As sequencing gets cheaper and cheaper, one would expect the answer for how to best sequence (and assemble!) any given genome would change. Most biologists assume something along these lines: everyone else has achieved some standard coverage (say 10x, or 100x) for their genome, so all we need to do is multiply that number times the size of my genome of interest, and then multiply that by the cost/bp, and voila! I will be able to have my very own genome sequence!
Naturally it's a bit more complicated than that, for a couple of reasons. First, the length of the reads matters quite a bit. If you're reading off a 1 GB eukaryotic genome in chunks of 100 bases, you're going to have trouble assembling the darn thing. First, you have to worry about complex repeats, which (in the context of assembly) are just plain evil, because they create connectivity structures that simply can't be resolved without additional information. Second, you need to think about sequencing bias, such as GC and AT rich regions -- most sequencers don't do that well on GC-rich regions, which are plentiful in big eukaryotic genomes. And third, normal sampling variation in shotgun coverage will screw you, on top of all of this, if you don't think about it.
So, what is the optimal sequencing strategy, then?
There's been some interesting discussion on the assemblathon mailing list about all of this, which, for the most part, I'll be paraphrasing and interpreting: the list archives are closed and the list policy about citing people is that I need to ask them for individual permission, and that's too much work :). If you're interested in the source messages, I recommend subscribing yourself and looking through the archives for messages from June 2011; if they open up the archives, I'll link directly to some of the more interesting messages.
A key component of any sequencing strategy discussion nowadays is that sequencing has become very commercial. While this drives down costs (pretty dramatically!), you also can't trust a damn thing that sequencing companies say, because the market is very competitive and there's very little percentage in straight-up honesty, much less full disclosure. (Paranoid much? Yeah, buy me beer sometime.) Moreover, there are several competing sequencing centers -- primarily the Broad Institute and the Beijing Genome Institute, as well as the Joint Genome Institute, Sanger, and St. Louis, and probably another five that I'm missing -- that all appear to have adopted different policies with respect to sequencing genomes. I don't really know what they are in detail, but (for example) Broad has a stereotyped sequencing strategy for which it has written its own software suite (see ALLPATHS-LG), and you can read the details in the PNAS paper. The bottom line is you need to talk to people who have experience with actual sequence, and not be overly trusting of either sequencing centers or company reps.
Another key component of any sequencing strategy discussion is the software being used to assemble. Some centers have their own assemblers (BGI has SOAPdenovo, Broad has ALLPATHS-LG), but there are literally dozens of assemblers out there. The assemblers can broadly be broken down into about four different types: overlap-layout-consensus, de Bruijn graph, greedy local, and "other". I'm most familiar with de Bruijn graph assemblers, since that's what I'm working with here at MSU, but there are advantages and disadvantages to the various kinds. Maybe more on that later. But the bottom line here is that there are many brilliant, passionate, opinionated people who have written their own assembler, and will swear by all that is holy that it is the best one. How do you choose?
A third key component of any sequencing strategy discussion is the genome itself. Mihai Pop's group just published a veddy interesting article on prokaryotic assembly (see Wetzel et al., 2011) in which they argue that the optimal sequencing strategy needs to be dynamically adjusted to the repeat structure of the genome: that is, you need to do a first sequencing run; analyze it for repeat structures; and then plan out your next rounds of sequencing based on that information. While I am always suspicious of plans that require intelligent thought (slow! expen$ive!) to be inserted into sequencing pipelines (fast! high throughput!), I think they make a pretty good argument -- and that's just for prokaryotic genomes, which are simple compared to eukaryotic genomes... for eukaryotic genomes, you also have to worry about heterozygosity (how much internal variation there is between the two haploid genomes you're sequencing). So how can you strategize to deal with your genome?
But let's back up. What are we doing, again?
Sequencing genomes is like this:
Long, not-terribly-random strings of (physical) DNA, O(10^7-10^10) in length.
Goal: determine full sequence and connectivity of strings of DNA.
Process: fragment into lots of bits, sequence in from both ends of each bit. Use overlaps, size of bits ("insert size"), to computationally reassemble.
The challenge, succinctly put, is this: in the face of uneven coverage and repetitive subsequences, devise the optimal coverage and range of insert sizes so that you can (a) sample most of the genome sufficiently and (b) resolve most repetitive regions by looking at pairs of ends. Do so (c) as cheaply as possible.
