As part of the 2012 Analyzing Next-Generation Sequencing Data course, I've been trying out ipython notebook for the tutorials.

In previous years, our tutorials all looked like this: Short read assembly with Velvet -- basically, reStructuredText files integrated with Sphinx. This had a lot of advantages, including Googleability and simplicity; but it also meant that students spent a lot of time copying and pasting commands.

This year, I tried mixing things up with some ipython notebook, using pre-written notebooks -- see for example a static view of the BLAST notebook. The notebooks are sourced at https://github.com/ngs-docs/ngs-notebooks, and can be automatically updated and placed on an EC2 instance for the students to run. The idea is that the students can simply shift-ENTER through the notebooks; shell commands can easily be run with '!', and we can integrate in python code that graphs and explores the outputs.

Once we got past the basic teething pains of badly written notebooks, broken delivery mechanisms, proper ipython parameters, etc., things seemed to work really well. It's been great to be able to add code, annotate code, and graph stuff interactively!

Along the way, though, a few points have emerged.

First, ipython notebook adds a little bit of confusion to the process. Even though it's pretty simple, when you're throwing it in on top of UNIX, EC2, bioinformatics, and Python, people's minds tend to boggle.

For this reason, it's not yet clear how good an addition ipynb is to the course. We can't get away with replacing the shell with ipynb, for a variety of reasons; so it represents an extra cognitive burden. I think for an entire term course it will be an unambiguous win, but for an intensive workshop it may be one thing too many.

I should have a better feeling for this next week.

Second, in practice, ipython notebooks need to be written so that they can be executed multiple times on the same machine. Workshop attendees start out very confused about the order of commands vs the order of execution, and even though ipynb makes this relatively simple, if they get into trouble it is nice to be able to tell them to just rerun the entire notebook. So the notebook commands have to be designed this way -- for one example, if you're copying a file, make sure to use 'cp -f' so that it doesn't ask if the file needs to be copied again.

Third, in practice, ipython notebooks cannot contain long commands. If the entire notebook can't be re-run in about 1 minute, then it's too long. This became really clear with Oases and Trinity, where Oases could easily be run on a small data set in about 1-2 minutes, while Trinity took an hour or more. Neither people nor browsers handle that well. Moreover, if you accidentally run the time-consuming task twice, you're stuck waiting for it to finish, and it's annoying and confusing to put multi-execution guards on tasks.

This point is a known challenge with ipython notebook, of course; I've been talking with Fernando and Brian, among others, about how to deal with long running tasks. I'm converging to the idea that long-running tasks should be run at the command line (maybe using 'make' or something better?) and then ipython notebook can be used for data analysis leading to summaries and/or visualization.

Fourth, ipython notebooks are a bit difficult to share in static form, which makes the site less useful. Right now I've been printing to HTML and then serving that HTML up statically, which is slow and not all that satisfying. There are probably easy solutions for this but I haven't invested in them ;).

---

In spite of these teething pains, feedback surrounding ipynb has been reasonably positive. Remember, these are biologists who may never have done any previous shell commands or programming, and we are throwing a lot at them; but overall the basic concepts of ipynb are simple, and they recognize that. Moreover, ipython notebook has enabled extra flexibility in what we present and make possible for them to do, and they seem to see and appreciate that.

The good news is that we figured all this out in the first week, and I still have a whole week with the guinea pigs, ahem, course attendees, under my thumb. We'll see how it goes!

--titus

p.s. Totally jonesing for a portfolio system that lets me specify a machine config, then with a single click spawns the machine, configures it, sucks down a bunch of ipython notebooks, and points me at the first one!

I'm a pretty big advocate of anything open -- open source, open access, and open science, in particular. I always have been. And now that I'm a professor, I've been trying to figure out how to actually practice open science effectively

What is open science? Well, I think of it as talking regularly about my unpublished research on the Internet, generally in my blog or on some other persistent, explicitly public forum. It should be done regularly, and it should be done with a certain amount of depth or self-reflection. (See, for example, the wunnerful Rosie Redfield and Nature's commentary on her blogging of the arsenic debacle & tests thereof.)

