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| Date | Start time | End time | Room | Track | Title | Confrimed presenter | Authors | Abstract |
|---|---|---|---|---|---|---|---|---|
| 2026-08-31 | 13:30:00 | 14:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | A Spectrum of Gut Microbiota Signatures Predicts Long-Term Health Status | Himmi Lindgren | Himmi Lindgren, Chandler Ross, Matti Ruuskanen, Veikko Salomaa, Rob Knight, Teemu Niiranen, Aki Havulinna, Leo Lahti |
Motivation: The human gut microbiota is a complex ecosystem composed of hundreds of unique microbial species
in a typical adult individual. Its composition has been linked to an individual's current and future health.
At the population level, joint analysis of co-abundant species can capture associations with health status
and other variables that cannot be robustly detected by analyzing individual taxa alone. Recently, the
concept of enterosignatures was proposed to detect such co-abundant groups, decomposing microbial community
variation into a mixture of five broad components of co-varying microbial taxa. However, their overall
robustness, generalizability, and prospective potential in predicting long-term health status remain to be
evaluated. Results: We demonstrate that robust signatures of co-abundant species can be prospectively associated with mortality, diabetes, liver disease, and sepsis risk among Finnish adults in the FINRISK population cohort based on a 20-year follow-up. We characterized dysbiosis-associated signatures across varying levels of sub-ecosystem granularity, encompassing a previously reported Escherichia-driven signature as well as novel Prevotella subspecies-driven signatures linked to compromised future health status. These results illustrate the benefits of analyzing latent signatures across multiple levels of ecosystem granularity. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | Improving metagenome binning by integrating intrinsic features and taxonomy | Simon Rasmussen | Simon Rasmussen, Svetlana Kutuzova, Jakob Nybo Andersen, Mads Nielsen, Lars Hestbjerg Hansen, Knud Nor Nielsen | A common procedure for studying the microbiome is binning the sequenced contigs into metagenome-assembled genomes. State-of-the-art binning methods use co-abundance and sequence based motifs such as tetranucleotide frequencies while taxonomic labels derived from alignment based classification have not been widely used. Here, we propose TaxVAMB, a metagenome binning tool based on semi-supervised bi-modal variational autoencoders, combining tetranucleotide frequencies and contig co-abundances with taxonomic information. TaxVAMB outperformed all other binners on CAMI2 human microbiome datasets, returning on average 29% more high quality assemblies than the next best binner, and performed on par with the best binners on short-read datasets. On a human gut long-read dataset TaxVAMB recovered 41% more high quality bins and 29% more species. In a typical single-sample setup, TaxVAMB on average returns 83% more high quality bins compared to VAMB. Furthermore, TaxVAMB enables high quality binning of eukaryotic organisms. Finally, TaxVAMB binned incomplete genomes better than any other tool, returning on average 300% more high quality bins of incomplete genomes than the next best binner. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | Exploring functional insights into the human gut microbiome via the structural proteome | Yu Li | Hongbin Liu, Jiuming Wang, Yu Li, Lei Dai |
Most gut microbiome proteins remain functionally uncharacterized because sequence-based methods miss remote
homologs from deep evolutionary divergence. We leverage AI-driven protein structure prediction and
representation learning to systematically decode this functional landscape. We constructed the Human Gut Microbial Protein Structure (GMPS) database of ~2.7 million AlphaFold2-predicted structures from 968 bacterial and 1,255 phage genomes. Structural alignment and clustering substantially outperformed sequence-based annotation, raising putative functional assignments for phage proteins from 24.8% to 54.5%. This revealed structural diversification of phage endolysins through domain shuffling; we experimentally validated nine endolysins with lytic activity against gut pathobionts, including Enterococcus gallinarum and Clostridium innocuum. Structural searches also identified bacterial enzymes for melatonin biosynthesis (DDC, AANAT, and ASMT), whose physiological roles were confirmed by enzymatic assays and in vivo mouse experiments showing direct impacts on host melatonin levels and colitis severity. To surpass the sensitivity ceiling of alignment-based methods, we developed Dense Enzyme Retrieval (DEER), an alignment-free model fusing pre-trained