170216 jts agbt_final

Nanopore sequencing of a human genome

AGBTFebruary 2017

Jared Simpson Ontario Institute for Cancer Research

& Department of Computer Science

University of Toronto

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

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

Nanopore Sequencing

4Illustration by David Eccles

Nanopore Sequencing

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* illustrative simulation - not real data

Nanopore Sequencing

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0.0 0.5 1.0time (s)

Cur

rent

(pA)


Signal-level Analysis

10

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0.0 0.5 1.0time (s)

Cur

rent

(pA)

What DNA sequence generated these samples?


11

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0.0 0.5 1.0time (s)

Cur

rent

(pA)

What DNA sequence generated these samples?

basecalling: predict sequence from the events


12

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30

40

50

60

70

0.0 0.5 1.0time (s)

Cur

rent

(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

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

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

…

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

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

Average Read Length

17Credit: John Tyson

Birmingham East Anglia Nottingham British Columbia Santa Cruz

AverageRead

Length

Fresh Cell DNA

Rapid Library Kit

Fresh Cell DNA

6.6kb

Accuracy

18

0

25

50

75

100

FAB2

3716

FAB3

9043

FAB3

9075

FAB3

9088

FAB4

1174

FAB4

2205

FAB4

2260

FAB4

2316

FAB4

2395

FAB4

2451

FAB4

2473

FAB4

2476

FAB4

2561

FAB4

2704

FAB4

2706

FAB4

2798

FAB4

2804

FAB4

2810

FAB4

2828

FAB4

3577

FAB4

4989

FAB4

5271

FAB4

5277

FAB4

5280

FAB4

5321

FAB4

5332

FAB4

6664

FAB4

6683

FAB4

9164

FAB4

9712

FAB4

9908

FAB4

9914

FAF0

1127

FAF0

1132

FAF0

1169

FAF0

1253

FAF0

1441

FAF0

4090

Flowcell

Perc

ent I

dent

ity

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

NG50 3.0Mbp

Canu Assembly Contiguity

Credit: Sergey Koren

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

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 Identitycanu 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%

Human Assembly Consensus

22

Remaining errors: some homopolymers, microsatellites, large differences that are difficult to polish.

Assembly Percent Identitycanu 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

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

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

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

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 100accuracy

density version

R7.3−2DR9.2−2DR9.4−1D^2

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

Detecting Base Modifications

28

Schreiber, et al. PNAS. (2013)Laszlo, et al. PNAS (2013)

Slide courtesy of Winston Timp

Training Methylation Models

29

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

NA12878 Methylation

30

Haplotype-Phased Methylation

31

nanopolish has experimental support for phasing methylation patterns


32


this haplotype is highly methylated


33


this haplotype isn’t


34

Het A>G SNP creates a CpG site, called as methylated

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

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