Question

Suppose you sequence a 30-base-pair segment of DNA in five different species (A, B, C, D, and E) and obtain the results at the right. Use cluster analysis to construct a phylogenetic tree for the five species. Number of nucleotide differences between species pairs
Please explain!

Answer 1

A phylogenetic tree uses data to indicate relatedness of different species. This webpage explains how to construct a phylogenetic tree using differences in molecular sequences (such as differences in amino acids, or differences in nucleotides).

Numbers in the table below represent mutational differences in a particular gene. Higher numbers indicate more genetic differences between two species. The longer two species (or subspecies) are isolated, the more likely there will be an accumulation of mutational differences.

discription of cluster analysis method that will help in constructing phylogenetic tree -

I. Cluster Analysis is a very general technique for inferring phylogenetic trees. The most widely used varient is the UPGMA method (unweighted pair-group method with arithmetic mean) and Fitch-Margoliash method

The UPGMA (Unweighted Pair Group Method with Arithmetic mean) -

methods produce rooted trees and require a constant-rate assumption - that is, it assumes an ultrametric tree in which the distances from the root to every branch tip are equal.

Fitch-Margoliash method-

The Fitch-Margoliash method uses a weighted least squares method for clustering based on genetic distance. Closely related sequences are given more weight in the tree construction process to correct for the increased inaccuracy in measuring distances between distantly related sequences. In practice, the distance correction is only necessary when the evolution rates differ among branches. The distances used as input to the algorithm must be normalized to prevent large artifacts in computing relationships between closely related and distantly related groups. The distances calculated by this method must be linear; the linearity criterion for distances requires that the expected values of the branch lengths for two individual branches must equal the expected value of the sum of the two branch distances - a property that applies to biological sequences only when they have been corrected for the possibility of back mutations at individual sites. This correction is done through the use of a substitution matrix such as that derived from the Jukes-Cantor model of DNA evolution.

The least-squares criterion applied to these distances is more accurate but less efficient than the neighbor-joining methods. An additional improvement that corrects for correlations between distances that arise from many closely related sequences in the data set can also be applied at increased computational cost. Finding the optimal least-squares tree with any correction factor is NP-complete,so heuristic search methods like those used in maximum-parsimony analysis are applied to the search through tree space.