‘KeyGene Nanopore Seminar’
April 2016
Clive G. Brown
C.T.O.
Clive G. Brown
Oxford Nanopore
CTO, Oxford Nanopore
ONT - Company Overview
Formed in 2005 : single-molecule sensing system for DNA/RNA/Prot
Scalable electronic products: MinION™ (USB), PromethION ™,
VolTRAX™
First commercial nanopore products (2014-2015).
Total investment to date > £180M
Experienced management and Board, 240+ employees
Broad intellectual property portfolio: in-house and through collaborations
including Harvard, Oxford, UCSC
www.oxfordnanopore.com
3
Ubiquitous analyses to enable
…an Internet Of Living Things
© Copyright 2016 Oxford
4
For the analysis of any living thing, by
anybody, anywhere and at any time
E.g. species identification
E.g. field testing in epidemics
E.g. environmental monitoring
E.g. infectious disease
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For monitoring pathogen outbreaks
in remote locations)
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© Copyright 2015 Oxford
NANOPORE SEQUENCING IS BEING USED IN TRADITIONAL
LABS IN 50 COUNTRIES… (~1500 INSTALLATIONS)
… and many
emerging
applications in the
field – only truly
portable device with
potential for clinic,
rural/outdoors, home
7
INTRODUCTION TO NANOPORE SEQUENCING - 1
ORIGINAL CONCEPT
Deamer & Branton et al.
~20 years ago
Hundreds of papers and patents
Over 40 MinION papers … since
‘Strand’
Deamer’s Notebook
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Strand Sequencing
Previous publications by Oxford Nanopore collaborators
1996
2009
2010
2012
Key ONT History
2009-2012
Using mostly Board update slides
Confidential
ONT History : Strand Sequencing – (Nov 2010)
Summary of Strand Sequencing Progress
Control of Strand Motion:
Demonstrated control of strand motion through Hemolysin using Phi29
Developed two modes of operation – Polymerase and Exo/Unzipping
Shown that Phi29 can operate as an exonuclease and a polymerase under an applied potential
Moved a complex strand (70mer PhiX fragment) through the nanopore in unzipping mode
Produced a consensus plot of 55 strands from a PhiX fragment showing consistent behaviour of strand
motion
GGATTTCGATGTAGCAGTTGCAATATAAAACTATCAAACTGCCAATTACGACCATTACCACCAAAAGAAGTTTTAAACAATCGG
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCACCCCCCCCCCCCCCCCCCCCTAAAGCTACATCGTCAACGTTATATTTTGATAGTTTGACGGTTAATGCTGGTAATGGTGGTTTTCTTCAAAATTTGTTAGCC
Confidential
ONT History : Strand Sequencing – Current /
Sequence : March 2011
Prediction of Strand States from Triplet Data
Standard UZ07 strand used as the basis for the a prediction from triplets
Consensus plot generated from multiple strands
G
T
G CC
T
A
A
T
T
C
T
G
A
T
T
T
T
T
A
Consensus plot can be manually
overlaid with predicted model
T
T
T
T
T
TT
T
T
G
T
T
T
G
T
G
T
T
G
T
T
T
G
A
C
C
T
T
T
C
A
T
T
C C
C
T
T
A
T
G
G
Data shows strong alignment for
bases in middle section
As the enzyme reaches the back
end of the strand, the states fail
to align
ONT History : Strand Sequencing – Read Length.
