selected organism subset.
- Analysis across a broad range of organisms revealed biologically relevant features.
- Identified features have potential utility for organism classification purposes.
Conclusions:
- Quantifying statistical properties of CG dinucleotide positioning is a valuable tool.
- This approach effectively characterizes broad organism classes across the spectrum of biological complexity.
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