Arabidopsis thaliana and a mutant defective in mRNA methylation (m6A). Here we show that m6A can be mapped in full-length mRNAs transcriptome-wide and reveal the combinatorial diversity of cap-associated transcription start sites, splicing events, poly(A) site choice and poly(A) tail length. Loss of m6A from 3’ untranslated regions is associated with decreased relative transcript abundance and defective RNA 3′ end formation. A functional consequence of disrupted m6A is a lengthening of the circadian period. We conclude that nanopore direct RNA sequencing can reveal the complexity of mRNA processing and modification in full-length single molecule reads. These findings can refine Arabidopsis genome annotation. Further, applying this approach to less well-studied species could transform our understanding of what their genomes encode.Understanding genome organization and gene regulation requires insight into RNA transcription, processing and modification. We adapted nanopore direct RNA sequencing to examine RNA from a wild-type accession of the model plant  Knowledge of RNA modifications and processing combinations is essential to understand gene expression and what genomes really encode.Patterns of pre-mRNA processing and base modifications determine eukaryotic mRNA coding potential and fate. Alternative transcripts produced from the same gene can differ in the position of the start site, the site of cleavage and polyadenylation, and the combination of exons spliced into the mature mRNA. Collectively termed the epitranscriptome, RNA modifications play crucial context-specific roles in gene expression . The mosRNA sequencing (RNAseq) is used to dissect transcriptome complexity: it involves copying RNA into complementary DNA (cDNA) with reverse transcriptases (RTs) and then sequencing the subsequent DNA copies. RNAseq reveals diverse features of transcriptomes, but limitations can include misidentification of 3′ ends through internal priming , spuriou6A  tail length) in full-length Arabidopsis mRNAs transcriptome-wide.We investigated whether long-read direct RNA sequencing (DRS) with nanopores  could re6A  and exos6A . We reveAT1G48090, spanning 63 exons, + RNA from four biological replicates of 14-day-old Arabidopsis Col-0 seedlings. We incorporated synthetic External RNA Controls Consortium (ERCC) RNA spike-in mixes into all replicates  and carr3 exons, , and 12.58 exons . These rons (U3B .We used ERCC RNA spike-ins  as interRH3 (AT5G26742), appear to be more susceptible, with up to 20% of reads affected across multiple sequencing experiments  (AT2G39730), consistent with the lack of antisense RNAs annotated at this locus. Consequently, we conclude that spurious antisense is rare or absent from nanopore DRS data  reads mapped antisense to the ERCC RNA spike-in collection . None ofDRS data . This siDRS data . For exaand PIN7 . The exi  tails of defined lengths ranging from 10 to 100 nt  with gene expression in Arabidopsis (Ligation of the motor protein adapter to RNA 3′ ends results in nanopores sequencing mRNA poly(A) tails first. We used the nanopolish-polyA software tool to estimate poly(A) tail lengths for individual reads . Nanopolo 100 nt . We firso 100 nt . ERCC-00o 100 nt . The genbidopsis , consistbidopsis .PTM intron 10 (supported by our Helicos DRS data) terminates transcripts prior to sequence encoding the transmembrane domain, hence bypassing established retrograde control.We previously mapped Arabidopsis mRNA 3′ ends transcriptome-wide using Helicos direct RNA sequencing (DRS)   We comp6 sequences, only four have read alignments terminating within 13 nt in all four datasets. Of these, two were not detectable after error correction with proovread  and the other two mapped to the terminal exon of coding sequence annotation, indicating that they may be authentic 3′ ends. Hence, internal priming is rare or absent in nanopore DRS data. It is possible that a standard nanopore DRS approach may be compromised in the quantitative detection of RNAs with very short poly(A) tails or tails incorporating uridylation for decay. However, overall, we conclude that nanopore DRS can identify multiple authentic features of RNA 3′ end processing.Since RT-dependent internal priming can result in the misinterpretation of authentic cleavage and polyadenylation sites , we nextNanopore DRS reads are frequently truncated prior to annotated transcription start sites, resulting in a 3′ bias of genomic alignments  Depledg. ConsequIn order to determine whether the 5′ ends we detected reflect full-length mRNAs, we compared them against transcription start sites in datasets derived from two orthogonal approaches. First, we compared the nanopore cap-capture 5’ ends with full-length Arabidopsis cDNA clones . We founin vitro transcribed RNAs also lacked ~11 nt of authentic 5′ sequence    is quite(AtRTD2) . The app(AtRTD2) , consist(AtRTD2) . ConsequTAS RNAs that are targeted by miR173  that mediates the thermosensitive response controlling flowering time  that can target mRNAs for nonsense-mediated decay (NMD). We could identify established cases of alternative splicing that lead to NMD  Supplem. In addiOverall, we conclude that nanopore DRS can reveal a greater complexity of splicing in the context of full-length mRNAs compared with short-read data. However, accurate splice pattern detection benefits from error correction with, for example, high-accuracy orthogonal short-read sequencing data. However, even with error-free sequences, accurate splice detection can be confounded by the existence of equivalent alternative junctions . Therefo6A has been mapped transcriptome-wide using approaches based on antibodies that recognize this mark  tail and sequenced using nanopore DRS. Using read alignments to the known sequence of the synthetic RNA, we applied a G-test to detect differences in the basecall profile of alignments at each reference position. Using this differential error rate approach, we could correctly map the position of m6A in these synthetic RNAs , a conserved m6A writer complex component, and four biological replicates of a line expressing VIR fused to Green Fluorescent Protein (GFP)  that restores VIR activity in the vir-1 mutant background (6A) and VIR-complemented lines. We identified 17,491 sites with a more than two-fold higher error rate (compared with the TAIR10 reference base) in the VIR-complemented line with restored m6A (−16), which closely resembles the established m6A target consensus  (6A with only 10 reads per replicate (in a four replicate per condition experiment).We next examined how the power of the differential error rate analysis approach to detect mondition . Since ted sites . We nextcontext) . For som6A on Arabidopsis gene expression. We found a global reduction in protein-coding gene expression in vir-1 (using either nanopore DRS or Illumina RNAseq data), corresponding to transcripts that were methylated in the VIR-complemented line  tail length estimates that peaked at approximately 20, 40 and 60 nt (vir-1 mutants had reduced abundance of CAB1 mRNAs with 20 and 40 nt poly(A) tails, and an increased abundance of poly(A) tails of 60 nt in length (FLOWERING LOCUS C (FLC) expression was reduced by more than 40-fold compared with the wild type  with an altered nanopore DRS 3′ position profile in the vir-1 mutant compared with the VIR-complemented line  and correspond to locations of increased cleavage downstream of m6A sites in the vir-1 mutant .In addition to measuring RNA expression, we examined the impact of mare tool  for analare tool . In contted line . Of thes1 mutant . A totalt  site usage, compared to only 166 genes displaying an increase in distal poly(A) site usage  site selection.In order to examine the impact of mnylation . DaPars-te usage . Consequ6A and 3’ end formation further, we asked if differential error sites were phased (associated) with specific poly(A) sites. We clustered reads from VIRc samples by their 3’ position using kernel density estimation. We then compared the error profiles of reads from different 3’ clusters using a G-test to identify whether 3’ end positions were associated with m6A levels . We identified 327 error sites which were phased with 3’ end position, overlapping 207 annotated genes. 149 (72%) of these also display a statistically detectable change in 3’ end position in the vir-1 mutant compared to VIRc. For example, at PRPL34 (AT1G29070), selection of the distal poly(A) sites pA2 and pA3 is associated with increased mismatches to the reference base (presumably resulting from higher levels of m6A) at an upstream error site, compared to the proximal poly(A) site pA1  to the poly(A) signal, and indicates that adenosine residues directly adjacent to, or within, the poly(A) signal itself may be methylated. Since loss of m6A activates the selection of upstream poly(A) signals, these findings are consistent with m6A at (or in the vicinity of) AAUAAA inhibiting recognition of the poly(A) signal for subsequent cleavage and polyadenylation.In the or sites . Notablyor sites . We ther6A motif . This fivir-1. The impacts of altered 3′ processing can be complex and have the potential to change the relative abundance of transcripts processed from the same gene but with different coding potential. For example, we detected increased readthrough of an intronic cleavage site in the Symplekin-like gene TANG1  that also results in chimeric RNA formation with the downstream gene GH3.9 . Chimeric RNAs were recently detected in mutants affecting other components of the Arabidopsis m6A writer complex, MTA and FIP37  in 3′ end formation or an indirect effect on factors required for 3′ processing. m6A independent roles for the human m6A methyltransferases METTL3 (6A cannot be overlooked (We next investigated those events where downstream readthrough was detected in a RNAseq  and confa RNAseq . We detend FIP37 . Howevers METTL3  and METTs METTL3  have beeerlooked .VIRILIZER-dependent m6A disrupts Arabidopsis mRNA 3’ formation. Although this includes readthrough at poly(A) sites with the formation of chimeric RNAs, the predominant impact is loss of suppression of proximal poly(A) sites.Overall, we conclude that loss of 6A modification of RNA. As a result, we could not only confirm previously established complexity in Arabidopsis RNA processing, but uncover novel events too. We found that spurious antisense signal was rare or absent, as were internal priming artefacts that complicate interpretation of authentic antisense RNAs and poly(A) sites respectively. Improvements in the utility of nanopore DRS likely depend upon more accurate base-calling, refined splitting of signal, increased read-depth and the development of software for splice-aware alignment of long sequencing reads.We used nanopore DRS to reveal the Arabidopsis transcriptome. Using orthogonal datasets as validation in each case, we could demonstrate the utility of nanopore DRS in revealing full-length mRNAs, mapping 5’ cap position, the position and estimated length of the poly(A) tail, patterns of alternative splicing, sites of internal RNA cleavage, and m6A transcriptome-wide. We show that a simple and transparent statistical test of basecalling error rate can identify m6A. Available basecalling software depend upon recurrent neural networks trained with signal produced from sequencing RNA where modified bases are