Most of my cool, sexy bloggable work is in bioinformatics; I do have a wet lab, and we're starting to get some neat stuff out of that (incl. both some ascidian evo-devo and some chick transcriptomics) but that's not as mature as the computational stuff I'm doing. And, as you know if you've seen any of my recent posts on this, I'm pretty bullish about the computational work we've been doing: the de novo assembly sequence foo is, frankly, super awesome and seems to solve most of the scaling problems we face in short-read assembly. And it provides a path to solving the problems that it doesn't outright solve. (I'm talking about partitioning and digital normalization.)

While I think we're doing awesome work, I've been uncharacteristically (for me) shy about proselytizing it prior to having papers ready. I occasionally reference it on mailing lists, or in blog posts, or on twitter, but the most I've talked about the details has been in talks -- and I've rarely posted those talks online. When I have, I don't point out the nifty awesomeness in the talks, either, which of course means it goes mostly unnoticed. This seems to be at odds with my oft-loudly stated position that open-everything is the way to go. What's going on?? That's what this blog post is about. I think it sheds some interesting light on how science is actually practiced, and why completely open science might waste a lot of people's time.

I'd like to dedicate this blog post to Greg Wilson. He and I chat irregularly about research, and he's always seemed interested in what I'm doing but is stymied because I don't talk about it much in public venues. And he's been a bit curious about why. Which made me curious about why. Which led to this blog post, explaining why I think why. (I've had it written for a few months, but was waiting until I posted diginorm.)

---

For the past two years or so, I've been unusually focused on the problem of putting together vast amounts of data -- the problem of de novo assembly of short-read sequences. This is because I work on unusual critters -- soil microbes & non-model animals -- that nobody has sequenced before, and so we can't make use of prior work. We're working in two fields primarily, metagenomics (sampling populations of wild microbes) and mRNAseq (quantitative sequencing of transcriptomes, mostly from non-model organisms).

The problems in this area are manifold, but basically boil down to two linked issues: vast underlying diversity, and dealing with the even vaster amounts of sequence necessary to thoroughly sample this diversity. There's lots of biology motivating this, but the computational issues are, to first order, dominant: we can generate more sequence than we can assemble. This is the problem that we've basically solved.

A rough timeline of our work is:

• mid/late 2009: Likit, a graduate student in my lab, points out that we're getting way better gene models from assembly of chick mRNAseq than from reference-based approaches. Motivates interest in assembly.
• mid/late 2009: our lamprey collaborators deliver vast amounts of lamprey mRNAseq to us. Reference genome sucks. Motivates interest in assembly.
• mid/late 2009: the JGI starts delivering ridiculous amount of soil sequencing data to us (specifically, Adina). We do everything possible to avoid assembly.
• early 2010: we realize that the least insane approach to analyzing soil sequencing data relies on assembly.
• early 2010: Qingpeng, a graduate student, convinces me that existing software for counting k-mers (tallymer, specifically) doesn't scale to samples with 20 or 30 billion unique k-mers. (He does this by repeatedly crashing our lab servers.)
• mid-2010: a computational cabal within the lab (Jason, Adina, Rose) figures out how to count k-mers really efficiently, using a CountMin Sketch data structure (which we reinvent, BTW, but eventually figure out isn't novel. o well). We implement this in khmer. (see k-mer filtering)
• mid-2010: We use khmer to figure out just how much Illumina sequence sucks. (see Illumina read phenomenology)
• mid-2010: Arend joins our computational cabal, bringing detailed and random knowledge of graph theory with him. We invent an actually novel use of Bloom filters for storing de Bruijn graphs. (blog post) The idea of partitioning large metagenomic data sets into (disconnected) components is born. (Not novel, as it turns out -- see MetaVelvet and MetaIDBA.)
• late 2010: Adina and Rose figure out that Illumina suckage prevents us from actually getting this to work.
• first half of 2011: Spent figuring out capacity of de Bruijn graph representation (Jason/Arend) and the right parameters to actually de-suckify large Illumina data sets (Adina). We slowly progress towards actually being able to partition large metagenomic data sets reliably. A friend browbeats me into applying the same technique to his ugly genomic data set, which magically seems to solve his assembly problems.
• fall 2011: the idea of digital normalization is born: throwing away redundant data FTW. Early results are very promising (we throw away 95% of data, get identical assembly) but it doesn't scale assembly as well as I'd hoped.
• October 2011: JGI talk at the metagenome informatics workshop - SLYT, where we present our ideas of partitioning and digital normalization, together, for the first time. We point out that this potentially solves all the scaling problems.
• November 2011: We figure out the right parameters for digital normalization, turning up the awesomeness level dramatically.
• through present: focus on actually writing this stuff up. See: de Bruijn graph preprint; digital normalization preprint.

