A model for phylogenetic inference using structural and chemical covariates.

We investigated whether or not evolutionary change in DNA sequence data was homogeneous across different classes of base pairs. DNA sequences for eight protein-coding mitochrondrial genes were obtained for 38 vertebrate taxa from GenBank. Each nucleotide site in the alignment was classified according to a number of covariates, including its codon position, genetic code degeneracy, and hydrophobicity. The evolutionary transition matrix for each base was estimated by tracing implied character changes under parsimony on a known phylogenetic tree. Canonical variates analyses of the inferred transition matrices were performed for each gene to determine whether or not different classes of bases behaved similarly. We found five distinct clusters of transition matrices that could be roughly defined by combinations of codon position and degeneracy. This pattern was consistent among all genes. A stochastic model of rate variation based on the interaction of the covariates was developed to assess the statistical significance of the clusters. The five-group classification was found to explain significantly more sequence variation than did a codon only classification, a codon degeneracy classification, or a codon and degeneracy classification. The same five-group classification was found for all genes tested, suggesting a common process underlying the molecular evolution of the mitochondrial genome. These results confirm that there are classes of base pairs that evolve differently, and suggest that models of sequence evolution that incorporate covariate information may be useful in developing nucleotide substitution models that more accurately reflect evolutionary history.

PAQ: Partition Analysis of Quasispecies.

MOTIVATION: The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. RESULTS: We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.