unlabelled and present in relatively low abundance. As a result, basecallers cannot accurately assign m6A. Since 5 RNA bases occupy a nanopore at any one time, m6A can affect the accuracy of basecalling of surrounding nucleotides. Formally then, an error-rate based approach has an approximate resolution of 9 nt. However, the methylated base can be interpreted using a position weight matrix of the consensus motif associated with error sites. Nanopore DRS technology and basecalling software continues to develop. In our experience, so long as the basecallers are not trained to directly identify m6A, and sequencing and analysis is performed on a common iteration of the nanopore DRS platform , then an error rate approach is still able to map m6A. Since this is a statistical test, it depends upon an aggregation of reads and does not map m6A directly in single molecules. Consequently, the power to detect m6A will depend upon read depth.The key technological question we addressed in this study, was whether nanopore DRS could be used to map m6A mapping has depended on antibodies that recognise this modification. However, antibody-based approaches such as MeRIP/m6A-seq and miCLIP are limited in some respects. For example, they cannot readily define stoichiometry, do not necessarily detect m6A only, have unclear sensitivity, and the fragmentation of RNA required for library preparation may result in the underrepresentation of sites close to RNA 3’ ends. As a result, antibody-independent approaches to mapping m6A have been sought  the majority of m6A sites in human or yeast cells cannot be detected  could reveal the functional impacts of m6A on Arabidopsis gene expression. We found that disrupting m6A was associated with altered patterns of mRNA cleavage and polyadenylation. Although we detected readthrough at some poly(A) sites, the predominant effect was the activation of proximal poly(A) sites in the absence of m6A, indicating that m6A can suppress proximal poly(A) site selection. We found that the distribution of m6A sites and the canonical poly(A) signal AAUAAA (and related hexamers with one mismatch position) closely overlapped, indicating that adenosines within the AAUAAA signal itself could be methylated. In mammals, recognition of AAUAAA involves direct binding by the CPSF30 and WDR33 proteins of the cleavage and polyadenylation machinery (6A (6A at AAUAAA either directly inhibits binding of CPSF30 and FY to the poly(A) signal, or that binding of m6A by YTH domain containing proteins (possibly CPSF30-YTH itself) blocks canonical recognition by CPSF30 and FY, and hence assembly of an active cleavage and polyadenylation complex. Future experiments will dissect the mechanisms involved.The key biological question we set out to address, was whether an unbiased transcriptome-wide sequencing approach . Consequently, the effect of m6A is likely to be mediated mostly through mRNA alternative polyadenylation, stability, translatability and the formation of specific protein complexes , an African orphan crop. Indeed, we are moving into an era where thousands of genome sequences are available and programmes such as the Earth BioGenome Project aim to sequence all eukaryotic life on Earth  lines (hen2–2 (Gabi_774HO7) mutant (Wild-type R) lines  were pro) mutant  was prov−2 sec−1 light for 9 days. Next, delayed fluorescence measurements were recorded every hour for 6 days at constant temperature (22°C) and constant light (20 µmol m−2 sec−1 red light and 20 µmol m−2 sec−1 blue light mix). Fast Fourier Transform (FFT) non-linear least-squares fitting to estimate period length was conducted using Biodare  and treated with TURBO DNase (Thermo Fisher Scientific). The total RNA concentration was measured using a Qubit 1.0 Fluorometer and Qubit RNA BR Assay Kit (Thermo Fisher Scientific), and RNA quality and integrity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and Agilent 2200 TapeStation System (Agilent).in vitro transcribed using a mMESSAGE mMACHINE T7 ULTRA Transcription Kit (Thermo Fisher Scientific) with and without the anti-reverse cap analogue, according to the manufacturer’s instructions. mGFP transcripts were treated with TURBO DNase, polyA-tailed using Escherichia coli poly(A) Polymerase (E-PAP) and ATP (Thermo Fisher Scientific) and recovered using a MEGAclear kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. The quantity of mGFP mRNAs was measured using a Qubit 1.0 Fluorometer (as described above), and the quality and integrity was checked using the NanoDrop 2000 spectrophotometer and agarose-gel electrophoresis. In vitro capped and non-capped mGFP mRNAs were used to prepare the libraries for DRS using nanopores.The mGFP coding sequence was amplified using CloneAmp HiFi PCR Premix (Clontech) and a forward primer containing the T7 promoter sequence Merck; . The PCR6A site were chemically synthesized by Dharmacon (Horizon Discovery). The sequence corresponds to a 60-nt portion of a human lncRNA MALAT1 5’-AAGUAAUUCAAGAUCAAGAGUAAUUACCAACUUAAUGUUUUUGCAUUGGACUUUGAGUUA(A50)−3’ (6A (highlighted in bold) (6A and MALAT1 control synthetic RNAs were used to prepare the libraries for DRS using nanopores.A matched set of synthetic RNAs differing only in a single m(A50)−3’  with a pin bold) . The 2’-Total RNA was reverse transcribed using SuperScript III polymerase or SuperScript IV VILO Master Mix (Thermo Fisher Scientific) according to the manufacturer’s protocol. For RT-PCR, reactions were performed using the Advantage 2 Polymerase Mix (Clontech) using the primers (Merck) listed in Illumina RNA sequencing libraries from purified mRNA were prepared and sequenced by the Centre for Genomic Research at University of Liverpool using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs). Paired-end sequencing with a read length of 150 bp was carried out on an Illumina HiSeq 4000. Illumina RNA libraries from ribosome-depleted RNA were prepared using the TruSeq Stranded Total RNA with Ribo-Zero Plant kit (Illumina). Paired-end sequencing with a read length of 100 bp was carried out on an Illumina HiSeq 2000 at the Genomic Sequencing Unit of the University of Dundee. ERCC RNA Spike-In mixes (Thermo Fisher Scientific)  were incReads were mapped to the TAIR10 reference genome with Araport11 reference annotation using STAR version 2.6.1 , a maximvir-1 mutant vs wild type and vir-1 mutant vs the VIR-complemented line were conducted in R version 3.5 using edgeR version 3.24.3 . Read counts for each chunk were generated using bedtools version 2.27.1  intersecvir-1 condition were identified using an FDR threshold of 0.05.We used the DaPars  algorithmRNA was isolated from approximately 75 μg of total RNA using the Dynabeads mRNA purification kit (Thermo Fisher Scientific) following the manufacturer’s instructions. The quality and quantity of mRNA was assessed using the NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). Nanopore libraries were prepared from 1 μg poly(A)+ RNA combined with 1 μl undiluted ERCC RNA Spike-In mix (Thermo Fisher Scientific) using the nanopore DRS Kit  according to manufacturer’s instructions. The poly(T) adapter was ligated to the mRNA using T4 DNA ligase (New England Biolabs) in the Quick Ligase reaction buffer (New England Biolabs) for 15 min at room temperature. First-strand cDNA was synthesized by SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) using the oligo(dT) adapter. The RNA–cDNA hybrid was then purified using Agencourt RNAClean XP magnetic beads (Beckman Coulter). The sequencing adapter was ligated to the mRNA using T4 DNA ligase (New England Biolabs) in the Quick Ligase reaction buffer (New England Biolabs) for 15 min at room temperature followed by a second purification step using Agencourt beads (as described above). Libraries were loaded onto R9.4 SpotON Flow Cells (Oxford Nanopore Technologies) and sequenced using a 48 hr run time.CGACUGGAGCACGAGGACACUGACAUGGACUGAAGGAGUAGAAA-3’) biotinylated at the 5′ end  and the 5′ cap was removed by Cap-Clip Acid Pyrophosphatase (Cambio) according to the manufacturer’s instructions. Next, the 5′ adapter RNA  using default RNA parameters and converted from RNA to DNA fastq using seqkit version 0.10.0 . Reads wa genome  and ERCCa genome  using mia genome  in splica genome . Variatia genome ; second,Proovread version 2.14.1  was usedError rate analysis of aligned reads was conducted on ERCC RNA Spike-In mix controls using pysam version 0.15.2  for BAM To identify read pairs resulting from over-splitting of the signal originating from a single RNA molecule, the sequencing summary files produced by Guppy were parsed for sequencing time and channel identifier and then used to identify pairs of consecutively sequenced reads. Genomic locations of reads were parsed from minimap2 mappings, and consecutively sequenced reads with adjacent alignment within a genomic distance of –10 nt to 1000 nt were identified. Samples sequenced before or during May 2018 had very low levels of over-splitting (between 0.01% and 0.05% of reads) compared with those sequenced in September 2018 onwards (between 0.8% and 1.5% of reads).To determine whether internal priming caused by the RT step can occur in nanopore data, the location of oligo(A) hexamers within Arabidopsis coding sequence (CDS) regions (Araport11) was determined and reads that terminated within a 20 nt window of each hexamer were counted. Of the 10,116 CDS oligo(A) runs, 160 (1.58%) had at least one supporting read in one Col-0 nanopore dataset. Of these, 137 were supported by only one replicate, and only four were supported by all four biological replicates. In total, 66 (41%) occurred in Araport11-annotated terminal exons, suggesting that they may be genuine sites of 3’ end formation.vir-1 mutant compared with the VIR-complemented line were identified using a Kolmogorov–Smirnov test. p-values were adjusted for multiple testing using Benjamini-Hochberg correction.Poly (A) tail length estimations were produced using nanopolish version 0.11.0  and addeHelicos data were prepared as previously described . PositioError-corrected full-length alignments were collapsed into clusters of reads with identical sets of introns. These clusters were then subdivided by 3′ end location by using a Gaussian kernel with sigma of 100 to find local minima between read ends, which were used as cut points to separate clusters. The read with the longest aligned length in each cluster was used as the representative in the figure.Splice junction locations, their flanking sequences and the read counts supporting them were extracted from Illumina RNA sequencing, nanopore DRS and nanopore error-corrected DRS reads using pysam version 0.14 , as wellTo validate novel splice junctions detected in nanopore DRS, five splice sites out of the 20 most highly expressed splice sites were selected for further validation; three of the five selected splice sites were successfully amplified in RT-PCR followed by Sanger sequencing (described above).Error corrected reads from Col-0 samples were filtered to retain only those that overlapped both annotated starts and termination codons from the Araport11 reference annotation . These rTo produce positive and negative examples of 5′ adapter-containing sequences, 5′ soft-clipped regions were extracted from aligned reads for the Col-0 replicate one datasets (with and without adapter ligation) using pysam . These sIn all, 72,083 positive and 123,739 negative training examples from Col-0 tissue replicate one were collected to train the neural network. We then estimated the amount of raw signal from the 5′ end of the squiggle that was required on average to capture the 5′ adapter. To do this, we used nanopolish eventalign version 0.11.0  to identThe model was trained in Python 3.6 using Keras version 2.2.4 with the Tensorflow version 1.10.0 backend . A ResNeTo evaluate the reduction in 3′ bias of adapter-ligated datasets, we used Araport11 exon annotations to produce per base coverage for each gene in the Col-0 replicate two dataset. Coverage was generated separately for reads predicted to contain adapters and for those predicted not to contain adapters. Leading zeros at the extreme 5′ and 3′ ends of genes were assumed to be caused by mis-annotation of UTRs and so were trimmed. The quartile coefficient of variation (interquartile range/median) was then used as a robust measure of variation in coverage across each gene.p-value thresholds of 0.5. Reproducible peaks across pairwise combinations of the three replicates were identified by irreproducible discovery rate (IDR) analysis using Python package idr version 2.0.3 with an IDR threshold of 0.05  . These w of 0.05 . The fin of 0.05 .Arabidopsis thaliana were downloaded from https://github.com/Gregor-Mendel-Institute/nanoPARE. miRNA target sites were predicted using GSTAr (https://github.com/MikeAxtell/GSTAr). miRNAs were also shuffled 1000 times and targets were predicted for shuffled sequences to model random chance predictions. P values for Allen scores of miRNA-target interactions were produced by permutation test, using the Allen scores of targets predicted from randomly shuffled versions of the miRNA as a null distribution. P values for the enrichment of nanopore DRS 5’ ends at the target cleavage sites were also produced by permutation test, comparing the log ratio of coverage in a 20 nt window upstream and downstream of each predicted cleavage site for an miRNA, to the coverage upstream and downstream of predicted cleavage sites of the shuffled miRNA. A 20 nt window was used instead of measuring 5' ends precisely at the cleavage site, because as we demonstrate in this study, approximately 11 nt are lost from the 5' end of RNA sequencing reads in standard nanopore DRS. Predictions which fell within 5’UTRs or across splice junctions were filtered out to reduce false positives. The P values derived from nanopore coverage and Allen scores were combined using the fisher method and corrected for multiple testing using the Benjamini-Hochberg method.Nanopore miRNA cleavage product identification was adapted from the EndCut method used previously . Known mVIRILIZER-dependent m6A RNA modifications, we developed scripts to test changes in per base error profiles of aligned reads. Pileup columns for each position with coverage of > 10 reads were generated using pysam  between the vir-1 and VIR-complemented lines was calculated using Haldane correction for zero counts. Bases that had a log fold change of > 1 were considered to have a reduced error rate in the vir-1 mutant.To detect sites of ng pysam  and read6A consensus.To identify motifs enriched at sites with a reduced error rate, reduced error rate sites were increased in size by five nt in each direction and overlapping sites were merged using bedtools version 2.27.1 . Sequenc6A motif which could be detected with all reads, was significant in the subsampled reads (FDR threshold 0.05). For the MALAT1 dataset, we also performed analyses on the effect of m6A stoichiometry on the ability to detect sites using differential error. For these analyses, reads from the m6A positive sample were replaced with reads from the m6A negative sample at rates of 0%, 25%, 50% and 75%, to simulate methylation levels of 100%, 75%, 50% and 25% respectively.To determine the effect of sequencing depth on the ability to call nanopore differential error sites, we performed a bootstrapped sub-sampling analysis. We conducted this analysis using MALAT1 synthetic RNA sequencing reads, and four differential error sites derived from three genes in the Arabidopsis data. 500 bootstrapped subsamples, at depths between 10 and 250 reads per condition, were generated for each gene and each base in the gene was then tested using differential error rate analysis as described above. Detection was considered successful if any of the differential error sites at the mvir-1 vs VIRc comparison. We identified reads overlapping each error site in the VIRc samples using pysam version 0.15.2 , which is featured as an example in To identify error sites which were phased with 3’ ends, we used error sites identified in the n 0.15.2 , and cluvir-1 and VIR-complemented lines was then performed in R version 3.5 using edgeR version 3.24.3 . The presence of m6A modifications at genes with differential 3′ end usage was assessed using bedtools intersect .Genes with differential 3′ end usage were identified by producing 3′ profiles of reads which overlapped with each annotated gene locus by > 20%. These profiles were then compared between the ntersect , and sigvir-1 mutant, we used Araport11 annotation  signal motif, we counted the occurrence of the sequence AAUAAA and related sequences  in a 100 nt window centred around m6A motifs detected de novo underneath nanopore error sites. To assess enrichment at these sites, we also shuffled the sequences derived from these windows 1000 times, maintaining the dinucleotide content using ushuffle version 1.1.0  analysis using Python package idr version 2.0.3 with an IDR threshold of 0.05  was performed as previously described (mescribed . Chromathttps://github.com/bartongroup/Simpson_Barton_Nanopore_1 (https://github.com/elifesciences-publications/Simpson_Barton_Nanopore_1).All scripts, pipelines and notebooks used for this study are available on GitHub at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:6A writer in Arabidopsis (vir-1 mutant), and vir-1 mutant reconstituted with vir-1, the authors used increased error rates in direct RNA sequencing to detect m6A modifications transcriptome-wide. The authors showed that m6A modifications impact RNA stability and circadian cycles are associated with 3' end formation in arabidopsis. The paper is very informative in the utility and limitations of using direct RNA sequencing to interrogate transcriptomes and the strategies described in the manuscript is widely applicable to not only Arabidopsis but also to any biologist interested in their transcriptome of choice.Studying the full transcriptome complexity and processing is key to understanding the biology of any organism. While the advent of RNA sequencing using short-read sequencing has greatly enriched our understanding of the transcriptome, challenges due to short-read sequencing include difficulties in mapping uniquely to individual isoforms, and assembling full transcripts, especially in repeat regions. In addition, the need to convert RNA into cDNA libraries for sequencing can result in artifacts in transcript annotation due to template switching of reverse transcriptase enzymes, and cDNA sequencing limited in its ability to detect RNA modifications. Using nanopore direct RNA sequencing, the authors updated the annotation of the Arabidopsis transcriptome by identifying new antisense transcripts, new splicing patterns, 3' end usage, polyA tail lengths and full length transcripts using 5'cap capture. In addition, using a mutant of the mDecision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Christian Hardtke as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Nanopore direct RNA sequencing maps an Arabidopsis N6 methyladenosine epitranscriptome" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Arabidopsis thaliana. In addition to studying the primary sequences of the transcriptome, including 5' ends, splicing and polyA+ tail length, the authors also utilized direct RNA sequencing to identify m6A modifications in vir-1 mutants and in vir-1 mutants with restored VIR activity. They found that m6A modifications is associated with 3' end processing in Arabidopsis thaliana. Overall the manuscript is well written and interesting.This is a timely manuscript to demonstrate the utility of direct RNA sequencing in discovering transcriptomic features in the model organism We have suggestions to improve the manuscript to increase the impact of the novelty and utility of using direct RNA sequencing, as well as the technical strength of the manuscript by adding controls and performing validations to determine the accuracy of the new signals that they find.Essential revisions:1) For polyA tail length determination- could the authors show accuracy of their polyA tail determination on a set of standards with known poly(A) tail lengths?2) 75% of the author's adapter ligated library failed to align to the transcriptome. The authors should look into the failed reads to explain why this is so.3) For transcripts with new 5' ends identified by 5'end capture, the authors should validate of the new 5'ends using 5'end RACE.6A sites should be generated to determine the accuracy and sensitivity of the author's method in detecting m6A to other available softwares, such as Tombo from Nanopore, as well as other published methods including (https://www.biorxiv.org/content/10.1101/525741v1). The authors should also comment on the robustness of their method with regards to new software changes.4) A set of RNAs with known m6A modifications. They should also do deeper analysis to look at the nucleotide composition of the called m6A modifications that they capture, as well as whether there is potential bias towards capturing modifications on specific transcripts.5) The authors should show data on the correlation between sequencing depth and the ability to detect m6) Direct RNA sequencing benefits from the aspect that it is long read, single molecule sequencing. As such, there is the possibility of "phasing" modifications that cannot be performed using short-read sequencing. The authors should show some examples of RNAs with more or less modifications than expected and attempt to associate that with RNA processing to demonstrate the utility of direct RNA sequencing.Reviewer #1:Arabidopsis thaliana. In the first part of the manuscript, the aim of this research is to: 1) prove that the Nanopore direct RNA sequencing has the ability to detect long, complex mRNAs under an acceptable error rate and to resolve a great complexity of splicing events; 2) prove that the over-splitting and spurious antisense reads generally occurs at low frequency in Nanopore direct RNA sequencing; 3) prove that Nanopore direct RNA sequencing can be used in estimation of poly-A tail length; 4) prove that Nanopore direct RNA sequencing using Cap-dependent capturing is effective in detecting authentic mRNA 5′ ends by using a convolutional neural network classifier of raw signals. In the second part, the authors present a direct RNA sequencing-based approach to detection of m6A methylation in data produced from both the vir-1 mutants defective in m6A and the vir-1 mutants restoring VIR activity. Furthermore, taking advantage of long read sequencing, the authors found that VIR activity is associated with the maintenance of 3' end formation of mRNAs. Overall, this manuscript