• Athma Pai

• Steven West

The decision between productive elongation and premature termination of promoterproximal RNA polymerase II (RNAPII) is fundamental to metazoan gene regulation. Integrator and Restrictor complexes are implicated in promoter-proximal termination, but why metazoans utilise two complexes and how they are coordinated remains unknown. Here, we show that Integrator and Restrictor act sequentially and nonredundantly to monitor distinct stages of transcription. Integrator predominantly engages with promoter-proximally paused RNAPII to trigger premature termination, which is prevented by cyclin-dependent kinase 7/9 activity. After pause release, RNAPII enters a previously unrecognised “restriction zone” universally imposed by Restrictor. Unproductive RNAPII terminates within this zone, while progression through it is promoted by U1 small nuclear ribonucleoprotein (snRNP), which antagonises Integrator and Restrictor in a U1-70K dependent manner. These findings reveal the principles of a sequential verification mechanism governing the balance between productive and attenuated transcription, rationalising the necessity of Integrator and Restrictor complexes in metazoans.

• Zihan Zhou, Zihan Zhou, Beijing Institute of Genomics
• Yuan Gao, Yuan Gao, Beijing Institute of Genomics

Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep’s adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.

• Juseong Kim, Juseong Kim, Division of Artificial Intelligence
• Sanghun Sel, Sanghun Sel, Division of Artificial Intelligence
• Giltae Song, Giltae Song, Division of Artificial Intelligence

Circular RNAs (circRNAs) function as key post-transcriptional regulators by interacting with microRNAs (miRNAs) to modulate gene expression. These interactions play a central role in gene regulatory networks and are implicated in various diseases. Accurate prediction of circRNA–miRNA interactions is therefore essential for understanding regulatory mechanisms and advancing therapeutic development. Notably, sequence variability among circRNA isoforms sharing the same back-splice junction can result in distinct miRNA binding profiles, highlighting the importance of isoform-level modeling. However, existing computational methods, including rule-based approaches (e.g., Miranda) and graph-based neural architectures, often fail to incorporate structural information and cannot effectively capture isoform-specific characteristics, thereby limiting their predictive performance. To address these challenges, we propose Thymba, a hybrid deep learning framework for structure-informed prediction of circRNA–miRNA interactions. Thymba combines Mamba modules, self-attention mechanisms, and one-dimensional convolutions to jointly model local sequence motifs and long-range dependencies. Furthermore, it employs a structure-aware pretraining strategy that concurrently optimizes masked language modeling and RNA secondary structure learning, enabling the model to generate representations that encode both sequential and structural contexts. We additionally construct a high-quality isoform-level dataset by integrating AGO-supported interaction data from public repositories and generating hard negative pairs via RNAhybrid-based thermodynamic and alignment filtering. This dataset supports both interaction prediction and binding site prediction tasks. Experimental results show that Thymba consistently outperforms existing methods, particularly on isoform-specific benchmarks, and demonstrates strong generalizability to related RNA–RNA interaction tasks such as circRNA–RBP binding prediction.

• Multiple

1-minute flash talks advertising iRNA posters

• Étienne Fafard-Couture, Étienne Fafard-Couture, Université de Sherbrooke
• Pierre-Étienne Jacques, Pierre-Étienne Jacques, Université de Sherbrooke
• Michelle S Scott, Michelle S Scott, Université de Sherbrooke

Small nucleolar RNAs (snoRNAs) are a group of noncoding RNAs identified in all eukaryotes. In human, C/D box snoRNAs are the most prevalent class, displaying crucial functions like regulating ribosome biogenesis and splicing. We have recently reported that less than a third of all annotated snoRNA genes are expressed in human. The remaining two-thirds, named the snoRNA pseudogenes, present features that are incompatible with their expression (e.g., mutations in their boxes). However, current annotations are often incomplete and overlook these snoRNA pseudogenes. To address this, we developed SnoBIRD. Based on DNABERT, SnoBIRD identifies C/D box snoRNA genes from any input sequence and classifies them as expressed or pseudogenes using sequence features (e.g., mutations in boxes). We show that SnoBIRD outperforms its competitor tools on a test set representative of all eukaryote kingdoms using relevant biological signal in the input sequence. By applying SnoBIRD on different genomes, we find that its runtime is adequate on the small Schizosaccharomyces pombe genome, and really outperforms the other tools on the large human genome (<13h compared to >3.5 days). Moreover, we identify with SnoBIRD most of the already annotated snoRNAs in these two species (respectively 19/32 and 358/403), as well as 8 and 22 novel expressed C/D box snoRNAs in their respective genome. Finally, we applied SnoBIRD on the genome of varied eukaryote species and show that it is an efficient and generalizable snoRNA predictor, as it identifies the known C/D box snoRNAs as well as dozens of novel expressed snoRNAs in these species.

