Real-time, portable genome sequencing for Ebola surveillance

Journal name:
Nature
Volume:
530,
Pages:
228–232
Date published:
DOI:
doi:10.1038/nature16996
Received
Accepted
Published online

The Ebola virus disease epidemic in West Africa is the largest on record, responsible for over 28,599 cases and more than 11,299 deaths1. Genome sequencing in viral outbreaks is desirable to characterize the infectious agent and determine its evolutionary rate. Genome sequencing also allows the identification of signatures of host adaptation, identification and monitoring of diagnostic targets, and characterization of responses to vaccines and treatments. The Ebola virus (EBOV) genome substitution rate in the Makona strain has been estimated at between 0.87 × 10−3 and 1.42 × 10−3 mutations per site per year. This is equivalent to 16–27 mutations in each genome, meaning that sequences diverge rapidly enough to identify distinct sub-lineages during a prolonged epidemic2, 3, 4, 5, 6, 7. Genome sequencing provides a high-resolution view of pathogen evolution and is increasingly sought after for outbreak surveillance. Sequence data may be used to guide control measures, but only if the results are generated quickly enough to inform interventions8. Genomic surveillance during the epidemic has been sporadic owing to a lack of local sequencing capacity coupled with practical difficulties transporting samples to remote sequencing facilities9. To address this problem, here we devise a genomic surveillance system that utilizes a novel nanopore DNA sequencing instrument. In April 2015 this system was transported in standard airline luggage to Guinea and used for real-time genomic surveillance of the ongoing epidemic. We present sequence data and analysis of 142 EBOV samples collected during the period March to October 2015. We were able to generate results less than 24 h after receiving an Ebola-positive sample, with the sequencing process taking as little as 15–60 min. We show that real-time genomic surveillance is possible in resource-limited settings and can be established rapidly to monitor outbreaks.

At a glance

Figures

  1. Deployment of the portable genome surveillance system in Guinea.
    Figure 1: Deployment of the portable genome surveillance system in Guinea.

    a, We were able to pack all instruments, reagents and disposable consumables within aircraft baggage. b, We initially established the genomic surveillance laboratory in Donka Hospital, Conakry, Guinea. c, Later we moved the laboratory to a dedicated sequencing laboratory in Coyah prefecture. d, Within this laboratory we separated the sequencing instruments (on the left) from the PCR bench (to the right). An uninterruptable power supply can be seen in the middle that provides power to the thermocycler. (Photographs taken by J.Q. and S.D.)

  2. Real-time genomics surveillance in context of the Guinea Ebola virus disease epidemic.
    Figure 2: Real-time genomics surveillance in context of the Guinea Ebola virus disease epidemic.

    a, Here we show the number of reported cases of Ebola virus disease in Guinea (red) in relation to the number of EBOV new patient samples (n = 137, in blue) generated during this study. b, For each of the 142 sequenced samples, we show the relationship between sample collection date (red) and the date of sequencing (blue). Twenty-eight samples were sequenced within three days of the sample being taken, and sixty-eight samples within a week. Larger gaps represent retrospective sequencing of cases to provide additional epidemiological context.

  3. Evolution of EBOV over the course of the Ebola virus disease epidemic.
    Figure 3: Evolution of EBOV over the course of the Ebola virus disease epidemic.

    a, Time-scaled phylogeny of 603 published sequences with 125 high quality sequences from this study. The shape of nodes on the tree demonstrates country of origin. Our results show Guinean samples (coloured circles) belong to two previously identified lineages, GN1 and SL3. b, GN1 is deeply branching with early epidemic samples. c, SL3 is related to cases identified in Sierra Leone. Samples are frequently clustered by geography (indicated by colour of circle) and this provides information as to origins of new introductions, such as in the Boké epidemic in May 2015. Map figure adapted from SimpleMaps website (http://simplemaps.com/resources/svg-gn).

  4. Primer schemes employed during the study.
    Extended Data Fig. 1: Primer schemes employed during the study.

    We designed PCR primers to generate amplicons that would span the EBOV genome. a, We initially designed 38 primer pairs which were used in the initial validation study and which cover >97% of the EBOV genome. During in-field sequencing we used a 19-reaction scheme or 11-reaction scheme, which generated longer products. The predicted amplicon products are shown with forward primers and reverse primers indicated by green bars on the forward and reverse strand, respectively, scaled according to the EBOV virus coordinates. b, c, The amplicon product sizes expected are shown for the 19-reaction scheme (b) and the 11-reaction scheme (c). No amplicon covers the extreme 3′ region of the genome. The last primer pair, 38_R, ends at position 18578, 381 bases away from the end of the virus genome. The primer diagram was created with Biopython33.

