Leszek P. Pryszcz’s research while affiliated with Barcelona Institute for Science and Technology and other places

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m⁶A), pseudouridine (ψ), and 5-methylcytosine (m⁵C) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data. Consequently, the field’s cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RNA Modification and Processing (RMaP) challenge to advance solutions for RNA modification detection and discuss ideas, problems and approaches. We show several computational methods to detect the most researched mRNA modifications (m⁶A, ψ, and m⁵C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms’ comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.

RNA modifications influence RNA function and fate, but detecting them in individual molecules remains challenging for most modifications. Here we present a novel methodology to generate training sets and build modification-aware basecalling models. Using this approach, we develop the m⁶ABasecaller, a basecalling model that predicts m⁶A modifications from raw nanopore signals. We validate its accuracy in vitro and in vivo, revealing stable m⁶A modification stoichiometry across isoforms, m⁶A co-occurrence within RNA molecules, and m⁶A-dependent effects on poly(A) tails. Finally, we demonstrate that our method generalizes to other RNA and DNA modifications, paving the path towards future efforts detecting other modifications.

Nanopore direct RNA sequencing (DRS) enables direct measurement of RNA molecules, including their native RNA modifications, without prior conversion to cDNA. However, commercial methods for molecular barcoding of multiple DRS samples are lacking, and community-driven efforts, such as DeePlexiCon, are not compatible with newer RNA chemistry flowcells and the latest-generation GPU cards. To overcome these limitations, we introduce SeqTagger, a rapid and robust method that can demultiplex direct RNA sequencing datasets with 99% precision and 95% recall. We demonstrate the applicability of SeqTagger in both RNA002/R9.4 and RNA004/RNA chemistries and show its robust performance both for long and short RNA libraries, including custom libraries that do not contain standard poly(A) tails, such as Nano-tRNAseq libraries. Finally, we demonstrate that increasing the multiplexing up to 96 barcodes yields highly accurate demultiplexing models. SeqTagger can be executed in a standalone manner or through the MasterOfPores NextFlow workflow. The availability of an efficient and simple multiplexing strategy improves the cost-effectiveness of this technology and facilitates the analysis of low-input biological samples.

RNA polyadenylation is crucial for RNA maturation, stability and function, with polyA tail lengths significantly influencing mRNA translation, efficiency and decay. Here, we provide a step-by-step protocol to perform Nanopore 3’ end-capture sequencing (Nano3P-seq), a nanopore-based cDNA sequencing method to simultaneously capture RNA abundances, tail composition and tail length estimates at single-molecule resolution. Taking advantage of a template switching-based protocol, Nano3P-seq can sequence any RNA molecule from its 3’ end, regardless of its polyadenylation status, without the need for PCR amplification or RNA adapter ligation. We provide an updated Nano3P-seq protocol that is compatible with R10.4 flowcells, as well as compatible software for polyA tail length and content prediction, which we term PolyTailor . We demonstrate that PolyTailor provides accurate estimates of transcript abundances, tail lengths and content information, while capturing both coding and non-coding RNA biotypes, including mRNAs, snRNAs, and rRNAs. This method can be applied to any RNA sample of interest (e.g. poly(A)-selected, ribodepleted, total RNA), and can be completed in one day. The Nano3P-seq protocol can be performed by researchers with moderate experience in molecular biology techniques and nanopore sequencing library preparation, and basic knowledge of linux bash syntax and R programming. This protocol makes Nano3P-seq accessible and easy to implement by future users aiming to study the tail dynamics and heterogeneity of both coding and non-coding transcriptome in a comprehensive and reproducible manner. Key Papers Beğik O, Diensthuber G, Liu H, Delgado-Tejedor A, Kontur C, Niazi AM, Valen E, Giraldez AJ, Beaudoin JD, Mattick JS, Novoa EM. Nano3P-seq: transcriptome-wide analysis of gene expression and tail dynamics using end-capture nanopore cDNA sequencing. Nature Methods 20 , 75–85 (2023). https://doi.org/10.1038/s41592-022-01714-w Delgado-Tejedor A, Medina M, Begik O, Cozzuto L, Lopez J, Blanco B, Ponomarenko J, Novoa EM. Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites. NatComm 15 , 10054 (2024). https://doi.org/10.1038/s41467-024-54368-x