• Xanthi Lida Katopodi, Xanthi Lida Katopodi, Centre for Genomic Regulation
• Laia Llovera Nadal, Laia Llovera Nadal, Centre for Genomic Regulation
• Alexane Ollivier, Alexane Ollivier, Centre for Genomic Regulation
• Leszek Pryszcz, Leszek Pryszcz, Centre for Genomic Regulation
• Cornelius Pauli, Cornelius Pauli, Heidelberg University Hospital
• Daniel Heid, Daniel Heid, Heidelberg University Hospital
• Thomas Muley, Thomas Muley, Heidelberg University Hospital
• Marc Schneider, Marc Schneider, Heidelberg University Hospital
• Laura Klotz, Laura Klotz, Heidelberg University Hospital
• Michael Allgäuer, Michael Allgäuer, Heidelberg University Hospital

Transfer RNAs (tRNAs) play a pivotal role in decoding genetic information, determining which transcripts are highly and poorly translated at a given moment. Dysregulation of tRNA abundances and their RNA modifications is a well-known feature in cancer cells, which leads to enhanced expression of specific oncogenic transcripts and proteins or, complementary, to the depletion of proteins essential to the proper cell function. A novel protocol named Nano-tRNAseq was recently developed to study tRNA populations using native RNA nanopore sequencing technologies, providing tRNA abundance and modification information from the same individual molecules.

To analyze information-rich nanopore native tRNA sequencing datasets, here we have developed AMaNITA (Abundance, Modifications, and Nanopore Intensity Toolbox/Application), a toolkit that facilitates Nano-tRNAseq analysis and provides a simple and user-friendly computational framework for the analysis of Nano-tRNAseq data. AMaNITA performs several steps, including filtering, quality control, batch effect estimation and automated correction, differential tRNA expression, and differential modification analyses, thus providing a start-to-end analysis of the data.

Harnessing the data produced by Nano-tRNAseq with AMaNITA, we then examine whether tRNAs can be used to distinguish biological states, tissue of origin, and disease state. We find that our method separately clusters tumor and normal samples and identifies individual tRNA molecules that are dysregulated in cancer, with potential diagnostic and therapeutic applications in the clinic. When applied on a lung cancer cohort consisting of 69 matched tumor/normal samples, our method reveals that tRNA information can segregate healthy and tumor samples with high accuracy.

• Zhao Li, Zhao Li, China National Center for Bioinformation
• Zhang Zhang, Zhang Zhang, China National Center for Bioinformation
• Lina Ma, Lina Ma, China National Center for Bioinformation

Chromatin-associated long non-coding RNAs (ca-lncRNAs) play crucial regulatory roles within the nucleus by preferentially binding to chromatin. Despite their importance, systematic identification and functional studies of ca-lncRNAs have been limited. Here, we identified and characterized human ca-lncRNAs genome-wide, utilizing 323,950 lncRNAs from LncBook 2.0 and integrating high-throughput sequencing datasets that assess RNA-chromatin association. We identified 14,138 high-confidence ca-lncRNAs enriched on chromatin across six cell lines, comprising nearly 80% of analyzed chromatin-associated RNAs, highlighting their significant role in chromatin localization. To explore the sequence basis for chromatin localization, we applied the LightGBM machine learning model to identify contributing nucleotide k-mers and derived 12 sequence elements through k-mer assembly and feature ablation. These sequence elements are frequently found within Alu repeats, with more Alu repeats enhancing chromatin localization. Meta-profiling of chromatin-binding sequencing segments further demonstrated that ca-lncRNAs bind to chromatin through Alu repeats. To delve deeper into the molecular mechanisms underlying the binding, we conducted integrative interactome analysis and computational prediction, revealing that Alu repeats primarily tether to chromatin through dsDNA-RNA triplex formation. Finally, to address sample constraints in ca-lncRNA identification, we developed a machine learning model based on sequential feature selection for large-scale prediction. This approach yielded 201,959 predicted ca-lncRNAs, approximately 70% of which are predicted to be preferentially located in the nucleus. Collectively, these high-throughput-identified and machine-learning-predicted ca-lncRNAs together form a robust resource for further functional studies.

