>First off, I’d like to say thanks to Paul and stajich for their comments. Paul for raising several points that I’d like to discuss tonight, and stajich for bringing my attention to the SHRiMP aligner. Obviously, I haven’t had much chance to look at SHRiMP, yet, but I’ll definitely get around to it.
So, paul mentioned several things that are worth discussing:
The Velvet roadmap for mRNA could be interesting. Sometimes the intron is snipped, sometimes it isn’t, get a nice little bubble. And the similar transcripts will do… interesting things.
Short reads would be fine for de-novo if DNA was completely random. Pity that it isn’t.
For the most part, I agree with Paul, Using Velvet on a transcriptome would be pretty cool. Yes, you’d get bubbles, but you’d get a lot of nifty information about new transcripts, or intermediate forms of transcripts. This neatly avoids one of the major hurdles I currently face when working with transcriptomes: I can either align against a genome, or a list of known genes, and neither one really gives me much opportunity to identify new genes. (Of course, I’m working on doing just that, with a colleague of mine, but I’m saving that for another post.)
Where I disagree with paul, however, is his final statement, that short reads would be fine for de novo assembly if they were truly random. I have two problems with that: The first is mapability (or sequencability), and the second is the short nature of the reads themselves.
Mappability has been defined in many different ways, but for general purposes, it’s the ability to identify a sequenced read that can be unambiguously aligned to that location on a chromosome. Fortunately, with 36-mer reads, as are produced by Illumina 1G’s, something like 70-80% of the genome is mappable. Not 100% mappable, but mappable to some degree. This may seem trivial, but it’s important.
Why it’s important is that a genome with 95% mapability doesn’t mean you get a single contig covering 95% of the genome, and a chunk of 5% of your reads that don’t cover your genome. It’s more like every 20-100kb you’ll find something that you just can’t assemble over. (I’m guestimating that number, by the way.) This means you have lots of small contigs that have no overlap. That is, of course, assuming you had enough highly mappable reads to do a relatively error free assembly, and, also of course, assuming your sequencing errors haven’t completely interfered with your assembly. I don’t think either can really be taken for granted.
In reality, the mappability of a genome isn’t a property you can figure out until you’ve sequenced it, so I don’t want to discourage anyone from trying. I’m aware of people working on using Illumina reads to do this, albeit they’ve supplemented the reads with BAC sequencing, ESTs and various other options, which will provide a nice scaffolding. This approach allows them to augment their sequencing power by obtaining a lot of coverage through the Illumina runs, rather than having to use them as the basis of a serious de novo assembly – which seems wise to me. (And even then, they’re only doing a fungus – not a higher vertebrate!)
And to return to my earlier point about the mappable genome not being 100% mappable, this is where I think paul’s point over simplifies. Although some bases may be 50% mappable, and are thus considered to be “mappable” in common discussion, that means that 50% of the possible sequencing reads in which they’re likely to participate will not yield an un-ambiguous fragment! That means you can say goodbye to 50% of the likely fragments which you would need to generate an overlap to span two forming contigs. That, to me, indicates that any de novo assembly is unlikely to correctly proceed past that base, and 50% mappability is not unusual in the genome.
The other point of paul’s that I wanted to discuss, was the assertion that the non-random fragmenting of DNA is what’s stopping us from doing a serious de novo assembly. While it’s true that shearing DNA isn’t an entirely random process, it’s also true that it doesn’t need to be. People have been doing restriction enzyme digests for decades now, in order to map vectors and inserts. (I learned how to do it in grade 11 biology, and that was back in 1994). So yes, while sonication or digests may not be random, what’s to stop someone from stripping DNA of it’s histones, and then doing 25 different digests? The net effect is just about the same (assuming you pick 25 good restriction enzymes with different base recognitions), and will yield fragments that will get around paul’s issue. But does that mean we can now do a de novo assembly?
No… I don’t think so. I doubt the lack of a random fragmentation pattern is the limiting factor in de novo assemblies. Unfortunately, the only way to prove myself or paul right or wrong is to simulate this: Take the mouse genome, fragment it into random 36-mers (the same size you get from an Illumina sequencing run), then inject 1 random base for every 1000 bases read (I’ll be generous and assume a .1% error rate, though the real thing is closer to 3-5% from what I’ve seen), and then try running velvet on it.
I bet you’ll observe that somewhere around 40x coverage you’ll start to saturate and discover that your assembly has covered anywhere from 60-80% of the genome (say 5% of that is just way wrong), and that it’ll have taken you longer to do that assembly than it would have to just sequenced the damned thing in the first place with PCR.
Anyhow, I take paul’s point to heart: We’re still early on in this game, and there are a lot of neat things we can try. I’m looking forward to seeing the results of many of them. (Who has time to try them all?)
By the way, FindPeaks 3.0.1 is done, and I’ll be presenting it as a poster at AGBT 2008 this week. If you’re interested in ChIP-Seq/Chip-Solexa (or for the pedantic, ChIP-Illumina), come find me, and I’ll tell you some of its new features.