describes a scientifically sound investigation of an important scientific area, that of epitranscriptomics. This manuscript will no doubt be a valuable resource to the growing field of direct RNA sequencing. I recommend this manuscript for publication in eLife.The possibility to perform RNA sequencing in its native form, without the requirement of cDNA synthesis, represents a major significant advance in RNA biology. Within this context, in this manuscript Matthew Parker and colleagues applied the Oxford Nanopore direct RNA sequencing to characterize the profiling of RNA transcripts in the model plant I have some specific points detailed below.1) In the supplementary Table 1, only 25% and 27% of sequencings reads in the 5' adapter ligation library could be mapped to the TAIR10 genome. The authors should check the sequencing reads to explain in detail the reason for the failed alignment in a large majority of sequencing reads.6A epitranscriptome”, 17,491 sites with a more than two-fold higher error rate in the VIR-complemented line with restored m6A. Are these error sites exclusively located in adenine positions? A nucleotide composition summary is required to improve clarity.2) Subsection “Differential error site analysis reveals the m3) Subsection “Spurious antisense reads are rare or absent in nanopore DRS”, the absence of reads mapping to antisense to RCA suggests that spurious antisense is rare or absent from Nanopore direct RNA sequencing data. Is it a generally accepted that the genomic loci of RCA do not generate antisense transcripts? If so, this information should be included.4) The webpage (https://github.com/bartongroup/Simpson_Barton_Nanopore_1) which is used to deposit scripts and pipelines on GitHub is not accessible. The code availability will represent a valuable addition to the Nanopore RNA sequencing community and can be used a guide for direct RNA methylation analysis.6A RNA modification detection and full-length RNA transcript sequencing using Cap-dependent capturing constitutes the major contribution of this paper, the recent advances relating to these topics should be included in the Introduction section.5) This is a technical paper to assess the performance of Nanopore direct RNA sequencing in a pioneer manner. Since m6A RNA modifications in native RNA sequences. bioRxiv 2019:525741.Liu H, et al. Accurate detection of mJiang F, et al. Long-read direct RNA sequencing by 5'-Cap capturing reveals the impact of Piwi on the widespread exonization of transposable elements in locusts. RNA Biol 2019:1-10.Reviewer #2:6A -modified positions in mRNAs. Finally, they applied an error-based m6A -detection method to find the association between the m6A modifications of 3′ UTR and RNA 3′-end processing in Arabidopsis thaliana.In this manuscript, Parker et al. demonstrated several interesting applications of nanopore direct RNA sequencing (DRS) to advance the current understanding of Arabidopsis transcriptome. They have assessed the primary performance factors of DRS that are related to the basecalling error profile, poly(A) length distribution, full-length splicing profile, and the reliability of antisense reads. Also, they have developed two new techniques for DRS to find the 5′-end positions of capped RNAs and the mThis manuscript lists many different types of RNA processing and modification that can be detected by DRS. This is a nice demonstration of the potential applications and strengths of DRS. But most parts do not connect to each other, and the conceptual novelty is limited. I have some concerns that will need to be addressed before publication.1) This technique is conceptually similar to the work by Liu et al. (https://www.biorxiv.org/content/10.1101/525741v1). Please clarify the similarities/differences between Liu et al. and the method described in this manuscript.2) Base-calling error profiles can vary depending on the basecaller version, the model in the basecaller, the processing/filtering parameters used for running the basecaller, the pore protein version, and the motor protein version. Recently, there was a huge upgrade for the basecalling model  introduced in Guppy 3.2. Moreover, according to the Oxford Nanopore Technologies (ONT), a faster motor protein and the new R10 pore will be introduced to their DRS kits very soon. The authors need to clarify and inform the readers that the error profile presented here may change depending on the basecaller software, a model, a pore, and a motor protein.The method described in this manuscript can work in the specific combinations that the authors used, but it does not guarantee its performance in the other settings. Considering the change in the flagship basecaller, Guppy, with higher accuracy after the version used in the manuscript, the authors need to check if this method works with comparable accuracy with the newer Guppy.3) In addition, Tombo from the ONT has a mode called "level_sample_compare" that enables the modified base detection by comparing control and experiment groups. Can you discuss a bit about the benefits and drawbacks of your method in comparison with Tombo?4) Subsection “Nanopore DRS confirms sites of RNA 3′ end formation and estimates poly(A) tail length”: The standard nanopore DRS library preparation uses the double-stranded RNA-DNA ligation assisted by an oligo(dT) splint. It inevitably introduces a substantial underrepresentation of short poly(A) tails. Even 10 nt oligo(dT) splint often results in a strong bias for > 20 nt poly(A) tails against the shorter tails. Moreover, short poly(A) tails often carry additional U tails which interfere with the ligation to the adapter. In addition, the means of the single-read "poly(A) length measurements" may not be a suitable summarization to estimate the means of "poly(A) lengths." Nanopolish gives the best approximations of poly(A) lengths at a single-read level. However, as the poly(A) dwell time roughly follows a gamma distribution with a long tail, the mean of the best approximations at a single-read level systematically overestimates the mean of poly(A) length.5) Subsection “Cap-dependent 5′ RNA detection by nanopore DRS”: The authors used RNA ligase 1 to mark the 5′ ends of RNAs. Due to the substrate specificity of this enzyme, circularized mRNAs and mRNA-mRNA concatamers might have been produced. Supplementary Table 1 shows that the libraries using the standard protocol yielded ~90% of mappable reads while the libraries with cap-dependent ligation yielded only ~25%. Please describe what the other 75% are. Also, it would be helpful if the sequence information of the 5′ adapter were presented in the manuscript.6A epitranscriptome”: How many of the miCLIP peaks could be detected with nanopore DRS? The authors only show the analyses using the population-level detection of m6A -modified sites. A real benefit of nanopore DRS lies in the associative analyses at a single-read level. Is it possible to use the DRS method to call m6A -modified sites within single molecules and analyze to see if a modified RNA has different polyadenylation status or 3′ end position from those in an unmodified RNA?6) Subsection “Differential error site analysis reveals the m6A modification in this manuscript can be better done using miCLIP than nanopore DRS.I feel that the current discoveries related to mReviewer #3:6A modification and use a vir-1 mutant strain to validate m6A sites. The identification of modified transcripts allowed the authors to identify a role of m6A in regulating length of circadian period. Overall the paper is well written and is easy to follow.In this manuscript, "Nanopore direct RNA sequencing maps an Arabidopsis N6 methyladenosine epitranscriptome" the authors use Nanopore direct-read RNA sequencing (DRS) to characterize the Arabidopsis transcriptome, including features that can't easily be determined by short read sequencing, such as cap-associated transcription start sites, splicing events, poly(A) site choice and poly(A) tail length. The authors also use this approach to map sites of mThe authors use cap-dependent ligation to enrich for capped mRNAs, reducing the 3' end bias observed in DRS. It would be of interest to discuss if the transcripts with early 5' ends result from technical artifacts  or represent transcripts present in the cell that lack a cap structure that is compatible with the ligation protocol. For example, the authors demonstrate that nanopore DRS can identify rare anti-sense transcripts. Could transcripts with early 5' ends, which are selected against in the cap dependent libraries, represent rare transcripts or degradation intermediary products?Additionally, the authors leverage DRS to expand the set of annotated transcripts and splicing isoforms. Can the authors use this data to describe new endogenous targets of NMD, poison exons, or find examples of proteins with new functional domains?6A modification on RNA metabolism. It would be informative if in addition to the ERCC controls, a set of RNAs with known m6A sites were also included.One feature of the transcriptome the authors focus on is the localization and effect of m6A biology that can't easily be studied with current methodologies is the stoichiometry of the modification at each position in each transcript. Can the authors comment at all on stoichiometry of m6A from DRS? Furthermore, can the authors comment on how many reads per transcript are necessary to detect a modification? Is there a bias towards more abundant transcripts? In mammalian cells it has been shown that modified RNAs tend to have shorter 3' UTRs (PMID: 27376769). Can this observation be tested in DRS, and if so, does the same phenomena occur in Arabidopsis? Information on stoichiometry can be validated for ACA sites with transcript specific assays using the MazF nuclease.One aspect of m6A through DRS?Lastly, can the authors comment on the ability of detecting modifications other than m Essential revisions:1) For polyA tail length determination- could the authors show accuracy of their polyA tail determination on a set of standards with known poly(A) tail lengths?We used the software tool nanopolish (polyA), developed by Jared Simpson’s lab, to estimate poly(A) tail length. Details on the development of the tool are incorporated within the Workman et al., pre-print (https://www.biorxiv.org/content/10.1101/459529v1). Importantly, in this context, the tool was tested on a set of RNA substrates with poly(A) tails of known lengths. Workman et al. state:S. cerevisiaeS. cerevisiae enolase transcripts appended with 3′ poly(A) tails of 10, 15, 30, 60, 80 or 100 nucleotides”“These datasets consisted of ionic current traces for synthetic The analysis of this set of standards with known poly(A) tail lengths in the Workman et al. study reports:“Median estimates fell within 4 nucleotides of the expected tail length for the 10-to-80 poly(A) datasets; for the 100 nt dataset, the median estimate was 109 nt.”To examine this question independently, using our own nanopore DRS data, we asked how well nanopolish (polyA) performed in identifying the poly(A) tail of the ERCC spike-ins included in our experiments. According to the manufacturers, most of the spike-ins have a terminal poly(A) stretch of 24 nt, with all spike-in poly(A) tails in the range 20-26 nt. For example, ERCC- 00002 has an expected poly(A) tail length of 24 nt, and the median