March 2011
Consensus plot for the 400 bp fragment
Abasic Section
from Primer
Indication that 400 bp fragment fully unzips
Large current deflection from abasic region in primer indicates correct start juncture
Consistently last about 2 minutes, as expected
Behaviour at the end of the strands are consistent between runs
About half of the states are present in most of the strands
Confidential
ONT History : Strand Sequencing – Current /
Sequence Base Calling. March 2011
Prediction of Strand States from Triplet Data
Consensus plot generated from multiple strands on standard UZ07 strand
A reference signal is produced from combining individual triplet signals
Initial base caller, derived from triplet set,
gives very good sequence agreement
Expected issues coming into the sequence
from the polyC leader
End of the strand shows different behaviour to
the predicted sequence – again, this is
expected as the enzyme loses it’s grip on the
strand
De Novo base call of sequence
Confidential
Strand Sequencing – May 2011
De Bruijn Training – SNPs
Use N to generate a mixture of static strands
Examine the current spread as N is moved from triplet
•
Expect to see single current level past first set
DDDDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDD
Can also examine the exit of the barrel
DDDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDDDDDDD
DDDDDDDDDDDDDNDDDDDDDDDDDDDDDDDDDDDDDDD
DDD – Triplet from De Bruijn
NNN– Random base (G, T, A or C)
DDD– De Bruijn Background
Confidential
Strand Sequencing – Sense and Antisense – May
2011
Reading Around a Hairpin – sequencing the sense and antisense strand
Recent work has shown that Phi29 is predominately a single stranded DNA polymerase
This should mean that if a DNA hairpin is used, the Phi29 will process both sides of the hairpin
Reading the sense and antisense strand from a single molecule would greatly increase sequencing accuracy
The G and T rich region can clearly be seen moving through the pore followed by the TTTT hairpin turn
The antisense strand, predominantly A and C is seen to follow the TTTT turn
Confidential
Strand Sequencing – Movement – June 2011
Effect of Poor Movement on Base-calling
Missing states from problems with movement make base calling from single molecule data difficult
Consensus plots can be generated from multiple single molecule reads
Consensus mapped to static:
Base calls can be made from a consensus of single molecule
reads by mapping to known model
Gaps in the sequence are evident even for known model
•
Need to make improvements to movement to drive progress
~JUNE 2011 ONT DITCHED POLYMERASES
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Confidential
Strand Sequencing – Movement : June’/July 2011
Base Calling Data from Helicase Data
Improvements to the movement allows base calling to be performed on a single molecule
Training set and moving strand chosen to be the same sequence (assumes triplet model fully describes data)
Base calling algorithms used using the triplet data from the De Bruijn k3 training set
States from static training data
Single molecule using a helicase
CCCGCGGGC CTGCTCGTGGTC TTGTTTCC AGCATCAC GAGGATGACTAGTATTACAAGAATAAACCCCTTTTT
||||| |X| ||XX|||||||| ||| |||| || | ||| X|||||X||||X|||| |||||||||||||||||||
CCCGC GTCATCTTATCGTGGTCTTTG TTCCAAG A CACAAAGGATTACTATTATT CAAGAATAAACCCCTTTTT
Single molecule data performs very well – some missed state and miss calls
Good starting point – system need optimisation to reduce errors
Reference
Called
Confidential
Strand Sequencing – Movement July 2011
Comparison Between Phi29 and Helicase Data
Problems with using Phi29 as a motor include:
•
missed states, fast and slow movement regimes, backwards movement at low potential, pausing
Initial work on helicases show that the distribution of movement is a lot more controlled
Helicase
Polymerase
Training Initial Guess – Sept 2011
Read head position map
Iterative methods often require an initial guess at
the model
–
Static training
•
•
•
–
Mapping existing model to a new experimental
condition
•
•
•
–
Mapping the read head position with a 3mer in a
homo-polymer background
Run all 64 3mers in the mapped read head position
Generate a 3mer model
Measured 3mer coefficients
Run a control strand through the pore
Apply linear transformations (shift and scale)
Minimise distance between model and data to
determine transformation
Upgrade to higher k-mer model
Replicate coefficients to upgrade
Linear transformations
to minimise “distance”
Shift
•
3mer to 4mer
upgrade
3mers: c20290
Linear transform: c19489
Scale
Abstraction of training process
Fit data to
model
Initial guess
at model
Model
Estimate model
from fit
Data
Model Out
Converged?
