Classification of Vector-Borne Virus Through Totally Ordered Set of Dinucleotide Interval Patterns

Abstract

In genome analysis, common approach to all word methods is use of long words to improve precision in biological findings. However, arbitrary increment in word length cannot always be fruitful, rather causing increase in space-time complexity. We observe that instead of mere increase in length, integration of word intervals along with order and frequency of their occurrence have great impact in extracting sequence information with much smaller word length and devise a method, Dinucleotide Interval Patterns (DIP), for entropy retrieval from ordered sets of dinucleotide intervals. Experiments on natural sequences of Flaviviridae virus with length 9 to 12 kbp establish that only word size of 2bp is capable of deriving precise taxonomic classification of the virus. This is in sharp contrast to standard word-based methods requiring a minimum of 6bp word size to achieve nearly 30% Topological Similarity in comparison to 60% score by DIP with only 2bp.