Efficient classification of sugarcane genomes

Abstract

A Phylogenetic tree construction to know to the relationship of the ancestral association of species. The genome sequences, outlining the transmission of functional and genetic classification. Analysing the quantitative conduct of phylogenetics in the conservation of biodiversity and the successful heuristics of obtaining an accurate distribution of trees plays a predominant role. The study to know higher accuracy from efficient algorithm to deducing phylogenetic relationship among Sugarcane (Saccharum) species. A sample of 431 Saccharum genome sequences was drawn from NCBI dataset. Efficient algorithms like Maximum Likelihood Estimation (MLE) method and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method were considered to construct the phylogenetic tree. The maximum likelihood with Tamura Nei model, Kimura 2-parameter model and achieves the highest precision, while MLE with Maximum likelihood with Jukes-Cantor model achieves the least. The computational biology of statistically results is justifiable and compares the functional relationship between various models in which error percentage has been reduced. The same algorithms perform on individual species under different models such as maximum likelihood with Kimura 2-parameter model and Tamura Nei model more efficient than others to differentiate the species genomic sequences and group them to correct taxon.