AGBT 2017 Talk

1. Nanopore sequencing of a human genome
AGBT
February 2017
Jared Simpson
Ontario Institute for Cancer Research
&
Department of Computer Science
University ofToronto

2. Overview
• Brief intro to nanopore sequencing and signal-level analysis
• Initial results from sequencing a human genome at high coverage
• Direct detection of cytosine methylation
2

3. Overview
• Brief intro to nanopore sequencing and signal-level analysis
• Initial results from sequencing a human genome at high coverage
• Direct detection of cytosine methylation
3
Disclosure: ONT provides research funding to my lab

4. Nanopore Sequencing
4
Illustration by David Eccles

5. Nanopore Sequencing
5
GCTACGATT
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0.0 0.5 1.0
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* illustrative simulation - not real data

6. Nanopore Sequencing
6
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30
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0.0 0.5 1.0
time (s)
Current(pA)
* illustrative simulation - not real data

7. Nanopore Sequencing
7
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30
40
50
60
70
0.0 0.5 1.0
time (s)
Current(pA)
* illustrative simulation - not real data

8. Nanopore Sequencing
8
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time (s)
Current(pA)
* illustrative simulation - not real data

9. Nanopore Sequencing
9
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0.0 0.5 1.0
time (s)
Current(pA)
* illustrative simulation - not real data

10. Signal-level Analysis
10
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30
40
50
60
70
0.0 0.5 1.0
time (s)
Current(pA)
What DNA sequence generated these samples?

11. Signal-level Analysis
11
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0.0 0.5 1.0
time (s)
Current(pA)
What DNA sequence generated these samples?
basecalling: predict sequence from the events

12. Signal-level Analysis
12
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40
50
60
70
0.0 0.5 1.0
time (s)
Current(pA)
What DNA sequence generated these samples?
GCTAC
basecalling: predict sequence from the events
observed signal depends on multiple bases;
events are labelled with 6-mers

13. Signal-level Analysis
13
First generation basecallers used hidden Markov models
Calculate best sequence of 6-mers using
gaussian emission distributions
Latest basecallers using recurrent neural networks to
capture longer range dependencies
Predict 6-mer label for each event using RNN,
assemble 6-mers into basecalled reads
input output
Examples: R7 metrichor, nanocall
Examples: R9 metrichor, nanonet, deepnano

14. Nanopolish
• Toolkit for working with signal-level data
• Originally designed for improving a consensus sequence using the
signals from multiple reads
• Extended to call SNPs for the mobile Ebola sequencing project
• A few new features in development that I’ll talk about later
14
P(D|S)
…ACTACGATCGACTTA…
…ACTACCATCGACTTA…
…ACTACGATC-ACTTA…
…ACTACCATC-ACTTA…
-176
-191
-168
-185
D1
D2
Dn
…

15. Human Sequencing Consortium
15
Group of MinION users put flowcells together to sequence a
human genome. Data publicly available on github/AWS.
https://github.com/nanopore-wgs-consortium/NA12878

16. Flowcell Yield
16
Fresh
Cell
DNA
Rapid
Library
Kit
Birmingham East
Anglia Nottingham British
Columbia Santa
Cruz
Credit: John Tyson
Fresh
Cell
DNA
Yield
2.3Gb

17. Average Read Length
17Credit: John Tyson
Birmingham East
Anglia Nottingham British
Columbia Santa
Cruz
Average
Read
Length
Fresh
Cell
DNA
Rapid
Library
Kit
Fresh
Cell
DNA
6.6kb

18. Accuracy
18
0
25
50
75
100
FAB23716
FAB39043
FAB39075
FAB39088
FAB41174
FAB42205
FAB42260
FAB42316
FAB42395
FAB42451
FAB42473
FAB42476
FAB42561
FAB42704
FAB42706
FAB42798
FAB42804
FAB42810
FAB42828
FAB43577
FAB44989
FAB45271
FAB45277
FAB45280
FAB45321
FAB45332
FAB46664
FAB46683
FAB49164
FAB49712
FAB49908
FAB49914
FAF01127
FAF01132
FAF01169
FAF01253
FAF01441
FAF04090
Flowcell
PercentIdentity

