The appeal of nanopore sequencing rests on a simple physical principle. As a native RNA molecule threads through a protein nanopore embedded in an electrically insulated membrane, it disturbs the ionic current flowing through the pore. Because each combination of nucleotides, known as a k-mer, produces a characteristic current signature, the raw squiggle of electrical data can in principle be decoded into sequence. Crucially, because the molecule is read directly rather than converted to complementary DNA and amplified, chemical modifications such as N6-methyladenosine (m6A), 5-methylcytosine (5mC) and pseudouridine leave their own fingerprints in the current trace. This is a capability that short-read platforms like Illumina fundamentally lack, since their reverse transcription and polymerase chain reaction steps erase the very epitranscriptomic marks researchers want to find.

The review traces how the technology matured from the portable MinION released in 2015, through the high-throughput PromethION and the budget-friendly Flongle adapter, to successive chemistry generations from R9.4.1 to the R10 series and the newer RNA004 flow cells. Equally important has been the evolution of the computational pipeline. Base calling, the conversion of raw current into nucleotide letters, has progressed from Hidden Markov Models to LSTM-based recurrent neural networks in ONT’s Guppy, through convolutional architectures in Bonito, to Dorado, which the authors note has been reported to reach around 99.5 percent raw read accuracy. Alignment tools have followed a parallel trajectory, from GraphMap’s gapped seeding strategy through the now-ubiquitous Minimap2, which the study describes as three to four times faster than mainstream short-read mappers at comparable accuracy, to specialists such as Winnowmap2, which achieved false-negative and false-positive rates of just 1.89 percent each on repetitive reference sequences, far outperforming Minimap2’s 39.62 and 5.88 percent respectively in the cited evaluation.

Between base calling and modification detection sits the re-squiggling step, where raw signals are re-aligned to the called sequence so that per-base current features can be extracted. The review dissects Tombo’s five-stage implementation, spanning genome mapping, signal normalization, event detection, sequence-to-signal assignment and the resolution of skipped bases, and notes that most re-squiggling tools share this conceptual skeleton. Quality control software such as NanoPlot, pycoQC, MinIONQC and ToulligQC then guards the integrity of the data before any biological interpretation begins. Together, the authors argue, this pipeline has matured into a sophisticated, community-driven framework that has steadily improved accuracy, scalability and accessibility across a wide range of applications.

The heart of the paper is its survey of modification detection methods, which have evolved through three distinct technological waves. The first wave relied on classical statistics: Nanocompore, xPore and Yanocomp employed Bayesian generative models built on Gaussian mixture models, while DiffErr and DRUMMER used G-tests and ELIGOS applied Fisher’s exact tests to compare error profiles between modified and control samples. The second wave brought machine learning and deep learning: Nanom6A applied XGBoost for single-base m6A detection, MINES used random forests on four specific sequence contexts, EpiNano turned to support vector machines, and m6Anet deployed a neural network for transcriptome-wide m6A quantification. Pseudouridine specialists emerged in parallel, with Penguin combining several machine learning models to reach 93.38 percent accuracy on a HEK293 benchmark, and NanoMUD using bidirectional LSTMs to detect both pseudouridine and its therapeutically important cousin N1-methylpseudouridine.

The third and most recent wave is the most ambitious. Multi-modification detectors such as ModiDeC, which identifies m6A, pseudouridine, Gm, m1A and inosine in a single pass, and RNANO, which leverages attention-enhanced multi-instance learning to detect seven modification types, are pushing toward comprehensive epitranscriptomic profiling. Perhaps most transformative is the emergence of modification-aware base callers, exemplified by m6ABasecaller and ONT’s own Dorado, which now supports modified base calling for m6A and pseudouridine directly. These tools fold modification prediction into the base calling step itself, bypassing computationally expensive re-squiggling and post hoc statistics, and can operate without matched control samples or prior motif knowledge. The catch, the authors caution, is that such models demand training data with precise ground-truth annotations of modification status at individual nucleotides, a resource that remains scarce.

To test whether this proliferation of methods translates into reliable science, the team benchmarked five m6A detection tools, m6Anet, MINES, nanom6A, DRUMMER and DiffErr, against an NGS-derived reference database stratified into four confidence tiers, using the HEK293_WT and HMEC_WT datasets from GEO accession GSE132971. The discrepancies were dramatic. In HEK293_WT, m6Anet predicted 36,688 m6A sites and MINES 35,870, while DRUMMER reported only 36 and DiffErr just 3. Even among the three prolific tools, only 5,088 sites were commonly detected, and a large share of their predictions mapped to the low and very low confidence tiers of the reference: m6Anet, for instance, placed 15.5 percent of its calls in the low group and 22.6 percent in the very low group, with only 1.6 percent reaching high confidence. The pattern repeated in HMEC_WT, where the three leading tools shared just 2,806 sites.