  5. List of equipment and consumables to establish the genome surveillance system.
    Extended Data Fig. 2: List of equipment and consumables to establish the genome surveillance system.

    ac, We show the list of equipment (a), disposable consumables (b) and reagents (c) to establish in-field genomic surveillance. Sufficient reagents were shipped for 20 samples. MinION sequencing requires a mix of chilled and frozen reagents. Recommended shipping conditions are specified. The picture underneath depicts MinION flowcells ready for shipping with insulating material (left) and frozen reagents (right).

  6. Bioinformatics workflow.
    Extended Data Fig. 3: Bioinformatics workflow.

    This figure summarizes the steps performed during bioinformatics analysis (ordered from top to bottom), in order to generate consensus sequences. The right column shows the example software command executed at each step.

  7. Results of MinION validation.
    Extended Data Fig. 4: Results of MinION validation.

    a, The results of comparing four MinION sequences with Illumina sequences generated as part of a previous study3 are shown. Each row in the table demonstrates the number of true positives, false positives and false negatives for a sample. False negatives may result in masked sequences, owing to being outside of regions covered by the amplicon scheme, having low coverage or falling within a primer binding site. Results before and after quality filtering (log likelihood ratio of >200) are shown. After quality filtering, no false positive calls were detected. All detected false negatives were masked with Ns in the final consensus sequence. No positions were called incorrectly. b, The four consensus sequences, plus an additional sample that had missing coverage in one amplicon are shown as part of a phylogenetic reconstruction with genomes from Carroll et al.3. Sample labels in red, blue, pink, yellow and blue represent pairs of sequences generated on MinION and llumina. These fall into identical clusters.

  8. Relationship between coverqage and log-likelihood ratio for sample 076769.
    Extended Data Fig. 5: Relationship between coverqage and log-likelihood ratio for sample 076769.

    Line-plot showing the relationship between sequence depth of coverage (x axis) and the log likelihood ratio for detected SNPs derived by subsampling reads from a single sequencing run to simulate the effect of low coverage. The horizontal and vertical line indicates the cut-offs (quality and coverage respectively) for consensus calling. Therefore, all variants are detected below 25× coverage, and the vast majority meet the threshold quality at 25× coverage or slightly above. Any combination of log likelihood ratio or coverage that placed variants in the grey box would be represented as a masked position in the final consensus sequence.

  9. Duration of MinION sequencing runs.
    Extended Data Fig. 6: Duration of MinION sequencing runs.

    For each sequence run the sequencing duration, measured as the difference between timestamp of the first read seen and the last read transferred for analysis. 127 runs are shown, with 15 outliers with duration greater than 200 min excluded.

  10. Histogram of Ct values for study samples.
    Extended Data Fig. 7: Histogram of Ct values for study samples.

    Ct values for samples in the study (where information was available) ranged between 13.8 and 35.7, with a mean of 22.

  11. Sequence accuracy for samples.
    Extended Data Fig. 8: Sequence accuracy for samples.

    a, b, Accuracy measurements for the entire set of two-direction reads were made for the validation samples, sequenced in the United Kingdom (a) and each of the 142 samples from real-time genomic surveillance (b). Accuracy is defined according to the definition from Quick et al.11. Vertical dashed lines indicate the mean accuracy for the sample.

  12. Maximum likelihood phylogenetic inference of 125 Ebola virus samples from this study with 603 previously published sequences.
    Extended Data Fig. 9: Maximum likelihood phylogenetic inference of 125 Ebola virus samples from this study with 603 previously published sequences.

    Coloured nodes are from this study. Node shape reflects country of origin. ac, the entire data set is shown (a), with zoomed regions focusing on lineages GN1 (b) and SL3 (c) identified during real-time sequencing. Map figure adapted from SimpleMaps website (http://simplemaps.com/resources/svg-gn).

