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Top 12 reasons you know you are a Big Data biologist

In our occasional series of guest blog articles, we are proud to present a piece on the topic of big data in biology, written by C. Titus Brown. This article has been reproduced from the original with his permission.

A few people have recently asked me what this "Big Data" thing is in biology. It's not an easy question to answer, though, because biology's a bit peculiar, and a lot of Big Data researchers are not working in bio. While I was thinking about this I kept on coming up with anecdotes -- and, well, that turned into this:

The Top 12 Reasons You Know You Are a Big Data Biologist.

  1. You no longer get enthused by the prospect of more data.

    I was at a conference a few months back, and an Brit colleague of mine rushed up to me and said, "Hey! We just got an Illumina HiSeq! Do you have anything you want sequenced?" My immediate, visceral response was "Hell no! We can't even deal with a 10th of the sequence we've already got! PLEASE don't send me any more! Can I PAY you to not send me any more?"

  2. Analysis is the bottleneck.

    I'm dangerously close to genomics bingo here, but: I was chatting with a colleague in the hallway here at MSU, pitching some ideas about microbiome work, and he said, "What a good idea! I have some samples here that I'd like to sequence that could help with that." I responded, "How about we stop producing data and think about the analysis?" He seemed only mildly offended -- I have a rep, you see -- but biology, as a field, is wholly unprepared to go from data to an analyses.

    My lab has finally turned the corner from "hey, let's generate more data!" to "cool, data that we can analyze and build hypotheses from!" -- yeah, we're probably late bloomers, but after about 3 years of tech development, there's very little we can't do at a basic sequence level. Analysis at that level is no longer the bottleneck. Next step? Trying to do some actual biology! But we are working in a few very specific areas, and I think the whole field needs to undergo this kind of shift.

  3. You've finally learned that 'for' loops aren't all they're cracked up to be.

    This was one of my early lessons. Someone had dumped a few 10s of millions of reads on me -- a mere gigabyte or so of data -- and I was looking for patterns in the reads. I sat down to write my usual opening lines of Python to look at the data: "for sequence in dataset:" and ... just stopped. The data was just too big to do iteration in a scripting language with it! Cleverness of some sort needed to be applied.

    Corollary: not just Excel, but BLAST, start to look like Really Bad Ideas That Don't Scale. Sean Eddy's HMMER 3 FTW...

  4. Addressing small questions just isn't that interesting.

    Let's face it, if you've just spent a few $k generating dozens of gigabytes amounts of hypothesis-neutral data on gene expression from (say) chick, the end goal of generating a list of genes that are differentially regulated is just not that exciting. (Frankly, you probably could have done it with qPCR, if you didn't want that cachet of "mRNAseq!") What more can you do with the data?

    (This point comes courtesy of Jim Tiedje, who says (I paraphrase): "The problem for assistant professors these days is that many of may not be thinking big enough." Also see: Don't be afraid of failure: really go for it)

    The biggest training challenge (in my opinion) is going to be training people in how to push past the obvious analysis of the data and go for deeper integrative insight. This will require training not just in biology but in data analysis, computational thinking, significant amounts of informed skepticism, etc. (See our course.) I think about it like this: generating hypotheses from large amounts of data isn't that interesting -- I can do that with publicly available data sets without spending any money! Constraining the space of hypotheses with big data sets is far more interesting, because it gives you the space of hypotheses that aren't ruled out; and its putting your data to good use. I'll, uh, let you know if I ever figure out how to do this myself...

    I think there are plenty of people that can learn to do this, but as Greg Wilson correctly points out, there has to be a tradeoff: what do you take out of existing training curricula, to be replaced with training in data analysis? I wish I knew.

  5. Transferring data around is getting more than a bit tedious.

    For some recent papers, I had to copy some big files from EC2 over to my lab computer, and from there to our HPC system. It was Slow, in the "time for lunch" sense. And these were small test data sets, compressed. Transferring our big data sets around is getting tedious. Luckily we have a lot of them, so I can usually work on analysis of one while another is transferring.

  6. Your sysadmin/HPC administrator yells at you on a regular basis about your disk usage.

    We regularly get nastygrams from our local sysadmins accusing of using too many terabytes of disk space. This is in contrast to the good ol' days of physics (which is where I got my sysadmin chops), where your sysadmin would yell at you for using too much CPU...

  7. You regularly crash big memory machines.

    My favorite quote of the year so far is from the GAGE paper), in which Salzberg et al (2012) say "For larger genomes, the choice of assemblers is often limited to those that will run without crashing." Basically, they took a reasonably big computer, threw some big data at various assembly packages, and watched the computer melt down. Repeatedly.

