[ginger_beer]

I do PhD research in metabolomics -- one of the latest omics in bioinfo-- with the CS department in my university. At the moment, we're working on alignment and identification of metabolite data. The data is not big in the sense of genomics data, but messy and complex due to the nature of the instruments (mass spectrometer), which will not get better THAT much in the foreseeable future.

Definitely a computationally difficult problem because while naive approaches work, they produce crappy results, wasting the result of tens of thousands of dollars of experiments. I see a big move towards applying statistical/machine learning methods, and graph theory stuffs in our field.

A lot of the rants in the original article are correct, with regards to prototyping and throwaway codes. That's because researchers are rushing to get an MVP out. The truly good ones got turned into (usually open-source) products, where the code quality hopefully improves a fair bit.

If you're a CS person who's interested or considering a move into bioinfo, I wrote a blog post about it recently: http://www.joewandy.com/2013/01/getting-into-bioinformatics....