The pseudouridine benchmark was even more sobering. NanoMUD predicted 82,581 pseudouridine sites in HEK293_WT, yet only 57 of them, about 0.1 percent, overlapped with the NGS-based reference, and NanoPsu managed just 31 overlaps. In HMEC_WT, Penguin predicted a staggering 644,248 sites but overlapped the reference at only 74 positions. Cross-tool concordance was similarly dismal, with NanoMUD and NanoPsu sharing only 396 sites in the first dataset and Penguin and NanoPsu agreeing on a single site in the second. The authors conclude that inconsistencies in pseudouridine identification are even more pronounced than for m6A, underscoring an urgent need for improved cross-tool consistency and methodological refinement.

Why does this matter beyond the technical community? RNA modifications are not decorative. m6A, the most prevalent internal modification in eukaryotic messenger RNA, regulates splicing, nuclear export, translation and mRNA stability, and its dysregulation has been implicated in heart failure, cancers and reproductive disorders. Pseudouridine enhances RNA stability and base stacking, and it is the key modification in chemically stabilized mRNA vaccines; NanoMUD has already been applied to quality control in vaccine design contexts. The review also contrasts nanopore approaches with alternatives: liquid chromatography-mass spectrometry offers accurate quantification but no sequence information, antibody-based methods lack single-nucleotide resolution, and newer NGS techniques such as GLORI and eTAM-seq achieve absolute m6A quantification but remain constrained by short reads and indirect chemistry. Nanopore’s direct, long-read, single-molecule approach remains uniquely positioned, but only if the computational interpretation of its signals can be trusted.

The authors are candid about the remaining obstacles. Nanopore error rates still exceed the 0.1 to 1 percent benchmark of Illumina short reads, and because some pipelines infer modifications from base calling errors, this intrinsic noise directly undermines detection. Direct RNA sequencing also demands substantial input, with the RNA004 protocol specifying 300 nanograms of poly(A) RNA or a microgram of total RNA, a barrier for clinical biopsies and single-cell work. Throughput remains modest at one to three gigabases per flow cell, and reliable modification detection requires coverage of at least 30-fold, while deep learning pipelines demand serious GPU infrastructure. Yet the trajectory is encouraging: multiplexed direct RNA protocols, real-time selective sequencing tools like SquiggleNet, signal-level mappers like Sigmap, and the integration of large language models and ensemble learning all point toward a future in which the epitranscriptome can be read as directly, and as reliably, as the genome itself. For now, the study’s message to researchers is clear: choose your tools carefully, validate against orthogonal methods, and treat any single algorithm’s modification calls with healthy skepticism.

Subject of Research: Benchmarking computational tools for detecting RNA modifications using Oxford Nanopore direct RNA sequencing

Article Title: Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking

Article References: Wu, Z., Li, J., Xia, R., Dai, J., Su, J., Meng, J., & Zhang, Y. (2026). Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking. Advanced Biotechnology, 4(1), Article 9. https://doi.org/10.1007/s44307-025-00093-5

Image Credits: AI Generated

DOI: 10.1007/s44307-025-00093-5

Keywords: nanopore sequencing, direct RNA sequencing, RNA modifications, m6A, pseudouridine, epitranscriptomics, base calling, machine learning, deep learning, benchmarking, Oxford Nanopore Technologies, computational tools

A new study published in MicrobiologyOpen has now combined two cutting-edge technologies, CRISPR interference and nanopore native RNA sequencing, to systematically probe the epitranscriptome of Escherichia coli. The research, led by Miranda E. Pitt of the University of Technology Sydney and Lachlan J. M. Coin of the University of Melbourne, together with colleagues, demonstrates a workflow in which individual RNA modification enzymes are switched off in living bacteria, and the resulting loss of chemical marks is read out directly at the single-molecule level. The approach not only confirmed several well-known modification sites on ribosomal RNA but also revealed candidate modification sites scattered across bacterial messenger RNA, a frontier that has remained largely unmapped.

