I was recently on a college campus for the first time in a while. It was autumn, and all the leaves had turned different colors. Undergrads were scurrying around, possibly studying for midterm exams. For some reason, college campuses, particularly in the fall, make me think of possibilities. Maybe they remind me of flipping through my own university's course catalog to pick out interesting-sounding classes: virology or biochemistry or French literature. There were so many different things to learn.
In this issue, Genome Technology profiles 25 young investigators in 'omics-related fields. Many of these researchers are faced with choosing from among different possible paths of what to study, and what they might uncover from that work. This group has already started down a variety of roads, focusing on a range of topics, from translating biomarkers for use in the clinic to developing biofuel.
We could not have compiled this list of up-and-coming researchers without help, and we extend a big thank-you to all the established PIs who responded to our requests for recommendation of young researchers doing exciting work.
Our careers column this month offers some advice for researchers early in their careers. Tracy Vence writes about navigating the transition from postdoc to PI. While most new faculty members know they have to get grants and publish papers, Tracy notes that there are other, sometimes overlooked, steps that may help get a lab running.
Elsewhere in this issue, GT takes a look at model organism research in the sequencing era. While nearly any organism that has its genome sequenced may be used as a model, well-established ones bring their long histories to newer avenues of research. As a few researchers point out, sequencing and genome-wide association studies often link a variant to a phenotype or disease — it may be up to experiments in models to uncover the cause or mechanism.
Finally, Matthew Dublin takes on the challenge of data management. Sequencing studies generate so much information that storage disk density cannot keep up. Matt reports that some researchers are trying to figure out what can be deleted while others are trying to compress data files, or buying more storage space and hoping for the best.