Category Archives: Science

Rapid, raw-read reference and identification (R4IDs): A flexible platform for rapid generic species ID using long-read sequencing technology.

Our paper on rapid identification of samples using partial, low-coverage, MinION-sequenced reference databases for ID (at the Kew Science Festival) is in preprint. See here on BiorXiv: doi: 10.1101/281048.

In it, we show (with empirical data and simulation) that the length and bias of MinION reads makes them ideal for sample ID – better than NGS, under certain conditions – even where no reference assembly is available and a genomic skim is used to BLAST against, instead. Because these genome-skim-DBs are quick to generate, we call them ‘rapid-raw-reaad-reference fo ID’, or ‘R4IDs’ for short.

The code to repeat these analyses or set up a R4IDs analysis is on GitHub but I’ve also packaged this as Docker containers:

A few rough corners to sand while we decide where to submit it, but comments welcome in the meantime. Thanks, as ever to my colleagues (long-suffering Alex and Andrew) for all their stress:

Rapid, raw-read reference and identification (R4IDs): A flexible platform for rapid generic species ID using long-read sequencing technology.

Joe Parker* Andrew J. HelmstetterAlexander S. T. Papadopulos*


The versatility of the current DNA sequencing platforms and the development of portable, nanopore sequencers means that it has never been easier to collect genetic data for unknown sample ID. DNA barcoding and meta-barcoding have become increasingly popular and barcode databases continue to grow at an impressive rate. However, the number of canonical genome assemblies (reference or draft) that are publicly available is relatively tiny, hindering the more widespread use of genome scale DNA sequencing technology for accurate species identification and discovery. Here, we show that rapid raw-read reference datasets, or R4IDs for short, generated in a matter of hours on the Oxford Nanopore MinION, can bridge this gap and accelerate the generation of useable reference sequence data. By exploiting the long read length of this technology, shotgun genomic sequencing of a small portion of an organism’s genome can act as a suitable reference database despite the low sequencing coverage. These R4IDs can then be used for accurate species identification with minimal amounts of re-sequencing effort (<1000s of reads). We demonstrated the capabilities of this approach with six vascular plant species for which we created R4IDs in the laboratory and then re-sequenced, live at the Kew Science Festival 2016. We further validated our method using simulations to determine the broader applicability of the approach. Our data analysis pipeline has been made available as a Dockerised workflow for simple, scalable deployment for a range of uses.

Field-based, real-time metagenomics and phylogenomics for responsive pathogen detection: lessons from nanopore analyses of Acute Oak Decline (AOD) sites in the UK.

Talk presented at the UK-India Joint Bioinformatics Workshop, Pirbright Institute, 09 Feb 2018


In a globalised world of increasing trade, novel threats to animal and plant health, as well as human diseases, can cross political and geographical borders spontaneously and rapidly. One such example is the rise of Acute Oak Decline (AOD) in the UK, a multifactorial decline syndrome with uncertain aetiology, vectors, and host risk factors first reported in the UK a decade ago. Affected oaks display significant morbidity and mortality, with symptoms including vascular interruption, crown loss and characteristic striking bark lesions breaching cambium and filled with a viscous, aromatic, dark-brown/black exudate, which may sometimes be released under considerable pressure. Although multiple bacterial species have been associated to lesion sites in affected oaks, and a putative insect vector identified, the basic risk factors, transmission, progression and treatment of the syndrome remain unclear.

This dispiriting state of affairs presents an ideal opportunity to exploit recent developments in nanopore sequencing to develop and test field-based methods of real-time phylogenomics and metagenomics to establish baseline data for healthy oaks, and contrast these with affected / dying oaks to shed light on syndrome causes and management. WGS metagenomic sampling was carried out on leaf and bark tissue from 37 affected, asymptomatic, and recovering individuals (nine Quercus species) at three field sites over a year. Extraction and DNA sequencing were performed in the field for a subset of samples with MinION nanopore rapid sequencing kits, and also using MinION and paired-end Illumina sequencing under laboratory conditions. Metagenomic analyses to determine microbial community composition were carried out, and real-time phylogenomic methods were also developed and applied. Early results from these analyses and lessons for future work are presented.

Metagenomic datasets can be rapidly generated in the field with minimal equipment using nanopore sequencing, providing a responsive capability for emerging disease threats and reducing transmission risks associated with transporting quantities of potentially infectious samples from outbreaks of novel diseases. Furthermore, real-time data analysis can provide rapid feedback to field teams, both to inform management decisions and also to allow for adaptive experimental protocols that dynamically target data collection to extract maximum information per unit effort.

