The Moore DDD Investigator competition: my presentation

Here are my talk notes for the Data Driven Discovery grant competition ("cage match" round). Talk slides are on slideshare You can see my full proposal here as well.

Hello, my name is Titus Brown, and I'm at Michigan State University where I run a biology group whose motto is "better science through superior software". I'm going to tell you about my vision for building infrastructure to support data-intensive biology.

Our research is focused on accelerating sequence-based biology with algorithmic prefilters that help scale downstream sequence analysis. The basic idea is to take the large amounts of data coming from sequencers and squeeze out the information in the data for downstream software to use. In most cases we make things 10 or 100x easier, in some cases we've been able to make analyses possible where they weren't doable before;

In pursuit of this goal, we've built three super awesome computer science advances: we built a low-memory approach for counting sequence elements, we created a new data structure for low-memory graph storage, and developed a streaming lossy compression algorithm that puts much of sequence analysis on a online and streaming basis. Collectively, these are applicable to a wide range of basic sequence analysis problems, including error removal, species sorting, and genome assembly.

We've implemented all three of these approaches in an open source analysis package, khmer, that we developed and maintain using good software engineering approaches. We have primarily focused on using this to drive our own research, but since you can do analyses with it can't be done any other way, we've had some pretty good adoption by others. It's a bit hard to tell how many people are using it because of the many ways people can download it, but we believe it to be in the 1000s and we know we have dozens of citations.

The most important part of our research is this: we have enabled some excellent biology! For a few examples, we've assembled the largest soil metagenome ever with Jim Tiedje and Janet Jansson, we've helped look at deep sea samples of bone eating worms with Shana Goffredi, we're about to publish the largest de novo mRNASeq analysis ever done, and we're enabling evo devo research at the phylogenetic extremes of the Molgula sea squirts. This was really what we set out to do at the beginning but the volume and velocity of data coming from sequencers turned out to be the blocking problem.

Coming from a bit of a physics background, when I started working in bioinformatics 6 years ago, I was surprised at our inability to replicate others' results. One of our explicit strategies now is to try to level up the field by doing high quality, novel, open science. For example, our lamprey analysis is now entirely automated, taking three weeks to go from 3 billion lamprey mRNASeq reads to an assembled, annotated transcriptome that we can interactively analyze in an IPython Notebook, which we will publish with the paper. Camille, who is working on this, is a combination software engineer and scientist, and this has turned out to be a really productive combination.

We've also found that 1000s of people want to do the kinds of things we're doing, but most don't have the expertise or access to computational infrastructure. So, we're also working on open protocols for analyzing sequence data in the cloud - going from raw mRNASeq data to finished analysis for about $100. These protocols are open, versioned, forkable, and highly replicable, and we've got about 20 different groups using them right now.

So that's what I work on now. But looking forward I see a really big problem looming over molecular biology. Soon we will have whatever 'omic data set we want from, to whatever resolution we want, limited only by sampling. But we basically don't have any good way of analyzing these data sets -- most groups don't have the capacity or capability to analyze them themselves, we can't store these data sets in one place and -- perhaps the biggest part of the catastrophe -- people aren't making these data available until after publication, which means that I expect many of them to vanish. We need to incentivise pre-publication sharing by making it useful to share your data. We can do individual analyses now, but we're missing the part that links these analyses to other data sets more broadly.

My proposal, therefore, is to build a distributed graph database system that will allow people to interconnect with open, walled-garden, and private data sets. The idea is that researchers will spin up their own server in the cloud, upload their raw or analyzed data, and have a query interface that lets them explore the data. They'll also have access to other public servers, and be able to opt-in to exploring pre-published data; this opt-in will be in the form of a walled-garden approach where researchers who use results from analyzing other unpublished data sets will be given citation information to those data sets. I hope and expect that this will start to incentivise people to open their data sets up a bit, but to be honest if all it does is make it so that people can analyze their own data in isolation it will already be a major win.

None of this is really a new idea. We published a paper exploring some of these ideas in 2009, but have been unable to find funding. In fact, I think this is the most traditionally unfundable project I have ever proposed, so I hope Moore feels properly honored. In any case, the main point here is that graph queries are a wonderful abstraction that lets you set up an architecture for flexibly querying annotations and data when certain precomputed results already exist. The pygr project showed me the power of this when implemented in a distributed way and it's still the best approach I've ever seen implemented in bioinformatics.

The idea would be to enable basic queries like this across multiple servers, so that we can begin to support the queries necessary for automated data mining and cross-validation.

My larger vision is very buzzwordy. I want to enable frictionless sharing, driven by immediate utility. I want to enable permissionless innovation, so that data mining folk can try out new approaches without first finding a collaborator with an interesting data set, or doing a lot of prep work. By building open, federated infrastructure, and avoiding centralized infrastructure, I am planning for poverty: everything we build will be sustainable and maintainable, so when my funding goes away others can pick it up. And my focus will be on solving people's current problems, which in biology are immense, while remaining agile in terms of what problems I tackle next.

The thing is, everybody needs this. I work across many funding agencies, and many fields, and there is nothing like this currently in existence. I'm even more sure of this because I posted my Moore proposal and requested feedback and discussed it with a number of people on Twitter. NGS has enabled research on non-model organisms but its promise is undermet due to lack of cyberinfrastructure, basically.

How would I start? I would hire two domain postdocs who are tackling challenging data analysis tasks, and support them with my existing lab; this would involve cross-training the postdocs in data intensive methodologies. For example, one pilot project is to work on the data from the DeepDOM cruise, where they did multi-omic sampling across about 20 points in the atlantic, and are trying to connect the dots between microbial activity and dissolved organic matter, with metagenomic and metabolomic data.

Integrated with my research, I would continue and expand my current efforts in training. I already run a number of workshops and generate quite a bit of popular bioinformatics training material; I would continue and expand that effort as part of my research. One thing that I particularly like about this approach is that it's deeply self-interested: I can find out what problems everyone has, and will be having soon, by working with them in workshops.

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