Training process
Currently using an custom adaptation Expectation-Maximisation (EM) algorithm
–
–
–
–
We call this a “Forced Path” model
Sequence of DNA is known
Properties of the system are modelled
The devil is in the detail
Step by step process
–
–
–
–
–
Sequence defines the underlying state path
State (k-mer) model defines the currents observed at each point in the state path
Properties of the movement system, coded in a transition matrix, define movement through the
state path
Calculated data path is combined with other observations of the same sequence
Observations from different sequences are combined and used to re-estimate the state (k-mer)
model
Observations from fragments can be combined to produce a complete PhiX path
Calculating a path (1)
State emission probabilities
Current (pA)
Current (pA)
State (Kmer)
Position in sequence
Transition matrix
Step back
(2->1)
Skip
(2->4)
Advance
(2->3)
Probability
Probability
Stay
(2->2)
Probability
Probability
GAGTTTTATCGCTTCCATGACGCAG
AAGTTAACACTTTCGGATATTTCTGA
TGAGTCGAAAAATTATCTTGATAAAG
CAGGAATTACTACTGCTTGTTTACG
AATTAAATCGAAGTGGACTGCTGGC
GGAAAATGAGAAAATTCGACCTATC
CTTGCGCAGCTCGAGAAGCTCTTAC
TTTGCGACCTTTCGCCATCAACTAA
CGATTCTGTCAAAAACTGACGCGTT
GGATGAGGAGAAGTGGCTTAATATG
CTTGGCACGTTCGTCAAGGACTGGT
TTAGATATGAGTCACATTTTGTTCAT
GGTAGAGATTCTCTTGTTGACATTTT
AAAAGAGCGTGGATTACTATC
Allowed to slip back to
any previous state
(low probability)
Calling The PhiX Path (Sense)
100 bases from the trained PhiX path
–
–
–
–
This is a “consensus”
We’ve derived the model from the same dataset
Where the model tracks the data well we can
determine the correct state sequence
States are translated back to base calls
Where the data and model are close we get the
states correct
Distance (pA)
Reference: GCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACA
*####*
*#################* * *##################* *######* *#####* * * ###########
*#* *##
Calls:
CATACTGCCCGAGATATTTCTAATGTCGTCAATTATGCTGCTTCTGGTGTGGTTCCTATTTTTCCTGGTATTCCTCAGGCTGTTGCCGAATATTGAGACA
Path Position
Sense-Antisense Calling
Sense
Reference: GCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACA
*####*
*#################* * *##################* *######* *#####* * * ###########
*#* *##
Calls:
CATACTGCCCGAGATATTTCTAATGTCGTCAATTATGCTGCTTCTGGTGTGGTTCCTATTTTTCCTGGTATTCCTCAGGCTGTTGCCGAATATTGAGACA
AntiSense
(reversed)
Reference: CGATGACGTTTCCTATAAAGATTACAGCAGTGACTACGACGAAGACCACACCAACTATAAAAAGTACCATAACTATTTCGACAACGGCTATGAACCTTGT
*
*###* * **#* * * * *
* * *##* *# * ###* *
*#* ** ##*#*
*#* ** ** *##*
Calls:
ACAAAGCGTTTAGTCAAAAGGGTCCGGAATGATTCAGAAGAAAGAAAACCCAAACTCCAGGGGCAACCCAAATAATTTCCGGCACGCATAAAAAATTTGT
MORE THAN THE
SUM OFTHE PARTS
Sense-AntiSense
Reference: GCTACTGCAAAGGATATTTCTAATGTCGTCACTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACA
##############################* *##################################################################*
Calls:
GCTACTGCAAAGGATATTTCTAATGTCGTCAGTGATGCTGCTTCTGGTGTGGTTGATATTTTTCATGGTATTGATAAAGCTGTTGCCGATACTTGGAACA
We are calling the path with the model used to generate that path
This is only a 3mer model
Sense path: 3760-3860
AntiSense path: 1524-1624
AGBT2012 : Online f1000
• Strand begun in 2009
• 2011 routine translocation
• 2011 recognition of k-mers
• 2011 use of be-bruijn training strands
• 2011 HMM base calling – de novo
• 2011 Move away from polymerase
• 2011 <=50kb reads
• 2011/2012 PhiX Genome sequencing.
Mostly commercial secrets pre AGBT2012 otherwise in
patents.
