• Christina Kalk, Christina Kalk, Institute for Computational Genomic Medicine
• Marcel Schulz, Marcel Schulz, Institute for Computational Genomic Medicine
• Michaela Müller-McNicoll, Michaela Müller-McNicoll, Institute of Molecular Biosciences
• Vladimir Despic, Vladimir Despic, Institute of Molecular Biosciences
• Mauro Siragusa, Mauro Siragusa, Institute for Vascular Signalling
• Justin Murtagh, Justin Murtagh, Goethe University Frankfurt

Background: Split Open Reading frames (Split-ORFs) exist on transcripts containing at least two open reading frames, each of which encodes a part of the same full-length protein. These multiple open reading frames arise from alternatively spliced transcript isoforms. The phenomenon of Split-ORFs has been observed for the SR protein family of splicing factors, where the Split-ORF proteins play important autoregulatory roles.
Aims/purpose: The aim of this study was to investigate the translation and expression of Split-ORFs.
Methods: We built a pipeline that predicts potential Split-ORFs for a user supplied set of transcripts and determines the regions unique to the potential Split-ORFs. These unique regions are absent from protein coding transcripts. The translation of the predicted Split-ORFs can be validated by finding their unique regions in Ribo-seq or proteomics data.
Results: The Split-ORF pipeline was applied to a set of transcripts containing premature termination codons or retained introns. Novel Split-ORF transcripts and their unique regions were predicted and a substantial fraction had significant Ribo-seq coverage in data from different cell types. Additionally, the Split-ORF candidate start sites had a significantly higher probability of being translation initiation sites than background sites as predicted by a deep neural network.
Outlook: These results suggest that the occurrence of Split-ORFs is more widespread than previously assumed and that they are expressed across different cell types. This paves the road for further functional investigations of the validated Split-ORF candidates and mechanisms of their biogenesis.

• Ilyes Baali, Ilyes Baali, Memorial Sloan Kettering Cancer Center
• Alexander Sasse, Alexander Sasse, Heidelberg University
• Quaid Morris, Quaid Morris, Memorial Sloan Kettering Cancer Center

Accurate identification of RNA-binding protein (RBP) binding sites is essential for understanding post-transcriptional gene regulation. However, current models face two major challenges: the limited availability of in vivo data and the poor generalization of models trained solely on in vitro assays. These limitations hinder our ability to make reliable in vivo predictions and obscure the true regulatory roles of RBPs in cellular contexts. This study aims to understand the root causes of discrepancies between these two assay types. By analyzing data from both assays, we investigate whether differences arise from biological context, experimental artifacts, or model limitations.
To address these challenges, we introduce a recalibration model that integrates in vitro and in vivo data to improve prediction accuracy and interpretability. We evaluate model performance across multiple generalization tasks—including chromosome, cell-type, and RBP-wise splits—and find that in-vitro-only models generalize poorly to in-vivo settings. In contrast, the recalibrated model significantly improves performance and even outperforms in-vivo-only models, demonstrating the added value of recalibrated in-vitro data. Feature importance analysis shows that the recalibration model corrects for incomplete binding preferences in in vitro assays and adjusts for assay-specific artifacts, such as G-rich motif enrichment in eCLIP. These findings suggest that many observed differences between assays are driven by technical biases rather than fundamental biological divergence and highlight the importance of accounting for such factors when modeling RBP binding in vivo.

• Adi Gershon, Adi Gershon, Department of Biochemistry and Molecular Biology
• Saira Jabeen, Saira Jabeen, Computer Science Department
• Asa Ben Hur, Asa Ben Hur, Computer Science Department
• Maayan Salton, Maayan Salton, Department of Biochemistry and Molecular Biology

