1. Jared Simpson
!
Ontario Institute for Cancer Research
&
Department of Computer Science
University ofToronto
Error correction, assembly
and consensus algorithms
for MinION data
London Calling, May 14th, 2015

2. Our collaboration
2

3. An overview of NGS assembly
• Illumina data: short reads, very accurate, very deep
• nearly all Illumina assembly is based on exact matching algorithms
• fragmented assemblies
!
• Algorithms for Illumina data do not work for long, noisy reads
• PacBio developed a pipeline (“HGAP”) to assemble their data
• We used this recipe as a starting point but with custom components
3

4. Long read assembly pipeline
4
Error correction
Celera Assembler
Consensus
Input reads
Genome Assembly

5. Input Data
• First challenge is finding overlaps for reads with 15-20% errors
5

6. Overlap Detection
6
we use github.com/thegenemyers/daligner to compute overlaps

7. Partial Order Graphs
7
add read GCTACGAT that we want to correct to graph

8. Partial Order Graphs
8
add sequence GCTCGAT to graph

9. Partial Order Graphs
9
add sequence GCTCGATT to graph

10. Partial Order Graphs
10
maximum weight path GCTCGAT is the corrected read

11. Error Correction
11

12. Contig Assembly
12
Celera Assembler produces one contig at 98.5% identity

13. Assembly Polishing
• Consensus problem is viewed as choosing a sequence C’ that maximizes
the probability of the event data
13
C0
= arg max
S2C
P(D|S)
P(D|S) =
rY
k=1
P(ei,k, ei+1,k, ..., ej,k|S, ⇥)
where

14. Selecting a Consensus
14
Mutate
ACTACGATCGACTTACGA
CCTACGATCGACTTACGA
TCTACGATCGACTTACGA
...
-CTACGATCGACTTACGA
G-TACGATCGACTTACGA
GC-ACGATCGACTTACGA
GCT-CGATCGACTTACGA
...
GACTACGATCGACTTACGA
GCCTACGATCGACTTACGA
GGCTACGATCGACTTACGA
GTCTACGATCGACTTACGA
P(D|S)
-190
-187
-192
-176
-191
-193
-168
-198
-191
-195
-181
GCTACGATCGACTTACGA

15. Selecting a Consensus
15
GCT-CGATCGACTTACGA
Mutate
A
C
T
...
-
G
GC
GCT
...
G
G
G
G
P(D|S)
-190
-187
-192
-176
-191
-193
-168
-198
-191
-195
-181
Select new consensus
GCT-CGATCGACTTACGA -168

16. Pore Models
16

17. Generating Events
• What do we expect events from a given sequence to look like?
17
GCTACGATT
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time (s)Current(pA)

18. Generating Events
• What do we expect events from a given sequence to look like?
18
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

19. Generating Events
• What do we expect events from a given sequence to look like?
19
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

20. Generating Events
• What do we expect events from a given sequence to look like?
20
GCTACGATT
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50
75
100
0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

21. Generating Events
• What do we expect events from a given sequence to look like?
21
CTACGATT
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22. Event Detection
22
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time (s)Current(pA)
Event mean current (pA) current stdv duration (s)
1 60.3 0.7 0.521
2 40.6 1.0 0.112
3 52.2 2.0 0.356
4 54.1 1.2 0.291
5 49.5 1.5 0.141

23. A simple model
• What is the probability of observing events E given a sequence S?
• Assuming for the moment there are no missing or extra events:
23
P(e1, e2, ..., en|s1, s2, ..., sn, ⇥) =
nY
i=1
P(ei|si, µsi , si )
P(ei|k, µk, k) = N(µk, 2
k)

24. Complications
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25. Complications
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time (s)
Current(pA)
Is this an event ?

26. Complications
26
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0.0 0.2 0.4 0.6
time (s)
Current(pA) One event or two ?

27. Nanopore HMM
• must consider:
• over segmentation
• under segmentation
• missed short events
• HMM:
• M states: match event to 5-mers
• E states: extra obs. of an event
• K states: no event obs. for 5-mer
27
P(D|S)
P(⇡, e1, e2, ..., en|S, ⇥) =
nY
i=1
P(ei|⇡i, µsi , si )P(⇡i|⇡i 1, S)
P(e1, e2, ..., en|S, ⇥) =
X
⇡
P(⇡, e1, e2, ..., en|S, ⇥)

28. Transition Probabilities
• Probability of not observing an
event is a function of absolute
difference between (expected)
current
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time (s)
Current(pA)

29. Transition Probabilities
• Probability of not observing an
event is a function of absolute
difference between (expected)
current
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time (s)
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30. Transition Probabilities
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absolute difference (pA)
SkipProbability

31. Assembly Accuracy
31
0
5000
10000
0 5000 10000
5 mer count in reference
5mercountindraftassembly
0
3000
6000
9000
12000
TTTTT AAAAA TTTTG CAAAA CTTTT AAAAG CCCCC GGGGG
kmer
count
draft
reference
y
12000
A
C
B
D
0
5000
0 5000 10000
5 mer count in reference
5mercou
0
5000
10000
0 5000 10000
5 mer count in reference
5mercountinpolishedassembly
C D
Draft: 98.5% accuracy Polished: 99.5% accuracy

32. Assembly Accuracy
32
0
3000
6000
9000
12000
TTTTT AAAAA TTTTG CAAAA CTTTT AAAAG CCCCC GGGGG
kmer
count
draft
reference
9000
12000
B
D
0
0 5000 10000
5 mer count in reference
5mer
0
3000
TTTTT AAAAA TTTTG CAAAA CTTTT AAAAG CCCCC GGGGG
kmer
0
5000
10000
0 5000 10000
5 mer count in reference
5mercountinpolishedassembly
0
3000
6000
9000
12000
TTTTT AAAAA TTTTG CAAAA CTTTT AAAAG CCCCC GGGGG
kmer
count
polished
reference
C D

33. Aligning Events to a Reference
• HMM can also align events to a reference genome
!
!
!
!
!
• Read about it here:
• http://simpsonlab.github.io/2015/04/08/eventalign/
33

34. Planned Improvements
• Model dwell duration to better call homopolymers
!
!
!
• SNP calling/genotyping
!
!
!
• Improve scalability to handle larger genomes
• Use signal data during error correction
34
CTAAAAAAAAAAAAGTACA
P(gi|D) =
P(D|gi)P(gi)
P(D)

35. Acknowledgements & Code
• Collaborators:
• Nick Loman, Josh Quick (Birmingham)
• Jonathan Dursi (OICR)
!
!
• Code:
• github.com/jts/nanocorrect (error correction)
• github.com/jts/nanopolish (signal-level algorithms)
• github.com/jts/nanopore-paper-analysis (reproduce our paper)
35