I just became a regular member of the NIH GCAT study section, and went to my first review on Thur and Fri. I heard that GCAT is the most frequently requested study sections now, and we have ~80 submissions each cycle. The review was a good experience, and here are some of my thoughts. Hope it will help others in their grant writing in the future.
- Reviewers appreciate proposals that have well motivated and focused biological questions, and reasonably (not overly sophisticated) computational methods. Experimental and computational biologists should think together of a good experimental design that really answer a good biological question.
- GCAT is a diverse study section, try to write something (at least in language and style) that appeals to people with to biological, genomics, computational, or statistical backgrounds alike.
- For computational PIs, it is better to motivate the methodology development on real data, have an experimental co-investigator, have some experimental validation, and have publication record to show that their computational work is biologically relevant. For experimental PIs, proposal shouldn’t just generate a lot of data, but should explain how to analyze the data and what biology can be learned from it.
- Make reviewers’ job easier. Write very clearly the hypothesis, aims, innovations, and significance. Also, have shorter paragraphs, use bold and italics to focus reviewers’ attention and improve the readability of a proposal.
- In the current funding environment, any grant with a single weakness could loose out. Any potential weakness needs to add co-investigators or support letters from real experts to address these weakness. Also, it is better to have publications to show previous collaboration record with the co-investigator, even if only mentioned in the support letter.
- Reviewers definitely look at productive publication record and previous work impact. It takes years of focused efforts to become a real expert and have a real impact in some fields. Use new technologies well and wisely, but don’t always chase the newest and hottest thing.
- PI should devote reasonable effort, usually 15-25% (new investigators could go higher). Reviewers prefer applicants with a few well focused PI grants to those with too many (either PI or co-PI) grants relative to their productivity.
- Make genomics data/resource generation and software publicly available and widely used. It will pay off for future applications. Do well by doing good.
- New investigators asking non-modular budget or non-modular proposal asking $499K could be conceived by the reviewers as too greedy. Also computational PIs proposing too many or too expensive wet lab experiments could be criticized.
- Proposals not discussed at one review might not be worth a revised submission. It is better to work hard on projects dear to your heart and submit a new proposal.
With the current funding environment and grant scoring system, grant reviews definitely have chance effect. So submit more proposals (e.g. one of my colleagues taught me to always have two grants pending), just so you have chance to get some.