Gordon Sanghera - Opening
Intro music - Arctic Monkeys
- A band that was around when most ONT staff were alive
- In 2005, they went straight in at number 1
- No sales, no marketing, used myspace to allow people to download one sing
- It was a bunch of kids
- Since then, there’s been no need to pander to the large organisations
- So many things have been proposed for nanopore sequencing
- The oil industry has problems with microbes
- The shipping industry has problems with ballast tanks
- The conference has 600 people - 500 customers, and 100 Nanopore people (with blue lanyards)
- Goal of ONT: Allowing anyone to sequence anything anywhere
- There are 7,000 MinIONs; 140 GridIONs; 40 beta PromethIONs; all done with community-based campaigns
What it all means
- We are at the cusp of the fourth industrial revolution
- We are seeing things that are in sci-fi films
- Uber’s actual goal: delivering anything to anyone anywhere
- We are bringing testing closer to the customers
- People can use Air B&B instead of hotels
- With nanopore, we are enabling the Internet of Living things
- There are 12500 sequencing centres in the world
- When people are provided access to real-time tools, we open up new opportunities
- Genoscope has 85 coral species
- General theme: More people, more diverse applications
- KeyGene is the first authorised PromethION service provider
- Providing service to the lettuce industry
The Dunbar Number - 150
- About the number that were in the first nanopore conference
- Need to work out how to help people
- Ninja programming schedule
- Repeating some breakout sessions
- A workshop to improve the digital community (Andy Davies)
- 300 people have downloaded the software App
- Interact button allows people to ask questions during plenary sessions
See the post-plenary questions here.
- Every cell has the same genome, but different cell types have different functions
- The transcriptome is what distinguishes cells
- Have non-coding as well as protein-coding
- Can be edited or modified; over 100 types of modifications exist
- Limitations of short-read sequencing
- Conversion into cDNA
- doing PCR (even a problem with long-read cDNA)
- Sequencing is 3´ to 5´ (not typical)
- RNA consortium was formed to look at human cell lines
- 6 universities sequencing GM12878
- The same cell culture was sent to five institutes, a separate culture was done by Birmingham
- Can go to github to see the data
- 13 million native RNA reads, 24 million cDNA reads
- Native RNA was longer than cDNA
- Error rates about the same
- Good correlation in gene expression levels
- Also good correlation with Illumina
- Birmingham clustered separately when using PCA
- Same sample clustered well together
- A lot of reads that seemed to be fragmented
- Read length doesn’t drop over time (so there’s no fragmentation happening within the flow cell)
- 45% of the reads were full length
- 20% were partial due to artefacts (e.g. basecaller truncating the read)
- 35% other partial reads, maybe due to sample prep, or degredation of RNA
- Using orthogonal information to distingush full-length transcripts
- e.g. ChipSeq - looking for the promoter region. If not correlated, then it’s probably not full-length
- Using short-read support for read correction
- Can look at full length and compare to annotation
- Can pick up novel transcripts
- Largest high-confidence transcript: 10313 nucleotides with 48 exons
How many sequenced reads are needed?
- At about 5M reads, the number of transcripts start to plateau
- Transcripts keep getting detected at high coverage (more than 75k transcripts)
- Allele bias
- Also looking at polyA tail lengths [see polya_estimator]
- Generally 30-150 nucleotides
- Detecting m6A, found that it was kmer-dependent
- More differences for some, but not others
- Remaining challenge: hard to distingush full from partial RNA
In-depth analysis of 100 reads
- Done manually by Mark Akeson
- Looked at squiggle for capturing the 5´ cap (methods in progress)
- Using minimap
- Longest read from titin (aiming for 100 kb)
See the video here.
Novel statements selected by David Eccles
- Live sample prep, sequencing, and WIMP analysis of Clive Brown’s DNA by Ruth Moysey
- Voucher for Series D ASIC; longer run time, potentially 30 Gb from a single MinION
- ONT has a roadmap for moving to 500 Gb from a single PromethION flow cell (NovaSeq-busting prices)
- [Read-until is coming back [in anger]
- MinIT is orderable in the store
- Mk1C sequencer (MinION combined with MinIT) should be available December
- Epi2me will be embedded in MinIT, GridION, PromethION (local analysis workflows)
- Epi2me will be commercialised via MetriCoin (workflow currency, included with flow cell / kit purchases)
- New 1D² kit coming that uses UMIs in adaptors, increasing accuracy to Q20 and allowing amplicon sequencing
- Miyagi tool for combining reads from different pores for increased consensus accuracy
- Slightly mutated forms of R9.4 have been tested; could release a variant-pore flow cell in a few weeks if there is interest
- Flongle early-access begins now, released commercially in Q3
- Zumbador’s official name is Ubiquibopsy (or Ubik tube)
- V2 VolTRAX (heat, PCR, 10 samples) is available for ordering in the store
- New SkunkWorx technology: a combined MinION + VolTRAX, dynamically embeds pores between droplets
- Too much novel stuff to summarise, see here
- Next year O2 arena – looks like a nanopore, fits 20,000 people
- Every year, I have been told that I need to do this: promised investors I would do a live demo.