OK, so what are the parameters you can twiddle?
It really boils down to these choices:
Sequencing technology: 454 or Illumina are the main production machines these days, although I hear things about PacBio, Ion Torrent, and ABI SOLiD. 454 is much more expensive per base, but gives longer reads (500bp +); Illumina is (much) cheaper per base, but the reads are annoyingly short (100-150 bp). With Illumina you can get ~600 bp inserts easily, larger inserts (3kb, 5kb, 10kb) with more difficulty. Not sure about 454.
Coverage: how much money do you want to spend, on what sequencing technology?
Insert sizes: larger inserts are really useful for bridging repeats, but also much more expensive.
And... I think that's about it. Or is it?
Well, you need to ask two more questions: can your assembler of choice take advantage of mixed read lengths, with mixed error models from different technologies, and/or various insert sizes? And can your sequencing center actually make all the different technologies work reliably?
(As I keep telling my students, if it were easy they wouldn't need brilliant people like us to work on it, now would they?)
When I get swamped with these kinds of questions, I usually try to retreat back into my reductionist hidey hole to clear my head. So let's back up again. What are the fundamental issues?
We can't do much about sequencing bias or heterozygosity, except to say that more coverage is generally going to make both biases and internal sequence variation stand out more reliably from random error. If we actually want to assemble our genome, we also can't do much about improving current assemblers, and it's unclear how to evaluate assemblers anyway, and most of them don't appear to do a great job on very heterogenous sequence types (i.e. from multiple types of sequencers) - anyway, these are the questions the assemblathon is asking, and they're doing a good job; just read the paper when it comes out. And we don't have much control over whether or not our sequencing center screws up.
So we're left with trying to decide on how much 454, how much Illumina, and what insert sizes. (Can you hear the shrieks of pain from sequencing and assembly aficionados as I ruthlessly strip all of the subtleties from the argument? Fun!)
For insert size, I like to point people to these two references:
Whiteford et al., Nuc. Acid Res, 2005 http://nar.oxfordjournals.org/content/33/19/e171.full
Butler et al., Genome Res, 2008 http://genome.cshlp.org/content/18/5/810.full
which make the nice point that there are many repeat structures that you simply cannot resolve with single-ended reads -- you need paired-end reads to do a good job of assembly. These two papers have recently been joined by a third, the Wetzel et al. paper above, which suggests that there are particular (and surprisingly frequent) repeat structures that cannot be resolved except by a very specific insert size. But barring advance knowledge of repeat structure, I would argue that a nice range of inserts, from 3k to 5k to 10k, should give you decent results. We have that for a parasitic nematode project in which I'm involved, and it's given us decent scaffold sizes.
With 454 vs Illumina, I am skeptical that 454 is a good expenditure of money at this point. The number of bases is so astonishingly low compared to what Illumina is outputting (~1m vs ~1bn for the same amount of money, I think? At any rate, at least 100x) that you really need to justify any 454 expenditure. That having been said, I may be so used to working with crappy genome assemblies (buy me beer, hear me weep) that I'm ignoring how much better they would be with ~10x 454 coverage. Certainly Greg Dick's group at U of M has shown me pretty good evidence that 454 sequences things that Illumina won't touch, in metagenomic data. So I can't give you much more than my experience that Illumina will get you ~80% of the way to a decent genome assembly -- which is something many people would love to have.
Is there an elephant in the room, and, if so, what is it? Well, this touches heavily on our lab's research, but I think that sequencing biases are screwing up the assembly game far more than people think. Right now assemblers have a bunch of poorly understood heuristics that address sequencer-specific bias, and our experience with these in metagenomic sequencing suggests that these artifacts and heuristics are a significant source of misassembly. More on that ... later.
I'm really at a loss about how to conclude any discussion of sequencing strategy. It's ridiculously complicated, comes down to a lot of guessing about what problems you're likely to run into, and involves an extremely rapidly changing technology suite. Getting a comprehensive answer out of anyone is hard... and won't get any easier for a while.