Intron retention is an alternative splicing event in which introns remain in mature mRNA, altering protein isoforms or triggering transcript decay. Recent evidence highlights IR’s involvement in key biological processes, including neurodevelopment. However, quantifying IR remains difficult due to intronic complexity and ambiguous read mapping.
We systematically analyzed IR in autism spectrum disorder (ASD) using three computational tools with distinct strategies. rMATS (junction-based modeling), IRFinder (intron/spliced read ratios), and iDiffIR (log fold-change in intron coverage). Our focus was on six splicing factors (NOVA2, RBFOX1, SRRM2, SART3, U2AF2, WBP4) implicated in syndromic ASD, alongside idiopathic ASD brain tissue. We aimed to identify shared IR events that might reflect underlying splicing dysregulation in ASD.
All tools revealed hundreds of significantly altered introns in ASD and splicing factor models, consistently showing increased retention in ASD or mutant conditions. Despite tool-specific differences, we identified 574 genes with significant intron retention in both splicing factor models and ASD brain, enriched for neurodevelopmental pathways and known autism genes. At the event level, 21 introns were detected across multiple splicing factor models and ASD brains, enriched for transcription factor motifs such as TFAP2A and PLAGL2, suggesting shared regulatory mechanisms.
Notably, rMATS and IRFinder detected more events and showed pronounced associations with intron length and GC content, whereas iDiffIR displayed greater variability. Our multi-tool approach highlights the complexity of IR detection and underscores the value of integrating complementary strategies to elucidate splicing dysregulation in ASD. These findings provides prioritized IR candidates for future functional studies in neurodevelopmental disorders.

• Simonida Zehr, Simonida Zehr, Goethe University Frankfurt
• Ralf Brandes, Ralf Brandes, Goethe University Frankfurt
• Marcel Schulz, Marcel Schulz, Goethe University Frankfurt
• Timothy Warwick, Timothy Warwick, Goethe University Frankfurt

Background:
Chromatin-localized RNAs play key roles in gene regulation and nuclear architecture. Genome-wide RNA-DNA interactions can be mapped using molecular methods like RADICL-seq, GRID-seq, Red-C, and ChAR-seq, which utilize bridging oligonucleotides for RNA-DNA ligation. Despite advancements in these methods, a computational tool for reliably identifying biologically meaningful RNA-DNA interactions is lacking.

Approach:
Herein, we present RADIAnT, a reads-to-interactions pipeline for analysing RNA-DNA ligation data. These data are often confounded by multiple factors, including nascent transcription and expression differences. To manage these confounders, RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance, genomic region bias and relative RNA abundance.

Results:
By calling interactions against the multifactor background described above, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. In addition to calling consistent interactions between different molecular methodologies, RADIAnT outperforms previously proposed methods in the accurate identification of genome-wide Malat1-DNA interactions in murine data, and NEAT1-DNA interactions in human cells, with orthogonal one-to-all data used to classify binding regions in each case. In a further use case, RADIAnT was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathologically-relevant process of endothelial-to-mesenchymal transition.

Conclusion:
RADIAnT represents a reproducible, generalisable approach for analysis of RNA-DNA ligation data, and provides users with statistically stratified RNA-DNA interactions which can be probed for biological function.

• Blake Sweeney

From the epitranscriptome and 3D structure prediction to large language models, RNA science is experiencing a transformative shift. Recent advances in RNA 3D structure prediction and RNA-focused language models represent early milestones in what's possible. The explosion in data availability and computational power will fundamentally change how we approach RNA research.

This computational revolution will be shaped by the tools we build today. This talk serves as an introduction to our special section and panel discussion, where we'll discuss frontiers in RNA tool development. This talk will outline the key themes that our following speakers and panel will explore in detail.

In the panel, we aim to tackle questions like: What are the highest-impact tools missing from our current toolkit? What problems can machine learning solve, and what limitations does it face in RNA science? How can these limitations be overcome? What would it take to make sophisticated RNA analysis accessible to every researcher?

We encourage anyone interested in RNA research or seeking new computational frontiers to attend this section and contribute to the following panel discussion.

• Etienne Boileau, Etienne Boileau, University Hospital Heidelberg
• Harald Wilhelmi, Harald Wilhelmi, University Hospital Heidelberg
• Anne Busch, Anne Busch, Johannes Gutenberg-University Mainz
• Andrea Cappannini, Andrea Cappannini, International Institute of Molecular and Cell Biology Warsaw
• Andreas Hildebrand, Andreas Hildebrand, Johannes Gutenberg-University Mainz
• Janusz M Bujnicki, Janusz M Bujnicki, International Institute of Molecular and Cell Biology Warsaw
• Christoph Dieterich, Christoph Dieterich, University Hospital Heidelberg

We recently presented Sci-ModoM [1], the first next-generation RNome database offering a one-stop source for RNA modifications originating from state-of-the-art high-resolution detection methods. Sci-ModoM provides quantitative measurements per site and dataset, enabling researchers, including non-experts, to assess the confidence level of the reported modifications across datasets. Currently, users can Search and Compare over seven million modifications across 162 datasets, Browse or download datasets, and retrieve metadata; and these figures keep growing as data is continuously added. Sci-ModoM addresses critical challenges that are foundational to open science such as the need for standardized nomenclatures, common standards and guidelines for data sharing. It promotes data reuse, as it relies solely on the authors' published results; data are accessible in a human-readable, interoperable format, developed in consultation with the community [2].

