With data intensive applications becoming more common in the life sciences, I'm often asked for advice on how to prepare for a career in bioinformatics or transition an existing career to focus on life science applications. A few years back, I responded to a post on Slashdot with some thoughts on bioinformatics career paths. Given a few recent conversations on the topic, I thought it would be good to share an updated version of that post here.
If you’re serious about getting into bioinformatics, there are a few good routes to take, all of which will provide you with a solid foundation to have a productive career.
The first thing to decide is what type of career you want. Three common career paths are Researcher, Analyst, and Engineer. The foundational fields for all are Biology, Computer Science (all inclusive through software engineering), and Statistics. Which career path you follow determines the mix.
Researchers have advanced degrees (typically a Ph.D.) and tend to pursue academic or government lab careers. Many research paths do lead to industry jobs, but these tend to morph into the analyst or engineer roles (much to the dismay of the researcher, usually). Bioinformatics researchers tend to have Ph.D.s in Biology, Computer Science, Physics, Math, or Statistics. Pursuing a Ph.D. in any of these areas and focusing your research on biologically relevant problems is a good starting point for a research career. However, there are currently more Ph.D.s produced than research jobs available, which is the reason that after years in school many bioinformatics-oriented Ph.D.s tend to end up in Analysis or Engineering jobs. As glamorous as research can be when you end up on the cover of Nature, on a daily basis a successful researcher will spend much of their time grant writing and running a research lab. Your graduate and post-doc work will be where you do most of your hands on research.
Bioinformatics Analysts (not really a standard term, but a useful distinction) focus on analyzing data for scientists or performing their own analyses. A strong background in statistics is essential (and, unfortunately, often missing) for this role along with a good understanding of biology. Focus on foundational stats - both frequentist and bayesian - and don't get too specialized in the stat-method du jour. Lab skills are not essential here, though familiarity with experimental protocols and their related statistical methods is.
A good way to train for an Analyst career path is to get an undergraduate degree in Math, Stats, or Physics. These provide the math background required to excel as an analyst along with exposure to ‘hard science’. Along the way, look for courses and research opportunities that involve computer science, bioinformatics, and Biology. Basic software skills are also needed, as most tools are Linux-based command line applications. Your day job here is working on teams to answer key questions from experiments. Good communication and teamwork skills are essential for this role.
Bioinformatics engineers/developers (again, not really a standard term, but bear with me) write the software tools used by analysts and researchers and may perform research themselves. A deep understanding of algorithms and data structures, software engineering, and high performance computing is required to really excel in this field, though good programming skills and a desire to learn the science are enough to get started.
The best education for a bioinformatics engineer is a Computer Science degree with a focus on bioinformatics and scientific computing (many problems that are starting to emerge in bioinformatics have good solutions from other scientific disciplines). Again, aligning additional coursework and undergraduate research with biologists is key to building a foundation. A double major in Biology would be useful, too. To fully round this out, a Masters in Statistics would make a great candidate, as long as their side projects were all biology related. Your day job here is building the tools and infrastructure to make bioinformatics function.
All three career paths can be rewarding and appeal to different mindsets.