150514 jts london_calling

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

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

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

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

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

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

8
add sequence GCTCGAT to graph

9
add sequence GCTCGATT to graph

10
maximum weight path GCTCGAT is the corrected read

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

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

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

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

Generating Events
• What do we expect events from a given sequence to look like?
17
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

Generating Events
18
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

Generating Events
19
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

Generating Events
20
GCTACGATT
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0.00 0.25 0.50 0.75 1.00 1.25
time (s)Current(pA)

Generating Events
21
CTACGATT
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time (s)Current(pA)

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

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)

Complications
24
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Current(pA)

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time (s)
Current(pA)
Is this an event ?

Complications
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50
60
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0.0 0.2 0.4 0.6
time (s)
Current(pA) One event or two ?

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, ⇥)

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)

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

30
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0.2
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0 5 10 15 20
absolute difference (pA)
SkipProbability

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
5mercou
0
5000
10000
0 5000 10000
5mercountinpolishedassembly
C D
Draft: 98.5% accuracy Polished: 99.5% accuracy

Assembly Accuracy
32
0
3000
6000
9000
12000
kmer
count
draft
reference
9000
12000
B
D
0
0 5000 10000
5mer
0
3000
kmer
0
5000
10000
0 5000 10000
5mercountinpolishedassembly
0
3000
6000
9000
12000
kmer
count
polished
reference
C D

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

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)

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

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