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The recent availability of complete genomic sequences of a diverse group of living organisms allows one to quantify basic mechanisms of molecular evolution on an unprecedented scale.
Simulated molecular evolution on a synthetic protein landscape shows the use of machine learning techniques to guide library design can be a powerful addition to library generation methods such as DNA shuffling.
It is relevant for experimental molecular evolution on bacteria as well, since post-treatment subsets are generally compared to the initial state [8].
The imperfection statistic also allows us to test several hypothesis about molecular evolution on genomic scales.
These results offer evidence for the influence of the rate of molecular evolution on diversification.
This is achieved by explicitly modeling the rate of molecular evolution on each branch in the tree.
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Because data were usually obtained from only one expressed X. borealis paralog but two X. laevis paralogs, most of our molecular analyses focused on molecular evolution of one X.
Much of the early focus on the molecular evolution of microcephaly genes centered on the role of two loci (ASPM and MCPH1) in human evolution [ 19, 20].
Recent studies have elucidated the effect of chromatin structure on molecular evolution of genomic DNA.
The constraints on molecular evolution of changed-tissues DE genes were the same as those for SE genes.
We investigated the molecular evolution of ebolaviruses based on many different features, from complete genomes to individual proteins.
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