nanopolish polyA estimate was 23.3 nt . ERCC-00074 also has an expected poly(A) tail length of 24 nt, and the median nanopore polyA estimate was 27.4 nt . We conclude that the median estimated poly(A) tail length by nanopolish (polyA) is similarly accurate to that reported by Workman et al., but with a broad margin of error.Text relating to these findings is now included in Results section and these data are now included as Figure 2—figure supplement 1A.2) 75% of the author's adapter ligated library failed to align to the transcriptome. The authors should look into the failed reads to explain why this is so.This finding is explained by the relatively low amount of input RNA used in the corresponding sequencing runs. In our standard nanopore DRS experiments we used 1,000ng of poly(A+) RNA for input libraries loaded onto the MinION flowcell. However, the recovery of RNA after cap capture was less than 250ng. This reduction in sample RNA has two consequences:First, relatively low amounts of input RNA reduces the ratio of the sample RNA to ONT’s internal standard RNA (yeast Enolase II [ENOII]). As a result, an order of magnitude more reads (11-20%) map to Enolase II in the adapter ligated samples compared to our standard nanopore DRS samples .1,2. Consistent with this, the remaining unmapped reads are very short (75% of unmapped reads are 100 nt or less in length) and of low quality  as illustrated in 1,2.Second, low input RNA results in low pore occupancy and “noise reads” consisting of signal from empty poresText has been inserted into the computational analysis methods section and the legend to Table 1, of the revised manuscript to explain this phenomenon.3) For transcripts with new 5' ends identified by 5'end capture, the authors should validate of the new 5'ends using 5'end RACE.Our 5’ cap capture method is essentially a cap-dependent 5’ RACE experiment: We ligate a biotinylated synthetic RNA oligonucleotide matching the oligo sequence used in the RLM GeneRacer kit designed for cap-dependent 5’ RACE (Thermo Fisher Scientific). Consequently, stronger validation of the new 5’ ends we identify could derive from a more orthogonal approach such as the Illumina-based high throughput 5’ end capture method called nanoPARE published at the end of 2018 by Michael Nodine’s group using Arabidopsis RNA3.NanoPARE is able to capture 5’ ends using reverse transcription of RNA with a template switching oligonucleotide (TSO), followed by tagmentation and PCR using TSO and transposase specific primers. PCR products are then sequenced using the IIlumina platform. Template switching using the TSO occurs most often at the 5’ end of the mRNA regardless of whether there is an m7G cap or not. Reads originating from the 5’ end of capped mRNAs can be distinguished from reads from uncapped 5’ ends and internal template switching by the addition of untemplated guanosine at the 5’ end of the read.RCA (AT2G39730), which agree with both our Illumina RNAseq coverage and the RIKEN RAFL clones, also agree well with the coverage from nanoPARE data . Furthermore, the alternative transcriptional start sites identified at AT1G17050 and AT5G18650 using nanopore DRS are also clearly visible in the nanoPARE data .To address whether novel 5’ ends identified by nanopore can be validated with another approach, we downloaded and reanalysed nanoPARE data, focussing on those reads with untemplated guanosine that originate from capped mRNAs. The 5’ ends identified by nanopore DRS at We reported in our original submission that 60% of nanopore 5’ ends sequenced using the cap capture protocol were within 13 nt of a transcription start site (TSS) identified using RIKEN RAFL cDNA clones . We can now also report that 75% of nanopore DRS 5' ends sequenced using cap capture are within 5 nt of a TSS detected using single base resolution peak-calling from nanoPARE reads, and 82% of nanopore DRS 5’ ends are within 13 nt of a TSS . This closer agreement is presumably due to the higher depth of the nanoPARE compared to RIKEN RAFL dataset, leading to the greater coverage of alternate TSSs.This information is now reported in the Results section of the manuscript and the data analysis has been incorporated into the Materials and mmethods section.6A sites should be generated to determine the accuracy and sensitivity of the author's method in detecting m6A to other available softwares, such as Tombo from Nanopore, as well as other published methods including (https://www.biorxiv.org/content/10.1101/525741v1). The authors should also comment on the robustness of their method with regards to new software changes.4) A set of RNAs with known m6A in a matched set of synthetic RNAs that differ only in the presence/absence of N6 methylation at a single adenosine. We selected a 60 nt portion of a human lncRNA (MALAT1) that has been reported to be modified by m6A, and for which base-specific validation of the modification had been obtained using the orthogonal technique, SCARLET4. The m6A -modified and unmodified RNAs were chemically synthesized by Dharmacon (Horizon Discovery), complete with a poly(A) tail 50 nt in length. We prepared and sequenced a library of each RNA using the nanopore DRS procedure.To address the first of the points raised by the reviewers, we examined whether we could detect mFollowing the application of our differential error rate analysis we could successfully identify the methylated adenosine.Text describing this approach has been added to the Results section, with an accompanying Figure 5—figure supplement 1A. In addition, details of the associated methodology involved have been added to the Materials and methods section.6A is not straightforward. No comparable assessment of the accuracy or sensitivity of any of the established m6A mapping techniques Me-RIP/ m6A -Seq5, mi-CLIP6, MAZTER-Seq7 or EpiNano8 exists. This is partly because no absolute “ground truth” for m6A sites exists, aside from individual synthesised RNAs. However, we have addressed this question in the following ways:The relative evaluation of our nanopore differential error analysis against other approaches detecting mNature Communications8. A distinction from the preprint is that the subsequent publication includes an assessment of EpiNano on biological data i.e. nanopore DRS of Saccharomyces cerevisiae strain SK1 defective in the S. cerevisiae m6A methyltransferase, IME4. S. cerevisiaeS. cerevisiae mRNAs are methylated during meiosis. Hence, synchronisation of cells is required to have appropriate starting material to detect m6A. Data corresponding to 3 biological replicates of the “wild-type” strain (Say841 comprising a deletion of NDT80 to facilitate meiosis synchronization) and 3 biological replicates of the ime4 mutant strain (Say966 in which both NDT80 and IME4 were deleted) were produced.Since our manuscript was reviewed, the EpiNano bioRxiv preprint has been published in 8 compared the relative performance of EpiNano against TOMBO using m6A sites identified by Me-RIP/ m6A -seq5 as the “ground truth”. However, while our manuscript was under review, another study was published in Cell that used an antibody-independent method called MAZTER-seq7 to map m6A. MAZTER-seq uses the enzyme MazF, which cleaves RNA in an ACA sequence context, unless the proximal A is methylated7. The MAZTER-seq study reported a minimal estimate of false positives and false negatives  in S. Cerevisiae Me-RIP/ m6A -seq data5,9. Consequently, Me-RIP/ m6A -seq data does not define an absolute “ground truth”. However, MAZTER-seq does not define an absolute “ground truth” either because only a fraction of S. Cerevisiae m6A sites are in an ACA contextLiu et al.S. cerevisiae8 is smaller than that which we report here for Arabidopsis . Consequently, this smaller dataset likely results in reduced power to detect m6A. Furthermore, only basecalled data appears to be publicly available and it is not certain whether data is basecalled using MinKNOW  or independently using a fixed version of the basecaller. Possibly for these reasons, we detect a large number of false positives in the differential error site approach when using an FDR threshold of 0.05 and log fold change threshold of 1 during analysis of these data. We therefore filtered the data with a more stringent FDR threshold of 10-6, resulting in the detection of 2,294 differential error sites (6A -seq5. Application of the identified motif to the sequences underlying error sites using FIMO (FDR < 0.1) identified 555 unique m6A motif sites. The most common motifs detected with nanopore DRS are shown in 6A -seq approach 5.The nanopore DRS dataset for or sites . A de noor sites , which rS. cerevisiae nanopore DRS datasets with a differential error rate approach is consistent with well-established features of m6A in this organism.We conclude that the output of analysis of these 8 and Me-RIP/ m6A -seq sites5 and MAZTER-seq sites7 as “ground truths”. However, it is important to bear in mind the limitations of such “ground truths” and hence the interpretation of “false positives” or “false negatives”. As a result, we refer here to “ground truth" and “predicted” sites that were “supported” or “unsupported” by either the Me-RIP/ m6A -seq or MAZTER-seq data.We assessed the performance of our nanopore DRS differential error rate approach by using the nanopore DRS data published in the Liu et al. EpiNano studyWe calculate three metrics for each set of predictions compared to the ground truth: (i) precision, (ii) recall and (iii) F1 score.Precision describes the fraction or percentage of predicted sites which have support in the ground truth dataset.Recall describes the fraction or percentage of ground truth sites which are detected in the predicted dataset.The F1-score an overall measure of accuracy and is defined as the harmonic mean of the precision and recall.6A -seq data as an m6A “ground truth” dataset  for which there is no supporting Me-RIP/ m6A -seq data. Indeed, using EpiNano sites with a threshold of zero  and filtered out m6A sites which were not in ACA contexts, we recovered only 7.5% of MAZTER-seq sites, but with an improved precision of 26.2%, for an F1 score of 0.12. Motifs detected de novo under nanopore DRS differential error sites recovered 9.2% of MAZTER-seq sites with a precision of 33.0%, for an F1-score of 0.14, when filtered to ACA contexts. Consequently, when analysed in this way, nanopore DRS differential error site analysis performed marginally better than Me-RIP/m6A-seq (with and without motif detection) and outperforms EpiNano.We next used MAZTER-seq datasets as a "ground truth” dataset . Garcia-Overall, we conclude that nanopore DRS differential error analysis can perform similarly to Me- RIP/m6A-seq and MAZTER-seq, but outperforms EpiNano.7. However, since MAZTER-seq only detects m6A in an ACA context , the majority of mRNA m6A sites are undetectable with this approach7.The strengths of MAZTER-seq are that single nucleotide resolution and stoichiometric data are obtainedin vitro in either the presence or absence of m6A. As a result, not only are RNAs with methylation profiles that have not been reported in nature used in the training set, but also the most frequently occurring sequence contexts found in nature e.g. AAm6ACA are missing from the training data. In order to address these limitations, EpiNano does not incorporate sites with more than