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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Integrated supplementary information
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.
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.
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..
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.
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.
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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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