In Bioinformatics, finding correlations between species allows us the better understand the important biological functions of those species and trace its evolution. This thesis considers sequence alignment, a method for obtaining these correlations. We improve upon sequence alignment tools designed for DNA with Plains, an algorithm than uses piecewise-linear gap functions and parameter-optimization to obtain correlations in remotely-related species pairs such as human and fugu using reasonable amounts of memory and space on an ordinary computer. We then discuss Planar, which is similar to Plains, but is designed for aligning RNA, and accounts for secondary structure. We also explore SEPA, a tool that uses p-value estimation based on exhaustive empirical data to better emphasize key results from an alignment with a measure of reliability. Using SEPA to measure the quality of an alignment, we proceed to compare Plains and Planar against similar alignment tools, emphaisizing the interesting correlations caught in the process.