The incredible availability of both data and methodological tools to understand complex biological phenomenon with data.

• Statistics
• Programming
• Probability theory
• Intuition for an interesting question
• Knowledge of the system

I am mostly self taught — I've been programming since I was a young teenager (initially on a TI-83 calculator). Along the way, I learned a lot from friends, Unix systems administrators, online programming communities, and books. I think the primary skill to learn first is patience — both computational and quantitative work can be incredibly discouraging at times, and having the ability to laugh off frustration and keep trying is a huge part of going forward with a project.

As far as statistics, I just fell in love with the subject. I had many teachers of both statistics and mathematics that would lead me to think these were uninteresting subjects, but I knew that there must be more there. I kept reading until I saw the beauty in both. I would highly recommend learning programming alongside statistics, so you can check solutions with simulations, as well as work with interesting real data.

My computational research work has varied. I've designed and implemented computational tools that find and characterize translocations in the human MLL gene (implicated in a subset of leukaemias), and participated in the Assemblathon, a collaborative effort to assess short read assemblers. More recent work has focused on plant genetics. I've developed computational methods to separate homeologs in tetraploid wheat transcriptomic data, and have written statistical genetics methods to phase and impute noisy genotype-by-sequencing data in maize and teosinte. Now, my primary focus is in the development of population genomics methods to understand evolutionary phenomenon.

Find something that ignites a passion in you, and do that! It's also essential to learn skills that aren't common, as this will make you a valuable asset.