- Have been spitting in tubes, but soomeone else will do the prep.
- Ruth Moysey takes a tubefull of spit from Clive.
Recap - get into rhythm
- Aim: Anybody to sequence anything anywhare
- Trying to make runnable outside labs
- real-time full-length reads
- ultra-low cost.
- In the past day, a few people have been demonstrating all features.
- This was a dream a few years ago.
Olbigatory nanopore slide
- An array of membranes on a chip that we designed
- catch DNA from solution
- stream through the pore
- generate tiny signals (a lot of work decoding) All asynchronous
- get a full-lenth read in a few seconds
- Electronics can be made small, cheap
- Design breaks the traditional mould - Would be stupid to make an electronic device with fluidics
- Also key for ONT was designing array in the factory
- There have been a number of previous talks [by Clive]
- recently spoke about sub-1000 sequencing run; mostly on PromethION ($600)
- GridION conceived Feb last year, has been relatively successful
- Product line as it stands, all in-field
- MinION is the most established
- Out there GridION is doing well; easy to own, people using it as service
- PromethION - a beast of a machine, now out, runnable, generating pretty high yields
- Over 1000 Customers for GridION; We think we can ship within a week of an order.
- Certification program, growing quite rapidly
- In your bags is a token that gets you access to a slightly modified version of the MinION flow cell: a series D ASIC
- It’s just better
- First design had a few faults, including current cross-talk; all things ate into yield
- This is a fixed-up version of the ASIC
- Can now enable very-long run times with sustainable yield of up to 100 hours
- Enables 30Gb on a MinION
- 48 individually-controllable flow cells
- 144,000 concurrent independent nanopore channels
- Can hot-swap
- Designed for quite lumpy workflows
Can be run as a high-throughput sequencer, just like any other.
- Now dependent on Ruth on the Maracas
- Now dependent on You lot for reporting on what’s happening on PromethION
- Now out of the Early access phase; $160,000 starter pack
- Can take box back
- When you buy PromethION can have access to upgrades
Next version will run 48 flow cells
- Have shipped 40 PromethION by this conference
- should get to 60 by end of June.
- What do new customer yields look like?
- Most getting over 50 Gb (this was the target)
- Some getting over 60
- ONT is getting internally over 170 Gb
- A roadmap to move to 500 Gb per flow cell
- These are NovaSeq-busting prices, provided you get the yields.
- New flow cell: single channel. Also a redesign of the multi-channel flow cell.
- Will make sure that all of MinION kits will work on PromethION (RNA coming in June)
- MinKNOW upgraded
- Very nice UI, innards of the GUI have been completely refactored.
- Thinking about things like read-until
- Including progressive unblock & dynamic voltage control, should increase data quality.
- Blockages when you shove DNA thought a hole. Single strand forms structures on the other side of the pore.
- Can unblock by inverting potential.
- The old software was the simplest thing that worked
- Unblocking was quite violent, would remove channels
- The new version doesn’t destroy the channel; a more subtle, sophisticated version of unblocking the pore
- Probably a larger uplift in difficult samples.
- Have generally improved kit quality
- 3-4 years ago pulled in software team to talk about Read Until
- As DNA is going through the pore, can basecall and do something with it before it’s gone through the pore
- An in-silico selection
We wrote an API.
Entire innards of MinKNOW have been rejigged, read-until is coming back
- balancing amplicons
Sequencing a certain region of human
- Coming back in second-half of year, coming back in anger
- Will be able to dynamically select molecules on the fly
- For MinION: Common complaint is the computer; that has caused a lot of trouble
- Making a device called MinIT
It should be almost zero configuration.
Plug it in, connect it, pair your iPhone display, ready to sequence.
Can run off batteries. Can run off 12V supply.
- Will put Guppy in there, will keep up with MinION basecalling, critical for ReadUntil
Also embedding Epi2Me.
