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

Sequencing landscape

• So many things have been proposed for nanopore sequencing
• The oil industry has problems with microbes
• The shipping industry has problems with ballast tanks

Statistics

• 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

Service Provision

• 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

Angela Brooks

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

Polyadenylated RNAs

• Have non-coding as well as protein-coding
• Can be edited or modified; over 100 types of modifications exist
• Limitations of short-read sequencing
  • Fragmentation
  • Conversion into cDNA
  • doing PCR (even a problem with long-read cDNA)

Nanopore Sequencing

• 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

Observations

• 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

Read fragmentation

• A lot of reads that seemed to be fragmented
• MTCO1
• 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

Full-length assembly

• 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)

Challenges

• 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

Questions

• Looked at squiggle for capturing the 5´ cap (methods in progress)
• Using minimap
• Longest read from titin (aiming for 100 kb)

Clive Brown

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

Introduction

• 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

Other talks

• 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

Platform

• 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

Grab bag

• 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

PromethION

• 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.

PromethION Yields

• 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

• 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.

Progressive unblock

• 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

Software

• 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

MinIT

• 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

Epi2Me

• 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

Epi2Me Commercialisation

• 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

Chemistry improvements

• 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.

Read Length

• 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

Yield Drop-off

• 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)

Unblocking

• 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.

Kits

• 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.

Targeted sequencing

• 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

Field sequencing

• 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

Consensus Accuracy

• 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.

Nanopore solutions

• 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

Pore changes

• 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.

Template changes

• 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]

Flongle

• 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.

Zumbador

• 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)

• Ubiquitious
• Kwick
• Liquid

• Biopsy

• 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.

VolTRAX

• 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.

SkunkWorx

• 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.

Timeline

• 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

Questions

• 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

Natacha Couto

• 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

Stefanos Siozios

• 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

Rainer Waldmann

• 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

Yutaka Suzuki

• 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

Markus Haak

• 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

Liana Kafetzopoulou

Intro

• 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

• RIPL
• 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

Processing

• Only doing DNAse treatment
• PoreChop
• Using Canu to assemble, then BLAST to find the closest reference
• Used De-novo approach to feed mapping

Diagnostics

• 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

Challenges

• MinION overheated in the first run
• Time scale changed massively, changed from pilot run to something else

Outcome

• Were able to make 35 lassa virus sequences in a month