• Valentina Boeva, Valentina Boeva, ETH Zurich

Chromatin interactions provide insights into which DNA regulatory elements connect with specific genes, informing the activation or repression of gene expression. Understanding these interactions is crucial for assessing the role of non-coding mutations or changes in chromatin organization due to cell differentiation or disease. Hi-C and single-cell Hi-C experiments can reveal chromatin interactions, but these methods are costly and labor-intensive.
Here, I will introduce our computational approach, UniversalEPI, an attention-based deep ensemble model that predicts regulatory interactions in unseen cell types with a receptive field of 2 million nucleotides, relying solely on DNA sequence data and chromatin accessibility profiles. Demonstrating significantly better performance than state-of-the-art methods, UniversalEPI—with a much lighter architecture—effectively predicts chromatin interactions across malignant and non-malignant cancer cell lines (Spearman’s Rho > 0.9 on unseen cell types).
To further expand its applicability, we integrate ASAP, our deep learning toolset that predicts the effects of genomic variants on ATAC-seq profiles. These predicted accessibility profiles can serve as input to UniversalEPI. Importantly, the accuracy of Hi-C interaction prediction remains virtually unchanged when replacing experimental ATAC-seq profiles with those generated by ASAP, indicating strong robustness and enabling predictions even in the absence of experimental accessibility data.
This combined framework represents an advancement in in-silico 3D chromatin modeling, essential for exploring genetic variant impacts on disease and monitoring chromatin architecture changes during development.

• Sarah Ouologuem, Sarah Ouologuem, Technical University Munich
• Laura D. Martens, Laura D. Martens, Technical University Munich
• Anna C. Schaar, Anna C. Schaar, Technical University Munich
• Maiia Shulman, Maiia Shulman, Helmholtz Center Munich
• Julien Gagneur, Julien Gagneur, Technical University Munich
• Fabian J. Theis, Fabian J. Theis, Helmholtz Center Munich

Motivation: Spatially resolved chromatin accessibility profiling offers the potential to investigate gene regulatory processes within the spatial context of tissues. However, current methods typically work at spot resolution, aggregating measurements from multiple cells, thereby obscuring cell-type-specific spatial patterns of accessibility. Spot deconvolution methods have been developed and extensively benchmarked for spatial transcriptomics, yet no dedicated methods exist for spatial chromatin accessibility, and it is unclear if RNA-based approaches are applicable to that modality.
Results: Here, we demonstrate that these RNA-based approaches can be applied to spot-based chromatin accessibility data by a systematic evaluation of five top-performing spatial transcriptomics deconvolution methods. To assess performance, we developed a simulation framework that generates both transcriptomic and accessibility spot data from dissociated single-cell and targeted multiomic datasets, enabling direct comparisons across both data modalities. Our results show that Cell2location and RCTD, in contrast to other methods, exhibit robust performance on spatial chromatin accessibility data, achieving accuracy comparable to RNA-based deconvolution. Generally, we observed that RNA-based deconvolution exhibited slightly better performance compared to chromatin accessibility-based deconvolution, especially for resolving rare cell types, indicating room for future development of specialized methods. In conclusion, our findings demonstrate that existing deconvolution methods can be readily applied to chromatin accessibility-based spatial data. Our work provides a simulation framework and establishes a performance baseline to guide the development and evaluation of methods optimized for spatial epigenomics.
Availability: All methods, simulation frameworks, peak selection strategies, analysis notebooks and scripts are available at https://github.com/theislab/deconvATAC.

• Tanmayee Narendra, Tanmayee Narendra, University of Dundee
• Giovanni Visonà, Giovanni Visonà, Max Planck Institute for Intelligent Systems
• Crhistian de Jesus Cardona, Crhistian de Jesus Cardona, University of Dundee
• James Abbott, James Abbott, University of Dundee
• Gabriele Schweikert, Gabriele Schweikert, University of Dundee

Epigenetic mechanisms enable cellular differentiation and the maintenance of distinct cell-types. They enable rapid responses to external signals through changes in gene regulation and their registration over longer time spans. Consequently, chromatin environments exhibit cell-type and individual specificity contributing to phenotypic diversity. Their genomic distributions are measured using ChIP-Seq and related methods. However, the chromatin landscape introduces significant biases into these measurements. Here, we introduce DecoDen to simultaneously learn shared chromatin landscapes while de-biasing individual measurement tracks. We demonstrate DecoDen's effectiveness on an integrative analysis of histone modification patterns across multiple tissues in personal epigenomes.

• Noor Pratap Singh, Noor Pratap Singh, Department of Computer Science
• Jamshed Khan, Jamshed Khan, Department of Computer Science
• Rob Patro, Rob Patro, Department of Computer Science

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses with minimal accuracy loss compared to alignment-based methods. However,
applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into “virtual colors”. These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct “colors” from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to
process and map single-cell ATAC-seq data in our new tool alevin-fry-atac. We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC. Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately 3 times less memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual color-enhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger.

