Highly parallel direct RNA sequencing on an array of nanopores (original) (raw)

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References

  1. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  2. Wu, J.Q. et al. Systematic analysis of transcribed loci in ENCODE regions using RACE sequencing reveals extensive transcription in the human genome. Genome Biol. 9, R3 (2008).
    Article PubMed PubMed Central Google Scholar
  3. Kozarewa, I. et al. Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes. Nat. Methods 6, 291–295 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  4. Lipson, D. et al. Quantification of the yeast transcriptome by single-molecule sequencing. Nat. Biotechnol. 27, 652–658 (2009).
    Article CAS PubMed Google Scholar
  5. Mamanova, L. et al. FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nat. Methods 7, 130–132 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  6. Ozsolak, F. et al. Direct RNA sequencing. Nature 461, 814–818 (2009).
    Article CAS PubMed Google Scholar
  7. Pan, Q., Shai, O., Lee, L.J., Frey, B.J. & Blencowe, B.J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).
    Article CAS PubMed Google Scholar
  8. Steijger, T. et al. Assessment of transcript reconstruction methods for RNA-seq. Nat. Methods 10, 1177–1184 (2013).
    CAS PubMed PubMed Central Google Scholar
  9. Thomas, S., Underwood, J.G., Tseng, E. & Holloway, A.K. Long-read sequencing of chicken transcripts and identification of new transcript isoforms. PLoS One 9, e94650 (2014).
    Article PubMed PubMed Central Google Scholar
  10. Vilfan, I.D. et al. Analysis of RNA base modification and structural rearrangement by single-molecule real-time detection of reverse transcription. J. Nanobiotechnology 11, 8 (2013).
    Article CAS PubMed PubMed Central Google Scholar
  11. Clamer, M., Höfler, L., Mikhailova, E., Viero, G. & Bayley, H. Detection of 3′-end RNA uridylation with a protein nanopore. ACS Nano 8, 1364–1374 (2014).
    Article CAS PubMed Google Scholar
  12. Smith, A.M., Abu-Shumays, R., Akeson, M. & Bernick, D.L. Capture, unfolding, and detection of individual tRNA molecules using a nanopore device. Front. Bioeng. Biotechnol. 3, 91 (2015).
    Article PubMed PubMed Central Google Scholar
  13. Wu, T.D. & Watanabe, C.K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).
    Article CAS PubMed Google Scholar
  14. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
    Article CAS PubMed PubMed Central Google Scholar
  15. Byrne, A. et al. Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat. Commun. 8, 16027 (2017).
    Article CAS PubMed PubMed Central Google Scholar
  16. Deamer, D., Akeson, M. & Branton, D. Three decades of nanopore sequencing. Nat. Biotechnol. 34, 518–524 (2016).
    Article CAS PubMed PubMed Central Google Scholar
  17. Oxford Nanopore Technologies Ltd. Direct RNA sequencing https://community.nanoporetech.com/protocols/direct-rna-sequencing/v/drs_9026_v1_revj_15dec201 (2016).
  18. The HDF Group. Hierarchical data format, version 5, 1997–2017. http://www.hdfgroup.org/HDF5/.
  19. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
    Article CAS PubMed PubMed Central Google Scholar
  20. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
    Article CAS PubMed PubMed Central Google Scholar
  21. Larkin, M.A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).
    Article CAS PubMed Google Scholar
  22. Li, H. & Durbin, R. Burrows–Wheeler Alignment Tool http://bio-bwa.sourceforge.net/bwa.shtml (2012).
  23. Fariselli, P., Martelli, P.L. & Casadio, R. A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins. BMC Bioinformatics 6, S12 (2005).
    Article PubMed PubMed Central Google Scholar

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Authors and Affiliations

  1. Oxford Nanopore Technologies Ltd., Oxford, UK
    Daniel R Garalde, Elizabeth A Snell, Daniel Jachimowicz, Botond Sipos, Joseph H Lloyd, Mark Bruce, Nadia Pantic, Tigist Admassu, Phillip James, Anthony Warland, Michael Jordan, Jonah Ciccone, Sabrina Serra, Jemma Keenan, Samuel Martin, Luke McNeill, E Jayne Wallace, Lakmal Jayasinghe, Chris Wright, Javier Blasco, Stephen Young, Denise Brocklebank, James Clarke, Andrew J Heron & Daniel J Turner
  2. Oxford Nanopore Technologies Inc., New York, New York, USA
    Sissel Juul

