A COMPARATIVE STUDY OF NEIGHBOR JOINING BASED APPROACHES FOR PHYLOGENETIC INFERENCE

Author

Maria Fernanda Correa, Southern Illinois University CarbondaleFollow

Date of Award

12-1-2010

Degree Name

Master of Science

Department

Computer Science

First Advisor

Che, Dunren

Abstract

One of the most relevant issues in the field of biology is the unveiling of the evolutionary history of different species and organisms. The evolutionary relationships of these species and organisms are explained by constructing phylogenetic trees whose leaves represent species and whose internal nodes represent hypothesized ancestors. The tree reconstruction process is known as Phylogenetic Inference. Phylogenies can be used not only for explaining the evolutionary history of organisms but also for many other purposes such as the design of new drugs by tracking the evolution of diseases. In the last few years, the amount of genetic data collected from organisms and species has increased greatly. Based on this, biologists have sought methods that are capable of computing phylogenies of small, medium, and even large datasets in a reasonable time and with accuracy. The neighbor-joining method is one used most for phylogenetic inference because of its computation efficiency. Since the increase of datasets, novel neighbor-joining- based approaches have been developed with the goal of computing efficiency and accurate phylogenies of thousands of sequences. Therefore, this study compared the canonical neighbor-joining method represented by MEGA software with two novel neighbor-joining-based approaches--the NINJA method and the FastTree method--to identify the most efficient and effective method for the computational performance, topological accuracy, and topological similarity through the scalability of the sequences size. The study was accomplished by executing experiments using small, medium, and large protein and nucleotide sequences. The FastTree method was the most successful at balancing the trade-off among the Computational Performance, Topological Accuracy, and Topological Similarity when scaling up the number of sequences in this study.