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Datamonkey news:

June 3, 2008: A number of updates for Spidermonkey/BGM and handling for protein/nucleotide data have been posted Details

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Welcome to the free public server for detecting signatures of positive and negative selection from coding sequence alignments using state-of-the-art statistical models. This service is brought to you by the viral evolution group at the Antivirial Research Center of the University of California, San Diego. The methods and software tools are developed and maintained by Sergei L Kosakovsky Pond, Simon Frost and Art Poon.

Datamonkey.org can help you answer the following questions ( publications citing datamonkey.org) :

Which codon sites are under positive or negative selection?

Three different codon-based maximum likelihood methods, SLAC, FEL and REL, can be used estimate the dN/dS (also known as Ka/Ks or ω) ratio at every codon in the alignment. An exhaustive discussion of each approach can be found in the methodology paper. All methods can also take recombination into account. This is done by screening the sequences for recombination breakpoints, identifying non-recombinant regions GARD tool and allowing each to have its own phylogentic tree.

Is there evidence of selection in my alignment?

The PARRIS method, developed by Konrad Scheffler and colleagues, extends traditional codon-based likelihood ratio tests to detect if a proportion of sites in the alignment evolve with dN/dS>1. The method takes recombination and synonymous rate variation into account.

Which codon sites are under positive or negative selection at the population level?

The codon-based maximum likelihood IFEL method can investigate whether sequences sampled from a population (e.g. viral sequences from different hosts) have been subject to selective pressure at the population level (i.e. along internal branches). A discussion of the method and its application can be found here

Did selective pressure vary along lineages, i.e. over time?

The codon-based genetic algorithm GABranch method can automatically partition all branches of the phylogeny describing non-recombinant data into groups according to dN/dS. Robust multi-model inference is used to collate results from all models examined during the run to provide confidence intervals on dN/dS for each branch and guard against model misspecification and overfitting (method details).

Did any sites co-evolve?

A Bayesian graphical model is deduced from reconstructed substitutions at each branch/site combination to infer conditional evolutionary dependancies of sites in the alignments, i.e. whether a site is more or less likely to experience a non-synonymous substitution at a branch when certain other sites do (or do not) experience non-synonymous substitutions at the same branch. The method was introduced in the evolutionary context in our paper on the evolution of the phenotypically important and highly variable V3 loop of the envelope glycoprotein in HIV-1.