Tuesday, February 2, 2016

How to (not) write a microbiome grant, part II. A deeper dive on preliminary data

As in Part I, a few quick notes that come to mind as I'm reviewing microbiome grants....

One of the biggest challenges and frustrations with grant writing is knowing just how much and what type preliminary data to include and how much detail on methods to provide. Within the context of the grant, preliminary data has a couple of different jobs. First, it's there to convince the reviewers that you can actually perform the type of experiments and analyses that you are proposing. Second, it's there to justify why the proposed experiments are interesting or necessary. There's certainly no magical formula, but I think there are a few things to keep in mind to when struggling over these two variables. (DISCLAIMER: just one person's opinion)

1. The amount of preliminary data required changes throughout the course of your career.

Don't kill the messenger, but track records matter. It's just the way it is. Early career researchers need to include more detail and need to justify their proposed experiments moreso than established researchers. If I'm reading a grant and I see that the PI has published (even as a preprint, because I can go and read the methods if there is a question in my mind) these kinds of analyses before, it's much easier to believe they'll be successful performing the proposed analyses. All else equal, that inherently gives established researches a leg up given page limits.

2. You must include enough detail to convince me that you know what you're talking about with the analyses.

If I'm reading a grant that proposes types of experiments that I'm familiar with, I probably have a decent idea of the associated pitfalls and critical variables. If you've done the experiments or understand how to do them well enough to carry them out, you should also have an idea of the critical points to include in your methods and analyses. It's very likely that, even if you don't have experience with specific protocols, that you'll know someone that does...do whatever you can to understand the ins and outs of the proposed experiments and write enough detail to cover the critical points. Assume that at least one reviewer is going to be familiar with the experimental protocols and include enough information to convince this reviewer you know what you're talking about. Assume that other reviewers may not understand the protocols and include enough basic information to give them an idea of what you're talking about.

3. The type of preliminary data required changes throughout the course of your career.

If you have a proven track record in the field, or if you've hit both of the above points in your grant, the preliminary data within your proposal should provide just enough smoke to convince the reviewer that there's a fire somewhere (metaphorical of course). It's very easy to propose "fishing expedition" experiments, one's where you are going to make a lot of observations and some magical result is going to come from combining together all this data.

When I was started as a PI, I kept proposing a few different RNAseq experiments that I thought would be very interesting and insightful. Inevitably, I'd get the reviews back and I'd get dinged for not having a hypothesis. "Pssssshhhhh" I'd say to myself as I gripped my stress relief ball, "The hypothesis is that gene expression WILL change!". With a bit more perspective gained from grant panel experience, I understand exactly now what the "fishing expedition" critique means. It's a combination of the psychology of having to review a bunch of related grants at a single time coupled with the reality that you have to bin grants into different piles as a reviewer.

Here's an example with microbiomes (in the style of Law and Order, this very example may be based on real events that are happening at this exact moment). Say a researcher has 10, 15-page microbiome grants to review before the panel meeting. A large percentage of these grants are interested in measuring microbiome dynamics over time, space, and across individuals. The methods and proposed analyses are usually very similar across a large swath of these grants. The only ways left to bin as a reviewer are by host species and whether there's enough smoke to think there may be a fire. If you can't make the case that your study system is different than other ones, chances are that your grant is going to get lumped in with those proposing similar methods and placed in the "others" pile unless someone else on the panel makes a good case during discussion.

Preliminary data is your way to make the grant stand out. If you are proposing that individual to individual variation matters, or that changes in microbiome dynamics over time matter, it's easy enough to get a few samples and sequence 16s. It doesn't have to be a full study, it just has to be enough to show that there is some signal that's interesting enough to follow up on. There's a surprising lack of pilot experiments in a lot of these microbiome grants (IMO), and the only thing I can think of is that it's hard to find sequencing centers that can process a handful of 16s samples relatively cheaply. I again assume that this is because you typically you need a certain threshold number of samples for a MiSeq run (vs. Sanger sequencing where you can perform just one reaction). One way to get around this is to find others that are interested in generating the same kind of data and pool resources together to pull together a whole MiSeq run. There seem to be a couple of places that could facilitate finding others to pool with (like GenoHub).

My null hypothesis as a reviewer is that microbiome dynamics are going to be the same in your system as they are in well studied systems. Use this preliminary data and pilot experiments to disprove my reviewer null hypothesis. Are there differences abundances or frequences for taxa that are important in other systems? For instance, if you're proposing a phyllosphere microbiome study, off the top of my head I can imagine a top five for the taxa you should find in high abundance. Is your system different (If you can't answer that, there's some reading you should do).  If you sequence a couple of plants, do the larger plants have differences in microbiomes that you can follow up on? Is there something unique about your microbiome of interest compared to others (i.e. the rice rhizosphere apparently has some archea). If there are differences between these experiments and previous ones from other systems, think about hypotheses to explain why these differences and build your grant off of that. "Sequence everything and sort out the important trends later" doesn't work when every grant is proposing to do the same thing.

4. "But I don't have any preliminary data to include"

Yes you do. It may not be your own, but there are enough public datasets for you to reanalyze other's work (to at least show that you can do the analyses and give yourself some sort of track record). It doesn't even have to focus on the system you are proposing to work in so long as it moves your narrative forward (see Points 1 and 2 above).

<slight update to point 4> There's a flipside to this. If you're proposing experiments similar to others that have been published before in the same system, don't just cite the previous papers. Give your reviewers a context for why your proposed study is going to be different than previously published studies.

1 comment:

  1. My grants have always been literacy based, and every district has literacy goals that your reading project can hopefully help support. Grant Money Search


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