150514 jts london_calling

Jared Simpson !

Ontario Institute for Cancer Research &

Department of Computer Science University of Toronto

Error correction, assembly and consensus algorithms

for MinION data

London Calling, May 14th, 2015

Our collaboration

2

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

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Long read assembly pipeline

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Error correction

Celera Assembler

Consensus

Input reads

Genome Assembly

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

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Overlap Detection

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we use github.com/thegenemyers/daligner to compute overlaps

Partial Order Graphs

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add read GCTACGAT that we want to correct to graph


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add sequence GCTCGAT to graph


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add sequence GCTCGATT to graph


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maximum weight path GCTCGAT is the corrected read

Error Correction

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Contig Assembly

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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

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C 0= argmax

S2CP (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

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GCT-CGATCGACTTACGAMutate

ACT

... -GGCGCT

... GGGG

P (D|S)

-190 -187 -192 -176 -191 -193 -168 -198 -191 -195 -181

Select new consensusGCT-CGATCGACTTACGA -168

Pore Models

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Generating Events• What do we expect events from a given sequence to look like?

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0.00 0.25 0.50 0.75 1.00 1.25time (s)

Cur

rent

(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,�2k)

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0.0 0.2 0.4 0.6time (s)

Cur

rent

(pA)

Is this an event ?

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0.0 0.2 0.4 0.6time (s)

Cur

rent

(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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Transition Probabilities• Probability of not observing an

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Aligning Events to a Reference• HMM can also align events to a reference genome

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!

• Read about it here: • http://simpsonlab.github.io/2015/04/08/eventalign/

33

http://simpsonlab.github.io/2015/04/08/eventalign/

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

http://github.com/jts/nanocorrect

http://github.com/jts/nanopolish

http://github.com/jts/nanopore-paper-analysis