Sean Eddy explains why sequencing is replacing many older assays, and why biologists need to learn to analyze their own data.
“High throughput sequencing for neuroscience”:
If we were talking about a well-defined resource like a genome sequence, where the problem is an engineering problem, I’m fine with outsourcing or building skilled teams of bioinformaticians. But if you’re a biologist pursuing a hypothesis-driven biological problem, and you’re using using a sequencing-based assay to ask part of your question, generically expecting a bioinformatician in your sequencing core to analyze your data is like handing all your gels over to some guy in the basement who uses a ruler and a lightbox really well.
Data analysis is not generic. To analyze data from a biological assay, you have to understand the question you’re asking, you have to understand the assay itself, and you have to have enough intuition to anticipate problems, recognize interesting anomalies, and design appropriate controls. If we were talking about gels, this would be obvious. You don’t analyze Northerns the same way you analyze Westerns, and you wouldn’t hand both your Westerns and your Northerns over to the generic gel-analyzing person with her ruler in the basement. But somehow this is what many people seem to want to do with bioinformaticians and sequence data.
It is true that sequencing generates a lot of data, and it is currently true that the skills needed to do sequencing data analysis are specialized and in short supply. What I want to tell you, though, is that those data analysis skills are easily acquired by biologists, that they must be acquired by biologists, and that that they will be. We need to rethink how we’re doing bioinformatics.
I would add this: it takes some time to learn, but in the end it’s not that hard, people. Students in chemistry and physics routinely learn the requisite skills. We need to educate biologists who expect to do programming, math, and statistics.
This kind of thinking drives me up a wall – scientists who are unwilling to approach the PhD labor market from a scientific perspective:
Living Science: Looking out for Future Scientists, Eve Marder, eLife:
I wonder at those who think they can predict which of our graduate applicants is likely to become a great scientist, and am dismayed by the hubris of those who think we should restrict access to PhD programs to a select few…
Ever since I can remember (and that is a long time), there have been wise heads who have counseled that we should drastically decrease the size of our PhD classes because there are not enough academic faculty positions to accommodate all of the able and interested candidates…
While these authors show a deep understanding of how increased competition for positions and funding have deleterious effects on the biomedical research and teaching enterprise, every time I think about substantially restricting access to graduate programs I wince…
There are some who argue that students who finish their PhDs (or spend years as postdocs) and then move into other careers have wasted their time. I disagree…
Society would be enriched if more of the people making decisions in industry, law, medicine, education and politics had lived through the rigors of a PhD program, and knew first-hand how difficult it is to extract knowledge from our imperfect measurement and analytical tools…
Continue reading “Perpetuating the PhD pyramid scheme”
The NY Times ran an op-ed by a Maryland Congressional representative arguing that younger biomedical investigators, who are at what should be the most creative time of their careers, are getting screwed in the current funding climate. He suggests that Congress should force the NIH to change this:
Congress should also mandate that the median age of first research awards to new investigators be under 40 within five years, and under 38 within 10 years. Failure to meet these benchmarks would result in penalties for the N.I.H., including possible funding cuts.
But people aren’t just getting funded later – it looks like they’re getting their first tenure-track jobs later as well. There are probably proportionally fewer younger investigators that the NIH could fund. The average age at which people get their first assistant professorships at U.S. medical schools appears to have climbed steadily, closely tracking the rise in age of investigators getting their first R01s. (There are some conflicting data; my guess is that it’s important to distinguish between first tenure-track job at any institution (NSF survey), and first tenure-track job at medical schools (AAMC data), where most people who apply for R01’s work.) This shouldn’t be surprising – competition for faculty jobs is growing, and as the economist Paula Stephan has argued, there is some evidence that those who go on to tenure track jobs do longer postdocs than those who don’t. This isn’t a problem that will be solved by forcing the NIH to fund more younger investigators.
So says E.O. Wilson in the Wall Street Journal.
But don’t just read the headline – be sure to catch the nuance in Wilson’s piece. He’s saying don’t let fear of math drive you from science, because you don’t need straight A’s through four semesters of calculus to be a good scientist.
I don’t quite agree with Wilson when he says you can always find a mathematician as a collaborator to handle the math you need. A mathematically illiterate biologist working with a biologically illiterate mathematician is usually not a fruitful combination. But good scientists pick up the necessary mental toolkit as it’s needed, including mathematical and statistical knowledge (as long as they’re willing to put some serious effort into gaining that knowledge, as opposed to, say, figuring out how to mindlessly apply t-tests).
Sean Eddy calls this approach “ante-disciplinary science”: Continue reading “Great scientists don’t need math”
From Rob Phillips’ list of publications on his lab website:
A First Exposure to Statistical Mechanics for Life Scientists. (with Hernan G. Garcia, Jane’ Kondev, Nigel Orme and Julie A. Theriot), Rejected by Am. J. Phys., 2007. [online full text]
The paper itself is a great read, with some important ideas for anyone who thinks about how to incorporate more quantitative/physical concepts into our program of biology education. It also tells you that stat mech is almost effortless once you understand the Boltzmann distribution: Continue reading “How to reference a rejected paper on your CV”