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170216 jts agbt_final

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AGBT 2017 Talk

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170216 jts agbt_final

  1. 1. Nanopore sequencing of a human genome AGBT February 2017 Jared Simpson Ontario Institute for Cancer Research & Department of Computer Science University ofToronto
  2. 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 2
  3. 3. 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
  4. 4. Nanopore Sequencing 4 Illustration by David Eccles
  5. 5. Nanopore Sequencing 5 GCTACGATT Sample Current ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) * illustrative simulation - not real data
  6. 6. Nanopore Sequencing 6 GCTACGATT Sample Current ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) * illustrative simulation - not real data
  7. 7. Nanopore Sequencing 7 GCTACGATT Sample Current ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) * illustrative simulation - not real data
  8. 8. Nanopore Sequencing 8 GCTACGATT Sample Current ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ●● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) * illustrative simulation - not real data
  9. 9. Nanopore Sequencing 9 CTACGATT Sample Current ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ●● ● ● ● ●●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ●●● ● ●●● ●● ●●● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●●● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) * illustrative simulation - not real data
  10. 10. Signal-level Analysis 10 ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ●● ● ● ● ●●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ●●● ● ●●● ●● ●●● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●●● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) What DNA sequence generated these samples?
  11. 11. Signal-level Analysis 11 ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ●● ● ● ● ●●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ●●● ● ●●● ●● ●●● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●●● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(pA) What DNA sequence generated these samples? basecalling: predict sequence from the events
  12. 12. Signal-level Analysis 12 ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ●●● ● ●● ● ● ●● ●● ● ● ●●●●● ● ● ●●● ●● ● ●● ● ●● ● ● ● ●● ●●●●●● ●● ● ● ● ● ● ● ● ●●● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ● ●●●●●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ●● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ●● ●●● ●● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ●●●● ●● ● ● ●● ●● ●● ●●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ●● ●●● ●● ●● ● ● ● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ●● ●●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●●●● ● ●● ● ● ● ●●●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ●● ●●● ● ●●● ●● ●●● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●●● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 30 40 50 60 70 0.0 0.5 1.0 time (s) Current(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. 13. Signal-level Analysis 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
  14. 14. 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 …
  15. 15. 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
  16. 16. 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
  17. 17. Average Read Length 17Credit: John Tyson Birmingham East  Anglia Nottingham British  Columbia Santa  Cruz Average Read Length Fresh  Cell  DNA Rapid  Library  Kit Fresh  Cell  DNA 6.6kb
  18. 18. Accuracy 18 0 25 50 75 100 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 Flowcell PercentIdentity
  19. 19. NG50 3.0Mbp Canu Assembly Contiguity Credit: Sergey Koren
  20. 20. 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
  21. 21. 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 Identity canu 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%
  22. 22. Human Assembly Consensus 22 Remaining errors: some homopolymers, microsatellites, large differences that are difficult to polish. Assembly Percent Identity canu 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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 95 100 accuracy density version R7.3−2D R9.2−2D R9.4−1D^2
  27. 27. 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
  28. 28. Detecting Base Modifications 28 Schreiber, et al. PNAS. (2013)Laszlo, et al. PNAS (2013) Slide courtesy of Winston Timp
  29. 29. Training Methylation Models 29 Learn emissions for k-mers over expanded alphabet using synthetically methylated DNA (w/ M.SssI)
  30. 30. NA12878 Methylation 30
  31. 31. Haplotype-Phased Methylation 31 nanopolish has experimental support for phasing methylation patterns
  32. 32. Haplotype-Phased Methylation 32 nanopolish has experimental support for phasing methylation patterns this haplotype is highly methylated
  33. 33. Haplotype-Phased Methylation 33 nanopolish has experimental support for phasing methylation patterns this haplotype isn’t
  34. 34. Haplotype-Phased Methylation 34 Het A>G SNP creates a CpG site, called as methylated
  35. 35. 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
  36. 36. 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

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