Read all about it!

Dead excited to say our Nature Science Reports paper on field-based DNA extraction, sequencing (and a bit of analysis) has been picked up by the BBC World Service and The Times (UK) newspaper! You can read all about it here (paywall).

If you can’t read it online, my Grandad has a copy he might lend you. We’re proper scientists now…

Tent-seq: the paper (aka ‘field-based, real-time phylogenomics’)

Really proud to report that the first of our bona fide real-time phylogenomics papers is now out in Scientific Reports!

In the paper we managed to show a number of things that are potentially really exciting, and I’ll get to them in a minute. First though, this is the first paper I’ve published where I got to drive every part: from conceiving the idea (with Alex) to getting funding, planning and carrying out the fieldwork/field-sequencing (with Alex and Dion), sequencing (all by Andrew) and analysis and writing (everyone). This was incredibly satisfying as normally a lot of my time is spent analysing downstream data. I feel like a proper grown-up scientist now. More please!

Firstly, what did we actually do? Pretty simple really:

  • Over a week (May 2016) in Snowdonia National Park, Wales,
  • We collected the flower Arabidopsis thaliana and congeneric A. lyrata,
  • Extracted their DNA and prepared sequencing libraries for MinION sequencing, in a tent with no mains power or running water,
  • Sequenced both species using Oxford Nanopore MinION, and
  • Analysed them in real-time with BLAST databases held locally, building trees with a handful of genes.

Later on back in the lab we repeated the sequencing (but not extractions) with Illumina MiSeq, so we could compare the platforms, and also developed a few more sophisticated bioinformatics analyses. To be honest, most of the pipelines we ran could have run in real-time (and now do) but at the time of the main fieldwork we just didn’t expect it would work as well as it did!

Result 1: Genomic DNA sequencing with MinION is fairly easy, even in the middle of nowhere.

Seriously, depending on how much patience and practical skill you have, this is either easy, or really easy. We used the Oxford Nanopore 1D Rapid sequencing kit (disclaimer: actually a prototype ONT provided; though the COTS one is much better now) for sequencing, and extracted using the Qiagen DNEasy Plant Miniprep kit, modified with a longer initial incubation and double-concentrated cleaning step, but essentially unchanged. The MinION itself, as is well documented, runs off USB into a laptop.

Hardware-wise you’ll need:

  • Two waterbaths (or in our case, two polyboxes, a gas kettle, and some thermometers)
  • A centrifuge
  • A generator to run said devices
  • Some poly boxes with -20ºC and ice for reagents

… that’s it. If you’re looking at this list and thinking ‘I could get all that together by next weekend, maybe we should go on a sequencing trip’ well, that’s the idea :)

There’s a lot of refinements possible. A portable freezer will make life easier, as will a dedicated 12v supply for USB power and a portable DNA quantification tool like a Quantus. Plus, all of the above don’t really like rain so a tent and/or van (as with Nick Loman and Josh Quick’s Zika trip last year) will help out a lot. But to get started, that’s it.

Result 2: Long MinION reads are really good at species ID – even better than Illumina in key respects.

The core goal of this project was to work out “can field-based genomic WGS sequencing identify closely-related species?”. So we deliberately picked two species from the same genus with publicly-available reference genome sequences (A. thaliana A. lyrata). The ID process would be simple. For each of the four datasets (two MinION runs, one from each species, and two MiSeq 2x300bp paired-end runs, one for each species), we’d simply:

  1. Trim adapters from each read
  2. Match each read to the A. thaliana genome using the best hit with BLASTN
  3. Match each read to the A. lyrata genome, using the same method
  4. Compare the hit scores for each reference genome
  5. Un-blind the read (reveal which of the two species it actually came from)
  6. Score the read as a true or false positive or negative, depending on the result.

Clearly many reads finding a BLAST alignment for one species will also find a significant alignment to the other species, since these are separated by only a couple of MYr (and are pretty similar phenotypically). So if both hits are ‘significant’, how would we distinguish the best one? Intuitively it seems sensible that the longest match / most identities will be better, but what threshold length difference should we use? 1bp longer? 10bp? 100bp?