- Pretty confident that a large number of people will want a hand with analysis
MinIT Should be orderable today.
- Have gone for GPUs in MinIT; going for neural-network based AI
- A bunch of sequential matrix computations
Quite a large number of bioinformatics things can be optimised on the same hardware with the same performance.
- MinION Mk1C - MinIT with the aility to load a flow cell, or flongle
- Target December
Should be able to run a single flow cell without any external needs
- It’s the kind of thing a dentist can run
- It also looks really cool
- A bioinformatics platform
- Embedded workflows doing the kind of analyses that people want to run
- Can be run in the cloud, or workflows can come to the computer: GridION, PromethION, MinIT.
Lots of reasons, 1) a lot of computing in the boxes.
- Expertise in optimising on GPUs
Sometimes people don’t want to pipe data over to the over side of the world
We think it’s more usable now, can upload custom reference, will provide data storage
- We will release Epi2Me on MinIT in Q3 - a MinIT with MinION will do this locally in Q3
- Will be commercialised
- When you buy a flow cell; have invented something called MetriCoin
- When you buy flow cells or reagents, will get MetriCoin, can use to run workflows
- People who don’t have bioinformatics support will have access to bioinformatics services
- Platform performance is now much closer to what Clive tweets
- Customer runs by kits is converging, variance is decreasing
- Largely due to kit simplification
- Early kits had a cholesterol tether; it sticks to everything that is hydrophobic
- We have been removing that from all of our kits.
- Generally customer performance has started to converge with nanopore.
- Fragment length is read length; largely in customer hands
- Most platforms, the optics / chemistry conks out
If you can present with long reads, it will sequence it
- Some of thought-leading developers have tried to present pore with enormous, intact lengths of DNA
- The developers found a bug in the software, ONT software was chopping up reads
- In principle, if you can do 2 Megabases, can do 20 Megabases
- Challenge is whole-chromosomal sequencing
- Initial software that classified data was the simplest that worked
- Software misclassified reads, which would degrade the data
Low-complexity regions, rectified in next software release in June
- Need to Scale data, scaling can be biased, to be fixed (fix went out 2 weeks ago)
- Extracting from chitinous insect won’t be the same as a drop of blood
- Not the whole story, can get DNA out
There have been issues with things sticking to tubes
- If initial yield was high, would drop off suddenly
Chickens do this
Software that you need to kick out DNA is different for different samples
Some kinds of DNA form secondary structure as they get to the other side of the pore, creating a block that kills the pore.
- Another upgrade coming to change the unblock logic that should level out yield differences over time.
Recently settled lawsuit about hairpin
- Came up with a system to do 1D²
- Version 1 went out a few months ago
- It is now due for a revamp
- Adapters will be improved to imporoved the performance of the system
- Looking for 60% reads 1D², should get to Q20
Software is antediluvian, 4 years old
- A lot of people have complained that they can’t do amplicons
- New kit optimised for PCR uses a special set of barcodes that will enable barcodes to be paired up (UMI).
- Will go back again, revamp it, and make a performant version.
- Technology was to take a deactivated Cas9
- Guide RNA, hybridised to sample, subset of reads is pulled down to pore
- Working quite nicely
- Will be out as a kit later in the year.
Direct RNA sequencing
- Can put RNA through the pore just like DNA
- Proven very popular, there’s lots of stuff in RNA
- Not as performant as DNA product
- Get full-length RNA molecules, can see all the modifications
Can really get to the full-length biology.
Nanopore is quantitative, see all the splice formas.
- Now focusing on making RNA as performant as DNA
- Want to increase speed RNA goes through the pore
- Will move to 110 bases per second
- Looking at improved adapters, simpler, fast library prep, incremental advances in basecalling
- Despair when looking at what people take to the field - polystyrene ice bucket kills me
- A lot of work has gone to ambient shipping
Looking to post flow cells without cooling
Can ship at ambient, most reagents
- Looking at storing ambient, a few kits left to lyophilise
- Especially an issue in places where don’t have a lab
- Looked at fixing this issue a few months ago
The thing that most people hold up to nanopore
- Cornering the rat, and then stamping on it
We’re now tickling the rat
- We’re not at the limit yet
- Worth talking to the team who are here
There are some limitations in the current software
- Current training set, issues with scaling / chunking of data
- Still aren’t learning all the context
- We still train on bacteria
- We don’t learn in damage and modifications
There are some software limitations, and other issues.
