(Morning after: Ugh, ugh, ugh -- I misread an axis, inserting an extra 0 -- so major crossouts in one section; why I shouldn't post late at night during pauses in day job stuff)
MinKNOW Upgrade Doubles PerformanceThe first bit of news was simply a software update, yet one which roughly doubled performance of MinION flowcells. Apparently a significant drag on pore lifetime was adapter complexes which would jam in the pore and choke it, preventing that pore from being used again. In effect MinKNOW performs a nano-Heimlich maneuver: by detecting this situation and reversing the voltage (ala read-until), the problem complexes could be ejected and allow the pore to continue being productive.
Immediately some of the most experienced labs started reporting flowcell yields over 10Gb (I think 12 is the public record), whereas Clive Brown from ONT has shown yields in excess of 20Gb. Clive's plots also show flowcell lifespans going out to 72 hours, rather than the currently programmed 48, although the yield flattens significantly by about 60 hours.
Consider that throughput range, either the "low" end of 12 gigabases or the high end of 24 gigabases. MiSeq is rated for 13-15Gb in 2x300 mode, the top output mode (when the kits are good). Ion Proton is rated for 10Gb with the P1 chip. So the highest output desktop sequencer is now MinION! PacBio Sequel, which also delivers long reads is rated at 8Gb per flowcell. Of course, there are vast differences in the data quality between these instruments, but there are certainly a number of applications in which the MinION data from the current basecallers is sufficient. I'm also avoiding an explicit cost comparison, as that brings in a lot of complex variables (such as volume discounts from ONT) but these are all in the same rough range -- or perhaps MinION is ahead. If you buy in bulk, then 1 library on 1 flowcell works out to about $600.How those sighting runs came out.— Clive G. Brown (@Clive_G_Brown) February 28, 2017
Need to find another few G/cell. pic.twitter.com/hhrfSsvNK7
Also just consider the amount of data here: 10Gb is enough for de novo assembly of Drosophila! 20Gb would be enough for Fugu! An awful lot of genomes can now be sequenced de novo on a single MinION flowcell, albeit with the caveat on final data quality.
All of these improvements should carry over to the PromethION, but as far as I can tell no field site has yet generated data. Oxford remains bullish on that instrument (as opposed to my less optimistic viewpoint). If PromethION can really launch with 4X the performance of MinION, then a genome resequencing of human should just about squeeze onto a single flowcell, and Clive has suggested on Twitter that Oxford can flatten coverage variation with read-until and therefore require less average coverage to fully call all variants.
The increased performance, as Clive has pointed out, also has some interesting side-effects. If someone was happy for their application with flowcell performance before these changes, then MinION is now overshooting. That means these applications can tolerate more variability in input yield. It also means that aging flowcells can be better tolerated. I don't know of any flowcell stability data out there, but my anecdotal observation from some we have (rest of experiment didn't gel in sync with deliveries) is that performance does degrade over time.
Improved performance also suggests that the Flongle and SmidgION could fit an awful lot of applications. Slated to have about half the number of pores as MinION, the software update means that SmidgION should perform about as MinION did before the update!
First Peek At Scrappie and 1D^2 BasecallsAt the December users' meeting in New York City, Clive mentioned that yet another basecaller, perhaps the third in a year, was under development. Called Scrappie, this caller is angling to improve on ONT's persistent problem with homopolymers. Scrappie, in combination with a new double-stranded scheme called 1D^2, appears to be capable of driving error rates down to about 1 in 500
In Jared Simpson's AGBT presentation he showed an example of Scrappie correctly calling a homopolymer. Oxford will likely show more such data at a mid-March Clive Brown web update and at May's London Calling meeting. No compute performance estimates out there for Scrappie yet nor a detailed analysis of how much it can improve reads, but it bears watching.This Is getting better as we refine base callers. Yet to use a scrappie 1D^2.. pic.twitter.com/S2yD9kVdVT— Clive G. Brown (@Clive_G_Brown) February 17, 2017
I didn't include this in counting news items, but Jared's talk did show methylation calling in action, including haplotype-aware methylation calling to show differential methylation by allele. Two methylation detection papers just published in Nature Methods, one from Simpson and Winston Timp and colleagues and the other from the UC Santa Cruz group.
The Huge News from Josh QuickOkay, now to the stunner. I've related before how our first, generally unimpressive MinION run stunned me by spitting out a 48Kb complete covering read from lambda phage. Clive Brown has long claimed that read lengths were limited by the library fragment lengths. The longest reads to date were a bit short of half a megabase and quite rare.
Josh Quick (aka @Scalene) tweeted this week an image showing a significant yield of reads in the 50-100Kb range.
Read lengths after playing with extraction and input amount with the rapid kit... need a longer x scale! pic.twitter.com/9kHgioaQSW— Josh Quick (@Scalene) March 1, 2017
How did Josh do it? The challenge with really long DNA is it tends to shear in solution, particularly during pipetting -- at the molecular scale even the most gentle pipetting generates significant shear forces. So Josh ran a standard phenol-chloroform prep of E.coli, which ends with spooling the goopy DNA onto a rod to get it out of an ethanol precipitation. That's great fun -- I did this back at Delaware once or twice, though it's also a long and tedious procedure once the novelty wears off. So Josh took the spooled DNA on the rod and ran the rapid 1D (transposase) prep on the rod. At the end of the prep, the still highly viscous DNA could be dripped into the flowcell.
It's interesting to contemplate these in context of some of the sample/library preparation automation which Oxford has proposed. VolTRAX might be particularly suitable to manipulating ultra-high molecular weight DNA, since very little shear should be imposed by short movements on the electrowetting surface. VolTRAX videos have been popping up on Twitter that resemble games, but as far as I've heard no actual libraries have yet been prepared. The Zumbador integrated sample+library prep device might also work well for preparing high molecular weight DNA, though the extreme viscosity of such DNA (nearly universally described as "like snot") could present interesting challenges for such a device.