Iterative progressive alignment method (IPAM) for multiple sequence alignment

Abstract

In order to design life saving drugs, such as cancer drugs, the design of Protein or DNA structures have to be accurate. These structures depend on Multiple Sequence Alignment (MSA). MSA is a combinatorial optimization problem which is used to find the accurate structure of Protein and DNA sequences from the existing sequences. In this paper, we have proposed a new iterative progressive alignment method, for multiple sequence alignment, which is a close variant of the MUSCEL algorithm. MUSCEL starts with the “kmer” distance table. However, based on the gene sequences length, our algorithm starts either with the “kmer” distance table or with the “Dynamic Programming (DP)” distance table. The other steps of this algorithm include: generating a Guide-tree using UPGMA, multiple sequence alignments, “kimura” distance calculation from aligned sequences and new techniques to improve multiple sequence alignments. We have introduced two new techniques in this research: the first technique is to generate Guide-trees with randomly selected sequences and the second is of shuffling the sequences inside that tree. The output of the tree is a multiple sequence alignment which has been evaluated by the Sum of Pairs Method (SPM) considering the real value data from PAM250. To test the performance of our algorithm, we have compared with the existing well known methods: T-Coffee, MUSCEL, MAFFT and Probcon, using BAliBase benchmarks and NCBI based our own datasets. The experimental results show that the proposed method works well for some situations, where other methods face difficulties in obtaining better solutions.