| A central focus of computational biology is to organize and make use of vast stores of molecular sequence data. Two of the most studied and fundamental problems in the field are sequence alignment and phylogeny inference. The problem of multiple sequence alignment is to take a set of DNA, RNA, or protein sequences and identify related segments of these sequences. Perhaps the most common use of alignments of multiple sequences is as input for methods designed to infer a phylogeny, or tree describing the evolutionary history of the sequences. The two problems are circularly related: standard phylogeny inference methods take a multiple sequence alignment as input, while computation of a rudimentary phylogeny is a step in the standard multiple sequence alignment method.;Efficient computation of high-quality alignments, and of high-quality phylogenies based on those alignments, are both open problems in the field of computational biology. The first part of the dissertation gives details of my efforts to identify a best-of-breed method for each stage of the standard form-and-polish heuristic for aligning multiple sequences; the result of these efforts is a tool, called Opal, that achieves state-of-the-art 84.7% accuracy on the BAliBASE alignment benchmark. The second part of the dissertation describes a new algorithm that dramatically increases the speed and scalability of a common method for phylogeny inference called neighbor-joining; this algorithm is implemented in a new tool, called NINJA, which is more than an order of magnitude faster than a very fast implementation of the canonical algorithm, for example building a tree on 218,000 sequences in under 6 days using a single processor computer. |
