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There are two different algorithms in distance based method, the cluster-based and the optimality-based. The cluster-based method algorithms build a phylogenetic tree based on a distance matrix starting from the most similar sequence pairs. The algorithms of cluster-bsed include unweighted pair group method using
Distance-based phylogeny problem. Input: distance matrix of pairwise distances for N species. Goal: find a tree consistent with the distance matrix. This means that the sum of edge lengths connecting each pair of leaves ij corresponds to a distance Mij.
A mutational model allows corrected distances. Jukes-Cantor model: D = 3 4 ln (1 4 3 Ds) D is the corrected distance (what we want) Dsis the raw count (what we have) ln is the natural log. Mutational models for DNA.
Pairwise Distances. If the evolutionary rate is constant over time, the distance will increase linearly with the time of divergence. A simplistic distance measure is the proportion of different sites between two sequences, known as the p distance.
discusses the verification of the metric property of distance matrices, including detections of errors if a distance matrix fails to satisfy the metric property, and then provides an algorithm in modifying the distance matrix to satisfy the metric property.
• Construct a pair-wise distance matrix between haplotypes using Hamming distances • Add smallest edge between all nodes which do not introduce a loop • If the smallest distance is greater than 1 add d-1 “hidden” nodes between the pair so that adjacent nodes have a hamming distance of 1 • Augment the distance matrix with the new nodes
The book has a stronger focus on hypothesis testing than the previous edition, with more extensive discussions on recombination analysis, detecting molecular adaptation and genealogy-based population genetics.