---

If you read this timeline (yeah, I know it's long, just skim) and look at the dates of "public disclosure", there's a 12-14 month gap between talking about k-mer counting (July 2010) and partitioning/etc (Oct 2011, metagenome informatics talk). And then there's another several-month gap before I really talk about digital normalization as a good solution (basically, mid/late January 2012).

Why??

1. I was really freakin' busy actually getting the stuff to work, not to mention teaching, traveling, and every now and then actually being at home.
2. I was definitely worried about "theft" of ideas. Looking back, this seems a mite ridiculous, but: I'm junior faculty in a fast-moving field. Eeek! I also have a duty to my grads and postdocs to get them published, which wouldn't be helped by being "scooped".
3. We kept on coming up with new solutions and approaches! Digital normalization didn't exist until August 2011, for example; appropriate de-suckifying of Illumina data took until April or May of 2011; and proving that it all worked was, frankly, quite tough and took until October. (More on this below.)
4. The code wasn't ready to use, and we hadn't worked out all the right parameters, and I wasn't ready to do the support necessary to address lots of people using the software.

All of these things meant I didn't talk about things openly on my blog. Is this me falling short of "open science" ideals??

In my defense, on the "open science" side:

• I gave plenty of invited talks in this period, including a few (one at JGI and one at UMD CBCB) attended by experts who certainly understood everything I was saying, probably better than me.
• I posted some of these talks on slideshare.
• all of our software development has been done on github, under github.com/ctb/khmer/. It's all open source, available, etc.

...but these are sad excuses for open science. None of these activities really disseminated my research openly. Why?

Well, invited talks by junior faculty like me are largely attended out of curiosity and habit, rather than out of a burning desire to understand what they're doing; odds are, the faculty in question hasn't done anything particularly neat, because if they had, they'd be well known/senior, right? And who the heck goes through other people's random presentations on slideshare? So that's not really dissemination, especially when the talks are given to an in group already.

What about the source code? The "but all my source code is available" dodge is particularly pernicious. Nobody, but nobody, looks at other people's source code in science, unless it's (a) been released, (b) been documented, and (c) claims to solve YOUR EXACT ACTUAL PROBLEM RIGHT NOW RIGHT NOW. The idea that someone is going to come along and swoop your awesome solution out of your repository seems to me to be ridiculous; you'd be lucky to be that relevant, frankly.

So I don't think any of that is a good way to disseminate what you've done. It's necessary for science, but it's not at all sufficient.

--

What do I think is sufficient for dissemination? In my case, how do you build solutions and write software that actually has an impact, either on the way people think or (even better) on actual practice? And is it compatible with open science?

1. Write effective solutions to common problems. The code doesn't have to be pretty or even work all that well, but it needs to work well enough to run and solve a common problem.
2. Make your software available. Duh. It doesn't have to be open source, as far as I can tell; I think it should be, but plenty of people have restrictive licenses on their code and software, and it gets used.
3. Write about it in an open setting. Blogs and mailing lists are ok; SeqAnswers is probably a good place for my field; but honestly, you've got to write it all down in a nice, coherent, well-thought out body of text. And if you're doing that? You might as well publish it. Here is where Open Access really helps, because The Google will make it possible for people to find it, read it, and then go out and find your software.

The interesting thing about this list is that in addition to all the less-than-salutary reasons (given above) for not blogging more regularly about our stuff, I had one very good reason for not doing so.

It's a combination of #1 and #3.

