Here are the award rates for 11 different postdoc fellowships in 2019.
There’s a huge variation in success rates: four different organizations fund fewer than 6% of applications that they receive, while the success rates for the K99 and F32 are >24%.
To back up - my appointment at CSHL let me run a lab without doing a postdoc, so I never had the experience of applying for these grants. To help out my current postdocs, I wanted to make up for my lack of experience by doing some research.
I collected the award rates for each of these grants either from the org’s website or by emailing them directly. (I included an asterisk to indicate uncertainty. For instance, Beckman said they received “over” 150 applications, and I used 150 as the denominator).
It was really striking to me how low the success rate is for some of these grants! <3% for LSRF and Hope! That’s less than half the award rate of the ultra-prestigious, ultra-competitive DP2 “New Innovator” grant for PIs.
The F32 has the highest award rate (28%), but there’s variability between institutes. It ranges from 0% (NHGRI: Human Genome) to 80% (NCCIH: Complementary & Integrative Medicine).
In related news, my lab’s postdocs will now be studying the effects of yoga on genomic instability.
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One week from today, the Nobel Prize in Medicine/Physiology will be announced.
Here are the 79 most likely awardees, each of whom has won two or more pre-Nobel “predictor” prizes:
My top picks: Horwich/Hartl for their work on chaperone-mediated protein folding. Their discoveries changed how we think about protein structure and has had significant ramifications for our understanding of neurodegenerative diseases.
Klenerman/Balasubramanian for the development of next-gen sequencing (this could be a Chem prize too). NGS has revolutionized multiple areas of medicine and medical research, and the committee likes recognizing tool/technique development (PCR, CRISPR, monoclonal antibodies, etc).
The new class of HHMI investigators average 3.9 papers as corresponding author in Cell, Nature, or Science. 26 out of 26 members of this group previously trained with a PI who is in the National Academy of Sciences or who was an HHMI investigator themselves.
To back up, I have a longstanding interest in understanding the trajectories of academic careers and uncovering “hidden” factors that influence success. Some of my published work on this topic:
Recently, there has been a push for funding bodies to look more closely at preprints and put less emphasis on journal names. However, if you look at data that I collected from HHMI’s competition in 2018, you can see that the results are pretty similar:
Check out our new study in @ScienceMagazine, where we take on a 100-year-old debate: what’s the role of aneuploidy in cancer?
We discovered that genetically removing extra chromosomes blocks cancer growth - a phenomenon we call “aneuploidy addiction”. science.org/doi/10.1126/sc…
In the 19th century, pathologists observing cancer cells under a microscope noticed that they frequently underwent weird mitoses. The chromosome bodies visible in these cells were not equally divided between daughter nuclei - in other words, they were aneuploid.
Early pathologists like Theodor Boveri proposed that it was this aneuploidy that actually caused cancer. But, there was no way to test it. Eventually, this theory fell out of favor - researchers discovered oncogenes and showed the impact that point mutations could have in cancer.
Very excited to share a new paper from my lab: using a set chromosome-engineering tools, we show that cancers are “addicted” to aneuploidy. If you genetically eliminate single aneuploid chromosomes, cancer cells totally lose their malignant potential! biorxiv.org/content/10.110…
To back up, for many years researchers have used the standard tools of molecular genetics to learn about the function of individual oncogenes and tumor suppressors. We can easily over-express, mutate, or knockout genes like KRAS and TP53 to study their biology.
Chromosome gain events are exceptionally common in cancer, but the genetic tools that allow us to manipulate individual genes don’t work for these chromosome-scale copy number changes. You can’t package a whole chromosome in a lentivirus to over-express it.
If you choose to transfer a manuscript between Nature-family journals, you can consult a web page that lists the acceptance rates for 124 journals published by the Springer Nature Group.
I haven’t seen this data circulated before, so I copied it to share here:
According to this data, "Nature" is not actually the most selective journal. Nature Med, Cancer, and Human Behavior all have lower acceptance rates.
This could be Simpson’s paradox. Maybe a cancer paper has a 2% acceptance rate at Nature and a 4% acceptance rate at Nature Cancer, but Nature also loves to accept ML papers, which increases the overall acceptance rate?
Westermann and colleagues were studying a gene believed to regulate YAP1 expression. They made two CRISPR knockout clones in the gene. Unexpectedly, they found that one KO clone upregulated YAP1 while one downregulated YAP1!
They then proceeded to assess YAP1 expression across a panel of wild-type clones that were not modified with CRISPR, and they saw similar variability.