- People assemble and polish, not fully-utilising all the signal
- People don’t exploit alll the information in the signal
Quite a lot of work to do.
Bases are not modelled, similar current for different sequences.
Progress on homopolymers, over past few months.
- Nanopolish is still the gold standard for treating these data, but medaka can do well in some contexts.
- Have medaka, only works in base-space
Comparable to nanopolish, but much faster
- Have been working on a new tool, miyagi, for correcting homopolymers
- HMM based, that’s the way to do it
- comparable or better to nanopolish in some circumstances
Fixing the problem
One way: improve the chemistry.
- People are alreads combining data with another certain company
- Has been long on our agenda to do the same thing on one platform
Would then not need the other company
If you could combine multiple pores, would get flattening out of errors
- R9.4 pore signal comes mostly from 3-4 bases
- A run of Ts bigger than 3-4 gives flat signal
- Can estimate bases using time domain
Would be better if we could de-flat the sections of data
- Using read-ahead to look at more bases, rather than less
Will always be averaging over more bases
- Deal with more bases using machine learning
- Use Pore R8 - Lysenin based
R10 has two points about 9 bases apart
- A particularly pathocogical error is a round of 5 Ts followed by two Gs
R10 spans the homopolymer giving an uppy-downy signal that the software can decode
- Equally, number of percentage correct homopolymers with R10 is better
Think that green accuracy line will shift up.
- Can generate orthogonal errors that can shift consensus
These are now under very active development as potential products
- What about combining together?
- 50-fold R9 with 50-fold R10
- can easily exceed Q40 in consensus, proof of concept
These methods are being implemented in miyagi tool, can use yourself (if you had R10 data).
- Still not using dwell in these data
Using time domain, can boost these further.
The principle is shown.
- Why not 5 pores?
Why not 500, each with completely different error modes?
We’re going to crack this this year, and get it out to you as soon as you can.
Doing very well on variant calling, specifically on SNVs.
- Have made at least one mutant of R9.4; R9.6. 9.4 gets one homopolymer wrong, 9.6 doesn’t.
- Can make even variants of 9.4 that have significantly uncorrelated signal. Could release this in 2-3 weeks.
- Could mess with the complement of the sequence
- About 20 minutes, a pot of nucleotides in the kits, swap T for U and train basecaller
A/C/G/U version has different errors from A/C/G/T version. More complicated.
- My preference is to put multiple pores in the chip.
Can provide these tools to allow people to mess with the pores.
- Combining all three: two pores plus complement
Combining data that isn’t correlated, as long as you pick the right data.
Will keep 9.4, but could make 9.6, could make R10
- Question: is this useful? is that of interest? When would you like it? [let ONT staff know]
It’s coming, really soon
- MiNION + 1-off adapter, that’s the flongle
- Has most of the electronics that’s in the MinION
- An array of copper electrodes
- Can then make a very cheap crappy plastic flow cell with magical chemistry
Separates the cheap bits from the expensive bits
128 channels, limited by the forces that are needed to make connections
- The data is the same, can flongleise a MinION
- Can flongleise a GridION
Projecting that v1 up to a Gb from one flow cell, should get 3Gb from a flongle.
- People don’t want lots of data, problem is about how quickly enough data can be obtained.
- Also good for bacteria and virused
People should buy more if it’s cheaper
Flongle works, just like MinION
Squiggles are squiggles, they are the same
Invites for early adopers have going out
Looking at $90-$100 per flow cells, about 5-10% of the total treatment cost for dentist
- Early access begins now, will release commercially in Q3
Where to go
- Drive is to get sequencing out of the lab
- Can survey what’s going on in a river
- Cheaper, easier, package it up, remove any extraneous equipment.
Clive had the largest office, but had to rename Zumbador.
- Idea is that you can take something very cheap, a piece of plastic, introduce a liquid because they’re easy
- After some reasonable time, like 10 minutes, can just sequence what’s in there.
New name: Ubikwibopsy (Ubik tube)
- Turns out that DNA is really informative
- Don’t want to ship samples off to california
Want anyone to be able to do this themselves, and have complete control over their own data.
Video: can put spit in a tube, a bunch of stuff happens in the tube, can bind DNA onto solid phase support, then separate. It drops onto the flow cell.
Have embedded the library prep onto the flow cell, comes ready to run.
DNA in, sample prep appears early in the pore
- Can get meaningful data really quickly
- Yields aren’t massive, but we’ve got it
10 minutes from spit prep to WIMP
Had bacterial infection, could fix it, see a drop in the bacteria.