In this talk, we will present Sci-ModoM in the context of a broader pan-European roadmap to (i) facilitate access to and sharing of high-throughput transcriptome-wide RNA modification data, and (ii) to promote data-driven sustainability in the development of reliable methods to map and identify RNA modifications. Our current work aims to expand the different RNA types (mRNA, non-coding RNA, tRNA, rRNA) in Sci-ModoM, to further establish FAIR data treatment, and to improve guidelines for data analysis and exchange, under the umbrella of the Human RNome project [3].

[1] Etienne Boileau, Harald Wilhelmi, Anne Busch, Andrea Cappannini, Andreas Hildebrand, Janusz M. Bujnicki, Christoph Dieterich. Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites Nucleic Acids Research, 2024, gkae972.
[2] https://dieterich-lab.github.io/euf-specs
[3] https://humanrnomeproject.org

• Sobhan Shukueian Tabrizi, Sobhan Shukueian Tabrizi, Bilkent University
• Sina Barazandeh, Sina Barazandeh, Carnegie Mellon University
• Helya Hashemi Aghdam, Helya Hashemi Aghdam, Bilkent University
• A. Ercument Cicek, A. Ercument Cicek, Bilkent University

Protein-RNA interactions are essential in gene regulation, splicing, RNA stability, and translation, making RNA a promising therapeutic agent for targeting proteins, including those considered undruggable. However, designing RNA sequences that selectively bind to proteins remains a significant challenge due to the vast sequence space and limitations of current experimental and computational methods. Traditional approaches rely on in vitro selection techniques or computational models that require post-generation optimization, restricting their applicability to well-characterized proteins.

We introduce RNAtranslator, a generative language model that formulates protein-conditional RNA design as a sequence-to-sequence natural language translation problem for the first time. By learning a joint representation of RNA and protein interactions from large-scale datasets, RNAtranslator directly generates binding RNA sequences for any given protein target without the need for additional optimization. Our results demonstrate that RNAtranslator produces RNA sequences with natural-like properties, high novelty, and enhanced binding affinity compared to existing methods. This approach enables efficient RNA design for a wide range of proteins, paving the way for new RNA-based therapeutics and synthetic biology applications. The model and the code is released at github.com/ciceklab/RNAtranslator.

• Ranjan Kumar Maji, Ranjan Kumar Maji, Goethe University Frankfurt
• Giulia Cantini, Giulia Cantini, Helmholtz Munich
• Hui Cheng, Hui Cheng, Helmholtz Munich
• Annalisa Marsico, Annalisa Marsico, Helmholtz Munich
• Marcel Schulz, Marcel Schulz, Goethe University Frankfurt

microRNAs (miRNAs) are short (~22 nt) RNA sequences key regulators of transcript expression. miRNAs bind to target mRNA sites to repress genes. isomiRs, generated with alternate processing of miRNA hairpins during biogenesis, exhibit variations that change the relative seed position to their canonical forms. This results in the selection of a different target transcript repertoire compared to canonical, diversifying miRNA regulation. However, mRNA configurations that enable miRNA target selection are still undetermined. isomiRs, together with canonical miRNA targets, have not been studied due to the lack of high-throughput experiments that capture exact miRNAs bound to their targets. Deep learning (DL) approaches have neither used such datasets nor have they investigated isomiR target interactions.
To address this gap, we developed a new transformer model, miRXplain, that predicts miRNA target interactions using miRNA and target sequences from CLIP-L chimeras. We analyzed CLIP-L experiments, which tether exact miRNA variations to their mRNA target site. We annotated these interactions and revealed nucleotide biases at the 5’ end of the target region. We addressed these biases and constructed miRNA and interacting site pairs to learn isomiR differences from their canonicals in their target interaction. miRXplain surpassed in performance all the benchmarked models and performed on par with TEC-miTarget, however ~2 times faster during training per epoch. Model attention weights revealed distinct importance of nucleotide positions for canonical and isomiR types. miRXplain can contribute to the discovery of isomiR targeting rules to enhance our understanding of miRNA biology.
Code availability: https://github.com/marsico-lab/miRXplain.

