Context is king!

Our marketing materials often quote the fact that our database contains billions of unique T- and B-Cell sequences. Seriously, that is an insane number. But in isolation, it can also be misleading. After all, your body pretty much creates sequences at random in its attempt to find ones that work — and a billion rolls of a die don’t tell you very much.

The magic happens when you add context to these sequences — tracking diagnoses and outcomes, demographics, medication use, other markers, really anything that can help complete the whole picture. This stuff combined with our sequence data is what’s (really) changing the world.

In fact, one of the toughest things about good science is picking the right attributes — traditionally they’re expensive and complex to track, and lots of them don’t even matter. This is the reason we try to help our customers with Analyzer features like projects and tagging. And over the next few years, we’ll continue to add more and more features that make it easier and quicker to figure out exactly what matters most in immunology.

Zooming out, though — what if it wasn’t so hard to collect and track this context? What if we could just track “everything” about our subjects and then use statistics to automatically figure out which attributes matter. Yes, this is the promise of big data we all love to talk about, but there are many more barriers to making it real than just more and bigger computers.

Companies like Patients Like Me and 23andMe have taken a really novel approach to this challenge. What if we enlist the subjects themselves to contribute data over time, from lots of different sources? What if researchers could re-contact those subjects to ask them new questions along the way? And what if the subjects gave consent for lots of different folks to use their information in flexible and informal ways, freeing up at least a little work from the slow-moving IRB process?

The tradeoff is a fascinating one — are we better off using small bits of highly reliable and curated data, or using tons and tons that we know is noisy? Well, actually it’s not a tradeoff at all. So long as you know what you’re working with, both can be incredibly productive ways to increase our understanding of the world.

We think about this a lot, and are making real investments to help us all better understand the adaptive immune system. Thanks for joining us on this really, really fun ride.

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