Saliva, lysis 10 minutes on Ubik tube.
Dentist offered to buy one.
If you encapsulated enrichment, could use read-until to exclude human, or include human.
We Might not be the right audience for that.
Future versions like flongle and smidgION will move towards anybody being able to touch a sample to a flow cell and know what’s in.
- Metrichor will make quantitative analysis of the self available direct ot the consumers.
- Version 1 a while back.
- Main use is the proper version, version 2 ready to go out a bit later this year.
- Supports all kinds of complex lab workflows.
- 15 inlets.
Can do heat, PCR, fluorescence detection. It is a lab on a chip.
Starter pack for V2 $8,000
- As I speak, the shop is open for orders, can buy a VolTRAX v2.
Will prioritise people who got a V1.
- Can do up to 10 samples, 2xGridION.
- Pricing starts to get quite attractive for multiple samples.
A few years ago, pulled people into my office, asked them to do a thing.
Amazingly, people went off and did it.
- A VolTRAX - MinION hybrid, amalgamating into a single device.
Now have another DNA sequencer that can be made.
- Completely automated, very complicated droplet-based workflows can be built in.
No physical handling, can work directly from cells.
- Form dynamic membranes between droplets on the VolTRAX.
- Can insert a pore, and have a sequencer.
Move the droplet to a certain part of the array and droplets look like a MinION channels.
Squiggles are the squggles; they are the same.
Droplets can be quite small, or quite big.
Can swap the droplet.
- Can access the trans side.
Can take the things you sequenced, and PCR them, or sequence again.
- Target is to get to 128 channels.
- More likely to be less than that.
Currently 8-channel breadboard.
Extraction zone, Library zone, sequencing zone.
- Henrietta Lacks cells being aligned and lysed.
No shearing; what if you could just get whole chromosomes and put it between two droplets.
- Lots of things you can do.
- We tend to stick DNA to the membrane.
- Can coat up the droplet with DNA, DNA whizzes around and will find pore within seconds.
If you have a tiny amount of material that you want to mine digitally, can mine out that cell.
With read-until, can eject high-abundant stuff.
Schemes where you can re-pair sequenced DNA and put it through again.
- Can also just hybridise things to DNA.
- Some Cas9 are SNP sensitive, can count thousands of presence - absense.
Spit blood in, all in one, very flexible measuring device.
- We have the proof of concept, all now about making the box.
Will be coming your way; not talking years.
- The mind boggles of what you can do.
- A small platform at relatively low plex. Our next likely major product launch.
- Revision D ASIC, can get now. Probably broadly by middle of Q3. Should see 20-30 Gb.
- Kit9 much improved ligation kit
- PromethION went on commerical release today
- PromethION flow cells orderable
- Epi2Me commericalisation
- Software umblocking fixes
- MinIT shipping in July
- Ambient shipping in July
- First flongle shipments in July
- Voltrax V2 in August
- Much more performant 1D² in August
- RNA upgrade in terms of yield / quality in September
- In December fully-performant PromethION (48 flow cells)
- ONT has just got 3Tb internally
- target is 12 Tb per run, equivalent to 3-4 NovaSeqs
- December Mk1C MinION
Flongle flow cells should be able to be chuckable; throwing away electronics is difficult.
Multiple pores - Coverage will be reduced because errors aren’t correlated. As long as you can pick the right one, which is the high and low quality base.
Trying to get the same kits and settings on all devices.
SmidgION - We can make it, that’s not a problem. ASIC is reallyy cheap. Flongle comes first.
R10 pore - frequency of homopolymers. Current software can call homopolymers as -1, some bias in software that is consistently doing it wrong. Will get a lot further with just incremental improvements.