08709449.6
EP2126588
18-Feb-08
PCT/08/000563
WO 2008/102121
18-Feb-08
12/527687
2010-0196203
18-Feb-08
08709448.8
EP2122344
18-Feb-08
PCT/08/000562
WO 2008/102120
18-Feb-08
12/527679
2011-0121840
18-Feb-08
PCT/GB2008/004127
WO 2009/077734
15-Dec-08
12/339,956
US2009/0167288
19-Dec-08
61/080,492
14-Jul-08
08863072
2232261
15-Dec-08
EP08006456.1
EP2107040
31-Mar-08
12/409007
US2009274870
23-Mar-09
09784654
EP2310534
06-Jul-09
PCT/GB2009/001690
WO2010/004273
06-Jul-09
13/002717
2011-0177498
06-Jul-09
61/078687
07-Jul-08
09784644
EP2307540
06-Jul-09
13187149.3
268260
02-Oct-13
PCT/GB2009/001679
WO2010/004265
06-Jul-09
13/968,778
US-2014-0051069
16-Sep-13
14/455,394
US2015 0031020
08/08/2014
61/078695
07-Jul-08
08806515.6
WO2010/055307
13-Nov-09
PCT/GB09/002666
WO2010/055307
13-Nov-09
WO2010/086603
29-Jan-10
14/334,285
PCT/GB10/000133
61/148726
30-Jan-09
PCT/GB10/000160
WO2010/086622
29-Jan-10
13/147159
2012-0058468
29-Jan-10
EP10703325
WO2010/086622
29-Jan-10
61/148737
30-Jan-09
EP 10705403
WO2010/086602
29-Jan-10
PCT/GB10/000132
WO2010/086602
29-Jan-10
13/147176
2012-0064599
29-Jan-10
10722740
EP2411538
25-Mar-10
13/260,178
WO2010/109197
25-Mar-10
PCT/GB10/000567
WO2010/109197
25-Mar-10
PCT/GB10/000789
WO2010/122293
19-Apr-10
10716404.8
2422198
19-Apr-10
13/265,448
2012-0133354
19-Apr-10
61/170,729
20-Apr-09
10793017.4
WO2011/067559
01-Dec-10
13/512,937
2012-0322679
01-Dec-10
61/265488
01-Dec-09
PCT/GB2010/002206
WO2011/067559
01-Dec-10
11823878.1
2614156
07-Sep-11
PCT/US2011/001552
WO2012/033524
07-Sep-11
13/821,156
US-2014-0051068
17-09-2013
61/402,903
07-Sep-10
61/574,236
30-Jul-11
61/574,240
30-Jul-11
61/574,237
30-Jul-11
61/574,239
30-Jul-11
61/574,238
30-Jul-11
61/574,235
30-Jul-11
61/574,233
30-Jul-11
PCT/GB2011/01432
30-Sep-11
PCT/GB11/001432
WO2012/042226
31-May-11
11770853.7
2622343
31-May-11
PCT/GB11/001432
WO2012/042226
31-May-11
12703872.7
2673638
10-Feb-12
INTRODUCTION TO NANOPORE SEQUENCING - 2
MANY CHEMISTRIES POSSIBLE
Many components to a nanopore sequencing system
All subject to continuous upgrades….
Motor
(E5, E6, E7)
Nanopore reader
(R7, R8, R9, R10 etc...)
Membrane
(M9, M10 etc...)
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Run Conditions
Salt, fuel, script, temperature...
PORES FOR THOUGHT ?
