Multiple sequence alignment (MSA) is one of the most useful tools in bioinformatics. However, the growth of sequencing data imposes further difficulties for aligning it with traditional tools. For large-scale alignments with thousands of sequences it will be necessary to use and take profit of the high performance computing (HPC). This paper, focused on the consistency-based T-Coffee MSA tool, presents several innovative solutions with the objective of improving its efficiency, scalability and accuracy. The results obtained show that our approach doubles the speed-up of the progressive alignment, thus allowing T-Coffee to align twice as many sequences while also improving the alignment accuracy.

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%.

SUMMARY: We present the first parallel implementation of the T-Coffee consistency-based multiple aligner. We benchmark it on the Amazon Elastic Cloud (EC2) and show that the parallelization procedure is reasonably effective. We also conclude that for a web server with moderate usage (10K hits/month) the cloud provides a cost-effective alternative to in-house deployment. AVAILABILITY: T-Coffee is a freeware open source package available from http://www.tcoffee.org/homepage.html