  13. Root-to-tip divergence plot and mean evolutionary rate estimate.
    Extended Data Fig. 10: Root-to-tip divergence plot and mean evolutionary rate estimate.

    a, Root-to-tip divergence plot for the 728 Ebola samples generated through maximum likelihood analysis. Samples from real-time genomic surveillance are coloured as per Fig. 3 and Extended Data Fig. 9. b, Mean evolutionary rate estimate (in substitutions per site per year) across the EBOV phylogeny recovered using BEAST under a relaxed lognormal molecular clock. Blue area corresponds to the 95% highest posterior density (HPD) (mean of the distribution is 1.19 × 10−3, 95% HPDs: 1.09–1.29 × 10−3 substitutions per site per year). Hatched regions in red are outside the 95% HPD intervals.

Accession codes

Primary accessions

European Nucleotide Archive

References

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  17. Goodfellow, I. et al. Recent evolution patterns of Ebola virus obtained by direct sequencing in Sierra Leone. http://virological.org/t/recent-evolution-patterns-of-ebola-virus-obtained-by-direct-sequencing-in-sierra-leone/150 (2015)
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Author information

  1. These authors contributed equally to this work.

    • Joshua Quick,
    • Nicholas J. Loman,
    • Sophie Duraffour,
    • Jared T. Simpson,
    • Ettore Severi &
    • Lauren Cowley

Affiliations

  1. Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK

    • Joshua Quick &
    • Nicholas J. Loman
  2. The European Mobile Laboratory Consortium, Bernhard-Nocht-Institute for Tropical Medicine, D-20359 Hamburg, Germany

    • Sophie Duraffour,
    • Joseph Akoi Bore,
    • Raymond Koundouno,
    • Babak Afrough,
    • Amadou Bah,
    • Jonathan H. J. Baum,
    • Beate Becker-Ziaja,
    • Jan Peter Boettcher,
    • Mar Cabeza-Cabrerizo,
    • Álvaro Camino-Sánchez,
    • Lisa L. Carter,
    • Juliane Doerrbecker,
    • Theresa Enkirch,
    • Isabel García- Dorival,
    • Nicole Hetzelt,
    • Julia Hinzmann,
    • Tobias Holm,
    • Liana Eleni Kafetzopoulou,
    • Michel Koropogui,
    • Abigael Kosgey,
    • Eeva Kuisma,
    • Christopher H. Logue,
    • Antonio Mazzarelli,
    • Sarah Meisel,
    • Marc Mertens,
    • Janine Michel,
    • Didier Ngabo,
    • Katja Nitzsche,
    • Elisa Pallasch,
    • Livia Victoria Patrono,
    • Jasmine Portmann,
    • Johanna Gabriella Repits,
    • Natasha Y. Rickett,
    • Andreas Sachse,
    • Katrin Singethan,
    • Inês Vitoriano,
    • Rahel L. Yemanaberhan,
    • Elsa G. Zekeng,
    • Antonino Di Caro,
    • Roman Wölfel,
    • Kilian Stoecker,
    • Erna Fleischmann,
    • Martin Gabriel,
    • Stephan Günther &
    • Miles W. Carroll
  3. Bernhard-Nocht-Institute for Tropical Medicine, D-20359 Hamburg, Germany

    • Sophie Duraffour,
    • Jonathan H. J. Baum,
    • Beate Becker-Ziaja,
    • Mar Cabeza-Cabrerizo,
    • Juliane Doerrbecker,
    • Tobias Holm,
    • Sarah Meisel,
    • Katja Nitzsche,
    • Elisa Pallasch,
    • Livia Victoria Patrono,
    • Rahel L. Yemanaberhan,
    • Martin Gabriel &
    • Stephan Günther
  4. Ontario Institute for Cancer Research, Toronto M5G 0A3, Canada

    • Jared T. Simpson
  5. Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada

    • Jared T. Simpson
  6. European Centre for Disease Prevention and Control (ECDC), 171 65 Solna, Sweden

    • Ettore Severi,
    • Natacha Milhano &
    • Christopher J. Williams
  7. National Infection Service, Public Health England, London NW9 5EQ, UK

    • Lauren Cowley &
    • Amy Mikhail
  8. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 2FL, UK

    • Gytis Dudas &
    • Andrew Rambaut
  9. Postgraduate Training for Applied Epidemiology (PAE, German FETP), Robert Koch Institute, D-13302 Berlin, Germany

    • Nobila Ouédraogo
  10. National Infection Service, Public Health England, Porton Down, Wiltshire SP4 0JG, UK

    • Babak Afrough,
    • Eeva Kuisma,
    • Christopher H. Logue,
    • Didier Ngabo,
    • Inês Vitoriano,
    • Kuiama Lewandowski &
    • Miles W. Carroll
  11. Swiss Tropical and Public Health Institute, 4002 Basel, Switzerland

