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Another example of the use of the Ka/Ks ratio is illustrated by Liberies et al. (2001), who studied adaptive evolution of amino acids at the genomic level. They calculated Ka/Ks values on nodes of branches within evolutionary lineages. The evolutionary lineages were taken from the Master

Catalog, a compilation of sequence alignments and evolutionary trees for all protein modules encoded by genes in Genbank, constructed by Benner et al. (2000). They focused on subtrees containing only chordates and Embryophyta (mosses, ferns, and higher plants) and could identify branches with high Ka/Ks values. These branches may be indicative of positive selection, where the mutated protein has a higher fitness than the ancestral form, probably associated with a change in function. The gene families that display high Ka/Ks values were stored in The Adaptive Evolution Database (TAED). Currently, TAED 2.1 (www. bioinfo.no/tools/TAED) contains 6657 families that are fully processed. In 10-20% of these families positive selection was determined in at least one branch. High Ka/Ks values on branch points in evolutionary protein trees may be caused by gene amplification followed by differentiation in function of the paralogues. Orthologous genes under different selective regimes in different species may also be found. However, the database cannot distinguish between paralogues and orthologues, so the results should be interpreted cautiously. Besides gene families that were identified previously to be under positive selection, such as the MHC proteins (proteins of the adaptive immune system), quite a number of families were newly identified in TAED to have undergone change in function. The authors conclude that TAED is a useful resource for biologists searching for potential examples of molecular adaptation as a starting point for further experimental study.

We can conclude that the use of Ka/Ks ratios for measuring molecular adaptation in coding sequences of the genome is a very effective approach. We expect that databases like TAED will become an important framework for ecological genomics to study adaptation at the molecular level. The challenge will be to integrate this information with gene-expression profiling and to link molecular variation to phenotypic differences between related species.

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