HIV-1 drug resistance testing using Oxford Nanopores MinION a - - PowerPoint PPT Presentation

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HIV-1 drug resistance testing using Oxford Nanopores MinION a long-range low error sequencing approach Alexander Thielen AREVIR 2017 Oxford Nanopores technology Oxford Nanopores technology long range sequencing Oxford

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HIV-1 drug resistance testing using Oxford Nanopore’s MinION – a long-range low error sequencing approach

Alexander Thielen

AREVIR 2017

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Oxford Nanopore’s technology

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– long range sequencing

Oxford Nanopore’s technology

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– long range sequencing – very fast

Oxford Nanopore’s technology

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Oxford Nanopore’s technology

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– long range sequencing – very fast – portability / global access (search for “Massimo Delledonne: MinIONs and Nanofrogs“)

Oxford Nanopore’s technology

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Oxford Nanopore’s MinION

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– long range sequencing – very fast – portability / global access (search for “Massimo Delledonne: MinIONs and Nanofrogs“)

Oxford Nanopore’s technology

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– long range sequencing – very fast – portability / global access (search for “Massimo Delledonne: MinIONs and Nanofrogs“) – relatively cheap, no capital cost – but: high error rate

Oxford Nanopore’s technology

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Oxford Nanopore’s error rate

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Error rate reduction using an Rolling Circle Amplification approach

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cDNA PR RT RNaseH INT 3.1kb

M13for M13rev

MID1 MID2

RCA Approach: 1. Amplification

PCR product

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Self-ligation using T4 ligase, digestion of non-circularized fragments

RCA Approach: 2. Circularization

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random priming

RCA Approach: 3. Rolling Circle Amplification

isothermal amplification by phi29 polymerase

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RCA Approach: 3. Rolling Circle Amplification

80-100 kb copyn copy1 copy2 Amplification products are sheared to ~20kb Final products:

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RCA Approach: 4. Library Preparation & Sequencing

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RCA Approach: 5. Analysis

Individual copies are detected in the sequenced reads and aligned against each other A consensus sequence is generated 80-100 kb copyn copy1 copy2 copyn copy1 copy2 ... consensus x x x x x x x x

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– except for homopolymers, errors shoud occur randomly – consensus sequences should therefore reduce error rates – accuracy simulations:

RCA accuracy estimation

Amplicon count Estimated RCA Accuracy R7 sim.

1 75.00% 80.00% 85.00% 90.00% 77.91% 3 84.38% 89.60% 93.93% 97.20% 84.41% 5 89.65% 94.21% 97.34% 99.14% 89.38% 7 92.94% 96.67% 98.79% 99.73% 91.49% 9 95.11% 98.04% 99.44% 99.91% 93.03%

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– 16 samples were prepared with the RCA approach (RCA- 1D) – for comparison, the same amplified 3.1kb products were also sequenced with

Illumina MiSeq, with Nextera XT tagmentation (MiSeq)
MinION with 1D Ligation Kit without fragmentation & RCA (1D-
nly)

– MiSeq results were used as gold-standard

Experiment

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– MinION output:

1D-only:

240.000 reads mapped onto HIV-1 1300 bp (range: 31-3098 bp)

RCA-1D:

198.000 reads mapped onto HIV-1 1698 bp (range: 173-82.556 bp)

Results

log10 read length Frequency 1 2 3 4 5 1000 2000 3000

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– Error rates (when only one copy of the amplified region is considered):

1D-only:

14.7% in total, 6.4% substitution errors, 5.6% deletions, and 1.1% insertions

RCA-1D:

14.7% in total, 6.0% mismatches, 6.3%, deletions 0.8% insertions  RCA has no effect on raw error rates

Results

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– median RCA consensus error rates:

Results

region copies total substitution deletion insertion 1 14.72% 6.03% 6.29% 0.77% 2 11.25% 5.63% 3.18% 1.00% 3 11.00% 3.49% 6.02% 0.18% 4 7.39% 1.64% 4.76% 0.00% 5 6.12% 1.22% 3.97% 0.00% 6 4.44% 0.94% 2.79% 0.00% 7 2.76% 0.00% 1.56% 0.00% 8 1.78% 0.00% 0.00% 0.00% 9 1.56% 0.00% 0.00% 0.00% 10 1.47% 0.00% 0.00% 0.00% 11 1.2% 0.00% 0.00% 0.00%

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Results

2,00%

0,00% 2,00% 4,00% 6,00% 8,00% 10,00% 12,00% 14,00% 16,00% 1 2 3 4 5 6 7 8 9 10 11 12

error rates

total substitution deletion insertions

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RCA accuracy estimation

vs. experiment

Amplicon count Estimated RCA Accuracy R7 sim. R9 exp.

1 75.00% 80.00% 85.00% 90.00% 77.91% 85.28% 3 84.38% 89.60% 93.93% 97.20% 84.41% 89.00% 5 89.65% 94.21% 97.34% 99.14% 89.38% 93.88% 7 92.94% 96.67% 98.79% 99.73% 91.49% 97.24% 9 95.11% 98.04% 99.44% 99.91% 93.03% 98.44%

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– Basecalling errors at different sensitivity cut-offs: – Basecalling errors almost entirely within or adjacent to homo-polymers

Results

0,00% 5,00% 10,00% 15,00% 20,00% 25,00% 30,00% 35,00% 2 4 6 8 10 12 5% 10% 15% 20%

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16-76608: M184V, K219R, E138K ~ 100% V108I: 77% V179I: 30% H221Y: 24% V106I: 4.4%

Outlook: Long-range covariation analysis

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Acknowledgments

Kirsten Becker Nina Engel Anja Förster Anna Memmer Bettina Spielberger Martin Däumer Bernhard Thiele