Michael W. Hunkapiller
Analyst
Well, I don't want to take the technology lightly, given the fact that it comes from a very successful company. That said, in looking at the molecular technology, we've compared the results that they've published on their website for an organism very similar in size and composition to arabidopsis. That organism, in their case, being drosophila, which is almost the same average GC content, even somewhat smaller genome, but not a lot. And what we see is exactly what we expect, that for certain regions that are relatively low in complexity, in terms of not having a lot of repeats and not having a lot of high GC or low GC content, the technology works great. Across the board, it doesn't work so good. And the problem is, it's an amplification-based approach to getting synthetic long reads. And the more amplification you do, the more bias you get, so what you see in their data is that once you get below 30% GC content, your yield or ability to see a sequence at all starts to disappear. And so you wind up increasing, in one sense, a lot of the gaps that you get. And if you look at the published data, the increase in contig length that they get by adding molecular data to their traditional mate-paired ends and so forth, regular luminous system, isn't that substantial. And so it really doesn't solve the problem at all. In some cases, it makes it harder to get certain regions out there. And if they come up with any magic bullets to make long PCR work or to get rid of the bias, that will help them. But as yet, I think, almost to a person, people that we've talked to or looked at the technology, recognized that it doesn't do a lot for them in that regard. And it certainly compares to the kinds of de novo assemblies that you can get on those, even much more difficult regions using our technology. It doesn't deal, for instance, with the whole repeat issues. Even when they do the synthetic reads, they frequently can't assemble those 8kb synthetic reads into a single contig because of the repeat problem. And so it fundamentally doesn't solve the problem because it's still stuck with short read sequences in order to generate the data. So they will be out there and it's a technology that very similar approaches have been used and some of the big laboratories have tried for a long time, several years, and it just hasn't made it into mainstream. And our reads continue to get longer and longer, they're already longer than the best you can do with that technology. And it's most of our read as opposed to a small number with a very expensive extra step in the sample prep.