• Zahra Zare Jousheghani, Zahra Zare Jousheghani, University of Maryland
• Noor Pratap Singh, Noor Pratap Singh, University of Maryland
• Rob Patro, Rob Patro, University of Maryland

Motivation: Long read sequencing technology is becoming an increasingly indispensable tool in genomic and transcriptomic analysis. In transcriptomics in particular, long reads offer the possibility of sequencing full-length isoforms, which can vastly simplify the identification of novel transcripts and transcript quantification. However, despite this promise, the focus of much long read method development to date has been on transcript identification, with comparatively little attention paid to quantification. Yet, due to differences in the underlying protocols and technologies, lower throughput (i.e. fewer reads sequenced per sample compared to short read technologies), as well as technical artifacts, long read quantification remains a challenge, motivating the continued development and assessment of quantification methods tailored to this increasingly prevalent type of data.
Results: We introduce a new method and corresponding user-friendly software tool for long read transcript quantification called oarfish. Our model incorporates a novel coverage score, which affects the conditional probability of fragment assignment in the underlying probabilistic model. We demonstrate, in both simulated and experimental data, that by accounting for this coverage information, oarfish is able to produce more accurate quantification estimates than existing long read quantification tools.
Availability and Implementation: Oarfish is implemented in the Rust programming language, and is made available as free and open-source software under the BSD 3-clause license. The source code is available at https://www.github.com/COMBINE-lab/oarfish.

• Leonardo Morelli, Leonardo Morelli, Laboratory of Chromatin Biology & Epigenetics
• Stefano Cretti, Stefano Cretti, Laboratory of Chromatin Biology & Epigenetics
• Daniela Michelatti, Daniela Michelatti, Laboratory of Chromatin Biology & Epigenetics
• Davide Cittaro, Davide Cittaro, Center for Omics Sciences
• Tiago P. Peixoto, Tiago P. Peixoto, Inverse Complexity Lab
• Alessio Zippo, Alessio Zippo, Laboratory of Chromatin Biology & Epigenetics

Understanding the structural principles of chromatin organization is a central challenge in computational epigenomics, largely due to the sparse, noisy, and complex nature of Hi-C data. Existing methods tend to focus either on local features, such as topologically associating domains (TADs), or global structures, like compartments. This methodological split often leads to poor agreement between models, limiting our ability to obtain a unified view of genome architecture. We introduce HiCONA, a novel graph-based framework that directly infers global 3D chromatin folding from both Hi-C contact maps and super resolution microscopy data. Unlike existing approaches, HiCONA optimizes a nested hierarchical representation of chromatin architecture by minimizing the entropy of the partition, thereby capturing the most informative and functionally relevant interactions. HiCONA enables simultaneous identification of topologically associating domains (TADs) and subcompartments using a single unified model, and performs robustly across gold-standard datasets. In benchmarking experiments, HiCONA recovers key chromatin contacts under both wild-type and cohesin-deficient conditions, offering insight into the structural consequences of architectural protein depletion. Furthermore, HiCONA provides a shared representation that facilitates direct comparison between imaging and sequencing-based data, bridging a major methodological gap in chromatin biology. By capturing chromatin folding from micro to mesoscale, HiCONA opens new avenues for understanding genome organization and its functional implications. This integrative and interpretable framework marks a significant advance in uncovering the forces that shape nuclear architecture, with potential applications in development, disease, and synthetic genome design.

• Wui Wang Lui, Wui Wang Lui, Johns Hopkins University
• Liliana Florea, Liliana Florea, Johns Hopkins University

We developed SpliSync, a deep learning method for accurate splice site correction in long read alignments. It combines a genomic language model, HyenaDNA, and a 1D U-net segmentation head, integrating genome sequence and alignment embeddings. SpliSync improves the detection of splice sites and introns and, when integrated with a short read transcript assembler, allows for improved transcript reconstruction, matching or outperforming reference methods like IsoQuant and FLAIR. The method shows promise for transcriptomic applications, especially in species with incomplete gene annotations or for discovering novel transcript variations.