Happily the


package in R lets you investigate the performance of test statistics on known classifier sets. We used this to produce the plot below, which shows the effect of increasing threshold length difference on true-positive (TP), false-positive (FP) and accuracy rates for MinION (black) and MiSeq (red) reads:Figure_2c-2d_length

The really key thing here is that MinION reads are beating MiSeq ones at most length difference (bias) thresholds greater than ~5-10bp, right up to 300bp (the MiSeq inserts top out at this length, of course). This is important because while here we’re matching orthogonal ID cases (A. lyrata against A. thaliana, and vice versa), in a practical application we might have a third species without a reference but two possible matches, and while some loci will be closer to the first, others could match the second. So while a threshold of 1bp might technically be the best (TP rate of ~90% and close to 100% accuracy), we may want to raise the threshold to a much higher value (>50bp) and accept lower TP rates to get a better confidence.

Result 3: Species ID does not need complete genomes for reference databases, and even works with a handful of MinION reads.

One very obvious and sensible criticism of our early drafts of this was that the reference genomes we used to build BLASTN databases with are largely completely assembled. While there’s been some handwringing recently about the structural variation of these plant genomes at population level, most people accept that a high proportion of the informative sequences in these genomes are now well determined.

For most people, in most places, this will not be the case; for instance, there’s ~300,000-400,000 plant species described, but only ~180-250 public genomes. Most of those are of the fairly-low-coverage HTS WGS variety as well, so are pretty bittily assembled. Quite often these come from first-year baselining experiments in the ‘get some DNA and run it through the MiSeq then SOAP or Abyss’ mould, with N50 values in the low ‘000s.

So to test the effect of this, we artificially digested the reference genomes a few thousand times to simulate N50 values from about 100 (virtually unassembled) up to 10^6 (essentially complete), shown here in Fig 3a for N50 values from 10^0 up to 10^4, with accuracy scores calculated for a range of cutoff values:


These results were pretty promising, so finally we asked ourselves: OK, we had tens of thousands of MinION reads to make our ID with, generated over a day or so: but how few reads we would need to have a stab at a correct ID? Again, we jacknifed our dataset to find out, shown above in Figs 3b and 3c. Promisingly, you can see that by about 10^2-10^3 reads (in practice, an hour or less) the confidence intervals on our ID score barely budge. So, after an hour of sequencing, you’re likely to get as good an answer as you can get. One. Hour…!!!

Result 4: Field-sequenced WGS MinION long-reads substantially improve downstream genomics with low-coverage HTS data.

When planning the fieldwork we hadn’t really known what we’d get, in terms of read length, quality, or yield: this was a prototype kit that not many people had played with yet, let alone taken into the field. But about the time we were writing this up we found out about various genome assemblers optimised (supposedly) for ONT reads, chiefly Canu and hybrid-SPAdes. We decided to give it a whirl.. the results are pretty amazing!

Data MiSeq only MiSeq + MinION
Assembler Abyss hybridSPAdes
Illumina reads, 300bp paired-end 8,033,488 8,033,488
Illumina data (yield) 2,418 Mbp 2,418 Mbp
MinION reads, R7.3 + R9 kits,

N50 ~ 4,410bp

MinION data (yield) 240 Mbp
Approx. coverage 19.49x 19.49x + 2.01x
Assembly key statistics:
# contigs 24,999 10,644
Longest contig 90 Kbp 414 Kbp
N50 contiguity 7,853 bp 48,730 bp
Fraction of reference genome (%) 82 88
Errors, per 100 kbp:                           #N’s 1.7 5.4
# mismatches 518 588
# indels 120 130
Largest alignment 76,935 bp 264,039 bp
CEGMA gene completeness estimate:
# genes 219 of 248 245 of 248
% genes 88% 99%

Result 5: Individual MinION reads can be directly, individually annotated for coding loci with no assembly required.

By now everyone was getting a bit sick of me going on about MinION reads, but there was one final hunch I wanted to test: If reads are about the same length as nuclear coding loci (~5000-50,000bp), does that mean we can annotate individual reads to pull out coding sequences, and use them to build phylogenies? SNAP was a great tool for this, not least because it’s trained on A. thaliana gene models already.

I want to be absolutely clear here, as sometimes people seem to miss this: I’m not talking about assembling reads before annotation as usual. I’m not even talking about assembling them in real-time, then annotating. I mean, each time a read finishes basecalling, immediately try and annotate that single read, and only that single read, to try and get a coding sequence. 