one A in the k-mer, and so like MAZTER-seq, the majority of m6A sites found in mRNA are undetectable using EpiNano.The limitations of EpiNano may derive from the training data used: RNAs were transcribed S. cerevisiae nanopore DRS data, we asked if these sites were enriched toward the 3’ end of mRNAs as has been previously reported5. We found that this was the case  changes to nanopore DRS.6A modifications. They should also do deeper analysis to look at the nucleotide composition of the called m6A modifications that they capture, as well as whether there is potential bias towards capturing modifications on specific transcripts.5) The authors should show data on the correlation between sequencing depth and the ability to detect m6A sites, we performed a bootstrapped subsampling approach using the nanopore DRS data derived from the synthetic MALAT1 RNAs (described in response to point 4), and example methylated positions in the Arabidopsis transcriptome. This analysis reveals that the ability to detect m6A from errors is dependent upon sequencing depth, as well as the fraction of reads which are methylated at the position in the methylation positive example.In order to examine the power of our differential error rate analysis approach at detecting m6A at the correct position in more than 95% of bootstraps using only 60 reads per condition. However, this represents an idealised scenario, as the synthetic reads are 100% unmethylated in one condition and 100% methylated in the other. We therefore also performed an in silico "dilution" experiment, where we diluted the bootstrapped sample of methylated reads with unmethylated reads and determined whether m6A could still be detected. Here we found that when 50% of reads in the methylation positive condition are sampled from the unmethylated condition, > 200 reads per condition are required to reliably detect m6A in > 95% of bootstraps, suggesting that the percentage of reads which are methylated at a position is an important factor in the power to detect differentially methylated sites.Using the single replicate MALAT1 data, we can reliably detect m6A varies between genes, and between methylated positions in the same gene. This is likely due to differences in the percentage of transcripts that are methylated at each position. We also cannot rule out that sequence context-specific error rates affect the sequencing depth required to detect m6A. However, we find that for some positions m6A can be reliably detected with as little as 10 reads per replicate (in a 4 replicate per condition experiment) suggesting that it is possible to detect m6A robustly at the threshold of a median coverage of 10 reads per replicate used in our original analysis.When performing subsampling experiments using example methylated positions from the Arabidopsis nanopore DRS dataset, we find that the number of reads required to detect mThese data have now been incorporated into the Results section of the revised manuscript and included as Figure 5—figure supplement 1E, F.6A modifications”, we examined the reference bases at differential error sites. We find that the error sites are not exclusively located at adenosine positions. This is explained by the fact that 5 RNA bases contribute to the nanopore signal at a particular moment in time. Consequently, the presence of a methylated adenosine in the mRNA can cause basecaller errors anywhere within approximately 5 nt of the methylated adenosine. Hence, detection of m6A using differential error rates has a resolution of around 9 nt, and multiple bases surrounding a single modified adenosine may be detected as having a differential error rate. Adenosines account for most error sites (61.3%). However, since m6A tends to occur in A rich sub-sequence (the most commonly detected methylated motifs in our dataset were AAm6ACA and AAm6ACU) it is also possible that this represents a mixture of methylated positions and adenosines surrounding methylated positions.In order to address the second point of “a deeper analysis at the nucleotide composition of the called m10 and also by Wan et al.11. In other words, the most frequently occurring m6A motifs we identify with nanopore DRS match those identified for Arabidopsis using orthogonal Me-RIP/m6A seq data produced and analysed by other labs.We previously characterised the motif that underpinned the differential error sites  and detailed the frequency of different sequences matching the motif . Notably, the two most frequently occurring motifs that we reported (AAACA and AAACU) match the two most enriched motifs detected in Arabidopsis Me-RIP peaks analysis reported by Chuan He and collaboratorsWe have included text on this further support to the validity of the approach we used into the revised manuscript.6) Direct RNA sequencing benefits from the aspect that it is long read, single molecule sequencing. As such, there is the possibility of "phasing" modifications that cannot be performed using short-read sequencing. The authors should show some examples of RNAs with more or less modifications than expected and attempt to associate that with RNA processing to demonstrate the utility of direct RNA sequencing.6A modification and other RNA processing events. However, at this stage, our method for detecting m6A is aggregated across all reads, meaning that true phasing is not possible.Indeed, the ultimate goal would be to distinguish modifications on a single read originating from an individual transcript molecule. We continue to work on this, with the aim of identifying relationships between m6A motifs detected in the vir-1 vs VIRc comparison, were clustered by their 3’ position using a kernel density estimate with a σ of 13. Clusters with fewer than 10 reads were discarded. We then compared the error profiles of reads from different clusters at the error site, using a G-test approach, to identify whether different 3’ end positions had different levels of methylation. We identified 327 error sites which were phased with 3’ end position, overlapping 207 annotated genes. 149 (72%) of these genes also display a statistically detectable change in 3’ end position in the vir-1 mutant compared to VIRc, indicating that the changes in 3’ end position in the vir-1 mutant may result directly from the loss of m6A methylation. See PRPL34 (AT1G29070) as an example.To address the question posed by the reviewers, we developed an approach to test the relationship between mRNA 3' end position and the error profile of the nanopore DRS reads at detected differential error sites using only the methylated (VIRc) samples. Reads from VIRc samples, which overlapped error sites with identifiable mWe have included these new analyses in to the Results section and as Figure 7C of the revised manuscript.Reviewer #1:[…] I have some specific points detailed below.1) In the supplementary Table 1, only 25% and 27% of sequencings reads in the 5' adapter ligation library could be mapped to the TAIR10 genome. The authors should check the sequencing reads to explain in detail the reason for the failed alignment in a large majority of sequencing reads.Our response to this point is covered in the Essential Revisions section, Point 2.6A epitranscriptome”, 17,491 sites with a more than two-fold higher error rate in the VIR-complemented line with restored m6A. Are these error sites exclusively located in adenine positions? A nucleotide composition summary is required to improve clarity.2) Subsection “Differential error site analysis reveals the mOur response to this point is covered in the Essential Revisions section, Point 5.3) Subsection “Spurious antisense reads are rare or absent in nanopore DRS”, the absence of reads mapping to antisense to RCA suggests that spurious antisense is rare or absent from Nanopore direct RNA sequencing data. Is it a generally accepted that the genomic loci of RCA do not generate antisense transcripts? If so, this information should be included.RCA in either the Araport 11 or TAIR10 annotations, so it is not generally accepted that antisense RNAs are found at this locus.Antisense transcripts are not annotated at RCA. To investigate this question with an orthogonal dataset, we consulted our Illumina RNA-seq dataset, in which we sequenced Col-0 and a hen2-2 exosome mutant, prepared using ribosomal RNA depletion. Illumina RNAseq datasets have been shown to suffer from spurious antisense reads produced during library construction which complicate the detection of genuine antisense RNAs. Spurious antisense signals can be identified in count data using ERCC spike-in controls, which should not have antisense RNAs. The ratio of sense to antisense reads mapping to the ERCC spike-in controls can be used as an estimate of the level of spurious antisense signal. In our datasets we find that the ratios of sense to antisense reads at RCA are similar to those found in the ERCC spike-ins, and do not change in the hen2-2 mutant (ind t-test p = 0.88), suggesting that the antisense reads detected at RCA in Illumina RNA-Seq data may be spurious and not from genuine antisense RNAs (Nanopore DRS indicates antisense RNAs are rare or absent at nse RNAs .4) The webpage (https://github.com/bartongroup/Simpson_Barton_Nanopore_1) which is used to deposit scripts and pipelines on GitHub is not accessible. The code availability will represent a valuable addition to the Nanopore RNA sequencing community and can be used a guide for direct RNA methylation analysis.eLife, was so that all source data and code would be freely available, open access and directly linked to the figures in which the analysis is presented.We have prepared a GitHub site and ENA sites for sequence data (detailed in the submitted manuscript). Access to these resources will be made available upon publication. Indeed, our rationale for submitting this work to 6A RNA modification detection and full-length RNA transcript sequencing using Cap-dependent capturing constitutes the major contribution of this paper, the recent advances relating to these topics should be included in the Introduction section.5) This is a technical paper to assess the performance of Nanopore direct RNA sequencing in a pioneer manner. Since m6A RNA modifications in native RNA sequences. bioRxiv 2019:525741.Liu H, et al. Accurate detection of mJiang F, et al. Long-read direct RNA sequencing by 5'-Cap capturing reveals the impact of Piwi on the widespread exonization of transposable elements in locusts. RNA Biol 2019:1-10.Nature Communications and is now cited in our revised manuscript.A manuscript corresponding to this pre-print has now been published in Reviewer #2:[…] 1) This technique is conceptually similar to the work by Liu et al. (https://www.biorxiv.org/content/10.1101/525741v1). Please clarify the similarities/differences between Liu et al. and the method described in this manuscript.Nature Communications8. Liu et al. used aggregated measurements of insertion, deletion, and mismatch frequencies at DRACH  k-mers, from RNAs transcribed in vitro using either ATP or m6ATP, to train a support vector machine model to classify methylated and unmethylated positions. There are several important similarities and distinctions between this methodology and our own:The general principle of using nanopore Direct RNA Sequencing to identify RNA modifications is shared with that used in the pre-print Liu et