Some recent progress with methods for evolutionary trees

Abstract

Sequences of macromolecules have "signals" or patterns that arise from a number of sources, particularly from shared common history or phylogeny. We discuss methods for inferring evolutionary trees from these patterns or signals under five properties desired for an ideal method. These five desiderata are that the methods be efficient (fast), consistent, powerful, robust, and falsifiable. Our conclusion is that corrections for multiple changes in sequences are the most important factor for any method to be consistent. Most optimality criteria, including compatibility and parsimony, become consistent when the sequences have appropriate corrections for multiple changes. Conversely, virtually no methods are consistent without adjustments for multiple changes. Hadamard conjugations are used to illustrate relationships between different methods and then illustrated by combining it with the closest tree optimality criterion. The data used to illustrate these recent developments include DNA sequences used to study B93018 Received 2 February 1993; accepted 13 July 1993 the origin of chloroplasts and also New Zealand skinks (Leiolopisma spp).

Colonization History of Atlantic Island Common Chaffinches (Fringilla coelebs) Revealed by Mitochondrial DNA

“…A choice of methods is offered for recoding multistate characters; we chose first to average the results from all methods (called ''sum-of-7'') and then to exclude the multistate characters. The closest tree method (a compatibility algorithm based on minimizing the sum of squares; Penny et al, 1993) was used to select a tree depicting the splits that determine the phylogenetic relationships among the taxa.…”

Evolution of chlorophyll and bacteriochlorophyll: the problem of invariant sites in sequence analysis.

“…To separate codon positions in our alignments, we used the program PREPARE (22). Jackknifing was carried out using SEQBOOT3.5 (20).…”

Testing the phylogeny of swordtail fishes using split decomposition and spectral analysis

“…One new approach to the analysis of biological sequences termed "spectral analysis" (Hendy and Penny 1993;Hendy et al 1994) emphasizes the identification of signals (patterns) in the data that may arise from different causes (e.g., Penny et al 1993;Lento et al 1995). This approach provides the means to examine such patterns when different corrections for unobserved multiple substitutions are made and hence to determine the usefulness of a given transformation to correct for these.…”