In other words, how quickly can we turn a tube of DNA, into a folder of sequence reads, into alignments of coding loci? The answer is, ‘bloody quickly’:


The dashed line shows ‘all gene models’. The solid line shows unique CDS. The axis is the number of predicted CDS and yes, those are thousands – recall that the total number of CDS for A. thaliana is only about 23,000. The axis is, well, actual sequencing time (!)*

Now, not all of these are complete genes, and error rate means distinguishing paralogs robustly in a real case (e.g. completely novel genome) would be tricky, but on the other hand, this was a completely unoptimised pipeline, really just hacked together over a couple of weeks. There’s a lot of scope to improve this…

*These are the read timestamps, but it wasn’t a live run. I actually ran the analysis back in the lab afterwards, as my code was too buggy on the fieldwork day and I just lost my shit. But the CPU demands aren’t high – I can and have run this live subsequently.


Result 6: Predicted coding loci from individual MinION reads can be aligned to orthologous sequences, and multilocus phylogeny inferred in real-time.

I build trees. I build trees. I build trees for a living. Did you seriously think, that having got as far as spitting out thousands of novel coding loci per day in field-based sequencing, I wasn’t going to try some real-time, field-based, multilocus phylogenomic inference?


Here’s a *BEAST tree from 53 loci, all of the A. thaliana ones coming from directly-annotated, field-sequenced reads. Seriously 😉


As you’ve gathered by now, I’m enormously happy with this research. I think this paper is easily a bigger contribution to general science than our 2013 molecular convergence one because, if we’re honest, it’s an interesting but niche phenomenon, whereas literally everyone who uses or categorises any kind of biological material can benefit from this paper.

I’m indebted to my colleagues, Alex S. T. Papadopulos, Dion Devey, Andrew Helmstetter and Tim Wilkinson, and our funders, the Kew Foundation. We didn’t invent the MinION – that took hundreds of incredibly clever people at Oxford Nanopore years and millions of pounds of investment to do – but in this study we’ve managed to show all of the really transformative aspects of this technology working in the field, in real-time. There is no technical reason, at all why we shouldn’t all expect that within a decade, all of the analyses we currently run on DNA data in labs can run in the field, within minutes of collecting biological samples. And that really is something.

What is ‘real-time’ phylogenomics?

Over the past few years I’ve been developing research, which I collectively refer to as ‘real-time phylogenomics’ – and this is the name of our mini-site for MinION-based rapid identification-by-sequencing. Since our paper on this will hopefully be published soon, it’s probably worth defining what I hope this term denotes now, what it does not – and ultimately where I hope this research is going.

‘Phylogenomics’ is simple enough, and Jonathan Eisen at UC Davis has been a fantastic advocate of the concept. Essentially, phylogenomics is scaled-up molecular systematics, with datasets (usually derived from a genome annotation and/or transcriptome) comprising many coding loci rather than a few genes. ‘Many’ in this case usually means hundreds, or thousands, so we’re typically looking at primarily nuclear genes, although organelles’ genomes may often be incorporated, since they’re usually far easier to reliably assemble and annotate. The aim is, basically to average phylogenetic signal over many loci by combining gene trees (or an analogous approach) to try and obtain phylogenies with higher confidence (single- or few-locus approaches, including barcodes no matter how judiciously chosen, capable of producing incorrect trees with high confidence). The process is intensive, since genomes must be sequenced and then assembled to a sufficient standard to be reasonably certain of identifying orthologous loci. This isn’t the only use of the term (which also refers to phylogenies produced from whole-genome metagenomics) but the most straightforward and common one as far as eukaryote genomics is concerned, and certainly the one uppermost in my mind.

However the results are often confusing, or at least more complex than we might hope: instead of a single phylogeny with high support from all loci, and robust to the model used, we often find a high proportion of gene trees (10-30%, perhaps) agree with each other, but not the modal (most common, e.g. majority rule consensus) tree topology. For instance among 2, 326 loci in our 2013 paper on phylogenomics of the major bat families, we found that position of a particular group of echolocators – which had been hotly debated for decades, based on morphological and single-locus approaches – showed such a pattern (sometimes supporting the traditional grouping of Microchiroptera + Megachiroptera, but over 60% of loci supporting the newer Yangochiroptera + Yinpterochiroptera system. This can be for a variety of reasons, some biological and some methodological. The point is that we have a sufficiently detailed picture to let us chose between competing phylogenetic hypothesis with both statistical confidence and intuition based on comparison.