• Cristina Cigana, Cristina Cigana, IRCCS San Raffaele Scientific Institute
• Elisa Lovo, Elisa Lovo, IRCCS San Raffaele Scientific Institute
• Alessandra Bragonzi, Alessandra Bragonzi, IRCCS San Raffaele Scientific Institute
• Giovanni Malerba, Giovanni Malerba, University of Verona
• Laura Veschetti, Laura Veschetti, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University

Background: miRNAs are key regulators in eukaryotes, yet little is known about their existence and function in bacteria. Although various noncoding RNAs have been identified in prokaryotes, only a few bacterial miRNAs have been validated. Given the clinical impact of Pseudomonas aeruginosa (PA) in chronic respiratory diseases, we investigated PA-derived miRNAs and their potential interactions with human genes.
Motivation: Research has mainly focused on eukaryotic miRNA and the lack of computational tools for prokaryotic miRNA prediction has slowed progress in microbial miRNA research. Our study aims to propose a computational framework for bacterial miRNA prediction, offering an application on PA.
Methods: We analyzed 36 RNAseq datasets from clinical PA isolates. Precursor miRNAs were predicted and filtered for structural stability. Mature miRNAs were identified through read mapping. Phylogenetic comparison was performed across organisms, and interactions with human UTRs were predicted. In silico validation across 4 PA reference strains was carried out through genome mapping, expression profiling, and de novo predictions.
Results: We identified a mean of 422 precursors and 247 mature miRNAs per sample. Some candidates showed homology with human and were conserved across species. Predicted targets were enriched in immune, metabolic, and signaling pathways. Fifty-six miRNAs scored high in the integrative in silico validation. Experimental confirmation is ongoing.
Conclusions: We propose a computational framework for identifying bacterial miRNAs with potential roles in host-pathogen interactions.
Significance: The knowledge generated through the study advances the characterization of currently under-studied microbial miRNAs, paving the way for therapeutic interventions in chronic respiratory disease.

• Sofia Kudasheva, Sofia Kudasheva, Earlham Institute
• Wilfried Haerty, Wilfried Haerty, Earlham Institute
• Terri Holmes, Terri Holmes, University of East Anglia
• James Smith, James Smith, University of East Anglia
• Vanda Knitlhoffer, Vanda Knitlhoffer, Earlham Institute

Deletions of the SNORD116 small nucleolar RNA cluster result in Prader-Willi syndrome (PWS), a developmental disorder with a complex multisystem phenotype. Emerging clinical data highlight a high incidence of congenital cardiac defects in individuals with PWS, whilst SNORD116 was found to be elevated in a human pluripotent stem cell (hPSC) model of cardiomyopathy. While previous research in neuronal cells has implicated SNORD116 in regulation of RNA processing, its molecular targets and function in the heart remain unclear.
To investigate this, we used an hPSC-derived cardiomyocyte model with SNORD116 knockout. We performed Oxford Nanopore long-read sequencing at three differentiation stages to simultaneously detect effects of SNORD116 knockout on alternative splicing, cleavage and polyadenylation (APA), and poly(A) tail length. We identified 40,018 novel isoforms; 174 of which were involved in significant isoform switches between control and SNORD116 knockout. Analysis of functional changes resulting from these switches revealed a developmental stage-dependent shift in 3’UTR usage in knockout cells, characterised by increased distal poly(A) site usage at day 2 and a reversal by day 30. Transcriptome-wide APA analysis confirmed these trends and revealed significant enrichment for predicted SNORD116 binding sites among APA-regulated genes. Notably, genes showing consistent poly(A) tail shortening in SNORD116 KO cells were enriched for ribosomal components, suggesting coordinated regulation of RNA stability and translation.
These findings highlight a previously unrecognised role for SNORD116 in modulating APA and poly(A) tail length during cardiomyocyte differentiation, with implications for understanding the molecular underpinnings of PWS-associated cardiac phenotypes.