al. (https://www.biorxiv.org/content/10.1101/525741v1), which is now published in 8, once trained on positive and negative examples, should be able to predict m6A in a normal sample without controls. In contrast, our method relies upon comparison between samples with methylation and reduced methylation controls.The models produced by Liu et al.8 makes predictions for reference positions and cannot identify the presence or absence of m6A in individual reads.Like our method, the model produced by Liu et al.8 should detect methylated positions in the analysed transcriptome independently of the writer complex or enzyme which catalyses the methylation. By contrast, our method, when using a mutant with reduced methylation such as vir-1, will only detect methylation sites which are lost in the mutant, e.g. VIR-dependent m6A sites.The models produced by Liu et al.8 rely on the prior knowledge of the DRACH motif to detect m6A, meaning that any modifications in other contexts will not be detected. In contrast, our method takes an unbiased statistical comparison approach to detecting m6A and is therefore sequence agnostic. We find that 4.8% of detected Arabidopsis m6A motifs are in the context AGAUU , and 9.55% of detected yeast m6A motifs are in the context DGACG . These modifications are also likely to cause basecalling errors, which the machine learning model has not been exposed to. This means that when the model is applied to biological samples and encounters errors caused by alternative modifications, it may behave unexpectedly, e.g. by calling them as m6A. Our method is unlikely to incorrectly flag other RNA modifications as m6A, unless the mutant used as a negative control alters the pattern of these modifications in RNA.The in vitro transcribed RNAs contain 100% methylated or 100% unmethylated adenosines only, biologically important k-mers with mixtures of A and m6A, such as AAm6ACA or AAm6ACU, are not sampled. Liu et al.8 acknowledge this and therefore do not make predictions in 5mer contexts with more than one A. However, by our estimates this would lead to the failure to detect 92% of m6A sites we detected in Arabidopsis, and 76.6% of m6A sites we detected in S. cerevisiae SK1. The two most frequently occurring motifs that we report in Arabidopsis (AAm6ACA and AAm6ACU) match the two most enriched motifs detected in Arabidopsis Me-RIP peaks analysis reported by Chuan He and collaborators10 and also by Wan et al.11, providing orthogonal support for these estimates.Because the 2) Base-calling error profiles can vary depending on the basecaller version, the model in the basecaller, the processing/filtering parameters used for running the basecaller, the pore protein version, and the motor protein version. Recently, there was a huge upgrade for the basecalling model  introduced in Guppy 3.2. Moreover, according to the Oxford Nanopore Technologies (ONT), a faster motor protein and the new R10 pore will be introduced to their DRS kits very soon. The authors need to clarify and inform the readers that the error profile presented here may change depending on the basecaller software, a model, a pore, and a motor protein.The method described in this manuscript can work in the specific combinations that the authors used, but it does not guarantee its performance in the other settings. Considering the change in the flagship basecaller, Guppy, with higher accuracy after the version used in the manuscript, the authors need to check if this method works with comparable accuracy with the newer Guppy.The reviewer raises an important point, namely that there are many variables controlling the error profiles of nanopore DRS reads. Indeed, we have found that comparing samples that have been sequenced with different kits or pores, or have been basecalled/mapped with different software can result in large numbers of false positives. We have therefore restricted analysis to samples that were sequenced with comparable kits, pores and used the same pipeline, tools, and parameters for basecalling and mapping of all samples.6A, we re-basecalled the data derived from the sequencing of the synthetic MALAT1 RNA using Guppy version 3.2.4. This version uses the so-called "flip-flop" approach to achieve higher accuracy. Using this basecaller, we found that there were still statistically detectable changes in error profile at the synthetically modified m6A site  In addition, Tombo from the ONT has a mode called "level_sample_compare" that enables the modified base detection by comparing control and experiment groups. Can you discuss a bit about the benefits and drawbacks of your method in comparison with Tombo?7; using yeast Me-RIP/m6A-seq data as a ground truth. The authors suggested that EpiNano had an increased precision over Tombo, but recovered fewer ground truth sites7. We understand that ONT is no longer supporting the further development of Tombo and are instead focussing on the development of a new tool called Megalodon for modification detection. However, this tool does not yet have support for RNA.The performance of Tombo has been evaluated by Liu et al.4) Subsection “Nanopore DRS confirms sites of RNA 3′ end formation and estimates poly(A) tail length”: The standard nanopore DRS library preparation uses the double-stranded RNA-DNA ligation assisted by an oligo(dT) splint. It inevitably introduces a substantial underrepresentation of short poly(A) tails. Even 10 nt oligo(dT) splint often results in a strong bias for > 20 nt poly(A) tails against the shorter tails. Moreover, short poly(A) tails often carry additional U tails which interfere with the ligation to the adapter. In addition, the means of the single-read "poly(A) length measurements" may not be a suitable summarization to estimate the means of "poly(A) lengths." Nanopolish gives the best approximations of poly(A) lengths at a single-read level. However, as the poly(A) dwell time roughly follows a gamma distribution with a long tail, the mean of the best approximations at a single-read level systematically overestimates the mean of poly(A) length.The reviewer makes very good points and we thank them for their informed insight. We have revised our wording in the manuscript text to refer to the poly(A) length of the sequenced reads rather than Arabidopsis poly(A) tail length. In addition, we have changed our data analysis and edited our text to use the median rather than the mean in these contexts. In addition we have included mention of the impact of short poly(A) tails and uridylated tails might have on quantitative gene expression analysis using the current standard nanopore DRS procedure.5) Subsection “Cap-dependent 5′ RNA detection by nanopore DRS”: The authors used RNA ligase 1 to mark the 5′ ends of RNAs. Due to the substrate specificity of this enzyme, circularized mRNAs and mRNA-mRNA concatamers might have been produced.in silico under-splitting errors , suggesting that the low levels of spurious concatemers are likely to be g errors .Supplementary Table 1 shows that the libraries using the standard protocol yielded ~90% of mappable reads while the libraries with cap-dependent ligation yielded only ~25%. Please describe what the other 75% are.Our response to the reviewer’s comment on the percentage of mappable reads in the adapter ligated datasets is given in the Essential Revisions section Point 2.Also, it would be helpful if the sequence information of the 5′ adapter is presented in the manuscript.The sequence of the 5’ adapter RNA has now been added to the Materials and methods section.6A epitranscriptome”: How many of the miCLIP peaks could be detected with nanopore DRS? The authors only show the analyses using the population-level detection of m6A -modified sites. A real benefit of nanopore DRS lies in the associative analyses at a single-read level. Is it possible to use the DRS method to call m6A -modified sites within single molecules and analyze to see if a modified RNA has different polyadenylation status or 3′ end position from those in an unmodified RNA?6) Subsection “Differential error site analysis reveals the mWhilst approaching this question we discovered an error in our miCLIP analysis pipeline that meant that the results from the irreproducible rate analysis were not thresholded correctly. Upon correcting this mistake we found that because replicate 3 of our miCLIP experiment was less well correlated with replicates 1 and 2 , it was reducing the quality of the results, and decided to exclude it from the downstream analysis. This has altered our analysis and some results slightly. The Materials and methods and Results sections of the manuscript have been updated accordingly.6A: we detected 93 thousand significant miCLIP sites compared to only 17 thousand nanopore differential error sites. Furthermore, miCLIP can detect a wider range of methylated adenosines in the transcriptome. Here we report a differential error rate method capable of detecting m6A sites dependent on VIR, in poly(A+) RNA. m6A sites in poly- or oligo- adenylated RNAs, which are detectable by miCLIP, but which are methylated by other enzymes or writer complexes are not detected by our experimental approach.We now find that 65.7% of nanopore error sites are within 5 nt of an miCLIP site detected in two replicates at IDR < 0.05 . Conversely, 31.2% of miCLIP peaks are within 5 nt of an error site detected with nanopore DRS. This is likely because miCLIP is a more sensitive but less precise technique (i.e. has antibody-dependent limitations) than nanopore DRS for mapping mWe address the issues regarding single molecule detection and phasing in the Essential Revisions section Point 6.6A modification in this manuscript can be better done using miCLIP than nanopore DRS.I feel that the current discoveries related to mArabidopsis thaliana Col-0 poly(A)+ RNA. Consequently, in addition to our nanopore DRS datasets, we provide here the first miCLIP-based map of m6A in any plant species. Compared to the read depth of our current nanopore DRS datasets, our miCLIP data does indeed detect more significant sites using the statistical approach described.To clarify, we carried out 3 biological replicates of miCLIP in this study using wild type 6A, such as miCLIP, are limited in some respects because (i) they cannot readily define stoichiometry (ii) they do not necessarily detect m6A only (iii) they have unclear sensitivity. These limitations account, in part, for the recent high-profile publication of the MAZTER-Seq procedure (published while our manuscript was in review) as an antibody-independent approach to mapping m6A that addresses these issues 7.However, antibody-based approaches to mapping m7. This appears to be explained by sites modified at low stoichiometries that are stochastically identified in one miCLIP experiment, but not another, due to the low efficiency of UV cross-linking7.The overlap of miCLIP sites mapped in different studies is low. Examination of 81,519 sites identified across six different published miCLIP experiments in human cell lines revealed a median of only 1,500 sites shared between any two datasets6A writer complex, or cross- hybridisation of the antibody to epitopes other than m6A. We used the Synaptic Systems anti-m6A antibody for miCLIP studies because it is widely used in the field and because the errors induced in miCLIP sequencing libraries had already been