PASTA estimates alignments and ML trees from unaligned sequences using an iterative approach. In each iteration, it first estimates a multiple sequence alignment using the current tree as a guide and then estimates a ML tree on (a masked version of) the alignment. By default, PASTA performs 3 iterations, but a host of options enable changing that behavior. In each iteration, a divide-and-conquer strategy is used for estimating the alignment. The set of sequences is divided into smaller subsets, each of which is aligned using an external alignment tool (default is MAFFT). These subset alignments are then pairwise merged (by default using Opal) and finally the pairwise merged alignments are merged into a final alignment using a transitivity merge technique. The division of the dataset into smaller subsets and selecting which alignments should be pairwise merged is guided by the tree from the previous iteration. The first step therefore needs an initial tree.
- HPC_PASTA_DIR - installation directory