• Cho Joohyun, Cho Joohyun, KAIST
• Daniil Melnichenko, Daniil Melnichenko, KAIST
• Jongmin Lim, Jongmin Lim, KAIST
• Dongsup Kim, Dongsup Kim, KAIST
• Young-suk Lee, Young-suk Lee, KAIST

The function of RNA is largely determined by its networks of protein-RNA interactions. A key challenge in RNA engineering is in designing the sequence in a manner that controls its interacting partners. Towards this effort, we built a RNA sequence generator using conditional diffusion models that automatically designs based on the structure of a given RNA-binding protein. The RNA generator is a single unified deep-learning framework of 64 million parameters and is trained on high-quality structure data of 1,190 distinct protein-RNA complexes. The model’s cross-attention mechanism suggests that it learns the evolutionary homology of protein-RNA interactions. When benchmarking on RoseTTAFoldNA’s training and test dataset, we find that our model generates RNA sequences with AlphaFold3-confidence scores comparable to the bound RNA sequence. In all, these results call for experimental confirmation from a complementary source of protein-RNA interaction, and expands the possibility of automatically designing functional RNAs for biomedical applications.

• Yiliang Ding

RNA structure plays an important role in the post-transcriptional regulations of gene expression. Using in vivo RNA structure profiling methods, we have determined the functional roles of RNA structure in diverse biological processes such as mRNA processing (splicing and polyadenylation), translation and RNA degradation in plants. We also developed a new method to reveal the existence of tertiary RNA G-quadruplex structures in eukaryotes and uncovered that RNA G-quadruplex structure serves as a molecular marker to facilitate plant adaptation to the cold during evolution. Additionally, we have developed the single-molecule RNA structure profiling method and revealed the functional importance of RNA structure in long noncoding RNAs. Recently, we established a powerful RNA foundation model, PlantRNA-FM, that facilitates the explorations of functional RNA structure motifs across transcriptomes.

• Baudouin Seguineau de Préval, Baudouin Seguineau de Préval, Université de Sherbrooke
• Laurence Faucher Giguere, Laurence Faucher Giguere, Université de Sherbrooke
• Virginie Marchand, Virginie Marchand, Université de Lorraine
• Pavan Lakshmi Narasimha, Pavan Lakshmi Narasimha, University of Lethbridge
• Nehal Thakor, Nehal Thakor, University of Lethbridge
• Yuri Motorin, Yuri Motorin, Université de Lorraine
• Sherif Abou Elela, Sherif Abou Elela, Université de Sherbrooke
• Michelle Scott, Michelle Scott, Université de Sherbrooke

The human ribosome is densely decorated by 235—sometimes overlapping—modified nucleosides interspersing the 28S, 18S, and 5.8S ribosomal RNA (rRNA). Their presence, although meshing the ribosome's spatial organization, may be site-dependent and occasional, which introduces structural heterogeneity at a post-transcriptomic level. Pseudouridines (Ψ) represent half of these modifications, and their abnormal rate in rRNA has been reported and serves as marker in various diseases, including cancers. Highly structured, small nucleolar RNAs (snoRNAs) target and modify uridine sites in association with other proteins through the snoRNP complex. While H/ACA snoRNAs guide Ψ formation, emerging evidence suggests their abundance does not always predict modification frequency, challenging the assumption of a direct relationship.
Here we coordinate direct signal quantification by nanopore and chemically reactive-based detection by HydraPsiSeq to quantify Ψ levels alongside snoRNA expression. Surprisingly, even upon depletion of core H/ACA proteins, only a few sites correlate strictly with snoRNA abundance, suggesting loose regulation. To explain how most pseudouridine sites escape from direct snoRNA abundance control, we fed a random forest classifier using features like snoRNA/site conservation, target accessibility, and expression dynamics. Our findings highlight complex dynamics among pseudouridine sites and beyond sole snoRNA guidance, insisting on timing and accessibility rather than proficiency to shape the ribosome and fine-tune its alternative translation programs.
This work underscores the need to consider snoRNAs beyond their canonic functions as necessary but insufficient actors of pseudouridylation and ribosome maturation that can deeply alter cellular phenotypes.

