Analysis of Molecular data
The principle of analyzing molecular data is similar to that of binary numerical data coded from morphological characters. However, which method should be used for particular molecular technique depending upon the principles and assumptions of the particular techniques. The methods
Analysis of Molecular data
The principle of analyzing molecular data is similar to that of binary numerical data coded from morphological characters. However, which method should be used for particular molecular technique depending upon the principles and assumptions of the particular techniques. The methods employed should not violate the principles and assumptions.
Types of Data
All the experimental data gathered by molecular techniques fall into one of two broad categories: discrete characters, and similarities (or distance). The discrete data are qualitative data with the possible states of two or more discrete values. The distance (or similarities) data are quantitative data with character of varying continuously and measuring on an interval scale.
Maximum Parsimony (MP) method
In this method, the DNA (or amino acid) sequences of ancestral species are inferred from those of extant species, considering a particular tree topology, and the minimum number of evolutionary changes that are required to explain all the observed differences among the sequences is computed. This number is obtained for all possible topologies, and the topology which shows the smallest number of evolutionary changes is chosen as the final tree.
This method is used mainly for finding the topology of a tree, but branch lengths can be estimated under certain assumptions. When the MP method is applied to morphological characters, it is customary to assume that the primitive and derived character states are known.
In the case of molecular data, this assumption generally does not hold, and different character states are often reversible. It is, therefore, important to use the MP method, which permits reversible mutations. In numerical taxonomy, this type of MP method is sometimes called the Wagner parsimony method.
Neighbor-Joining method (Saitou & Nei, 1987)
In contrast to cluster analysis, neighbor joining keeps track of nodes on a tree rather than taxa or clusters of taxa. The raw data are provided as 