    Someone recently sent me an e-mail saying "hey, we did it! we took 3 Gb of sequence data from soil and assembled it in only 1 week in 3 TB of RAM!" Pretty cool stuff -- but consider that at least four to six runs would need to be done (parameter sweep!), and it takes only about 1 week and $10k to generate twice that data. In the long run, this does not seem cost effective. (It currently takes us 1-2 months to analyze this data in 300 GB of RAM. I'm not saying we have the answer. ;)

  8. Throwing away data looks better and better.

    I made a kind of offhanded comment to a NY Times reporter once (hint: don't do this) about how at some point we're going to need to start throwing away data. He put it as the ultimate quote in his article. People laughed at me for it. (BUT I WAS RIGHT! HISTORY WILL SHOW!)

    But seriously, if someone came up to you and said "we can get rid of 90% of your data for you and give you an answer that's just as good", many biologists would have an instant negative response. But I think there's a ground truth in there: a lot of Big Data is noise. If you can figure out how to get rid of it... why wouldn't you? This is an interesting shift in thinking from the "every data point is precious and special" that you adopt when it takes you a !#%!# week to generate each data point.

    I attended a talk that David Haussler gave at Caltech recently. He was talking about how eventually we would need to resequence millions of individual cancer cells to look for linked sets of mutations. At 50-300 Gb of sequence per cell, that's a lot of data. But most of that data is going to be uninteresting -- wouldn't it be great if we could pick out the interesting people and then throw the rest away? It would certainly help with data archiving and analysis...

  9. Big computer companies call you because they're curious about why you're buying such big computers.

    True story: a Big Computer Company called up our local HPC to ask why we were buying so many bigmem machines. They said "It's the damned biologists -- they keep on wanting more memory. Why? We don't know - we suspect the software's badly written, but can't tell. Why don't you talk to Titus? He pretends to understand this stuff." I don't think it's weird to get calls trying to sell me stuff -- but it is a bit weird to have our local HPC's buying habits be so out of character, due to work that I and others are doing, that Big Computer Companies notice.

    (Note: the software's not mine, and it's not badly written, either.)

  10. Your choice must increasingly be "improve algorithms" rather than "buy bigger computers".

    I've been banging this drum for a while. Sequencing capacity is outpacing Moore's Law, and so we need to rethink algorithms. An algorithm that was nlogn used to be good enough; now, if analysis requires a supra-linear algorithm, we need to figure out how to make it linear. (Sublinear would be better.)

    Anecdote: we developed a nifty data structure for attacking metagenome assembly (see: It scaled (scales) assembly by a factor of about 20x, which got us pretty excited -- that meant we could in theory assemble things like MetaHIT and rumen on commodity hardware without doing abundance filtering. Literally the day that we told our collaborators we had it working, they dumped 10x more data on us and told that they could send us more any time we wanted. (...and that, boys and girls, was our introduction to the HiSeq!) 20x wasn't enough. Sigh.

    The MG-RAST folk have told me a similar story. They did some awesomely cool engineering and got their pipeline running about 100x faster. That'll hold them for a year or so against the tidalwave of data.

    Corollary: don't waste your time with 2% improvements in sensitivity and specificity unless you also deliver 10x in compute performance.

  11. You spend a lot of time worrying about biased noise, cross-validation, and the incorrect statistical models used.

    We were delayed in some of our research by about a year, because of some systematic biases being placed in our sequencing data by Illumina. Figuring out that these non-biological features were there took about two months; figuring out how to remove them robustly took another 6 months; and then making sure that removing didn't screw up the actual biological signal took another four months.

    This is a fairly typical story from people who do a lot of data analysis. We developed a variety of cross-validation techniques and ways of intuiting whether or not something was "real" or "noise", and we spent a certain amount of time discussing what statistical approaches to use to assess significance. In the end we more or less gave up and pointed out that on simulated data what we were doing didn't screw things up.

  12. Silicon Valley wants to hire your students to go work on non-biology problems.

    Hey, it's all Big Data, right?

So: what is Big Data in biology?

First, I've talked mostly about DNA sequence analysis, because that's what I work on. But I know that proteomics and image analysis people are facing similar dilemmas. So it's not just sequence data.

Second, compute technology is constantly improving. So I think we need moving definitions.

Here are some more serious points that I think bear on what, exactly, problems for Big Data in biology. (They're not all specific to biology, but I can defend them on my home ground more easily, you see.)