    • Amadou Bah
  12. Robert Koch Institute, D-13302 Berlin, Germany

    • Jan Peter Boettcher,
    • Nicole Hetzelt,
    • Julia Hinzmann,
    • Janine Michel &
    • Andreas Sachse
  13. University College London, London WC1E 6BT, UK

    • Lisa L. Carter
  14. Paul-Ehrlich-Institut, Division of Veterinary Medicine, D-63225 Langen, Germany

    • Theresa Enkirch
  15. Institute of Infection and Global Health, University of Liverpool, Liverpool L69 7BE, UK

    • Isabel García- Dorival,
    • Natasha Y. Rickett,
    • Elsa G. Zekeng,
    • Georgios Pollakis &
    • Julian A. Hiscox
  16. Laboratory for Clinical and Epidemiological Virology, Department of Microbiology and Immunology, KU Leuven, Leuven B-3000, Belgium

    • Liana Eleni Kafetzopoulou
  17. Ministry of Health Guinea, Conakry BP 585, Guinea

    • Michel Koropogui,
    • Facinet Yattara &
    • Sakoba Keïta
  18. Kenya Medical Research Institute, Nairobi P.O. BOX 54840 - 00200, Kenya

    • Abigael Kosgey
  19. National Institute for Infectious Diseases L. Spallanzani, 00149 Rome, Italy

    • Antonio Mazzarelli &
    • Antonino Di Caro
  20. Friedrich-Loeffler-Institute, D-17493 Greifswald, Germany

    • Marc Mertens
  21. Federal Office for Civil Protection, Spiez Laboratory, 3700 Spiez, Switzerland

    • Jasmine Portmann
  22. Janssen-Cilag, Stockholm, Box 7073 – 19207, Sweden

    • Johanna Gabriella Repits
  23. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool L69 7BE, UK

    • Natasha Y. Rickett,
    • Elsa G. Zekeng,
    • Georgios Pollakis &
    • Julian A. Hiscox
  24. Institute of Virology, Technische Universität München, D-81675 Munich, Germany

    • Katrin Singethan
  25. Public Health Agency of Canada, Winnipeg, Manitoba R3E 3R2, Canada

    • Trina Racine &
    • Alexander Bello
  26. Institut Pasteur Dakar, Dakar, DP 220 Senegal

    • Amadou Alpha Sall,
    • Ousmane Faye &
    • Oumar Faye
  27. Laboratoire de Fièvres Hémorragiques de Guinée, Conakry BP 5680, Guinea

    • N’Faly Magassouba
  28. Sandia National Laboratories, PO Box 5800 MS1363, Albuquerque, New Mexico 87185-1363, USA

    • Cecelia V. Williams,
    • Victoria Amburgey &
    • Linda Winona
  29. Ratoma Ebola Diagnostic Center, Conakry, Guinea

    • Cecelia V. Williams,
    • Victoria Amburgey,
    • Linda Winona,
    • Emily Davis,
    • Jon Gerlach &
    • Frank Washington
  30. MRIGlobal, Kansas City, Missouri 64110-2241, USA

    • Emily Davis,
    • Jon Gerlach &
    • Frank Washington
  31. Expertise France, Laboratoire K-plan de Forecariah en Guinée, 75006 Paris, France

    • Vanessa Monteil,
    • Marine Jourdain,
    • Marion Bererd,
    • Alimou Camara,
    • Hermann Somlare,
    • Abdoulaye Camara,
    • Marianne Gerard,
    • Guillaume Bado &
    • Bernard Baillet
  32. Fédération des Laboratoires - HIA Bégin, 94163 Saint-Mandé cedex, France

    • Déborah Delaune
  33. Laboratoire de Biologie - Centre de Traitement des Soignants, Conakry, Guinea

    • Déborah Delaune
  34. World Health Organization, Conakry BP 817, Guinea

    • Koumpingnin Yacouba Nebie,
    • Abdoulaye Diarra,
    • Yacouba Savane,
    • Raymond Bernard Pallawo,
    • Isabelle Roger,
    • Boubacar Diallo &
    • Pierre Formenty
  35. London School of Hygiene and Tropical Medicine, London EC1E 7HT, UK