DIFFERENT SHAPES AND SIZES HAVE BEEN ENGINEERED FOR SEQUENCING
Some public crystal structures available, others obtained by ONT and collaborators
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RELEASED PORE – R9
R9 is…..CsgG PORE FROM E.coli
Nonameric lipoprotein (nine subunits) with a 36 stranded Beta-barrel
Shape, dimensions, and the position of the constriction of R9 make it a better pore for
DNA sequencing
CsgG wildtype has been engineered heavily to enhance its properties > 700 mutants
Considerable ‘head room’ for further improvement
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INTRODUCTION TO NANOPORE SEQUENCING - 3
BASIC PLATFORM DYNAMICS
Pore
Membrane array
ASIC Channels
A single nanopore per well
100s to 100,000s of channels
Many analytes per pore, per channel, per run
Channels/pores asynchronous – no ‘cycles’
MinION currently offers 100s of channels;
products will scale up and down
33
INTRODUCTION TO NANOPORE SEQUENCING - 4
MinION SEQUENCER – USB DEVICE AND FLOWCELL
Consumable flowcell
contains sensing
chemistry, nanopore, and
electronics
Sample added to flowcell here
Sensor chip with
multiple nanopores
Sensor chip works with
custom ASIC for control
and data processing
USB powers device
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MinION docks with flowcell, data
streamed to USB
ASIC is the Core
ONT’s ASIC is the core component of the MinION
•
System chemistry → electronic signal
ASIC influences:
•
Number of parallel recording channels
•
Signal to noise of the nanopore recording
R&D on new generation ASICs progressing well
35
INTRODUCTION TO NANOPORE SEQUENCING - 5
1D AND 2D EXPLAINED
1D - Linear
1D is a rapid library preparation
allowing sequencing of the template
strand.
2D is a slightly longer preparation,
but give more accurate calls using
template and complement reads
…template…
(exit)
2D - Hairpin
Template…
Template…
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…template…hairpin…complement…
…complement…
(exit)
RAPID 1D LIBRARY PREPARATION
GREAT DEMAND FOR A RAPID, LONG READ, LIBRARY PREP
MuA fragments gDNA & adds adapters at same time – 10 min prep
Good performance from MuA libraries
– Modal fragment size is lower than for g-tube shearing, but with very long tail
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LONG READS ON ONT PLATFORM – TOWARDS 1MB
READ LENGTH IS LIMITED BY INPUT DNA, NOT PLATFORM
Improvements in sample-prep protocol yields long-read libraries
Size selection (BluePippin) after library preparation allows exclusively long
fragments
Image of complete 2D read of 250 kb
250 kbase
dsDNA
250 kbase
E.coli prep (extracted using Qiagen
500 tip)
Start - Template
Complement - Exit
Hairpin “turn”
MinION at 40 G per run
6,400G
MkII
Number of Pores
MkI
120G
Throughput per Day (Gb)
R9.2
20G
Translocation Speed (b/s/pore)
© Copyright 2015 Oxford Nanopore Technologies
39
NEW KITS - ALL FAST MODE
NANOPORE SEQUENCING KIT
RAPID SEQUENCING KIT
SK-NSK007 (R9 Version)
SQK-RAD001 (R9 Version)
Ligation Based generating 1D and 2D Reads
Transposome based generating 1D reads
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New Enzyme
2 tubes -> 10 minutes -> Done
Higher speed (250 bps)
Runs at 250 bps
Premixed fuel with running buffer
Premixed fuel with running buffer
https://www.nanoporetech.com/products-services/voltrax
VolTRAX– large format alpha produced
Smaller format being for direct use with the MinION platform
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FAST MODE
ENZYME “MOTOR” CONTROLS SPEED OF DNA MOVING THROUGH NANOPORE
Running slow → enzyme kinetics dominated by single process → exponential kinetics
– Exponential distribution of event length → event detector misses short events
Exponential
Missed data
Minimum event
length
Non-Exponential
fewer deletions
R9
Running fast → enzyme kinetics across multiple processes → non-exponential kinetics
– Fewer missed events leads to high accuracy with increased throughput
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SYSTEM SLOWDOWN AND THROUGHPUT
LOTS OF SCOPE FOR IMPROVING FLOWCELL YIELD
Typically hitting ~30 % theoretical maximum throughput over 24 hours
Blocking is a key factor – lots like sample prep contamination is a major contributor
Control
Nicked
0.0
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Read length (kb)
5.0
‘ACCURACY’ IMPROVEMENTS
UPDATED CHEMISTRY
1D
2D
R7 @ 70 b/s
HMM
R9 @ 250 b/s
HMM
1D
% Accuracy
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2D
‘ACCURACY’ IMPROVEMENTS – MARCH 2016 @ 180MV
UPDATED ALGORITHMS
1D
2D
R7 @ 70 b/s
HMM
R9 @ 250 b/s
RNN
1D
1D ~= Pb
2D ~= Capillary
% Accuracy
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2D
ACCURACY IMPROVEMENTS – APRIL 2016 @200MV
UPDATED ALGORITHMS
1D
2D
R7 @ 70 b/s
HMM
R9 @ 250 b/s
RNN
1D
~92% 1D
~98% 2D
% Accuracy
250mv -> 350mv ? > 99%
2D
INTRODUCTION TO BASECALLING - 1
EVENT DETECTION
Event detection is used to convert raw data to “events” by looking at current transitions
Performed using a non-linear filter based on local t-statistics
Similar starting point for many of the
algorithms, but assumptions less
appropriate at faster speeds.