• Michael Apostolides, Michael Apostolides, McGill University
• Jichen Wang, Jichen Wang, McGill University
• Ali Saberi, Ali Saberi, McGill University
• Benedict Choi, Benedict Choi, University of California
• Hani Goodarzi, Hani Goodarzi, University of California
• Hamed Najafabadi, Hamed Najafabadi, McGill University

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have important limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, especially in single-cell data where substantial positional biases are common; long-read (LR) sequencing offers isoform resolution but suffers from lower depth, higher noise, and technical biases. To address these challenges, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of multiple RNA-seq platforms to achieve state-of-the-art isoform-resolved quantification. MPAQT explicitly models platform-specific biases, including positional biases in short-read single-cell data and sequence-dependent biases in LR data. We show that MPAQT enables state-of-the-art gene- and isoform-level quantification both in SR-only single-cell data and in bulk datasets integrating SR and LR reads. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage. This effect is mediated through isoform-specific sequence features embedded in UTRs, which interact with RNA-binding proteins that modulate mRNA stability. We further demonstrate that machine learning-based predictions can be fed back into MPAQT to resolve ambiguities in read-to-isoform assignment, resulting in more accurate abundance estimates. These findings highlight MPAQT’s potential to enhance our understanding of transcriptomic complexity across platforms and cell types, while bridging statistical quantification with machine learning models of isoform regulation.

• Yoshihisa Tanaka, Yoshihisa Tanaka, Daiichi Sankyo Co.
• Naohiro Sunamura, Naohiro Sunamura, Daiichi Sankyo Co.
• Rei Kajitani, Rei Kajitani, Daiichi Sankyo Co.
• Marie Ikeguchi, Marie Ikeguchi, Daiichi Sankyo Co.
• Ryo Kunimoto, Ryo Kunimoto, Daiichi Sankyo Co.

Understanding the role of transcript isoforms is crucial for dissecting disease mechanisms. TAR DNA binding protein-43 (TDP-43) is a key regulator of RNA splicing, and its dysfunction in neurons is a hallmark of some neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). Specifically, TDP-43 maintains proper splicing by preventing the aberrant inclusion of cryptic exons into mRNA, thereby preserving normal transcript isoforms. Although TDP-43-dependent cryptic exons have been implicated in disease pathogenesis, an approach to investigate how cryptic exons disrupt transcript isoforms has yet to be established. To address this, we developed IsoRefiner, a novel method for identifying full-length transcript structures using long-read RNA-seq. Our results show that IsoRefiner outperforms existing long-read analysis tools. Leveraging this method, we conducted long-read RNA-seq, guided by prior short-read RNA-seq, to comprehensively resolve the full-length structures of aberrant transcripts caused by TDP-43 depletion in human induced pluripotent stem cell (iPSC)-derived motor neurons. This led to the discovery of a novel TDP-43-dependent cryptic exon in the MNAT1 gene, along with its full-length transcript structure. Furthermore, we confirmed the presence of the MNAT1 cryptic exon in tissues derived from patients with ALS and FTD. Our findings deepen understanding of TDP-43 proteinopathy, and our approach provides a powerful framework for investigating splicing mechanisms across diverse cellular and disease contexts.

• Tianyuan Liu, Tianyuan Liu, Spanish National Research Council
• Adam Frankish, Adam Frankish, European Bioinformatics Institute
• Ana Conesa, Ana Conesa, Spanish National Research Council
• Alejandro Paniagua, Alejandro Paniagua, Spanish National Research Council
• Fabian Jetzinger, Fabian Jetzinger, BioBam Bioinformatics

Long-read sequencing (LRS) platforms, such as Oxford Nanopore (ONT) and Pacific Biosciences (PacBio), enable comprehensive transcriptome analysis but face challenges such as sequencing errors, sample quality variability, and library preparation biases. Current benchmarking approaches address these issues insufficiently: BUSCO assesses transcriptome completeness using conserved single-copy orthologs but can misinterpret alternative splicing as gene duplications, while spike-ins (SIRVs, ERCCs) oversimplify real-sample complexity, neglecting RNA degradation and RNA extraction artifacts, thus inflating performance metrics. To overcome these limitations, we introduce the Transcriptome Universal Single-isoform COntrol (TUSCO), a curated internal reference set of conserved genes lacking alternative isoforms. TUSCO evaluates precision by identifying transcripts deviating from reference annotations and assesses sensitivity by verifying detection completeness in human and mouse samples. Our validation demonstrates that TUSCO provides accurate and reliable benchmarking without external controls, significantly improving quality control standards for transcriptome reconstruction using LRS.

• Maayan Salton