established6. However, it has also previously been shown that this antibody cross-reacts with non-methylated purine-rich RNA sequences5, highlighting limitations to this approach.We used miCLIP as an orthogonal technique here, using only wild-type material. Although we detect enrichment of miCLIP sites in 3’UTRs , the consensus sequence associated with miCLIP is oligo(A) – indicating either the identification of sites distinct from those written by the mRNA m6A to changes in RNA abundance and processing , in the same experiment. For an miCLIP-only approach to provide the same insight, further analysis would be required, such as (i) control procedures for mapping m6A sites in a methylation defective mutant that are not yet routinely established or accepted, (ii) different datasets of RNAseq to investigate changes in RNA levels, (iii) further datasets of specialised RNAseq to map 3’ ends (eg 3P-Seq12), (iv) since we demonstrate that some of the changes in pre-mRNA processing in the absence of VIRILIZER-dependent m6A  are distinct from events annotated in TAIR10, Araport11 and AtRTD2, long read sequencing of the vir-1 transcriptome with PacBio, for example, would be necessary to establish an appropriate Reference Transcriptome to be used to quantify the different de novoRNA processing events identified in this study. Overall, nanopore DRS can give m6A positions in the context of full- length RNA reads which can be used for other analyses, but miCLIP cannot.We use nanopore DRS datasets to link mReviewer #3:[…] The authors use cap-dependent ligation to enrich for capped mRNAs, reducing the 3' end bias observed in DRS. It would be of interest to discuss if the transcripts with early 5' ends result from technical artifacts  or represent transcripts present in the cell that lack a cap structure that is compatible with the ligation protocol. For example, the authors demonstrate that nanopore DRS can identify rare anti-sense transcripts. Could transcripts with early 5' ends, which are selected against in the cap dependent libraries, represent rare transcripts or degradation intermediary products?We thank the reviewer for their insightful comment. We had considered it difficult to clearly distinguish what might be biology from what might be experimental induced RNA decay aside from our RNA quality control measures. Consequently, we focused instead on capturing RNAs that did have a 5’ cap to establish which RNAs were full-length, by at least this criterion. However, in response to the reviewer's question, we looked at this in more detail.3. Target sites for known Arabidopsis small (s)RNAs  in protein coding transcripts were predicted using GSTAr. To prevent false positives caused by poor alignments and full-length mRNAs, target sites spanning introns and in 5'UTRs were filtered out. For each putative target site, the number of nanopore DRS reads terminating in a 20 nt region upstream and downstream of the cleavage site was calculated and used to produce an enrichment score for reads terminating immediately downstream of the site. This method was used instead of measuring 5' ends precisely at the cleavage site, because as we demonstrated in our study, approximately 11 nt are lost from the 5' end of RNA reads in standard nanopore DRS. Enrichment scores and Allen scores for sRNA-target alignments were tested for significance using the same permutation testing method as in Schon et al.3 sRNA sequences were shuffled 1,000 times and target sites for shuffled sequences were predicted. Null distributions for Allen scores and enrichments scores were then produced and used to generate p values for significance. Enrichment and Allen score p values were combined using Fisher's method and multiple testing correction was applied.To address what might be biology-dependent decay in the datasets that we had, we asked if there was an enrichment of 5’ nanopore DRS reads terminating in proximity to established miRNA cleavage sites in our datasets prepared without cap capture. We approached this question in a similar way to that used by Michael Nodine’s recent study using Illumina nanoPARE dataTAS genes also reveal clear evidence of established cleavage products  and HAM2 (miR170/miR171); NAC1 (miR164); ARF proteins (miR160); and SPL domain containing proteins (miR156/7). Nanopore DRS reads aligning to the non-protein coding products . The fulTAS ncRNA) cleavage and we thank the reviewer for making this suggestion. Since we have shown that nanopore DRS does not sequence the extreme 5’ end of an RNA accurately, the ideal way to address this question in the future would be to compare adapter ligated libraries that were cap-dependent with those that were cap-independent in a conceptually similar approach to that used in Michael Nodine’s nanoPARE study3. In addition, the use of mutants defective in the Arabidopsis cytoplasmic 5’-3’ exonuclease XRN4 would be useful in revealing degradation intermediary products that are otherwise too unstable to be readily detected.We conclude that, in principle, nanopore DRS can be used to reveal sites of internal mRNA .upf1 mutants13 , establishing the principle that nanopore DRS can be used to identify novel alternative splicing products which may be targets of NMD. We suggest that the best way to study these phenomena in the future would be to use biological material with genotypes that disrupt the processes of interest in order to provide a means to validate the events under study. For example, sequencing an NMD mutant such as upf1, as was previously done with Illumina RNAseq, would validate the discovery of potential new NMD substrates.We can, for example, identify well-established examples of cassette exons, and alternative donor and acceptor sites which are known to be targets of the NMD pathway as indicated by upregulation in utants13 . This ints13 . Translation of these APA transcripts would be predicted to produce transcription factors which have similar activity to the cleaved N-terminal fragment generated from full length PTM. Consequently, regulation of this APA site may provide an alternative mechanism for the production of active PTM that bypasses the retrograde signalling pathway.In addition to the detection of novel alternative splicing events, nanopore DRS is also able to identify novel alternative cleavage and polyadenylation (APA) sites. For example, PTM is a plant homeodomain transcription factor with C-terminal transmembrane domains that sequester it to the chloroplast. PTM is cleaved from the membrane in high light conditions and the N-terminal fragment translocates to the nucleus, where it acts as a retrograde transcription factor positively regulating flowering through the control of This finding is now included in the Results section related to RNA 3’ end detection.6A modification on RNA metabolism. It would be informative if in addition to the ERCC controls, a set of RNAs with known m6A sites were also included.One feature of the transcriptome the authors focus on is the localization and effect of mThis point is addressed in response to the Essential Revisions Point 4.6A biology that can't easily be studied with current methodologies is the stoichiometry of the modification at each position in each transcript. Can the authors comment at all on stoichiometry of m6A from DRS? Furthermore, can the authors comment on how many reads per transcript are necessary to detect a modification? Is there a bias towards more abundant transcripts?One aspect of mThese points are addressed in response to Essential Revisions Point 5.In mammalian cells it has been shown that modified RNAs tend to have shorter 3' UTRs (PMID: 27376769). Can this observation be tested in DRS, and if so, does the same phenomena occur in Arabidopsis?6A in the vir-1 m6A defective mutant on 3’ end formation, we found a clear directional shift to proximal poly(A) site selection. These findings were reported in Figure 6E of our originally submitted manuscript. In addition, our analysis of phasing, described in Essential Revisions section Point 6, revealed a clear directional shift to more proximal poly(A) site selection in transcript isoforms detected from the same gene in m6A defective mutant backgrounds compared to complemented line VIRc .When considering the impact of loss of m15 algorithm which is designed to detect shifts in RNA 3’ end formation in Illumina RNAseq data. We used our Illumina RNAseq datasets that we carried out in parallel to nanopore DRS, comparing the vir-1 mutant with the VIR complemented line. We find that DaPars analysis also identifies a global shortening of 3'UTRs in the vir-1 mutant .We have now also addressed this question in an orthogonal manner. We used the DaParsWe have incorporated the DaPars analysis  into our revised manuscript.6A and 3’ end formation in published mammalian cell studies. For example, Ke et al.16, reported that dense methylation was associated with an inhibition of proximal poly(A) site selection and the use of more distal poly(A) sites. Therefore, our findings are consistent with the suggestion made by Ke et al.16 that m6A could inhibit the use of proximal sites and hence favour longer 3’UTR transcript isoforms of the same gene. Moline et al. state the following in the Discussion of their publication:Our interpretation is that there is not yet a clear consensus on all the possible interrelationships between m6A peaks. Consistent with this observation, we found that m6A levels of genes are positively correlated with maximum 3′-UTR lengths as measured in input steady-state RNAseq data . Detailed analyses showed that genes with higher proximal APA usage in the m6A-positive fraction  had significantly longer 3′UTRs  and lower ratios of proximal versus distal signals  in the input RNAseq data as compared to genes with higher distal APA usage in the m6A-positive fraction  or no change in APA usage between fractions .”“A recent study reported that genes with longer last exons have a higher density of mThe further analysis of 3’ end formation prompted by this reviewer’s question and the phasing analysis requested in Essential revisions point 6 led us to look more closely at the shift in 3’ formation. When we plotted the position of the most-frequently occurring motifs at differential error sites and the poly(A) signal AAUAAA (and single nucleotide variants) we detected a close overlap in position . We have included this new finding into the Results section of the revised manuscript.6A through DRS?Lastly, can the authors comment on the ability of detecting modifications other than m17 has already been demonstrated.We have not yet examined other RNA modifications. A crucial tool in such analyses is the availability of a genotype that disrupts the modification. However, the ability to use nanopore to map other modifications in DNA and indeed nucleotide analoguesConsequently, we would anticipate that other RNA modifications will be detectable by nanopore DRS.ReferencesLife Sci Space Res (Amst)18, 80-86, doi:10.1016/j.lssr.2018.05.004 (2018).1 Pontefract, A., Hachey, J., Zuber, M. T., Ruvkun, G. and Carr, C. E. Sequencing nothing: Exploring failure modes of nanopore sensing and implications for life detection. BMC Bioinformatics19, 108, doi:10.1186/s12859-018-2124-3 (2018).2 Mojarro, A., Hachey, J., Ruvkun, G., Zuber, M. T. and Carr, C. E. 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