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INTRODUCTION TO BASECALLING - 4
LSTM-RNN NEURAL NETWORK BASECALLING
Squiggles contain a memory beyond that which is modelled by HMM
Network based on bidirectional long short-term memory (BLSTM) recurrent cells
Input features derived from a window of events
Output is posterior matrix as in HMM Forwards-Backwards, decode in similar way
Achieves better results than HMM, can tune for performance/speed
Scaling z-scaling (zero mean std)
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ANALYSIS OF SEQUENCING DATA
MinKNOW: MinION
experimental control,
QC, data acquisition


Epi2Me (Metrichor Ltd):
cloud-based analyses
designed to let users without
bioinformatics expertise
resolve biological questions.
Currently, basecalling is also
provided to customers
through the cloud
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© Copyright 2016 Oxford Nanopore Technologies
PROMETHION
INSTRUMENT
SEQUENCING MODULE
•
•
•
•
48 individually addressable flow cells
Can also be run together (by sample)
New 3K ASIC - 144,000 channels Total
R9.x chemistry to be shipped.
COMPUTE MODULE
• Local cluster of high performance compute
• Real time data analysis in the box
• Web based administration…can be run by a simple tablet
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Detachable Sensing Chip
Sensor Array Chemistry
Sensor chip architecture redesigned for more controlled membrane formation
Micro-patterning of sensor surface
controls the polymer fluid used to form
membranes
1000s of individually addressable
nanopore membranes formed
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PROMETHION COMPUTE
COMPUTE MODULE – INITIAL SPECIFICATIONS
12/24x quad-core i7 slave nodes plus one i7
management node
2x 1 GBit ethernet management ports
12/24x 2 TB SSD data buffer storage
"Slurm” job scheduling cluster
Runs Ubuntu 14.04 LTS internally [possibly 16.04
LTS later this year]
Web-based administration and operation
interface
Will mount CIFS or NFS external storage,
[possibly others]
2x 10 GBit fibre uplinks
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[1x 1 GBit ethernet developer port (debugging)]
PROMETHION
BUILDING CAPACITY
THE PEAP QUEUE
First box shipment is imminent
First come first served
Team prepared to build initially 4-6 per month
Later this year 10 per month will achievable
with current set up
2017 scale up is being planned
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Humbling to see the number of deposits coming in
Targeting delivery to everyone before the end of
the year
Talk to us if having the deposit committed is an
issue and you need to leave the queue
Sensing from Biology
Membrane chemistry robust to sensing from biological samples
Polymer
membrane
replaces
traditional “lipid
bilayer”
Sequencing from blood is possible with the MinION system
Minimal library preparation
needed to get the sample
ready for sequencing
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SUMMARY
Nanopore sequencing is here and maturing rapidly.
Accuracy, read lengths and throughput.
Usable for Human and other large genomes.
Devices to sample directly from biology (e.g. blood).
Direct detection of base modifications.
Cloud based analyses.
Direct RNA sequencing.
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Where might it all go?
201x ?
© Copyright 2015 Oxford
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Where could devices go?
Remote
environmental
At home
Rural monitoring eg
crops
Food chain/quality assurance
Farm
Food
processing
Retail
Home
London Calling Conference 2016
26-27 May. London. Open to all
Plenary sessions │ Breakouts │ Posters │ Conference dinner
Register at londoncallingconf.co.uk
@nanoporeconf
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