Sentence examples for evolution rate variation from inspiring English sources

The 16S rRNA alignment served to construct a reference tree by maximum-likelihood (ML) using TREE-PUZZLE with a HKY nucleotide evolution model [ 48] and a Gamma model [ 49] for evolution rate variation between sites.

For both approaches, trees were inferred based on the General Time Reversible model of evolution with gamma-distributed rate variation and a proportion of invariable sites.

The model used was the generalized time-reversible (GTR) model of sequence evolution, allowing for among-site rate variation and a proportion of invariable sites.

Maximum likelihood trees were reconstructed using PhyML [54] with the JTT model of sequence evolution and taking among-site rate variation into account by using a six-category discrete approximation of a distribution and a proportion of invariable sites.

Four simultaneous Markov chains, three heated and one cold, were run under a mixed model of sequence evolution and gamma approximation for rate variation among sites.

For Bayesian tree estimation we used MrBayes [ 66] with the generalized time reversible (GTR) model of sequence evolution, allowing for among site rate variation and a proportion of invariable sites.

Bayesian trees were obtained using MrBayes 3.1.2 [ 98], as implemented in the ADOPS pipeline, using the GTR model of sequence evolution, allowing for among-site rate variation and a proportion of invariable sites.

According to this criterion the GTR model with gamma-distributed rates was chosen for the nucleotide data set and a mixed model that is a linear combination of 14 empirical models of protein evolution and considering among-site rate variation with a five-category discrete gamma-distribution for rates was chosen for the amino acid data set.

We checked that this clustering was not an artifact due to variation in evolution rate at synonymous sites.

These codons were strongly clustered in hypervariable regions, and we checked that this observation was not an artifact due to variation in evolution rate at synonymous sites.

Understanding the causes of rate variation in protein evolution is central for many fields including molecular evolution, comparative genomics and structural biology.