Cluster Analysis Method for Multiple Sequence Alignment

With the addition of more data in the field of proteomics, the computational methods need to be more efficient. The fraction or the part of molecular sequence that is more resistant to change is functionally more important to the molecule. Comparative approaches are used to ensure the reliability of sequence alignment. The problem of multiple sequence alignment (MSA) is a proposition of evolutionary history. The explicit homologous correspondence of each individual sequence position is established for each column in the alignment. In the present work, the different pair-wise sequence alignment methods are discussed. The limitation of these methods is that they are capable for aligning the limited number of sequences having small sequence length. A new method is proposed for sequence alignment based on the local alignment with consensus sequence. The triticum wheat varieties sequences are considered which are loaded from the NCBI databank. The dataset is divided into two parts and two phylogenetic trees are constructed for each dataset. Using advanced pruning techniques, a single tree is constructed from the two trees generated. Then by applying the threshold condition, the closely related sequences are extracted and optimal MSA is obtained using shift operations in both directions