• Michal Rahimi, Michal Rahimi, Bar-Ilan University
• Yaron Orenstein, Yaron Orenstein, Bar-Ilan University

CRISPR/Cas9 has transformed gene editing, enabling targeted modification of genomic loci using a 20-nt guide RNA followed by an NGG motif. However, editing efficiency varies due to target sequence, flanking regions, and epigenetic context. Measuring endogenous efficiency experimentally is labor-intensive, prompting the development of predictive models. Prior models were trained on small datasets, limiting generalizability. Leenay et al. recently released a dataset of ~1,600 endogenous efficiency measurements in T cells. We present EpiCRISPR, a neural network trained on this dataset that integrates guide RNA sequence, flanking regions, epigenetic marks, and high-throughput predictions. We found that incorporating downstream flanking sequences improved prediction (Spearman correlation from 0.309 to 0.375). Including epigenetic features—especially open chromatin, H3K4me3, and H3K27ac—boosted performance to 0.496. Adding high-throughput-based predictions further raised correlation to 0.514. Importantly, EpiCRISPR generalized well across cell types and revealed biologically meaningful feature importance via saliency maps. EpiCRISPR is publicly available at github.com/OrensteinLab/EpiCRISPR.

• Stefano Roncelli, Stefano Roncelli, Center for non-coding RNA in Technology and Health
• Gül Sude Demircan, Gül Sude Demircan, Center for non-coding RNA in Technology and Health
• Christian Anthon, Christian Anthon, Center for non-coding RNA in Technology and Health
• Lars Juhl Jensen, Lars Juhl Jensen, ZS Associaties
• Jan Gorodkin, Jan Gorodkin, Center for non-coding RNA in Technology and Health

CRISPR/Cas systems have significantly advanced genome editing, yet the precise design of guide RNAs (gRNAs) for optimal efficiency and specificity remains a persistent challenge. The CRISPRnet project seeks to enhance model performance and predictive accuracy by generating new data from gRNAs that are strategically selected to enrich existing datasets. To determine which gRNAs should be validated experimentally, we utilize methods for estimating prediction uncertainty. The idea being that the gRNAs, for which the efficiency prediction models are most uncertain, are the ones that would be the most valuable to experimentally validate. A key difficulty in this effort lies in the absence of definitive ground truth for model uncertainty. To address this, we modified the state-of-the-art CRISPRon model, which was trained on 30mer gRNA targets with context sequence and the binding energy between the gRNA spacer and the target DNA, by using deep neural networks to predict the editing efficiency. We implemented two approaches: (1) an ensemble of CRISPRon models trained with nested cross-validation to quantify prediction variance, and (2) an ensemble of modified CRISPRon models, extended with an additional classifier head and a customized loss function for uncertainty estimation. The effectiveness of these methods is evaluated through benchmarking against a curated set of candidate gRNAs, enabling data augmentation based on the recommendations made by the models.

• Syed Faraz Ahmed, Syed Faraz Ahmed, University of Melbourne
• Mohamed Fareh, Mohamed Fareh, Peter MacCallum Cancer Institute
• Wenxin Hu, Wenxin Hu, Peter MacCallum Cancer Institute
• Matthew R McKay, Matthew R McKay, University of Melbourne

The advancement of RNA therapeutics hinges on developing precise RNA-editing tools with high specificity and minimal off-target effects. We present a framework for optimizing CRISPR PspCas13b, a programmable RNA nuclease with a 30-nucleotide spacer sequence that offers potentially superior targeting specificity. Through single-base tiled screening and computational analyses, we identified critical design principles governing effective RNA recognition and cleavage in human cells. Our analyses revealed position-specific nucleotide preferences that significantly impact crRNA efficiency. Specifically, guanosine bases at positions 1-2 enhance catalytic activity, while cytosine bases at positions 1-4 and 11-17 dramatically reduce efficiency. This positional weighting system forms the foundation of our algorithm, which predicts highly effective crRNAs with ~90% accuracy. Comprehensive spacer-target mutagenesis analysis, implemented through computational modeling, demonstrated that PspCas13b requires ~26-nucleotide base pairing and tolerates only up to four mismatches to activate its nuclease domains. This computational insight explains PspCas13b's superior specificity compared to other RNA interference tools and predicts an extremely low probability of off-target effects, subsequently validated through proteomic analysis. We developed an open-source, R-based computational tool (https://cas13target.azurewebsites.net/) that implements these design principles to generate optimized crRNAs for any target sequence. The tool scores potential crRNAs based on nucleotide composition and position. Additionally, it performs off-target analysis by assessing sequence complementarity with human transcriptome data.
This computational approach represents a significant advancement in RNA targeting technology and offers a powerful platform for the development of more effective RNA therapeutics with minimized off-target effects.

