Chromosome-Scale Scaffolds And The State of Genome Assembly

A new paper on using Hi-C sequencing appeared in Science recently, demonstrating the generation of chromosome-length scaffolds for human as well as several insect genomes.  The authors even provide a cost model, proposing that by processing multiple genomes in parallel the sequencing reagent cost (but not labor) of this approach should be about $10K per human genome. In the case of the insect genomes, the paper enables a look at chromosome evolution which is simply impossible with lower resolution.  These findings resonate with a number of pieces I've written over the years, but particularly with my recent criticism of the proposal Earth BioGenome project and a spirited defense of that concept made in the comments of my piece by a member of the steering committee.

Differential Mammalian Toxicity: Why Do Some Human Foods Kill Dogs?

I've been contemplating this post for a while, but it can be seen as another angle on my recent post on the challenges of drug discovery, so it finally left the mental queue.  We often use other mammalian species in drug development to predict human toxicity.  We know animals aren't the same as people, but lacking a better alternative that's what we do.  Now, as regular readers know I keep company with a dog, and that sometimes has me wondering: how well do we understand the cases of things we can eat but which are dangerous for our canines?

Targets: Drugability Revisited

My correspondent @datarade shot a tweet my way on his quest to understand drug discovery. He does this despite the fact I've promised posts on previous tweets that are submerged in my mental queue.  But the best part of teaching is forcing yourself to rethink what you think you know, so I'm going to actually take this one on in the space of "what is a target, how do we pick them and how do we drug them".  Which I've found to be enlightening and frustrating.  It's a messy space because so much is empirical, and I keep devising and then discarding taxonomies and explanatory approaches because they all seem unsatisfactory.

Obviousness: Rarely Obvious

Pacific Biosciences has made new thrusts in their ongoing intellectual property action against Oxford Nanopore, adding two recently issued patents to the fray.  Oxford has publicly brushed these off as "another pore excuse for a lawsuit", but certainly the battle is not over.  One of these patents, 9,542,527 "Compositions and methods for nucleic acid sequencing", appears to concern using hairpin linkages to read both strands, much like the 9,404,146 "Compositions and methods for nucleic acid sequencing"  patent that PacBio led with.  Since Oxford has announced they will abandon their "2D" methods that use such hairpins, this angle would seem to be soon irrelevant (as I predicted back when PacBio originally attacked).  But the other, US 9,546,400 "Nanopore sequencing using n-mers" covers basecalling methods, which is a new twist.  A route to challenge any patent is to identify "prior art", information which was publicly available at the time of the patent filing which impinges on the claims in the patent application.  Not only can exact matches to prior art be an issue, but also anything which would be "obvious" to a skilled practitioner.  And that can certainly be a can of worms

plexWell: Illumina Libraries by the Plateload

The advent of so-called next generation sequencers, particularly those from Illumina, have brought the price of sequence data down dramatically.  However, there is a catch: the cost of preparing DNA to go into the sequencer, the process known as library preparation, has glided downwards on a much shallower trajectory.  This means that for projects wishing to sequence very large numbers of small genomes or large constructs the cost of library preparation can be similar to or even exceed the cost of data generation.  A small company north of Boston called seqWell Inc™ has a new approach to Illumina library generation which they are on the cusp of making widely available, and not only does this bring the cost per well down but it is designed to yield normalized libraries from relatively unnormalized samples.

ONT Updates: GridION X5, PromethION, 1D^2, Scrappie, FPGAs and More

Clive Brown gave a webcast today with updates on a number of Oxford Nanopore topics, but clearly the flagship announcement was a new instrument, GridION X5.  Due to the raging snowstorm in the Boston area I was home with my teammate and we've been doggedly going through the tweets (now storified) and my notes (plus David Eccles' nice set) to retrieve the juiciest bones therein.

Blog team member intently watching @Clive_G_Brown webcast - now must confer & write-up impressions pic.twitter.com/jPGpw1w0lg

— Keith Robison (@OmicsOmicsBlog) March 14, 2017

MinION Leviathan Reads: An Update

Last week I posted a piece on some amazing new nanopore data, only to be red-faced to discover the next morning that I had misread the axes.  So I re-posted the piece with the offending data and subsequent analysis in strike-thru font.  After I did that, I was informed that the same dataset actually did have leviathan reads, bigger than my misinterpretation.

Catching Up On Oxford Nanopore News: More, Better, Meth & Huge

Oxford Nanopore and its collaborators have shown at least three interesting advances in the last few months which I haven't yet covered; the most astounding of which was announced this week.  I'll take these three in an order which works logically for me, though it isn't strictly chronological plus I'll touch on some parts of their platform which have not made advances which were perhaps expected.

(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)