You see, until near to the metagenome informatics meeting, I didn't know if partitioning or digital normalization really worked. We had really good indications that partitioning worked, but it was never solid enough for me to push it strongly as an actual solution to big data problems. And digital normalization made so much sense that it almost had to work, but, um, proving it was a different problem. Only in October did we do a bunch of cross-validation that basically convinced me that partitioning worked really well, and only in November did we figure out how awesome digital normalization was.

So we thought we had solutions, but we weren't sure they were effective, and we sure didn't have it neatly wrapped in a bow for other people to use. So #1 wasn't satisfied.

And, once we did have it working, we started to put a lot of energy into demonstrating that it worked and writing it up for publication -- #3 -- which took a few months.

In fact, I would actually argue that before October 2011, we could have wasted people's time by pushing our solutions out for general use when we basically didn't know if they worked well. Again, we thought they did, but we didn't really know.

This is a conundrum for open science: how do you know that someone else's work is worth your attention? Research is really hard, and it may take months or years to nail down all the details; do you really want to invest significant time or effort in someone else's research before that's done? And when they are done -- well, that's when they submit it for publication, so you might as well just read that first!

--

This is basically the format for open science I'm evolving. I'll blog as I see fit, I'll post code and interact with people that I know who need solutions, but I will wait until we have written a paper to really open up about what we're doing. A big part of that is trying to only push solid science, such that I don't waste other people's time, energy, and attention.

So: I'm planning to continue to post all my senior-author papers to arXiv just before their first submission. The papers will come with open source and the full set of data necessary to recapitulate our results. And I'll blog about the papers, and the code, and the work, and try to convince people that it's nifty and awesome and solves some useful problems, or addresses cool science. But I don't see any much point in broadly discussing my stuff before a preprint is available.

Is this open science? I don't really think so. I'd really like to talk more openly about our actual research, but for all the reasons above, it doesn't seem like a good idea. So I'll stick to trying to give presentations on our stuff at conferences, and maybe posting the presentations to slideshare when I think of it, and interacting with people privately where I can understand what problems they're running into.

What I'm doing is more about open access than open science: people won't find out details of our work until I think it's ready for publication, but they also won't have to wait for the review process to finish. While I'm not a huge fan of the way peer review is done, I accept it as a necessary evil for getting my papers into a journal. By the time I submit a paper, I'll be prepared to argue, confidently and with actual evidence, that the approach is sound. If the reviewers disagree with me and find an actual mistake, I'll fix the paper and apologize profusely & publicly; if reviewers just want more experiments done to round out the story, I'll do 'em, but it's easy to argue that additional experiments generally don't detract from the paper unless they discover flaws (see above, re "apologize"). The main thing reviewers seem to care about is softening grandiose claims, anyway; this can be dealt with by (a) not making them and (b) sending to impact-oblivious journals like PLoS One. I see no problem with posting the paper, in any of these circumstances.

Maybe I'm wrong; experience will tell if this is a good idea. It'll be interesting to see where I am once we get these papers out... which may take a year or two, given all the stuff we are writing up.

I've also come to realize that most people don't have the time or (mental) energy to spare to really come to grips with other people's research. We were doing some pretty weird stuff (sketch graph representations? streaming sketch algorithms for throwing away data?), and I don't have a prior body of work in this area; most people probably wouldn't be able to guess at whether I was a quack without really reading through my code and presentations, and understanding it in depth. That takes a lot of effort. And most people don't really understand the underlying issues anyway; those who do probably care about them sufficiently to have their own research ideas and are pursuing them instead, and don't have time to understand mine. The rest just want a solution that runs and isn't obviously wrong.

In the medium term, the best I can hope for is that preprints and blog posts will spur people to either use our software and approaches, or that -- even better -- they will come up with nifty new approaches that solve the problems in some new way that I'd never have thought of. And then I can read their work and build on their ideas. This is what we should strive for in science: the shortest round trip between solid scientific inspiration in different labs. This does not necessarily mean open notebooks.

Overall, it's been an interesting personal journey from "blind optimism" about openness to a more, ahem, "nuanced" set of thoughts (i.e., I was wrong before :). I'd be interested to hear what other people have to say... drop me a note or make a comment.

