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Updated: Aug 10, 2026
10:41
Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
Published on: June 24, 2019
Identification of physicochemical selective pressure on protein encoding nucleotide sequences
Wendy S W Wong1, Raazesh Sainudiin, Rasmus Nielsen
1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. ww3@sanger.ac.uk
BMC Bioinformatics
|March 18, 2006
Summary
This study introduces a new statistical model for evolutionary bioinformatics. It identifies positive selection in protein-coding genes by considering amino acid physicochemical properties, improving accuracy.
Area of Science:
- Evolutionary bioinformatics
- Molecular evolution
- Computational biology
Background:
- Statistical methods are crucial for identifying positive selection in protein-coding genes.
- Existing methods often overlook the physicochemical properties of amino acids, limiting their scope.
Purpose of the Study:
- To develop a novel codon-based likelihood model for detecting site-specific selection pressures.
- To incorporate the influence of specific amino acid physicochemical properties into selection analyses.
Main Methods:
- A new codon-based likelihood model was developed.
- Nonsynonymous substitutions were categorized based on physicochemical property changes.
- Substitution rates (gamma and omega) relative to synonymous rates were analyzed.
- Likelihood ratio tests were employed to detect selection on specific physicochemical properties.
Main Results:
- The model demonstrated good power and accuracy in detecting physicochemical selective pressure on simulated data.
- The method was successfully applied to analyze human Major Histocompatibility Complex (MHC) class-I alleles.
- The approach was also used to analyze data from abalone sperm lysine.
Conclusions:
- The developed method provides a more flexible framework for identifying selection pressures.
- It enables the detection of selection acting on particular physicochemical properties of amino acids.
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