Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called the "discrete gamma model," uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good, and four such categories appear to be sufficient to produce both an optimum, or near-optimum fit by the model to the data, and also an acceptable approximation to the continuous distribution. The second method, called "fixed-rates model", classifies sites into several classes according to their rates predicted assuming the star tree. Sites in different classes are then assumed to be evolving at these fixed rates when other tree topologies are evaluated. Analyses of the data sets suggest that this method can produce reasonable results, but it seems to share some properties of a least-squares pairwise comparison; for example, interior branch lengths in nonbest trees are often found to be zero. The computational requirements of the two methods are comparable to that of Felsenstein's (1981, J Mol Evol 17:368-376) model, which assumes a single rate for all the sites.

/pdf/maximum-likelihood-phylogenetic-estimation-from-dna-368ndqbgd9.pdf

Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods

As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.

/pdf/bayesian-phylogeography-finds-its-roots-4zx5e4eoid.pdf

Bayesian phylogeography finds its roots.

For the first time a single experimental approach, 16S ribosomal RNA sequence characterization, has been used to develop an overview of phylogenetic relationships in the bacterial world. The techni...

https://science.sciencemag.org/content/sci/209/4455/457.full.pdf

The Phylogeny of Prokaryotes

http://dambe.bio.uottawa.ca/publications/2003MPE.pdf

An index of substitution saturation and its application.

Gene expression is central to the genotype-phenotype relationship in all organisms, and it is an important component of the genetic basis for evolutionary change in diverse aspects of phenotype. However, the evolution of transcriptional regulation remains understudied and poorly understood. Here we review the evolutionary dynamics of promoter, or cis-regulatory, sequences and the evolutionary mechanisms that shape them. Existing evidence indicates that populations harbor extensive genetic variation in promoter sequences, that a substantial fraction of this variation has consequences for both biochemical and organismal phenotype, and that some of this functional variation is sorted by selection. As with protein-coding sequences, rates and patterns of promoter sequence evolution differ considerably among loci and among clades for reasons that are not well understood. Studying the evolution of transcriptional regulation poses empirical and conceptual challenges beyond those typically encountered in analyses of coding sequence evolution: promoter organization is much less regular than that of coding sequences, and sequences required for the transcription of each locus reside at multiple other loci in the genome. Because of the strong context-dependence of transcriptional regulation, sequence inspection alone provides limited information about promoter function. Understanding the functional consequences of sequence differences among promoters generally requires biochemical and in vivo functional assays. Despite these challenges, important insights have already been gained into the evolution of transcriptional regulation, and the pace of discovery is accelerating.

/pdf/the-evolution-of-transcriptional-regulation-in-eukaryotes-16fy43qset.pdf

The Evolution of Transcriptional Regulation in Eukaryotes

If one has the amino acid sequences of a set of homologous proteins as well as their phylogenetic relationships, one can easily determine the minimum number of mutations (nucleotide replacements) which must have been fixed in each codon since their common ancestor. It is found that for 29 species of cytochrome c the data fit the assumption that there is a group of approximately 32 invariant codons and that the remainder compose two Poisson-distributed groups of size 65 and 16 codons, the latter smaller group fixing mutations at about 3.2 times the rate of the larger. It is further found that the size of the invariant group increases as the range of species is narrowed. Extrapolation suggests that less than 10% of the codons in a given mammalian cytochrome c gene are capable of accepting a mutation. This is consistent with the view that at any one point in time only a very restricted number of positions can fix mutations but that as mutations are fixed the positions capable of accepting mutations also change so that examination of a wide range of species reveals a wide range of altered positions. We define this restricted group as the concomitantly variable codons. Given this restriction, the fixation rates for mutations in concomitantly variable codons in cytochrome c and fibrinopeptide A are not very different, a result which should be the case if most of these mutations are in fact selectively neutral as Kimura suggests.

An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution

Three models of codon fixation variability are formulated. The first assumes that all codons have the same fixation rate. The second assumes that some codons are invariant and that all variable codons have the same fixation rate. The third assumes that some codons are invariant and that the variable codons comprise two classes, all members of each class having the same fixation rate. Maximum likelihood estimates of the parameters and tests of goodness-of-fit are given for each model.

Etan Markowitz

Papers

An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution

Estimation and testing goodness-of-fit for some models of codon fixation variability.