    • Giovanna Jaramillo Gutierrez
  36. Norwegian Institute of Public Health, PO Box 4404 Nydalen, 0403 Oslo, Norway

    • Natacha Milhano
  37. Public Health Wales, Cardiff CF11 9LJ, UK

    • Christopher J. Williams
  38. Defence Science and Technology Laboratory (Dstl) Porton Down, Salisbury SP4 0JQ, UK

    • James Taylor,
    • Phillip Rachwal &
    • Simon A. Weller
  39. Oxford Nanopore Technologies, Oxford OX4 4GA, UK

    • Daniel J. Turner
  40. Department of Cellular and Molecular Medicine, School of Medical Sciences, University of Bristol, Bristol BS8 1TD, UK

    • David A. Matthews
  41. Academic Department of Military Medicine, Royal Centre for Defence Medicine, Birmingham B15 2TH, UK

    • Matthew K. O’ Shea,
    • Andrew McD. Johnston &
    • Duncan Wilson
  42. Centre of Defence Pathology, Royal Centre for Defence Medicine, Birmingham B15 2TH, UK

    • Emma Hutley
  43. Queen Elizabeth Hospital, Birmingham B12 2TH, UK

    • Erasmus Smit
  44. Bundeswehr Institute of Microbiology, D-80937 Munich, Germany

    • Roman Wölfel,
    • Kilian Stoecker &
    • Erna Fleischmann
  45. Institut National de Santé Publique, Conakry BP 1147, Guinea

    • Lamine Koivogui
  46. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220, USA

    • Andrew Rambaut
  47. Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh EH9 2FL, UK

    • Andrew Rambaut
  48. University of Southampton, South General Hospital, Southampton SO16 6YD, UK

    • Miles W. Carroll
  49. NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, PHE Porton Down, UK

    • Miles W. Carroll

Contributions

N.J.L., J.Q., M.K.O’S., D.W., S.G., M.W.C. conceived the study. N.J.L., J.Q., M.K.O’S., S.A.W., J.T., P.R., D.T. designed the lab in a suitcase and laboratory protocol and initial validation. J.Q., S.D., L.C., J.A.B., R.K., L.E.K., and A.Ma. performed MinION sequencing. N.J.L., J.Q. and J.T.S. performed bioinformatics analysis and wrote software. J.T.S. added variant calling support to the nanopolish software. N.J.L., J.Q., S.D., E.S., P.F., L.C., A.Mi., N.M. and I.R. analysed the data. G.D., A.R., N.J.L., J.Q. and G.P. performed phylogenetic analysis. J.A.H., D.A.M., G.P., K.L., B.A. assisted further validation experiments. M.W.C., M.Ga., S.G., A.D.C., K.S., E.F. and R.W. coordinated activities for the European Mobile Laboratories. N.J.L., J.Q., S.D., M.W.C., S.G., M.K.O’S., A.R., E.S., P.F., I.R., A.Mi., and L.C. wrote the manuscript. All other authors were involved either in sample collection, and/or logistical support and strategic oversight for the work.

Competing financial interests

J.Q., N.J.L. and J.T.S. have all received travel expenses and accommodation from Oxford Nanopore to speak at organised symposia. J.Q. and N.J.L. have received an honorarium payment to speak at an Oxford Nanopore meeting. N.J.L. is a member of the Oxford Nanopore MinION Access Programme and has received reagents free of charge as part of the MinION Access Programme and in support of this project but does not receive other financial compensation or hold shares. D.T. is an employee of Oxford Nanopore.

Corresponding author

Correspondence to:

MinION and Illumina raw sequence files have been deposited into the European Nucleotide Archive under project code PRJEB10571.

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Primer schemes employed during the study. (176 KB)

    We designed PCR primers to generate amplicons that would span the EBOV genome. a, We initially designed 38 primer pairs which were used in the initial validation study and which cover >97% of the EBOV genome. During in-field sequencing we used a 19-reaction scheme or 11-reaction scheme, which generated longer products. The predicted amplicon products are shown with forward primers and reverse primers indicated by green bars on the forward and reverse strand, respectively, scaled according to the EBOV virus coordinates. b, c, The amplicon product sizes expected are shown for the 19-reaction scheme (b) and the 11-reaction scheme (c). No amplicon covers the extreme 3′ region of the genome. The last primer pair, 38_R, ends at position 18578, 381 bases away from the end of the virus genome. The primer diagram was created with Biopython33.