--titus

p.s. I recognize that it's too early to really defend the claim that our stuff provides a broad set of solutions. That's not up to me to say, for one thing. For another, it'll take years to prove out. So I'm really talking about the hypothetical solution where it is widely useful in practice, and how that intersects with open science goals & practice.

I'm going to pick on Mick Watson today. (It's OK. He's just a foil for this discussion, and I hope he doesn't take it too personally.)

Mick made the following comment on my earlier Big Data Biology blog post:

I do wonder whether there is just a bit too much hand wringing about "big data".

For e.g., the rumen metagenomic data you mentioned above, I can assemble using MetaVelvet on our server in less than a day (admittedly it has 512Gb of RAM, but doesn't everyone?). I can count the 17mers in it using Jellyfish in a few hours.

So I just set the processes running, two days later, I have my analysis. What's the problem? Does it matter that you can do it quicker?

Big data doesn't really worry me.

...

I know I am being flippant, but really to me the challenge isn't the data, it's the biology. I don't care if it takes 2 hours, 2 days or 2 weeks to process the data.

Improve your computing efficiency by 100x, I don't care; improve your ability to extract biological information by 100x, then I'm interested :)

He makes one very, very, very good point -- who cares if you can run an analysis (whatever it is) and it doesn't provide any value? The end goal of my sequencing analysis is to provide insight into biological processes; I might as well just delete the data (an O(1) "analysis" operation, if one with a big constant in front of it..) if the analysis isn't going to yield useful information.

But he also seems to think that speed and efficiency of analyses doesn't matter for science. And I don't just think he's dead wrong, I know he's dead wrong.

This is both an academic point and a practical point. And, in fact, an algorithmic point.

I'm out at a Cloud Computing for the Human Microbiome Workshop and I've been trying to convince people of the importance of digital normalization. When I posted the paper the reaction was reasonably positive, but I haven't had much luck explaining why it's so awesome.

At the workshop, people were still confused. So I tried something new.

I first made a simulated metagenome by taking three genomes worth of data from the Chitsaz et al. (2011) paper (see http://bix.ucsd.edu/projects/singlecell/) and shuffling them together. I combined the sequences in a ratio of 10:25:50 for the E. coli sequences, the Staph sequences, and the SAR sequences, respectively; the latter two were single-cell MDA genomic DNA. I took the first 10m reads of this mix and then estimated the coverage.

You can see the coverage of these genomic data sets estimated by using the known reference sequences in the first figure. E. coli looks nice and Gaussian; Staph is smeared from here to heck; and much of the SAR sequence is low coverage. This reflects the realities of single cell sequencing: you get really weird copy number biases out of multiple displacement amplification.

Then I applied three-pass digital normalization (see the paper) and plotted the new abundances. As a reminder, this operates without knowing the reference in advance; we're just using the known reference here to check the effects.

As you can see, digital normalization literally "normalizes" the data to the best of its ability. That is, it cannot create higher coverage where high coverage doesn't exist (for the SAR), but it can convert the existing high coverage into nice, Gaussian distributions centered around a much lower number. You also discard quite a bit of data (look at the X axes -- about 85% of the reads were discarded in downsampling the coverage like this).

When you assemble this, you get as good or better results than assembling the unnormalized data, despite having discarded so much data. This is because no low-coverage data is discarded, so you still retain as much overall covered bases -- just in fewer reads. To boot, it works pretty generically for single genomes, MDA genomes, transcriptomes, and metagenomes.

And, as a reminder? Digital normalization does this in fixed, low memory; in a single pass; and without any reference sequence needed.

Pretty neat.

--titus

I'm out at a Cloud Computing for the Human Microbiome Workshop and I've been trying to convince people of the importance of digital normalization. When I posted the paper the reaction was reasonably positive, but I haven't had much luck explaining why it's so awesome.

At the workshop, people were still confused. So I tried something new.

