What to do with lots of (sequencing) data

On a recent west coast speaking junket where I spoke at OSU, OHSU, and VanBUG (Brown PNW '15!), I put together a new talk that tried to connect our past work on scaling metagenome assembly with our future work on driving data sharing and data integration. As you can maybe guess from the first few talk slides, the motivating chain was something like

  1. We want to help biologists move more quickly to hypotheses;
  2. This can in part be done by aiding in hypothesis generation and refinement;
  3. Right now it's painful to analyze large sequencing data sets;
  4. Let's make it less painful!

At both OHSU and OSU, where I gave very similar talks, I got important and critical feedback on these points. The three most valuable points of feedback were,

  • what exactly do you mean by data integration, anyway, Titus?
  • you never talked about generating hypotheses!
  • no, seriously, you never talked about how to actually generate hypotheses!?

The culmination point of this satori-like experience was when Stephen Giovannoni leaned across the table at dinner and said, "Perhaps you can tell me what all this data is actually good for?" This led to a very robust and energetic dinner conversation which led to this blog post :). (Note, Stephen clearly had his own ideas, but wanted to hear mine!)

The underlying point is this: I, and others, are proselytizing the free and open availability of large data sets; we're pushing the development of big data analytic tools; and we're arguing that this is important to the future of science. Why? What good is it all? Why should we worry about this, rather than ignoring it and focusing instead on (for example) physiological characterization of complex environments, clinical trials, etc. etc.?

So, without further ado,

What is all this data potentially good for?

Suppose you set out to use a body of sequencing data in your scientific research. This sequencing data is maybe something you generated yourself, or perhaps it's from a public data set, or from multiple public data sets. Either way, it's a bunch of data that you would like to make use of. What could you do with it?

(This isn't specific to sequencing data, although I think the exploratory approaches are particularly important in biology and sequencing data are well suited to exploratory analysis.)

  1. Computational hypothesis falsification.

    "I thought A was happening. If A is happening, then when I looked at my data I should have seen B. I didn't see B. Therefore A is probably not happening."

    For example, if you are convinced that a specific biogeochemical process is at work, but can't find the relevant molecules in a metagenomic survey, then either you did something wrong, had insufficient sensitivity, or your hypothesis is incorrect in the first place.

    This is one place where pre-existing data can really accelerate the scientific process, and where data availability is really important.

  2. Determination of model sufficiency.

    "I have a Boolean or quantitative model that I believe captures the essential components of my system under investigation. When I fit my actual data to this model, I see several important mismatches. Therefore my model needs more work."

    For gene regulatory networks, or metabolic modeling, this kind of approach is where we need to go. See, for example, work from my graduate lab on sea urchin GRNs - this approach is used there implicitly to drive forward the investigation of underdetermined parts of the GRN.

  3. Comparison with a null or neutral model.

    "If interesting interactions were happening, I would see patterns that deviated from my model of what an uninteresting system should look like. I don't, therefore my model of what is interesting or uninteresting needs to change."

    Somewhat of an elaboration of the above "model sufficiency", here we are choosing an explicit "null model" to interpret our data and concluding that our data is either interesting or boring. For me, the difference is that these models need not be mechanistic, while the models in the previous point are often mechanistic. One example I'd point to is Ethan White's work on maximum entropy models.

  4. Hypothesis generation (or, "fishing expedition.")

    "I have no idea what processes are at work here. Let's look at the data and come up with some ideas."

    A thoroughly underappreciated yet increasingly default approach in various areas of biology, fishing expeditions can feed the masses. (Get it? Hah!)

    But, seriously, this is an important part of biology; I wrote about why at some length back in 2011. All of the previous points rely on us already knowing or believing something, while in reality most of biology is poorly understood and in many cases we have almost no idea what is going on mechanistically. Just looking at systems can be very informative in this situation.

So, this is my first take on the reasons why I think large-scale data generation, availability, analysis, and integration can and should be first class citizens in biology. But I'd be interested in pushback and other thoughts, as well as references to places where this approach has worked well (or poorly) in biology!


p.s. Thanks to Stephen Giovannoni, Thomas Sharpton, Ryan Mueller, and
David Koslicki for the dinner conversation at OSU!

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