• Roser Vento-Tormo, Roser Vento-Tormo, Wellcome Sanger Institute

The study of human tissues requires a systems biology approach. Their development starts in utero and during adulthood, they change their organization and cell composition. Our team has integrated comprehensive maps of human developing and adult tissues generated by us and others using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy. We utilise these maps to guide the development and interpretability of in vitro models. To do so, we develop and apply bioinformatic tools that allow us to quantitatively compare both systems and predict changes.

• Jacob Munro, Jacob Munro, The Walter and Eliza Hall Institute
• Melanie Bahlo, Melanie Bahlo, The Walter and Eliza Hall Institute
• Brendan Ansell, Brendan Ansell, The Walter and Eliza Hall Institute

Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional modification catalyzed by ADAR enzymes that can alter codons, splicing patterns, and RNA secondary structures. This process is essential for neuronal development and immune function, with dysregulation implicated in neurological disorders, cancers, and autoimmune diseases. Despite its biological importance, accurate detection of RNA editing from RNA-seq data remains technically challenging, and robust inference of differential editing between experimental conditions is not straightforward.

To address these challenges, we have developed EdiSetFlow, a reproducible and scalable pipeline for transcriptome-wide A-to-I RNA editing analysis from bulk RNA-seq data. EdiSetFlow is implemented in Nextflow takes raw FASTQ files as input, performs read trimming and quality filtering, aligns reads to the reference genome, and identifies editing sites with JACUSA. Common genetic variants are excluded based on the gnomAD population database. Identified sites are annotated for gene context and predicted functional consequences, with results summarized in a user-friendly HTML report. The pipeline is designed to efficiently scale to hundreds or thousands of samples, making it suitable for large datasets such as GTEx.

An accompanying R package enables advanced analyses, including model fitting, hypothesis testing, false discovery rate control, and visualisations, facilitating reliable statistical comparisons of editing between experimental groups. Applying EdiSetFlow to GTEx brain RNA-seq data, we uncovered distinct RNA editing signatures across brain regions, identifying both known and previously uncharacterized regional editing patterns. EdiSetFlow provides researchers with a robust, end-to-end solution to efficiently discover and interpret biologically meaningful RNA editing events in diverse transcriptomic datasets.

• Ning Dai, Ning Dai, Oregon State University
• Tianshuo Zhou, Tianshuo Zhou, Oregon State University
• Wei Yu Tang, Wei Yu Tang, Oregon State University
• David Mathews, David Mathews, University of Rochester
• Liang Huang, Liang Huang, Oregon State University

The task of designing optimized messenger RNA (mRNA) sequences has received much attention in recent years thanks to breakthroughs in mRNA vaccines during the COVID-19 pandemic. Because most previous work aimed to minimize the minimum free energy (MFE) of the mRNA in order to improve stability and protein expression, which only considers one particular structure per mRNA sequence, millions of alternative conformations in equilibrium are neglected. More importantly, we prefer an mRNA to populate multiple stable structures and be flexible among them during translation when the ribosome unwinds it. Therefore, we consider a new objective to minimize the ensemble free energy of an mRNA, which includes all possible structures in its Boltzmann ensemble. However, this new problem is much harder to solve than the original MFE optimization. To address the increased complexity of this problem, we introduce EnsembleDesign, a novel algorithm that employs continuous relaxation to optimize the expected ensemble free energy over a distribution of candidate sequences. EnsembleDesign extends both the lattice representation of the design space and the dynamic programming algorithm from LinearDesign to their probabilistic counterparts. Our algorithm consistently outperforms LinearDesign in terms of ensemble free energy, especially on long sequences. Interestingly, as byproducts, our designs also enjoy lower average unpaired probabilities (AUP, which correlates with degradation) and flatter Boltzmann ensembles (more flexibility between conformations). Our code is available on: https://github.com/LinearFold/EnsembleDesign.