Enhancing the Scalability of Consistency-based Progressive Multiple Sequences Alignment Applications

Multiple Sequence Alignment (MSA) is an extremely powerful tool for important biological applications, such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In this paper we propose a new approach to reduce the computational requirements of TCoffee, a memory demanding MSA tool that uses a consistency based scheme to produce more accurate alignments. Our goal is to minimize the memory constraints in order to increase the performance and scalability of the application. The experimental results show that our approach is able to reduce the memory consumption and increase both the time performance and the number and the length of sequences that the method can align. In summary, it is able to reduce the memory requirements by between 72% and 58% depending on the optimization level, improving the alignment scalability as a whole. Also, the library reduction yields a further improvement: the alignment execution time can be reduced by up to 92%. These results are obtained without a significant impact on the final alignment quality, that declines by less than 3%.