The technical challenge the team faced is formidable. Traditional methods for detecting RNA modifications, such as immunoprecipitation or chemical treatments like bisulfite conversion, are laborious, require highly purified and concentrated RNA, and generally capture only one modification type at a time. Bacterial RNA adds further complications: rigorous lysis procedures and complex cell membranes accelerate RNA degradation, and bacterial transcripts have notoriously short half-lives, in many cases under one minute. Enzymes called RNases can rapidly destroy extracted RNA unless inhibited immediately. Direct RNA sequencing, released by Oxford Nanopore Technologies in 2017, offers a faster alternative because it reads native RNA molecules as they pass through protein nanopores, preserving the chemical modifications that conventional methods destroy when RNA is converted to complementary DNA. However, the chemistry was originally incompatible with bacterial transcripts, which lack the polyadenylated tails found in eukaryotic messenger RNA.

The researchers overcame this barrier by artificially adding poly(A) tails to bacterial transcripts before sequencing, a technique they had refined in previous work. To create the perturbations needed to detect modifications, they turned to CRISPR interference, or CRISPRi. Unlike standard CRISPR gene editing, which cuts DNA, CRISPRi uses a catalytically inactive form of the Cas9 protein, dCas9, that binds to a target gene guided by a matching guide RNA and simply blocks its transcription. This knocks down, rather than knocks out, gene expression, avoiding the toxicity and off-target DNA disruption associated with genome cutting while allowing inducible, tunable silencing. The team used a plasmid system optimized for E. coli, in which dCas9 expression is triggered by the small molecule anhydrotetracycline.

Five genes encoding known ribosomal RNA modification enzymes were targeted: rlmF and rlmJ, both responsible for N6-methyladenosine marks on 23S ribosomal RNA; rsmF, which deposits 5-methylcytosine on 16S ribosomal RNA; rsmG, which installs 7-methylguanosine on 16S ribosomal RNA; and rluD, a pseudouridine synthase that modifies three positions in a functionally critical stem-loop of the 23S subunit. A sixth gene, add, encoding adenosine deaminase, was included to explore whether it might carry previously unrecognized RNA editing activity. The knockdown system was tested across multiple E. coli reference strains, including the type strain ATCC 11775, ATCC 8739, ATCC 25922, and a multidrug-resistant isolate, ATCC BAA-2452. Quantitative reverse transcription PCR confirmed that most target genes were repressed by more than 80 percent, although rsmG proved harder to silence, reaching only about 54 percent reduction, and the resistant isolate could not be transformed with the plasmid without a severe fitness cost.

With knockdown strains in hand, the researchers sequenced both total RNA and ribosomal RNA-depleted, messenger RNA-enriched samples on nanopore flow cells, using computational tools to compare ionic current signals between knockdown and control conditions. The software nanocompore, which does not require prior training and is agnostic to modification type, compared each perturbed sample against two controls: the unmodified original isolate and a plasmid control expressing dCas9 without a guide RNA. For rluD, all three known pseudouridine sites in the 23S ribosomal RNA were detected with strong statistical significance, and the sequence motifs surrounding those sites matched published reports. The rsmG knockdown likewise produced a clear signal at the known 7-methylguanosine position in 16S ribosomal RNA. In contrast, the sites modified by rlmF, rlmJ, and rsmF yielded weak signals that were difficult to distinguish from noise, echoing the findings of earlier benchmarking studies showing that certain modification types, particularly 5-methylcytosine and some N6-methyladenosine sites, are inherently harder to detect with current nanopore basecalling and current-difference tools.

Extending the analysis to messenger RNA, the team identified significant modification-site changes on several bacterial transcripts shared across strains. Genes harboring candidate modification sites included ompC, which encodes an outer membrane porin; lpp1; salPB; cspC; dbhA and dbhB; and secY, which encodes a core component of the protein secretion machinery. For the rluD and rsmG knockdowns, which had produced the strongest ribosomal RNA signals, the researchers searched messenger RNA for sequence motifs matching those seen at the known ribosomal sites. Strikingly, a significant change was found on the well-characterized pseudouridine motif in the gene encoding elongation factor Tu, the workhorse protein of translation, as well as on transcripts of lpp1, gapA, and fusA. For rsmG, significant sites matching the known 7-methylguanosine motif appeared in hupA, lpp1, rplT, rpsA, and ompC. Because these messenger RNA sites were detected in more than one strain background, they represent plausible, though not yet chemically verified, targets of bacterial modification enzymes acting beyond the ribosome.

