On not reinventing the wheel and using Biopython

The wheels of open-source,” is a plea to not keep reinventing the wheel. It resonated with me because I love reinventing wheels. I’ve been writing code since 1997. My favorite thing about writing code is implementing my own idea and seeing it work. It’s addictive. The fact is that someone else had that idea, implemented it first, then tested it and improved it. Using their code would have huge advantages, but I don’t get the same dopamine rush.

Logic and preference diverge.

I’ve been learning to use BioPython to analyze high throughput sequencing data. I’ve tried k-mer analysis based on an article from 2011, and that sort-of worked. I got an aptamer… but it didn’t bind native protein, only recombinant protein. And it was the most successful candidate that came out of the k-mer analysis.


So: back to the bench, or back to bioinformatics?

Might as well try to get more from the data we have, I thought. The trouble was that… bioinformatics is hard. I could figure out how to do k-mer analysis by trial and error. How about phylogenetic trees? How about a scoring system for sequence similarity? These things have been done carefully and checked for statistical correctness and computational efficiency.

So… it’s time to not reinvent the wheel. It’s time to follow logic.