Fuzzy logic in the phylogenetics of HIV-1 using the W-curve, a three-dimensional visualization tool

Determining a generic sequence poses a large and complex problem in the area of computational biology. One major approach to this problem is based on the use of a method for finding genetic homology. Although a wide variety of techniques have been used to study genetic homology, the most effective tend to be applicable to short genetic sequences only.; A successful classification of microorganisms into taxa is expected to yield new knowledge about both the current genetic interrelationships between organisms and their possible phenotypes. Some surprising discoveries of genetic homology between viral quasispecies may lead to controversial reclassification of species. For instance, ongoing research relying upon genetic homology in viruses such as HIV yields numerous unexpected results. For example, the lethality of HIV-1 to a Patient, has been correlated with a threshold of its quasispecies diversity. Diversity can be examined by counting the number of branch points in a phylogenetic tree of its history. These trees are obtained by the "standard" nearest neighbor difference matrix method of sequences compared by typical one-dimensional linear homology alignment analysis. A new algorithm, the W-curve, allows 3-dimensional sequence alignment comparison. It is a numerical map of an autoregressive walk along a sequence, in an X-Y plane, that is projected onto a nucleotide position of the sequence (Z axis). This dissertation seeks to improve the field of computational biology by introducing the concept of fuzzy logic into W-curve based phylogenetic classification of HIV-1 quasispecies. In this project we took a DNA sequences an proceed with the following: (1) Using the W-curve we obtained a 3D description of the sequence. (2) This information is then collected by the genoutplot and stored in *.seq files. (3) Using Fast Fourier Transform concept we digitized these curve's information and computed their energy densities. (4) A fuzzy set of the sequence is then derived from these energies. These can serve as ground for a fuzzy reasoning tool in a future research.; A point to point mapping between biological reasoning and computational reasoning (fuzzy logic), should make maximum use of the great power of computing in our understanding of genetic homology.