  2. Extended Data Figure 2: List of equipment and consumables to establish the genome surveillance system. (479 KB)

    ac, We show the list of equipment (a), disposable consumables (b) and reagents (c) to establish in-field genomic surveillance. Sufficient reagents were shipped for 20 samples. MinION sequencing requires a mix of chilled and frozen reagents. Recommended shipping conditions are specified. The picture underneath depicts MinION flowcells ready for shipping with insulating material (left) and frozen reagents (right).

  3. Extended Data Figure 3: Bioinformatics workflow. (220 KB)

    This figure summarizes the steps performed during bioinformatics analysis (ordered from top to bottom), in order to generate consensus sequences. The right column shows the example software command executed at each step.

  4. Extended Data Figure 4: Results of MinION validation. (554 KB)

    a, The results of comparing four MinION sequences with Illumina sequences generated as part of a previous study3 are shown. Each row in the table demonstrates the number of true positives, false positives and false negatives for a sample. False negatives may result in masked sequences, owing to being outside of regions covered by the amplicon scheme, having low coverage or falling within a primer binding site. Results before and after quality filtering (log likelihood ratio of >200) are shown. After quality filtering, no false positive calls were detected. All detected false negatives were masked with Ns in the final consensus sequence. No positions were called incorrectly. b, The four consensus sequences, plus an additional sample that had missing coverage in one amplicon are shown as part of a phylogenetic reconstruction with genomes from Carroll et al.3. Sample labels in red, blue, pink, yellow and blue represent pairs of sequences generated on MinION and llumina. These fall into identical clusters.

  5. Extended Data Figure 5: Relationship between coverqage and log-likelihood ratio for sample 076769. (240 KB)

    Line-plot showing the relationship between sequence depth of coverage (x axis) and the log likelihood ratio for detected SNPs derived by subsampling reads from a single sequencing run to simulate the effect of low coverage. The horizontal and vertical line indicates the cut-offs (quality and coverage respectively) for consensus calling. Therefore, all variants are detected below 25× coverage, and the vast majority meet the threshold quality at 25× coverage or slightly above. Any combination of log likelihood ratio or coverage that placed variants in the grey box would be represented as a masked position in the final consensus sequence.

  6. Extended Data Figure 6: Duration of MinION sequencing runs. (75 KB)

    For each sequence run the sequencing duration, measured as the difference between timestamp of the first read seen and the last read transferred for analysis. 127 runs are shown, with 15 outliers with duration greater than 200 min excluded.

  7. Extended Data Figure 7: Histogram of Ct values for study samples. (62 KB)

    Ct values for samples in the study (where information was available) ranged between 13.8 and 35.7, with a mean of 22.

  8. Extended Data Figure 8: Sequence accuracy for samples. (577 KB)

    a, b, Accuracy measurements for the entire set of two-direction reads were made for the validation samples, sequenced in the United Kingdom (a) and each of the 142 samples from real-time genomic surveillance (b). Accuracy is defined according to the definition from Quick et al.11. Vertical dashed lines indicate the mean accuracy for the sample.

  9. Extended Data Figure 9: Maximum likelihood phylogenetic inference of 125 Ebola virus samples from this study with 603 previously published sequences. (263 KB)

    Coloured nodes are from this study. Node shape reflects country of origin. ac, the entire data set is shown (a), with zoomed regions focusing on lineages GN1 (b) and SL3 (c) identified during real-time sequencing. Map figure adapted from SimpleMaps website (http://simplemaps.com/resources/svg-gn).

  10. Extended Data Figure 10: Root-to-tip divergence plot and mean evolutionary rate estimate. (259 KB)

    a, Root-to-tip divergence plot for the 728 Ebola samples generated through maximum likelihood analysis. Samples from real-time genomic surveillance are coloured as per Fig. 3 and Extended Data Fig. 9. b, Mean evolutionary rate estimate (in substitutions per site per year) across the EBOV phylogeny recovered using BEAST under a relaxed lognormal molecular clock. Blue area corresponds to the 95% highest posterior density (HPD) (mean of the distribution is 1.19 × 10−3, 95% HPDs: 1.09–1.29 × 10−3 substitutions per site per year). Hatched regions in red are outside the 95% HPD intervals.

Supplementary information

PDF files

  1. Supplementary Information (428 KB)

    This file contains a Field Guide to Nanopore Sequencing - a detailed discussion of logistical issues that arose during this project and Supplementary Tables 1-4.

Additional data