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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach.

Adi Stern¹, Adi Doron-Faigenboim, Elana Erez

¹Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.

Nucleic Acids Research

|June 26, 2007

Summary

Selecton 2.2 identifies biologically significant protein sites by analyzing synonymous (Ks) and non-synonymous (Ka) substitution rates. This tool visualizes evolutionary selection pressures, aiding in understanding protein evolution.

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Area of Science:

Evolutionary biology
Bioinformatics
Molecular evolution

Background:

Biologically significant protein sites can be identified by comparing synonymous (Ks) and non-synonymous (Ka) substitution rates.
This comparison allows for the inference of site-specific positive Darwinian and purifying selection pressures.

Purpose of the Study:

To present Selecton version 2.2, a web server for calculating and visualizing the Ka/Ks ratio (omega) at each protein site.
To enable statistical testing for positive selection using various evolutionary models, including a new mechanistic-empirical model.

Main Methods:

The Selecton 2.2 web server calculates the Ka/Ks ratio for each protein site.
It implements diverse evolutionary models and a mechanistic-empirical model considering amino acid physicochemical properties.
Advanced options include statistical support calculation, 3D structure visualization, and customizable genetic codes and phylogenetic trees.

Main Results:

Selecton 2.2 graphically displays the Ka/Ks ratio using color-coding to indicate positive selection, purifying selection, or lack of selection.
The server provides statistical support for omega values and visualizes selection forces on protein sequences and structures.

Conclusions:

Selecton 2.2 is a user-friendly, freely available web server for computing and visualizing site-specific evolutionary selection forces.
It offers advanced features for detailed analysis of protein evolution and identification of functionally important sites.