These techniques have been on the horizon for a while (certainly since at least 2000) and gathered pace over the last decade with improvements in computing, informatics, and especially next-generation sequencing. The other half of this equation, ‘real-time’ sequencing, has emerged much more recently and centres, obviously, on the MinION sequencer. Most work using this so far has focused either on the very impressive potential long-read data offers for genomic analyses, particularly assembly, or rapid ID of samples e.g. the Quick/Loman Zika and Ebola monitoring studies; and our own work.

So what, exactly, do we hope to achieve with phylogenomic-type analyses using real-time MinION data, and why?

Well, firstly, our work so far has shown that the existing pipeline (sample -> transport -> sequence-> assemble genome-> annotate genes-> align loci-> build trees) has lots of room for speedups, and we’re fairly confident that the inevitable tradeoff with accuracy when you omit or simplify certain steps (laboratory-standard sequencing, assembly) is at least compensated for by the volume of data alone. Recall that a ‘normal’ phylogenomic tree similar to our bat one might take two or more postdocs/students a year to generate from biological samples, often longer. A process taking a week instead would let you generate something like 50 more analyses in a year! The most obvious application for this is just accelerating existing research, but the potential for transforming fieldwork and citizen science is considerable. This is because you can build trees that inform species relationships, even if the species in question isn’t known. In other words a phylogenome can both reliably identify an unknown sample, and also identify if it is a new species.

More excitingly, I think we need to have a deeper look at how we both construct and analyse evolutionary models. Life on earth can be accurately and fully described best by a network, not a bifurcating tree, but this applies to loci as well as single genes. In other words, there is a single network that connects every locus in every living thing. Phylogenetic trees are only a bifurcating projection of this, while single- or multi-locus networks only comprise a part.

We’ve hitherto ignored this fact, largely because (a) trees are often a good approximation, especially in the case of eukaryote nuclear genes, and (b) the data and computation requirements a ‘network-of-life’ analysis implies are formidable. However, cracks are beginning to appear, in both faces. Firstly, many loci are subject to real biological phenomena (horizontal gene transfer, selection leading to adaptive convergence, etc) which give erroneous trees as discussed above. Meanwhile prokaryotic and viral inference is rarely even this straightforward. Secondly, expanding computing power, algorithmic complexity, and sequencing capacity (imagine just 1,000 high schools across the world, regularly using a MinION for class projects…) mean the question for us today really isn’t ‘how do we get data’, but ‘how ambitious do we want to be with it?’

Since my PhD but especially since 2012, I’ve been developing this theme. Ultimately I think the answer lies in the continuous analysis of public phylogenomic data. Imagine multiple distributed but linked analyses, continuously running to infer parts of the network of life, updating their model asynchronously both as new data flood in, and by exchanging information with each other. This is really what we mean by real-time phylogenomics – nothing less than a complete Network of Life, living in the cloud, publicly available and collaboratively and continuously inferred from real-time sequence data.

So… that’s what I plan to spend the 2020s doing, anyway.


Some aspects of BLASTing long-read data

Quick note to explain some of the differences we’ve observed working with long-read data (MinION, PacBio) for sample ID via BLAST. I’ll publish a proper paper on this, but for now:

  • Long reads aren’t just a bit longer than Illumina data, but two, three, four or possibly even five orders of magnitude longer (up to 10^6 already, vs 10^2). This is mathematically obvious, but extremely important…
  • … the massive length means lots of the yield is in comparatively few reads. This makes yield stats based on numbers of reads positively useless for comparison with NGS. Also…
  • Any given long read contains significantly more information than a short one does. Most obviously the genomics facilities of the world have focused on their potential for improving genome assembly contiguity and repeat spanning (as well as using synteny to spot rearrangements etc) but we’ve also shown (Parker et al, submitted) that whole coding loci can be directly recovered from single reads and used in phylogenomics without assembly and annotation. This makes sense (a ~kb long read can easily span a whole gene, also ~kb in scale) but it certainly wasn’t initially obvious, and given error rates, etc, it’s surprising it actually works.
  • Sample ID using BLAST actually works very differently. In particular, the normal ‘rubbish in, rubbish out’ rule is inverted. In other words, nanopore reads (for the time being) may be long, but inevitably contain errors. However, this length means that assuming BLAST database sequences are approximately as long/contiguous, Nanopore queries tend to either match database targets correctly, with very long alignments (hundreds/thousands of identities), or not at all.

This last point is the most important. What it means is that, for a read, interpreting the match is simple – you’ll either have a very long alignment to a target, or you won’t. Even when a read has regions of identity to more than one species, the correct read has a much longer cumulative alignment length overall for the correct one. This is the main result of our paper.

