Proposal gambit

I am currently (this minute… well, not THIS minute, but just a minute ago, and in a minute) in the throes of revising a resubmission of a previously submitted R01 proposal to NIH. This proposal generally covers novel methods to build protein-sequence-based classifiers for problematic functional classes- that is, groups of proteins that have a shared function but either are very divergent in their sequence (meaning that they can’t be associated by traditional sequence similarity approaches) or have a lot of similar sequences with divergent functions (and the function that’s interesting can’t be easily disambiguated).

I got good feedback from reviewers on the previous version (though I did not get discussed- for those who aren’t familiar with the process, to get a score- and thus a chance at funding- your grant has to be in the top 50% of the grants that the review panel reads, then it moves on to actual discussion in the panel and scoring). Their main complaint was that I had not described the novel method I was proposing in sufficient detail, and so they were intrigued but couldn’t assess if this would really work or not. The format of NIH R01-level grants (12 pages for the research part) means that to provide details of methods you really need to have published your preliminary results. Also- if it’s published it really lends weight to the fact that you can do it and get it through peer review (or pay your way into a publication in an fly-by-night journal).

So anyway. I’ve put this resubmission off since last year and I’m not getting any younger and I don’t have a publication to reference on the method in the proposal yet. So here’s my gambit. I’ve been working on the paper that will provide preliminary data and it was really nearly finished it just needed a good push to get it finalized, which came in the form of this grant. My plan is to finish up the last couple of details on the paper and submit it to F1000 Research because it offers online publication immediately with subsequent peer review. I’ve been intrigued by this emerging model recently and wanted to try it anyway. But this allows me to reference the online version very soon after I upload it (maybe tomorrow) and include it as a bona fide citation for my grant. The idea is that by the time it’s reviewed (3 months hence) it will have passed peer review and will be an actual citation.

But it’s a gambit. It’s possible that the paper will still be under review or will have received harsh reviews by the time the reviewers look at it. It’s also possible that since I won’t have a traditional journal citation in text for the proposal- I’ll need to supply a URL to my online version- that the reviewers will just frown on this whole idea and it might even piss them off making them think I’m trying to get away with something (which I totally am, though it’s not unethical or against the rules in any way that I can see). However, I’m pretty sure that this is a lot more common on the CS side (preprint servers, and the like) so I’m betting on that flying.

Anyway, I’ll have an update in 3+ months on how this worked out for me. I actually have high hopes for this proposal- which does scare me a little. But I’m totally used to dealing with rejection, as I’ve mentioned before on numerous occasions. Wish me luck!

Big Data Showdown

One of the toughest parts of collaborative science is communication across disciplines. I’ve had many (generally initial) conversations with bench biologists, clinicians, and sometimes others that go approximately like:

“So, tell me what you can do with my data.”

“OK- tell me what questions you’re asking.”

“Um,.. that kinda depends on what you can do with it.”

“Well, that kinda depends on what you’re interested in…”

And this continues.

But the great part- the part about it that I really love- is that given two interested parties you’ll sometimes work to a point of mutual understanding, figuring out the borders and potential of each other’s skills and knowledge. And you generally work out a way of communicating that suits both sides and (mostly) works to get the job done. This is really when you start to hit the point of synergistic collaboration- and also, sadly, usually about the time you run out of funding to do the research.
BigDataShowdown_v1