  1. You have archival issues on a large scale.

    You have lots of homogeneously formatted data that probably contains answers you don't know you're looking for yet, so you need to save it, metadata it, and catalog it. For a long time.

  2. The rate at which data is arriving is itself increasing.

    You aren't just getting one data set. You're getting dozens (or hundreds) this year. And you'll get more than that next year.

    One implication of this is that you'd better have a largely automated analysis pipeline, or else you will need an increasing number of people just to work with the data, much less do anything interesting. Another implication is that software reuse becomes increasingly important: if you're building custom software for each data set, you will fall behind. A third implication is that you need a long-term plan for scaling your compute capacity.

  3. Data structure and algorithm research is increasingly needed.

    You cannot rely on many heavyweight iterations over your data, or simple data structures for lookup: the data is just too big and existing algorithms are tailored to smaller data. For example, BLAST works fine for a few gigabytes of data; past that, it becomes prohibitively slow.

  4. Infrastructure designers are needed.

    Issues of straightforward data transfer, network partitioning, and bus bandwidth start to come to the forefront. Bottleneck analysis needs to be done regularly. In the past, you could get away with "good enough", but as throughput continues to increase, bottlenecks will need to be tackled on a regular basis. For this, you need a person who is immersed in your problems on a regular basis; they are hard to find and hard to keep.

    One interesting implication here is for cloud computing, where smart people set up a menu of infrastructure options and you can tailor your software to those options. So far I like the idea, but I'm told by aficionados that (for example) Amazon still falls short.

  5. You have specialized hardware needs.

    Sort of a corollary of the above: what kind of analyses do you need to do? And what's the hardware bottleneck? That's where you'll get the most benefit from focused hardware investment.

  6. Hardware, infrastructure design, and algorithms all need to work together.

    Again, a corollary of the above, but: if your bottleneck is memory, focus on memory improvements. If your bottleneck is disk I/O, focus on hardware speed and caching. If your bottleneck is data transfer, try to bring your compute to your data.

  7. Software needs to change to be reusable and portable.

    Robust, reusable software platforms are needed, with good execution guarantees; that way you have a foundation to build on. This software needs to be flexible (practically speaking, scriptable in a language like Python or Ruby or Perl), well developed and tested, and should fade into the background so that you can focus on more interesting things like your actual analysis. It should also be portable so that you can "scale out" -- bring the compute to your data, rather than vice versa. This is where Hadoop and Pig and other such approaches fit now, and where we seriously need to build software infrastructure in biology.

  8. Analysis thinking needs to change.

    Comprehensively analyzing your data sets is tough when your data sets are really big and noisy. Extracting significant signals from them is potentially much easier, and some approaches and algorithms for doing this in biology exist or are being developed (see especially Lior Pachter's eXpress). But this is a real shift in algorithmic thinking, and it's also a real shift in scientific thinking, because you're no longer trying do understand the entire data set -- you're trying to focus on the bits that might be interesting.

  9. Analyses are increasingly integrative.

    It's hard to make sense of lots of data on its own: you need to link it in to other data sets. Data standards and software interoperability and "standard" software pipelines are incredibly important for doing this.

  10. The interesting problems are still discipline-specific.

    There are many people working on Big Data, and there is big business in generic solutions. There's lots of Open Source stuff going on, too. Don't reinvent those wheels; figure out how to connect them to your biology, and then focus on the bits that are interesting to you and important for your science.

  11. New machine learning, data mining, and statistical models need to be developed for data-intensive biological science.

    As data volume increases, and integrative hypothesis development proceeds, we need to figure out how to assess the quality and significance of hypotheses. Right now, a lot of people throw their data at several programs, pick their favorite answer, and then recite the result as if it's correct. Since often they will have tried out many programs, this presents an obvious multiple testing problem. And, since users are generally putting in data that violates one or more of the precepts of the program developers, the results may not be applicable.

  12. A lack of fear of computational approaches is a competitive advantage.

    The ability to approach computational analyses as just another black box upon which controls must be placed is essential. Even if you can open up the black box and understand what's inside, it needs to be evaluated not on what you think it's doing but on what it's actually doing at the black box level. If there's one thing I try to teach to students, it's to engage with the unknown without fear, so that they can think critically about new approaches.

Well, that's it for now. I'd be interested in hearing about what other people think I've missed. And, while I'm at it, a hat tip to Erich Schwarz, Greg Wilson, and Adina Howe for reading and commenting on a draft.


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