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Highly parallel direct RNA sequencing on an array of nanopores

Abstract

Sequencing the RNA in a biological sample can unlock a wealth of information, including the identity of bacteria and viruses, the nuances of alternative splicing or the transcriptional state of organisms. However, current methods have limitations due to short read lengths and reverse transcription or amplification biases. Here we demonstrate nanopore direct RNA-seq, a highly parallel, real-time, single-molecule method that circumvents reverse transcription or amplification steps. This method yields full-length, strand-specific RNA sequences and enables the direct detection of nucleotide analogs in RNA.

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Figure 1: Direct RNA-seq.
Figure 2: Analysis of the Saccharomyces cerevisiae S228C transcriptome by direct RNA-seq.
Figure 3: Analysis of quantitative and length biases using the ERCC spike-in panel.
Figure 4: Detection of splice variants using the SIRV E2 spike-in panel.
Figure 5: Detection of modified bases in synthetic RNA strands.

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Authors and Affiliations

Authors

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 to Daniel J Turner.

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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.

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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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