The study also examined the downstream consequences of losing these modifications, measuring bacterial growth and profiling the proteome by quantitative mass spectrometry. Growth effects varied by strain: activation of dCas9 itself imposed a measurable burden in ATCC 11775, which carries an extra plasmid, while ATCC 8739 tolerated the system well. Statistically significant growth delays were observed for knockdowns of rluD and rsmF in one strain and rlmF, rluD, and rsmF in another, consistent with prior reports that the phenotypic impact of ribosomal RNA modification loss depends heavily on genetic background. Proteomic analysis detected roughly 1,800 to 2,400 proteins per strain and revealed modest but reproducible changes, including alterations in citrate lyase, the trehalose-specific phosphotransferase component TreB, the xylose-binding protein XylF, the sodium/proline symporter PutP, formate hydrogenlyase subunit HycB, and the oligopeptide transporter OppC. The authors note these changes require further validation and may only become pronounced under stress conditions such as antibiotic exposure or temperature shifts, which previous work has shown can reshape the bacterial epitranscriptome.

The broader significance of the work lies in its implications for antimicrobial resistance and bacterial virulence. RNA modifications are already known to help pathogens evade entire antibiotic classes, including aminoglycosides, chloramphenicol, and macrolides, and to contribute to antibiotic tolerance in organisms such as Vibrio cholerae. Loss of tRNA modification enzymes can impair a bacterium’s ability to infect and cause disease in a host. A reliable, rapid, genome-wide method for mapping modification dynamics in bacteria could therefore illuminate how pathogens respond to clinical interventions at the level of their RNA. The authors caution that fine-tuning is still needed, particularly for messenger RNA modification detection across diverse bacterial species and under varying growth conditions, and newer nanopore chemistry promises higher accuracy and greater yield. Yet as a proof of principle, pairing CRISPR interference with native RNA sequencing offers a scalable blueprint for interrogating the bacterial epitranscriptome, one silenced enzyme and one molecule of RNA at a time.

Subject of Research: Mapping RNA modifications in the Escherichia coli epitranscriptome using CRISPR interference gene knockdown and nanopore direct RNA sequencing

Article Title: Interrogating the Escherichia coli Epitranscriptome Via CRISPR Interference and Nanopore Native RNA Sequencing

Article References: Pitt, M. E., Zhang, J., Nguyen, A. N. T., Hall, M. B., Jebeli, L., Featherstone, L. A., Myers, G. S. A., Scott, N. E., & Coin, L. J. M. (2026). Interrogating the Escherichia coli Epitranscriptome Via CRISPR Interference and Nanopore Native RNA Sequencing. MicrobiologyOpen, 15(5), Article e70413. https://doi.org/10.1002/mbo3.70413

Image Credits: AI Generated

DOI: 10.1002/mbo3.70413

Keywords: epitranscriptome, E. coli, CRISPR interference, nanopore sequencing, direct RNA sequencing, RNA modifications, ribosomal RNA, messenger RNA, pseudouridine, m6A, antimicrobial resistance, MicrobiologyOpen

Nanopore sequencing has transformed genomics by allowing DNA and RNA molecules to be read in their entirety, producing long reads that span entire transcripts and reveal complex splicing patterns that short-read technologies often miss. Oxford Nanopore platforms thread individual molecules through protein nanopores and infer their sequence from disruptions in an electrical current. This approach delivers reads that can stretch tens of thousands of bases, capturing full-length transcript isoforms and preserving modifications that are lost when RNA is fragmented and amplified. Yet the technology has long been hampered by a higher raw error rate compared with Illumina short-read sequencing, and this limitation has been particularly consequential for SNP calling, where a single misread base can be mistaken for a genuine variant.

Conventional approaches to correcting these errors typically rely on consensus building, aligning many reads to a reference genome and counting the frequency of each base at every position. Such methods work reasonably well for DNA sequencing at high coverage, but transcriptome data presents unique complications. Expression levels vary enormously across genes, meaning some transcripts are covered by thousands of reads while others yield only a handful. Low-coverage transcripts offer too few observations for statistical consensus methods to separate signal from noise, and highly expressed genes can harbor natural RNA editing events that complicate variant interpretation further. The result has been a landscape in which SNP detection from nanopore transcriptome data has demanded heavy computational filtering and often produced inconsistent results across tools and datasets.