Nanopore direct RNA sequencing (DRS) enables direct measurement of RNA molecules, including their native RNA modifications, without prior conversion to cDNA. However, commercial methods for molecular barcoding of multiple DRS samples are lacking, and community-driven efforts, such as DeePlexiCon, are not compatible with newer RNA chemistry flowcells and the latest-generation GPU cards. To overcome these limitations, we introduce SeqTagger, a rapid and robust method that can demultiplex direct RNA sequencing datasets with 99% precision and 95% recall. We demonstrate the applicability of SeqTagger in both RNA002/R9.4 and RNA004/RNA chemistries and show its robust performance both for long and short RNA libraries, including custom libraries that do not contain standard poly-(A) tails, such as Nano-tRNAseq libraries. Finally, we demonstrate that increasing the multiplexing up to 96 barcodes yields highly accurate demultiplexing models. SeqTagger can be executed in a standalone manner or through the MasterOfPores NextFlow workflow. The availability of an efficient and simple multiplexing strategy improves the cost-effectiveness of this technology and facilitates the analysis of low-input biological samples.

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m ⁶ A), pseudouridine (ψ), and 5-methylcytosine (m ⁵ C) became acknowledged for playing critical roles in gene expression regulation, RNA stability, and translation efficiency. Among modification-aware sequencing approaches, direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by capturing and storing properties of noncanonical RNA nucleosides in raw data. Consequently, the field's cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RMaP challenge to advance solutions for RNA modification detection and discuss current ideas, problems and approaches. Here, we show several computational methods to detect the most researched mRNA modifications (m ⁶ A, ψ, and m ⁵ C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms' comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.

In recent years, nanopore direct RNA sequencing (DRS) became a valuable tool for studying the epitranscriptome, due to its ability to detect multiple modifications within the same full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling 'errors' in DRS datasets, N6-methyladenosine (m6A) modifications produce relatively low 'errors' compared to other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the 'error'signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads –especially in shorter RNA fractions– and increased basecalling error signatures at pseudouridine (Ψ) and N1-methylpseudouridine (m1Ψ) modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS datasets.

In recent years, nanopore direct RNA sequencing (DRS) has established itself as a valuable tool for studying the epitranscriptome, due to its ability to detect multiple modifications within the same full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling errors in DRS datasets, N6-methyladenosine (m6A) modifications produce relatively low errors compared to other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the error signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads, especially in shorter RNA fractions, and increased basecalling error signatures at pseudouridine (Y) and N1-methylpseudouridine (m1Y) modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS datasets.

RNA modifications hold pivotal roles in shaping the fate and function of RNA molecules. Although nanopore sequencing technologies have proven successful at transcriptome-wide detection of RNA modifications, current algorithms are limited to predicting modifications at a per-site level rather than within individual RNA molecules. Herein, we introduce m6ABasecaller, an innovative method enabling direct basecalling of m6A modifications from raw nanopore signals within individual RNA molecules. This approach facilitates de novo prediction of m6A modifications with precision down to the single nucleotide and single molecule levels, without the need of paired knockout or control conditions. Using the m6ABasecaller, we find that the median transcriptome-wide m6A modification stoichiometry is ~10-15% in human, mouse and zebrafish. Furthermore, we show that m6A modifications affect polyA tail lengths, exhibit a propensity for co-occurrence within the same RNA molecules, and show relatively consistent stoichiometry levels across isoforms. We further validate the m6ABasecaller by treating mESC with increasing concentrations of STM2457, a METTL3 inhibitor as well as in inducible METTL3 knockout systems. Overall, this work demonstrates the feasibility de novo basecalling of m6A modifications, opening novel avenues for the application of nanopore sequencing to samples with limited RNA availability and for which control knockout conditions are unavailable, such as patient-derived samples.

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.