Nanopores allow direct sequencing of
RNA and modified RNA nucleotides
RNA strands can be sequenced on the MinION
need to convert to double-stranded DNA
TM
and PromethION
without the
TM
AAAAAAAAAAAAAAAAAAAAAAA
full-length
mRNA
a)
ligation of sequencing adapter
TTTTTTTTTT
Current (pA)
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120
80
40
0
AAAAAAAAAAAAAAAAAAAAAA
TTTTTTTTTT
b)
0
1,000
norovirus
2,000
3,000
4,000
5,000
Position in HRV genome
rhinovirus
6,000
7,000
influenza A
tether annealing
100
Read depth
AAAAAAAAAAAAAAAAAAAAAA
TTTTTTTTTT
80
60
40
20
0
sequence RNA strand
0
1,000
2,000
3,000
4,000
5,000
Position in HRV genome
6,000
7,000
Fig. 1 Direct RNA-sequencing library-prep workflow
Fig. 2 Full-length direct RNA reads of HRV a) 7.5 kb squiggle b) aligned reads
Direct RNA offers native strand
sequencing and quick prep
Direct RNA sequencing of human
rhinovirus
Nanopore-based sequencing technologies are capable of analysing RNA
as well as DNA, but in contrast to other sequencing technologies,
nanopores are the only sequencing technology which can sequence RNA
directly, rather than depending on reverse transcription and PCR. There
are several ways to prepare RNA strands for sequencing. Fig. 1 shows a
method in which an adapter is ligated onto the RNA strand. This adapter
allows efficient threading of the RNA strand into the nanopore, and is
pre-loaded with the motor protein which controls the speed of
translocation of the strand. This library prep is considerably quicker than
conventional cDNA preparation methods.
When an RNA strand passes through a nanopore, the current level
changes with the sequence in a similar way to when the analyte is DNA.
We prepared a 1D RNA template from the ~7.5 kb human rhinovirus
(HRV) using the prep shown in Fig. 1 and sequenced the library using R9
pores, at ~90 nucleotides per second. Fig. 2a shows the current trace
from a full-length strand of HRV. The data was basecalled using a
bespoke 1D Hidden Markov Model-based basecaller, and reads were
aligned to the norovirus, HRV and influenza A reference sequences using
LAST. Fig. 2b shows the alignment, beneath a Savant representation of
the NCBI Reference sequence. No off-target mapping was observed.
60
40
20
0
b)
Expected values
RNA 1
RNA 2
RNA 3
100
Mix A
Mix B
Read count
200
100
CUGUCGAAUUAAUUCGCCCGGCGAAUGUGCC
Unmodifed
m6A
90
Mix C
Current (pA)
Relative abundance (%)
a)
80
70
0
60
210
220
Position in reference
230
Fig. 3 Direct RNA reads a) quantitative measurement b) yeast transcriptome
Fig. 4 Detecting the m6A modification by direct RNA sequencing
Direct measurement of RNA
reduces bias
Direct RNA sequencing can
identify modified bases
Having no amplification or reverse transcription as part of the library prep
means that direct RNA sequencing should be free from the bias and size
limitations of these steps. To investigate this, we pooled 3 transcripts in
10:30:60 ratios and counted the reads of each. Fig. 3a) shows that the
read counts were close to the expected values, incidating low bias. We
next took total mRNA from Saccharomyces cerevisiae and prepared a
whole transcriptome library. Although the throughput of direct RNA
sequencing is not yet sufficient for full transcriptomic profiling, we were
able to map the most prevalent transcripts to the reference transcriptome,
and observed differences in their abundance (Fig. 3b).
Direct RNA sequencing measures the current blockade caused by RNA
bases in the narrowest part of the nanopore. Base modifications that
affect this current can also be analysed. To determine the effect of a
common RNA modification (m6A) on the current trace we sequenced fully
modified and unmodified strands of the FLuc transcript and aligned the
current traces. Fig. 4 shows a region of this alignment. The levels
corresponding to unmodified and fully modified strands are shown in red
and blue respectively, beneath the base sequence of the region, taking the
middle base in the kmer for alignment. m6A-containing kmers can be
discriminated from rAMP-containing kmers by the nanopore.
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