Skip to main content
Premium Trial:

Request an Annual Quote

A Special Issue for Special Scientists


You're a busy person. If you're like most of our readers, you probably spend your day scrambling from one meeting to the next, squeezing in experiments and data analysis whenever you can, and after your long, hard day, you finally go home — where you catch up on all of your work e-mail. Sound familiar?

In a field where speed is essential — you need results now, you have to release your data immediately, and there's always a grant application or project presentation looming — it's a rare thing indeed to step back and actually take a moment to appreciate what you and your colleagues have accomplished.

It's that rare moment we offer to you with this issue of Genome Technology, aimed at celebrating the accomplishments of a select group of researchers in this community. In the past several months, readers have asked me for more profiles of up-and-coming scientists. So when we decided to add a bonus tenth issue to our calendar, choosing the theme was simple: who would be the PIs of tomorrow's labs? Who are the rising stars people should be watching right now?

We tapped today's leading PIs to find out, and they had no shortage of names to share with us. The tough part was narrowing the field to the 30 most promising scientists whose profiles you will find on the following pages. Our criteria were simple: they had to be involved in the disciplines that comprise systems biology, and could be no more than five years into their first faculty or equivalent post.

In what has been perhaps the most fun issue we've ever put together, the GT staff got to spend hours talking with these bright researchers not only about what they're doing today, but also about where they see the field going in the years to come (we did get mocked soundly, though, for my own favorite question: “If you were to one day win the Nobel Prize, what accomplishment would you like that to be for?”). What we found was that these scientists are already fluent in some key attributes: if you read the profiles carefully, you'll notice a theme of highly collaborative people who understand the importance of networking and surrounding themselves with other very smart people.

I'd like to thank all of the current lab heads who recommended people for inclusion in this issue, and also GT reporter Matt Dublin for heading up this project. And though we keep our editorial and advertising departments completely separate, I will take a moment to thank our advertisers, whose contributions for this issue have allowed us to give travel stipend honoraria to our profiled investigators.

You'll notice that this issue doesn't look like a typical Genome Technology. With different content comes a different designer, and GenomeWeb's own Elena Coronado has done an outstanding job in giving our bonus issue a very special look. We'll be back to our usual designers, the talented folks at Three Bears, with our next issue.

Finally, for those of you who thought we'd forgotten about the cartoon caption contest we offered earlier this year, don't miss the Blunt End. We held results till now since so many entries were plays on the PI/postdoc dynamic. Check out p. 50 for the winning caption and our honorable mention.

The Scan

Study Finds Few FDA Post-Market Regulatory Actions Backed by Research, Public Assessments

A Yale University-led team examines in The BMJ safety signals from the US FDA Adverse Event Reporting System and whether they led to regulatory action.

Duke University Team Develops Programmable RNA Tool for Cell Editing

Researchers have developed an RNA-based editing tool that can target specific cells, as they describe in Nature.

Novel Gene Editing Approach for Treating Cystic Fibrosis

Researchers in Science Advances report on their development of a non-nuclease-based gene editing approach they hope to apply to treat cystic fibrosis.

Study Tracks Responses in Patients Pursuing Polygenic Risk Score Profiling

Using interviews, researchers in the European Journal of Human Genetics qualitatively assess individuals' motivations for, and experiences with, direct-to-consumer polygenic risk score testing.