Parallel multiple sequence alignment with decentralized cache support

The problem of multiple sequence alignment is one of the most important problems in computational biology. In this paper we present a new method that simultaneously performs multiple sequence alignment and phylogenetic tree inference for large input data sets. We describe a parallel implementation of our method that utilises simulated annealing metaheuristic to find locally optimal phylogenetic trees in reasonable time. To validate the method, we perform a set of experiments with synthetic as well as real–life data.

Parallel multiple sequence alignment with decentralized cache support

Abstract. In this paper we present a new method for aligning large sets of biological sequences. The method performs a sequence alignment in parallel and uses a decentralized cache to store intermediate results. The method allows alignments to be recomputed efficiently when new sequences are added or when alignments of different precisions are requested. Our method can be used to solve important biological problems like the adaptive update of a complete evolution tree when new sequences are added (without recomputing the whole tree). To validate the method, some experiments were performed using up to 512 Small Subunit Ribosomal RNA sequences, which were analyzed with different levels of precision.