Implementing Hierarchical Clustering Method for Multiple Sequence Alignment and Phylogenetic Tree Construction

Abstract

In the field of proteomics because of more data is added, the computational methods need to be more efficient. The part of molecular sequences is functionally more important to the molecule wis more resistant to change. To ensure the reliability of sequence alignment, comparative approaches are used. The problem of multiple sequence alignment is a proposition of evolutionary history. For each column in the alignment, the explicit homologous correspondence of each individual sequence position is established. The different pair-wise sequence alignment methods are elaborated in the present work. But these methods are only used for aligning the limited number of sequences having small sequencelength. For aligning sequences based on the local alignment with consensus sequences, a new method is introduced. From NCBI databank triticum wheat varieties are loaded. Phylogenetic trees are constructed for divided parts of dataset. A single new tree isconstructed from previous generated trees using advanced pruning technique. Then, the closely related sequences are extracted by applying threshold conditions and by using shift operations in the both directions optimal sequence alignment is obtained.