This is a fun challenge series.

While there are simple solutions to these problems given their relatively small size, bioinformatics can scale up past your RAM quickly.

I have given myself the extra challenge of making my solutions scalable and mindful of big O (the worst case run scenario). They let you use any language to solve the challenges, and Python is my main one, but I have passed the first 4 challenges using the power of C language subroutines in the Numpy package.

My answers were the same as the simpler solutions, but they can handle scaling. If I had to deal with 1,000,000 nucleotides instead of 1,000, my solution would not break.

Going to keep that momentum going throughout the challenges.

The stronghold section requires you to create the algorithms to solve the problems... in the following section, the "armory", the challenges are to be solved with existing industry wide software packages like Biopython in python and Bioconductor in the R language.

They briefly touched on the power of Numpy to make powerful reductions in big O problems, but the majority of what I learned about the practical application of Numpy and efficient coding came directly from the lottery hobby!

Even though this lottery problem was impossible to solve, the skills learned are literally directly transferred to other domains! Markov models are all over the bioinformatics domain.

Who knows, maybe I will pick up something in this pursuit that can be brought back to the lottery domain...