The second implication is that, as it has been put to me, for nanopore reads to be any good for an ID, you have to have a genomic database. While this is true in the narrow sense, our current work (and again, this is partly in our paper, and partly in preparation) shows that in fact all that matters is for the length distribution in the reference database to be similar to the query nanopore one. In particular, we’ve demonstrated that a rapid nanopore sequencing run, with no assembly, can itself serve as a perfectly good reference for future sample ID. This has important implications for sample ID but as I said, more on that later 😉

Science and (small) business

Over the last 10-20 years there’s been a revolution in academic science (or should that be ‘coup’?) where many aspects of the job have been professionalised and formalised, especially project management but management in general. This generally includes tools like GANTTs, milestones, workload models, targets and many other things previously unmentionable in academia but common in industry, especially large organisations. Lots of academics will tell you they think it’s bureaucratic overkill, intrusive, a waste of time, and worse (to put it mildly) but the awkward truth is that, as lab groups steadily increased in size (as fewer, larger grants went to increasingly senior PIs or consortia) many of the limitations of the collegiate style of the past, centred on a single academic with a tight-knit group, have been exposed.

Frequently the introduction of ‘management practices’, often after hiring expensive consultants, is accompanied by compulsory management training. Sometimes it can be an improvement. More normally (in my experience) whether an improvement in outcomes (as distinct from ‘efficiency’) has been achieved probably depends on whether you cost in staff time (or overtime) and morale. You can make arguments either way.

But I can’t help thinking: why are we attempting to replicate practices from big/massive private sector organisations, anyway? I suspect, the answer in part is because those are the clients management consultants have the most experience working with. More seriously, those organisations differ in fundamental respects from even the largest universities, let alone individual research projects. This is because large companies:

  • Add value to inputs to create physical goods or services that are easily costed by the market mechanism (this is the big one)
  • Usually have large cash reserves, or easy access to finance (tellingly when this ends they usually get liquidated)
  • Keep an eye on longer-term outcomes, but primarily focus on the 5-10 year horizon
  • Compete directly with others for customers (in some respects an infinite resource)
  • Are answerable, at least, yearly, to shareholders – with share value and dividends being the primary drivers of shareholder satisfaction.

Meanwhile, universities (and to an even greater extreme, research groups/PIs):

  • Produce knowledge outputs with zero market value*
  • Live hand-to-mouth on short grants
  • Need long-term, strategic thinking to succeed (really, this is why we get paid at all)
  • Compete indirectly for finite resources grants and publications, based partly on track record and partly on potential.
  • Answer, ultimately, to posterity, their families, and their Head Of Department

I want to be clear here – I’m not saying, by any means, that previous management techniques (ie, ‘none’) work well in today’s science environment – but I do think we should probably look to other models than, say General Motors, or GlaxoSmithKline. The problem is often compounded because PIs have no business experience (certainly not in startups) while consultants often come from big business – their ability to meet in the middle is limited.

Instead small and medium enterprises (SME)s are a much closer model to how science works. Here good management of resources and people is extremely important, but the scale is much smaller, permitting different management methods, often focussing on flexibility and results, not hierarchies and systems. For instance, project goals are often still designed to be SMART (specific, measurable, achievable, realistic, time-scaled) but these will be revisited often and informally, and adjusted whenever necessary. Failure is a recognised part of the ongoing process. This is the exact opposite to how a GANTT, say, is used in academia: often drawn up at the project proposal (design) stage, it is then ignored until the end of the grant, when the PI scrabbles to fudge the outcomes, goals, or both to make the work actually carried out fit, so they don’t get a black mark from the funder/HoD for missing targets.

There are plenty of other models, and they vary not just by organisation size/type (e.g. tech startup, games studio, SkunkWorks, logistics distributor, niche construction subcontractor) but you see what I mean: copying ‘big business’ wholesale without looking at the whole ecosystem of business practices makes little sense…

*Obviously not all, or even most, scientific output will never realise any economic value – but it can be years, or centuries removed from the work to create it. And spin-outs are relevant to a tiny proportion of PI’s work, even in applied fields.

Single-molecule real-time (SMRT) Nanopore sequencing for Plant Pathology applications

A short presentation to the British Society for Plant Pathology’s ‘Grand Challenges in Plant Pathology’ workshop on the uses of real-time DNA/RNA sequencing technology for plant health applications.

Doctoral Training Centre, University of Oxford, 14th September 2016.

Slides [SlideShare]: cc-by-nc-nd