Implementing UPGMA and NJ Method For Phylogenetic Tree Construction Using Hierarchical Clustering

The research in bioinformatics has accumulated large amount of data. As the hardware technology advancing, the cost of storing is decreasing. The biological data is available in different formats and is comparatively more complex. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of biology. To construct a phyloenetic tree is a very challenging problem. The main purpose of phylogenetic tree is to determine the structure of unknown sequence and to predict the genetic difference between different species. There are different methods for phylogenetic tree construction from character or distance data. There are different methods to compute distance which include the comparative distance from two sequences, distance using UPGMA and Neighbour Joining. Computing distance from the available sequences is itself an intricate problem and each method has its own merits and demerits. In the present project work, distance is computed using comparative method (scoring using differences) and using UPGMA. Distance data for human phylogenetic problem is considered for the present work. There are different approaches to construct tree. UPGMA and Neighbour Joining Methods are used to retrieve the results. The final trees give the anthromorphical information for the human being. The results are also shown in Hierarchical clustering form.