• Ghislain Fievet, Ghislain Fievet, Université de Lorraine
• Julien Broséus, Julien Broséus, Université de Lorraine
• David Meyre, David Meyre, Université de Lorraine
• Sébastien Hergalant, Sébastien Hergalant, INSERM

Adversarial attacks pose a significant risk to machine learning (ML) tools designed for classifying single-cell RNA-sequencing (scRNA-seq) data, with potential implications for biomedical research and future clinical applications. We present adverSCarial, a novel R package that evaluates the vulnerability of scRNA-seq classifiers to various adversarial perturbations, ranging from barely detectable, subtle changes in gene expression to large-scale modifications. We demonstrate how five representative classifiers spanning marker-based, hierarchical, support vector machine, random forest, and neural network algorithms, respond to these attacks on four hallmarks scRNA-seq datasets. Our findings reveal that all classifiers eventually fail under different amplitudes of perturbations, which depend on the ML algorithm they are based on and on the nature of the modifications.
Beyond security concerns, adversarial attacks help uncover the inner decision-making mechanisms of the classifiers. The various attack modes and customizable parameters proposed in adverSCarial are useful to identify which gene or set of genes is crucial for correct classification and to highlight the genes that can be substantially altered without detection. These functionalities are critical for the development of more robust and interpretable models, a step toward integrating scRNA-seq classifiers into routine research and clinical workflows. The R package is freely available on Bioconductor (10.18129/B9.bioc.adverSCarial) and helps evaluate scRNA-seq-based ML models vulnerabilities in a computationally-cheap and time-efficient framework.

• Netanya Keil, Netanya Keil, University of Florida
• Carolina Monzó, Carolina Monzó, Institute for Integrative Systems Biology (I2SysBio)
• Lauren McIntyre, Lauren McIntyre, Institute for Integrative Systems Biology (I2SysBio)
• Ana Conesa, Ana Conesa, Institute for Integrative Systems Biology (I2SysBio)

SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multisample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for 1) the identification of potentially under-annotated genes and putative novel transcripts and for 2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different datasets, a Drosophila developmental experiment and a multiplatform dataset from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites. SQANTI-reads is open source and is available in versions ≥ 5.3.0 in the SQANTI3 GitHub repository.

• Qian Shi, Qian Shi, The Pennsylvania State University
• Qimin Zhang, Qimin Zhang, The Pennsylvania State University
• Mingfu Shao, Mingfu Shao, The Pennsylvania State University

Motivation: The established single-cell RNA sequencing technologies (scRNA-seq) has revolutionized biological and biomedical research by enabling the measurement of gene expression at single-cell resolution. However, the fundamental challenge of reconstructing full-length transcripts for individual cells remains unresolved. Existing single-sample assembly approaches cannot leverage shared information across cells while meta-assembly approaches often fail to strike a balance between consensus assembly and preserving cell-specific expression signatures.
Results: We present Beaver, a cell-specific transcript assembler designed for short-read scRNA-seq data. Beaver implements a transcript fragment graph to organize individual assemblies and designs an efficient dynamic programming algorithm that searches for candidate full-length transcripts from the graph. Beaver incorporates two random forest models trained on 51 meticulously engineered features that accurately estimate the likelihood of each candidate transcript being expressed in individual cells. Our experiments, performed using both real and simulated Smart-seq3 scRNA-seq data, firmly show that Beaver substantially outperforms existing meta-assemblers and single-sample assemblers. At the same level of sensitivity, Beaver achieved 32.0%-64.6%, 13.5%-36.6%, and 9.8%-36.3% higher precision in average compared to meta-assemblers Aletsch, TransMeta, and PsiCLASS, respectively, with similar improvements over single-sample assemblers Scallop2 (10.1%-43.6%) and StringTie2 (24.3%-67.0%).
Availability: Beaver is freely available at https://github.com/Shao-Group/beaver. Scripts that reproduce the
experimental results of this manuscript are available at https://github.com/Shao-Group/beaver-test.

• Elizabeth Tseng, Elizabeth Tseng, PacBio

Long-read RNA sequencing has emerged as a powerful tool in transcriptomics, offering the ability to sequence full-length cDNAs—often exceeding 10 kb—without the need for transcript assembly. This capability shifted early bioinformatics efforts toward the discovery of novel isoforms, enabling the development of new nomenclature to describe isoform features previously undetectable by short reads. Renewed focus was also placed on identifying and filtering potential cDNA artifacts.

With long read lengths and high accuracy, PacBio’s Iso-Seq data prompted new tool developments covering cancer fusion detection, direct open reading frame predictions, allele-specific isoform expression, and finally, differential isoform expression analyses. However, gaps remain the tool space that need to be addressed with the advent of large, population-scale long-read RNA-Seq projects.

In this talk, I will explore how Iso-Seq has propelled the long-read sequencing field forward, highlight the current challenges in tool development and data analysis, and discuss the promising avenues for discovery that lie ahead.

• Jonathan Göke

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar. Furthermore, the addition of post-transcriptional RNA modifications further impacts their function. The availability of long read RNA-Seq provides an opportunity to sequence entire RNA transcripts, enabling the analysis of individual RNA isoforms and their modifications. In this presentation I will show how the raw nanopore signal data can be used to identify and distinguish multiple RNA modifications from direct RNA-Seq data, I will summarise new results from the Singapore Nanopore Expression Project (SG-NEx) and describe computational methods that analyse long read RNA-Seq data to estimate isoform expression, track full length reads, and identify novel isoforms at single cell and spatial resolution.