How does it change what you do, though? I did signal processing with massive data streams rather than bioinformatics, but I assume the situation is similar. The algorithms are what they are. They are complex mathematical equations or transformations that need to be run on data and are often optimized without being able to change their asymptotic complexity.

Skiena's Algorithm Design Manual mentions him being brought in as an algorithmic consultant to modify some genetics analysis software so that it'd actually finish but I don't really remember the details or know enough about the field to give you plausible examples.

I can see that; I did a lot of similar work with signal processing algorithms. None of what I did affected asymptotic complexity at all, though. The asymptotic complexity was tied to the algorithms chosen, and changing those was an issue of trading computational performance for system performance.

I pulled it up and found it; the problem involved modeling genome sequences as strings and finding possible substrings.