NanoTS takes a fundamentally different approach by framing SNP calling as a pattern recognition problem that deep learning is exceptionally well suited to solve. Rather than relying solely on base-level alignments, the tool learns from the rich features embedded in nanopore signal data, the raw electrical current measurements that underlie every base call. Neural networks can detect subtle signatures that distinguish systematic sequencing errors, which tend to follow repeatable patterns tied to sequence context and pore dynamics, from genuine genetic variation, which manifests consistently across independent molecules. This capacity to model complex, high-dimensional relationships in the data allows NanoTS to make confident genotype calls even in regions where traditional statistical methods falter.

The implications of accurate SNP calling from direct RNA sequencing extend across a wide range of biological and biomedical applications. Variant detection at the transcript level reveals which alleles are actively expressed, a dimension invisible to DNA-based genotyping. Allele-specific expression, in which one copy of a gene is transcribed more abundantly than the other, plays a critical role in imprinted genes, autoimmune disease, cancer biology and X-chromosome inactivation. Researchers studying these phenomena have historically needed to combine DNA genotyping with RNA expression analysis from separate experiments, introducing uncertainty about whether observed expression patterns truly reflect the same cells and conditions. A tool that can call genotypes directly from transcriptome data collapses these two measurements into a single, internally consistent dataset.

The deep learning architecture at the heart of NanoTS represents part of a broader movement in genomics toward learned models that outperform hand-crafted algorithms. Basecalling, the process of converting raw nanopore current signals into nucleotide sequences, was revolutionized years ago by neural networks that replaced earlier hidden Markov model approaches, dramatically improving read accuracy. Variant calling is now following a similar trajectory. DeepVariant, developed by Google, demonstrated that convolutional neural networks could outperform classical statistical variant callers on Illumina data by treating genomic alignment piles as images. NanoTS extends this paradigm to the specific and demanding context of long-read transcriptome sequencing, where error profiles, coverage distributions and biological variation patterns differ substantially from genomic DNA sequencing.

Training such a model requires data of exceptional quality, since the network must learn to associate signal and alignment patterns with known ground-truth genotypes. The developers of NanoTS tackled this challenge by curating training examples in which true variants could be established with high confidence, allowing the network to internalize the distinctions between authentic polymorphism and sequencing artifact. Once trained, the model generalizes across genes, transcripts and coverage regimes, producing variant calls that remain reliable even for transcripts sequenced at low depth, a scenario that has historically been the Achilles heel of long-read transcriptome analysis.

The clinical dimension of this work is particularly compelling. Personalized medicine increasingly depends on knowing both which variants a patient carries and how those variants are expressed in relevant tissues. RNA sequencing of patient samples, whether from tumor biopsies or blood cells, is already routine in many clinical and research settings. If those same RNA reads can yield accurate genotype information without a parallel DNA sequencing run, the cost and complexity of comprehensive molecular profiling drops considerably. Cancer genomics stands to benefit substantially, as tumor transcripts carry both expressed mutations and the allele-specific expression patterns that reveal loss of heterozygosity and other genome alterations relevant to treatment decisions.

Beyond clinical applications, NanoTS opens doors for basic research into population genetics and evolutionary biology at the transcript level. Understanding how genetic variation shapes transcript diversity across individuals and species requires tools that can reliably measure both. Long-read transcriptome sequencing captures the full complexity of isoform expression, and with accurate SNP calling layered on top, researchers can begin to map how specific variants influence splicing, expression levels and transcript structure in ways that short-read studies have only approximated. The combination of full-length isoform resolution and direct genotype calling provides a uniquely integrated view of the relationship between genome and transcriptome.

The publication of NanoTS in Nature Methods underscores the growing recognition that computational innovation is as essential to unlocking the potential of new sequencing technologies as the hardware itself. As nanopore sequencing continues its rapid improvements in accuracy and throughput, tools like NanoTS ensure that the analytical pipeline keeps pace, converting noisy raw signals into biologically meaningful and clinically actionable insights. For laboratories worldwide working with direct RNA sequencing, the arrival of a robust deep learning solution for SNP calling marks a significant step toward making long-read transcriptomics a complete, standalone platform for both discovery and diagnosis.

Subject of Research: A deep learning tool for accurate SNP calling and genotype detection in nanopore long-read transcriptome sequencing data.

Article Title: NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data

Article References: NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data. (n.d.). https://doi.org/10.1038/s41592-026-03225-4

Image Credits: AI Generated

DOI: 10.1038/s41592-026-03225-4

Keywords: NanoTS, deep learning, SNP calling, nanopore sequencing, long-read transcriptomics, genotyping, direct RNA sequencing, variant detection, neural networks, Nature Methods, allele-specific expression, computational genomics