Lightning Talks / Day 2 (Thomas Bray)
Franz Josef Müller
- Nanopore sequencing of short repetitive DNA
- A number of short repeat disorders (e.g. fragile X: GGC repeat, usually 30-50, but can expand up to 200 repeats)
- Why nanopore sequencing? Looking at kilobase stretches of 100% GC sequence, southern blot remains a diagnostic tool
- Good news: raw signal allows tracing / counting cases
- Regardless of the system, no real ground truth of the repeat number (in the normal case)
- Used nanopore-simulation (crohrandt)
- Pulls data, generates raw signal
- Entered defined repeat expansions based on the ground truth
- Made another tool: nanostrike
- Uses a signal model to identify repeat boundaries, quite good agreement with manual counts
- Cpf1 enrichment
- Poster actually describes a different tool
- University of gronigen
- Looking at vancomycin-resistant enterococci
- Intrinsic resistance to several microbial classes
- VRE dissemination can exchange mobile genetic elements
- Colleagues studying 36 isolates
- Did Illumina sequencing, found 7 clusters
- Some strains were clustered differently, but isolated from the same patient, or the same ward
- Wanted to look at transposons
- [Showed photo of PI - John, because he kept showing photos of her] – she did the sequencing herself, it wasn’t John’s work
- No shearing, assemblies using Unicycler
- Could see transposons, look for differences, two on the chromosome, other two in a plasmid
- Looking back at the tree, found that core genes could be linked by common transposons
- Important to study structures, combine short-read sequencing with long-read sequencing
- Works with microbes and arthropods
- Microbes that pass from mother to offspring
- Image of maternal transmission to oocytes, paternal transmission as well (but rare)
- Biologically important, can alter vector competence
- Can, for example, kill of male offspring
- Has been hard to complete genomes
- Only a minority of microbes can be cultured outside the host
- Microbes have highly repetitive genomes
- Repetitive content is difficult to resolve
- Trying to make an improved reference genome (estimated size of about 3.5 Mb)
- Large amount of extra-chromosomal elements; PacBio also failed
- Two different library protocols. Very long reads provided the ability to assemble the main chromosome into a single circular contig - got extra-chromosomal elements as well
- Likes idea of VolTRAX-based single-cell sequencer
- Doing Illumina sequencing, need to fragment DNA, end up with only 3´ sequences
- Problem with amplification, need to correctly identify UMIs and cell barcodes
- Clustering gets easier if you know the barcode + UMI sequences
- Did 1 nanopore run with 190 cells, 30M reads needed for 5X UMI
- Did 951 cells using targeted nanopore sequencing
- With one cell: reads with the same UMI should be the same molecule; some outliers
- Nanopore data can be linked to Illumina
- Good idea to use Illumina data with single-cell data
- Sequencing core laboratory
- Started analysis with cancer cells
- 9 patients, some initial success for cancer results
- Used the same process to genotype malaria patients
- Making a geographic distribution of modifications
- Moving to Papua, needed to modify protocols
- Replaced PCR with isothermal amplification
- Expanded to global G-RAID meeting
- BBC (UK) found out about this
- Wanted to do the first human cancer genome
- With the field kit, first do Malaria in Indonesia
- In spare time, likes to detect unnatural bases (e.g. isoG, isoC)
- Detection method should be PCR-free and polymerase-independent
- MAX: looking at different restriction enzymes, method applicable for certain sequencing contexts
- Developed iCG ased on sequencing reference DNA samples
- Unnatural bases have substantial influence
- Automated model, does event correction
- During event correction, bad events are redistributed
- Data points are based on Dynamic Time Warping, shift to correct misalignment due to unnatural base calling
- Quite potentially adoptible to other bases or base calling
- Haven’t tested against existing modified natural bases
- ONT managed to fit her name on a MinION two years ago
- Want to be able to sequence all RNA viruses with a single protocol
- Charles Chiu published a protocol: reverse transcription using a random nonamer [9-mer]
- creates a cDNA library
Samples for testing
- Samples that were the most prevalent: chikungunya & Dengue
- Had to test sensitivity as well
- Even at higher CTs, can get the whole genome with the random nonamer protocol
- Percentage of reads (coverage) on MinION match Illumina and MinION
- What about when you don’t know what is there?
- When you can assemble 80% of the genome, it’s probably there
- Three different kits, consistent results
Taking it into the field
- Testing on Lassa virus, a very divergent RNA virus
- Lassa is endemic in Nigeria, outbreaks twice a year
- Take positive samples for sequencing
- Working great until Nigeria hit more cases than were ever before seen
- Got data out as soon as it was generated
- Only doing DNAse treatment
- Using Canu to assemble, then BLAST to find the closest reference
- Used De-novo approach to feed mapping
- Existing in-country diagnosis uses two RTPCRs
- Looked at the phylogeny; MinION assemblies were all spread around, all independent spillovers
- MinION is definitely accurate enough to tell if there’s human to human transmission
- There were two samples that looked marvelously identical
- Looked at patient forms, found out that they were from the same patient, taken 6 hours apart, one SNP difference
- NCDC and WHO had a great need to know what was happening; the country as well…
- Country took the time and attention to listen
- MinION overheated in the first run
- Time scale changed massively, changed from pilot run to something else
- Were able to make 35 lassa virus sequences in a month