I couldn't imagine working in computational biology when I was an undergraduate! As I developed my own research project during graduate school, though, I realized the power of computational tools in making biological research more efficient and interesting. More importantly, I found out I enjoyed programming, and learning these analytical techniques wasn't as insurmountable a task as I previously thought.

• Communicating with people who have expertise that is different than mine
• Finding good resources for teaching myself things (programming languages, statistical tests, etc)
• Patience and persistence in troubleshooting
• Applying methods of analysis in interesting and creative ways
• Asking for help from people who know more than me!

I wish I had taken more math, statistics, and computer science classes in school. I am mostly self taught, but have had a few fantastic mentors who also helped me find workshops. I learned a lot from the folks at Software Carpentry (http://software-carpentry.org) and am now learning how to teach their classes. I also like free online classes like those through Coursera (https://www.coursera.org).

I'm interested in how genomic changes affect organismal evolution. I work in lots of different organisms (plants, animals, bacteria, etc) to characterize patterns of genomic variation, which I then correlate to changes in phenotype (morphology, life history, habitat). Some examples of questions I address include: Is genome size related to growth habit? Are rates of molecular evolution related to life history traits?

I just started my dream job as assistant professor, specializing in genomics and bioinformatics, and there is space in my lab for a master's student interested in working on genome evolution while developing bioinformatics skills. Don't be afraid to talk to me or other professors about how you can learn more as a computational biologist!