I first made a simulated metagenome by taking three genomes worth of data from the Chitsaz et al. (2011) paper (see http://bix.ucsd.edu/projects/singlecell/) and shuffling them together. I combined the sequences in a ratio of 10:25:50 for the E. coli sequences, the Staph sequences, and the SAR sequences, respectively; the latter two were single-cell MDA genomic DNA. I took the first 10m reads of this mix and then estimated the coverage.

You can see the coverage of these genomic data sets estimated by using the known reference sequences in the first figure. E. coli looks nice and Gaussian; Staph is smeared from here to heck; and much of the SAR sequence is low coverage. This reflects the realities of single cell sequencing: you get really weird copy number biases out of multiple displacement amplification.

Then I applied three-pass digital normalization (see the paper) and plotted the new abundances. As a reminder, this operates without knowing the reference in advance; we're just using the known reference here to check the effects.

As you can see, digital normalization literally "normalizes" the data to the best of its ability. That is, it cannot create higher coverage where high coverage doesn't exist (for the SAR), but it can convert the existing high coverage into nice, Gaussian distributions centered around a much lower number. You also discard quite a bit of data (look at the X axes -- about 85% of the reads were discarded in downsampling the coverage like this).

When you assemble this, you get as good or better results than assembling the unnormalized data, despite having discarded so much data. This is because no low-coverage data is discarded, so you still retain as much overall covered bases -- just in fewer reads. To boot, it works pretty generically for single genomes, MDA genomes, transcriptomes, and metagenomes.

And, as a reminder? Digital normalization does this in fixed, low memory; in a single pass; and without any reference sequence needed.

Pretty neat.

--titus

I'm pretty proud of our most recently posted paper, which is on a sequence analysis concept we call digital normalization. I think the paper is pretty kick-ass, but so is the way in which we're approaching replication. This blog post is about the latter.

(Quick note re "replication" vs "reproduction": The distinction between replication and reproducibility is, from what I understand, that "replicable" means "other people get exactly the same results when doing exactly the same thing", while "reproducible" means "something similar happens in other people's hands". The latter is far stronger, in general, because it indicates that your results are not merely some quirk of your setup and may actually be right.)

So what did we do to make this paper extra super replicable?

If you go to the paper Web site, you'll find:

• a link to the paper itself, in preprint form, stored at the arXiv site;
• a tutorial for running the software on a Linux machine hosted in the Amazon cloud;
• a git repository for the software itself (hosted on github);
• a git repository for the LaTeX paper and analysis scripts (also hosted on github), including an ipython notebook for generating the figures (more about that in my next blog post);
• instructions on how to start up an EC2 cloud instance, install the software and paper pipeline, and build most of the analyses and all of the figures from scratch;
• the data necessary to run the pipeline;
• some of the output data discussed in the paper.

(Whew, it makes me a little tired just to type all that...)

What this means is that you can regenerate substantial amounts (but not all) of the data and analyses underlying the paper from scratch, all on your own, on a machine that you can rent for something like 50 cents an hour. (It'll cost you about $4 -- 8 hours of CPU -- to re-run everything, plus some incidental costs for things like downloads.)

Not only can you do this, but if you try it, it will actually work. I've done my best to make sure the darn thing works, and this is the actual pipeline we ourselves ran to produce the figures in the paper. All the data is there, and all of the code used to process the data, analyze the results, and produce the figures is also there. In version control.

When you combine that with the ability to run this on a specific EC2 instance -- a combination of a frozen virtual machine installation and a specific set of hardware -- I feel pretty confident that at least this component of our paper is something that can be replicated.

I'm pretty proud of our most recently posted paper, which is on a sequence analysis concept we call digital normalization. I think the paper is pretty kick-ass, but so is the way in which we're approaching replication. This blog post is about the latter.

(Quick note re "replication" vs "reproduction": The distinction between replication and reproducibility is, from what I understand, that "replicable" means "other people get exactly the same results when doing exactly the same thing", while "reproducible" means "something similar happens in other people's hands". The latter is far stronger, in general, because it indicates that your results are not merely some quirk of your setup and may actually be right.)

So what did we do to make this paper extra super replicable?

