  The theoretical analysis of performance has been an important tool in the
engineering of algorithms in many application domains. Its goals are to predict
the empirical performance of an algorithm and to be a yardstick that drives the
design of novel algorithms that perform well in practice. While these goals
have been achieved in many instances, they have not been achieved ubiquitously
across crucial application domains. I provide a case study in the area of
sequencing bioinformatics, an inter-disciplinary field that uses algorithms to
extract biological meaning from genome sequencing data. In particular, I give
three concrete examples: two showing how theoretical analysis has failed to
achieve its goals and one showing how it has been successful. I will then
catalog some of the challenges of applying theoretical analysis to sequencing
bioinformatics, argue why empirical analysis is not enough, and give a vision
for improving the relevance of theoretical analysis to sequencing
bioinformatics. By recognizing the problem, understanding its roots, and
providing potential solutions, this work can hopefully be a crucial first step
towards making theoretical analysis more relevant in sequencing bioinformatics
and potentially other fast-paced application domains.