Authors

  1. Daniel R Garalde
  2. Elizabeth A Snell
  3. Daniel Jachimowicz
  4. Botond Sipos
  5. Joseph H Lloyd
  6. Mark Bruce
  7. Nadia Pantic
  8. Tigist Admassu
  9. Phillip James
  10. Anthony Warland
  11. Michael Jordan
  12. Jonah Ciccone
  13. Sabrina Serra
  14. Jemma Keenan
  15. Samuel Martin
  16. Luke McNeill
  17. E Jayne Wallace
  18. Lakmal Jayasinghe
  19. Chris Wright
  20. Javier Blasco
  21. Stephen Young
  22. Denise Brocklebank
  23. Sissel Juul
  24. James Clarke
  25. Andrew J Heron
  26. Daniel J Turner

Contributions

D.R.G., A.J.H., J. Clarke and D.J.T. conceived the experiments. D.R.G. led the project. D.R.G., E.A.S., D.J., A.J.H., J.H.L., P.J., A.W., M.J., J.K., S.M. and L.M. designed and performed the experiments. J.H.L. tested, engineered and developed the motor protein. J.H.L., S.M., L.M., D.R.G., E.A.S., A.J.H., M.B., D.J., A.W. and E.J.W. designed or assessed motor protein mutations and the sequencing adaptor. D.J.T., D.R.G. and E.A.S. developed the library preparation. E.A.S. and J.K. created custom RNA templates. B.S. wrote custom analysis tools and performed analysis of all sequence data sets. N.P., T.A. and M.B. expressed and purified proteins. M.J., J. Ciccone and S.S. designed and prepared plasmids. M.J., E.J.W., L.J., S.Y., D.R.G., E.A.S., D.J., A.J.H., M.B., J.H.L. and D.B. assessed sequencing performance of buffers, voltages and pores. C.W. wrote squiggle-consensus algorithms. J.B., C.W., D.B., J.H.L., M.B. and S.Y. trained RNA basecallers or analyzed modified base data. D.J.T., B.S., D.R.G., S.J. and C.W. wrote the manuscript. A.J.H., S.Y. and P.J. contributed to the figures or to editing of the manuscript.

Corresponding author

Correspondence toDaniel J Turner.

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Competing interests

All authors are employees of Oxford Nanopore Technologies and are shareholders and/or share option holders.

Integrated supplementary information

Supplementary Figure 1 Read-length distributions for direct RNA and nanopore cDNA datasets

Supplementary Figure 2 Analysis of direct RNA method

a) Distribution of mean quality values for all reads in the direct RNA yeast dataset. b) Distribution of read accuracies from the retrained direct RNA basecaller.

Supplementary Figure 3 Technical replicates of the direct RNA method.

The correlation between read counts after mapping to the yeast transcriptome for 5 technical replicates of the Direct RNA method. The five technical replicates were separate library preparations of yeast run on separate MinION Chips. Above the diagonal are pairwise scatter plots and below the diagonal are pairwise density plots (Rho from Spearman’s rank correlation is shown over each plot). Each scatter or density plot includes all transcripts in the annotation: n = 6713 transcripts.

Supplementary Figure 4 Effect of increasing number of PCR cycles

The effect of number of PCR cycles on bias, read length and deviation from expected read counts for ERCC spike-ins. Three independent replicates were performed at each cycle number totaling 24 separate nanopore cDNA sequencing runs. Error bars denote s.e.m..

Supplementary Figure 5 Direct RNA versus Illumina: comparison of bias.

Correlation between read counts and transcript length for a) direct RNA (Pearson’s r = 0.13, p = 5.4e-29) or b) Illumina (Pearson’s r = 0.3, p = 7e-141) yeast datasets. Correlation between read counts and GC content for c) direct RNA (Pearson’s r = 0.013, p = 0.29) or d) Illumina (Pearson’s r = 0.19, p = 1.6e-58) yeast datasets. In each of (a-d), all transcripts were included: n = 6713 transcripts. e) Correlation between mean quality of aligned read portions and the GC content of aligned reference portions for direct RNA yeast dataset (Pearson’s r = 0.082, p = 0, n = 2,777,523 alignments). The correlation coefficients and the corresponding two-sided p-values were calculated using the stats.pearsonr function from the scipy Python package.

Supplementary Figure 6 Gene-level and transcript-level correlations to SIRV control.

Reads aligned using the spliced-alignment strategy and correlations calculated a) at the transcript level (Spearman’s Rho = 0.62, p = 9.5e-9, n = 69 transcripts) or b) at the gene level (Spearman’s Rho = 0.61, p = 0.15, n = 7 genes) for the SIRV E2 dataset. The correlation coefficients and the corresponding two-sided p-values were calculated using the stats.spearmanr function from the scipy Python package.

Supplementary Figure 7 Coverage of individual exons in the SIRV E0 dataset.

Supplementary information

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Garalde, D., Snell, E., Jachimowicz, D. et al. Highly parallel direct RNA sequencing on an array of nanopores.Nat Methods 15, 201–206 (2018). https://doi.org/10.1038/nmeth.4577

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