If you go to the paper Web site, you'll find:

• a link to the paper itself, in preprint form, stored at the arXiv site;
• a tutorial for running the software on a Linux machine hosted in the Amazon cloud;
• a git repository for the software itself (hosted on github);
• a git repository for the LaTeX paper and analysis scripts (also hosted on github), including an ipython notebook for generating the figures (more about that in my next blog post);
• instructions on how to start up an EC2 cloud instance, install the software and paper pipeline, and build most of the analyses and all of the figures from scratch;
• the data necessary to run the pipeline;
• some of the output data discussed in the paper.

(Whew, it makes me a little tired just to type all that...)

What this means is that you can regenerate substantial amounts (but not all) of the data and analyses underlying the paper from scratch, all on your own, on a machine that you can rent for something like 50 cents an hour. (It'll cost you about $4 -- 8 hours of CPU -- to re-run everything, plus some incidental costs for things like downloads.)

Not only can you do this, but if you try it, it will actually work. I've done my best to make sure the darn thing works, and this is the actual pipeline we ourselves ran to produce the figures in the paper. All the data is there, and all of the code used to process the data, analyze the results, and produce the figures is also there. In version control.

When you combine that with the ability to run this on a specific EC2 instance -- a combination of a frozen virtual machine installation and a specific set of hardware -- I feel pretty confident that at least this component of our paper is something that can be replicated.

We just posted a pre-submission paper to arXiv.org:

A single pass approach to reducing sampling variation, removing errors, and scaling de novo assembly of shotgun sequences

Authors: C. Titus Brown, Adina Howe, Qingpeng Zhang, Alexis B. Pyrkosz, and Timothy H. Brom

arXiv link

Paper Web site, with source code and tutorials

Abstract:

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes enable the sensitive investigation of a wide range of biological phenomena. However, it is increasingly difficult to deal with the volume of data emerging from deep short-read sequencers, in part because of random and systematic sampling variation as well as a high sequencing error rate. These challenges have led to the development of entire new classes of short-read mapping tools, as well as new de novo assemblers. Even newer assembly strategies for dealing with transcriptomes, single-cell genomes, and metagenomes have also emerged. Despite these advances, algorithms and compute capacity continue to be challenged by the continued improvements in sequencing technology throughput. We here describe an approach we term digital normalization, a single-pass computational algorithm that discards redundant data and both sampling variation and the number of errors present in deep sequencing data sets. Digital normalization substantially reduces the size of data sets and accordingly decreases the memory and time requirements for de novo sequence assembly, all without significantly impacting content of the generated contigs. In doing so, it converts high random coverage to low systematic coverage. Digital normalization is an effective and efficient approach to normalizing coverage, removing errors, and reducing data set size for shotgun sequencing data sets. It is particularly useful for reducing the compute requirements for de novo sequence assembly. We demonstrate this for the assembly of microbial genomes, amplified single-cell genomic data, and transcriptomic data. The software is freely available for use and modification.

---

I'll blog more about this stuff over the next few days, but, briefly, this paper presents a single pass, fixed-memory approach to downsampling sequencing data (yeah, the stuff I've been talking about for a while now). This approach is called "digital normalization", or "diginorm" for sure. It eliminates lots of data, evens out coverage, and removes errors from shotgun sequencing data sets. The net effect is an often massive amount of data reduction combined with significant scaling of de novo assembly.

Or, to put it another way, it's a streaming lossy compression algorithm that primarily "loses" errors in sequencing data.

We've implemented it as a preprocessing filter that should work on any data set you want to assemble, with potentially any assembler. It's written in C++, with a Python wrapper, as part of khmer. And, of course, it's freely available for use, re-use, modification, and redistribution under the BSD license, 'cause why the heck not?

If you want to try it out, we've linked to some tutorials for running microbial genome assemblies with Velvet, as well as eukaryotic transcriptome assemblies with Oases and Trinity, on the paper Web site

We'll submit this paper to PNAS on Friday; I'm still waiting for some final proofreading.

Together with our other paper, which is currently under revision for resubmission to PNAS, these two papers form the theoretical basis for our attack on soil metagenome assembly. (Our plan is sheer elegance in its simplicity!)

--titus