That having been said, I'd appreciate pointers to blog posts and open discussions of these issues on mailing lists. Having (tried to) teach some biologists in this area recently, as part of my NGS course, I think actually providing these discussions could be incredibly valuable and could raise the level of discourse a fair bit.
Posted by Cameron Neylon on 2011-08-10 at 03:48.
It's interesting how things come back around. Towards the end of his PhD Nava, of the Whiteford et al paper, looked at putting together a benchmarking framework for the growing set of assemblers that were appearing at that point. Trying to think about what would be a meaningful benchmark was something of a challenge, I think he settled on taking a reference sequence and then breaking it up, introducing errors, and biases and then feeding each assembler a series of fragment collections but it never got that far. And it was unclear whether the results would be meaningful without running large numbers of tests that at the time was computationally intractable for us. I don't know if it's useful but the other half of the 2005 paper that we eventually ripped out before submitting to NAR because no-one seemed to believe was a discussion of characterising the repeat structures of sequences. We developed some plots that did seem to be useful at fingerprinting various repeat structures. Unfortunately we struggled to get it published and it eventually appeared in Complex Systems where I suspect no-one has ever read it: <a href="http://eprints.ecs.s oton.ac.uk/20919/">http://eprints.ecs.soton.ac.uk/20919/</a>
Posted by Mick Watson on 2011-08-10 at 15:25.
Very interested in sequencing bias, however, there is a barrier to this: should one publish data showing there is a bias in your data, how do you avoid everyone else saying "Well, ours is fine, it must be just your machine".. and all of a sudden your reputation takes a hit. How do we create a system whereby sequencing providers can safely discuss problems with their sequencers without their reputation taking a hit? I am struck by an anecdote I heard recently: a lab manager at a particular sequencing company's annual event found that EVERY academic laboratory was having problems with that provider's instrument, however, every commercial provider insisted they had no problem. Reputation in science is key. Finally, on assembly. I wonder if some of the reasons behind de bruijn are now falling away. As I understand it, de bruijn graph assembly came along due to the short reads, and the sheer noise created when you compare (via an overlap approach) millions of 36bp reads with one another. However, reads are now much longer, and advances in computation mean we can compare reads pairwise. I'm also struck by the performance of SGA in Assemblathon, which has a (string graph based) overlap approach to assembly. So were/are de bruijn graph assemblers just a flash in the pan, only needed for short reads? Will we see the return of the overlap-layout-consensus assembly algorithms?
Posted by Titus Brown on 2011-08-10 at 23:01.
Mick, thanks for your comments! We've been tracking down a problem in metagenome assembly where sequencing bias is causing systematic misassemblies. How would you NOT discuss that in the context of your own science?! In our case we can show it's coming from multiple sequencers and is present in many different data sets. For mRNAseq and metagenomic sequencing, I don't think that long reads will solve the scaling problem. You need deep sampling for both, which is something that long reads don't provide yet (and when they do, voila! scaling problem!) They should make much better assemblies possible. I am intrigued by SGA but know very little about it. I eagerly await awesomeness :)
Posted by Nick Loman on 2011-08-11 at 04:15.
One point I'd like to make is that many groups have gone off writing de novo assemblers as a theoretical exercise and very few have established whether they are doing a good job in reconstructing the biology. Now we have efforts like the Assemblathon and GAGE to try and bring order to this chaos. Users are now in the habit of running a pipeline, chosen for good reasons or ill, and being forced to uncritically accept the output because they have no way of knowing whether their assembly is good or not. As you point out; the results from 454, Illumina PE, Illumina MP etc. sequencing can be quite different. I would like to see some standardised datasets for all platforms with well-validated reference sequences (e.g. lab finished) so that software can be objectively tested. I take issue with your idea you NEED mate-pairs. Actually even better than that is really long reads, which are unambiguous and easy to assemble - so PacBio is going to be the one to watch. They may kill the remaining market for 454 de novo. I think Mick makes an interesting point that de Bruijn was optimised for noisy, high-coverage data. Now we have better quality, longer Illumina reads it may well be that new approaches like SGA will come to the fore. I may have a play with this now. Traditional overlap-layout-consensus algorithms cannot handle ultra-deep Illumina coverage, in my experience. It may be though that accuraet 150bp base reads treated in a conventional way does not require such depth of coverage as we are used to generating.