Furthermore, numerical characterisation techniques based on graphical representations have enabled quantitative estimations of sequence similarities and dissimilarities [see review 14] . Basically there have been two approaches for numerical characterization, both of which use the graphical representation to map a DNA sequence into a set of numbers. One approach using geometrical mapping proposed by Raychaudhury and Nandy [20] have been found to be useful for several calculations based on the 2D graphical representation [14] , and extended recently to an abstract 20D modelling for protein sequences [21] , where individual sequences are indexed by numerical descriptors. The other approach is to use matrix methods by forming ratios of graph theoretic and Euclidean distances between nodes of the graphical plots, first formulated for DNA sequences in Randic et al [22] . Since invariants associated with matrix formulation are well-known, individual sequences can be indexed by one or more such invariants of various orders; it is expected that these would be sufficiently characteristic of the underlying sequences to enable unique characterization. This technique has been the most widely used method of choice for the researchers in this field who have defined different types of matrices to construct various invariants to describe the DNA sequences. However, the difficulties associated with computing various parameters for very large matrices that are natural for large sequences have restricted the numerical characterizations to leading eigenvalues and the like [14] .