19. NG50 3.0Mbp
Canu Assembly Contiguity
Credit: Sergey Koren

20. NG50 45.8Mbp
Canu Assembly + HiC Scaffolding
Topological domains in mammalian genomes identified by analysis of chromatin interactions. Dixon et al. Nature Methods (2012)
Scaffolding of long read assemblies using long range contact information. Ghurye et al. Biorxiv (2016)
Credit: Sergey Koren

21. Human Assembly Consensus
21
- This is a work in progress
- Polishing a single 6 Mbp chr20 contig
- 30X data set is NA12878 consortium data only
- 60X data set includes 30X PCR-amplified NA12878 provided by ONT
- stats calculated from bwa mem alignments to GRCh38
- differences that matched an NA12878 variant were not consider an error
Assembly Percent Identity
canu 94.8%
canu+racon 96.5%
canu+racon+nanopolish (30X) 99.1%
canu+racon+nanopolish (60X) 99.4%
canu+racon+nanopolish (60X) + pilon 99.6%

22. Human Assembly Consensus
22
Remaining errors: some homopolymers,
microsatellites, large differences that are difficult
to polish.
Assembly Percent Identity
canu 94.8%
canu+racon 96.5%
canu+racon+nanopolish (30X) 99.1%
canu+racon+nanopolish (60X) 99.4%
canu+racon+nanopolish (60X) + pilon 99.6%
- This is a work in progress

23. Data Improvements
• Homopolymers have been the main source of residual errors in
ONT assemblies
• Earlier basecallers would collapse homopolymers to a 6-mer
• Newest ONT basecaller (“scrappie”) estimates the homopolymer
length
23

24. Scrappie basecalls
24
Sequence
Scrappie
Nanonet
[0 - 28]
[0 - 23]
[0 - 28]
Metrichor Coverage
Nanonet Coverage
Scrappie Coverage
Metrichor
23,389,180 bp 23,389,200 bp 23,389,220 bp 23,389,240 bp 23,389,260 bp 23,389,280 bp 23,389,300 bp
135 bp
chr20
Credit: Sergey Koren

25. Data Improvements
• “2D” reads use a hairpin adaptor to read both strands of DNA
• 2D reads have higher accuracy but with high variance due to effect of base
pairing after the pore
• New method of reading both strands: 1D2
25Figure provided by ONT

26. 1D2 Accuracy
26
- All runs are E. coli
- R7.3-2D from Nick Loman
- R9.2-2D from OICR
- R9.4-1D2 provided by ONT
0.0
0.1
0.2
75 80 85 90 95 100
accuracy
density
version
R7.3−2D
R9.2−2D
R9.4−1D^2

27. Next step: Human Assembly v2
• Improvements to basecalling and read accuracy will help our
assembly
• Using scrappie reads from chromosome 20 improves canu
assembly from ~95% to 97.5%
• Planning to polish this assembly
27

28. Detecting Base Modifications
28
Schreiber, et al. PNAS. (2013)Laszlo, et al. PNAS (2013)
Slide courtesy of Winston Timp

29. Training Methylation Models
29
Learn emissions for k-mers
over expanded alphabet
using synthetically
methylated DNA (w/ M.SssI)

30. NA12878 Methylation
30

31. Haplotype-Phased Methylation
31
nanopolish has experimental support for phasing methylation patterns

32. Haplotype-Phased Methylation
32
nanopolish has experimental support for phasing methylation patterns
this haplotype is highly methylated

33. Haplotype-Phased Methylation
33
nanopolish has experimental support for phasing methylation patterns
this haplotype isn’t

34. Haplotype-Phased Methylation
34
Het A>G SNP creates a CpG site, called as methylated

35. Summary
• Improvements to ONT throughput and accuracy have allowed
sequencing of large genomes
• Initial human assembly is highly contiguous
• Further improvements to accuracy are needed, new “scrappie” basecaller is
promising
• 5-mC can be detected directly from signal-level data, concordant
with bisulfite sequencing
35

36. Acknowledgements
OICR: Matei David, Phil Zuzarte, Jonathan Dursi, Lars Jorgensen
Birmingham: Nick Loman, Josh Quick
Johns Hopkins University: Winston Timp, Rachael Workman
NHGRI: Sergey Koren, Adam Phillippy
NA12878 Sequencing: Matt Loose, John Tyson, Miten
Jain, Mark Akeson, Justin O’Grady and many others
contributing analysis
Oxford Nanopore Technologies: Chris Wright, Clive
Brown, Tim Massingham