protein language models (ESM-2 and SaProt) through contrastive learning to project enzymes into a unified latent space where functionally related proteins cluster regardless of sequence or structural divergence. On a cross-kingdom benchmark, DEER outperformed sequence (BLASTp, MMseqs2) and structure (Foldseek, US-align, DALI) aligners for bacteria–human isozyme detection while being orders of magnitude faster, and recovered validated bacterial melatonin enzymes among its top candidates. Our work establishes an AI-powered framework for functional discovery in the human gut microbiome, illuminating uncharacterized protein space across microbial ecosystems, with broad implications for microbiome-targeted therapeutics and host–microbe interaction discovery. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room 8 | Biodiversity, sustainability, envirobioinformatic | TheBIGbam: compression and interactive exploration of large-scale sequencing alignments with circular mapping support | Martin Boutroux | Martin Boutroux, Evan Thomas, Wai Hoe Chin, Hannes Peter | TheBIGbam (https://github.com/bhagavadgitadu22/theBIGbam) is a genome browser and alignment viewer designed for massive metagenomic and metatranscriptomic datasets. The tool accepts genome assemblies in FASTA format or annotated genome sequences in GenBank format, together with BAM files containing read alignments. Alternatively, it can start from raw FASTQ reads and generate alignments using a modified mapper that supports circular genomes, enabling seamless read mapping across genome ends. TheBIGbam can compress hundreds of gigabytes of input files 10- to 100-fold into dedicated databases while retaining key per-position information, including coverage depth and recurrent mismatches, insertions, and deletions between reads and the reference. These databases can be served to a local web browser, enabling interactive exploration of any contig in any sample using DNAFeaturesViewer for genome maps and Bokeh for mapping-derived features. Contig-sample pairs available for visualization can be filtered using a range of summary metrics calculated per contig, per sample, and per contig-sample pair to guide users toward the most relevant signals. Through its interactive visualization, theBIGbam facilitates the exploration of complex datasets, while its integrated database, combining assembly features, annotated features, and mapping-derived features, provides the information needed to investigate biological hypotheses systematically. Designed to complement existing browsing tools like IGV and Anvi'o, theBIGbam is particularly suited for examining misassemblies, subpopulations, microdiversity, and contig topology in large-scale datasets. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room 8 | Biodiversity, sustainability, envirobioinformatic | Cleanifier: contamination removal from microbial sequences using spaced seeds of a human pangenome index | Jens Zentgraf | Johanna Elena Schmitz, Jens Zentgraf, Sven Rahmann |
Motivation: The first step when working with DNA data of human-derived microbiomes is to remove human
contamination for two reasons. First, many countries have strict privacy and data protection guidelines for
human sequence data, so microbiome data containing partly human data cannot be easily further processed or
published. Second, human contamination may cause problems in downstream analysis, such as metagenomic
binning or genome assembly. For large-scale metagenomics projects, fast and accurate removal of human
contamination is therefore critical. Results: We introduce Cleanifier, a fast and memory frugal alignment-free tool for detecting and removing human contamination based on gapped k-mers, or spaced seeds. Cleanifier uses a pangenome index of known human gapped k-mers, and the creation and use of alternative references is also possible. Reads are classified and filtered according to their gapped k-mer content. Cleanifier supports two filtering modes: one that queries all gapped k-mers and one that queries only a sample of them. A comparison of Cleanifier with other state-of-the-art tools shows that the sampling mode makes Cleanifier the fastest method with comparable accuracy. When using a probabilistic Cuckoo filter to store the complete k-mer set, Cleanifier has similar memory requirements to methods that use a sampled minimizer index. At the same time, Cleanifier is more flexible, because it can use different sampling methods on the same index. Availability and implementation: Cleanifier is available via gitlab (https://gitlab.com/rahmannlab/cleanifier), PyPi (https://pypi.org/project/cleanifier/), and Bioconda (https://anaconda.org/bioconda/cleanifier). The pre-computed human pangenome index is available at Zenodo (https://doi.org/10.5281/zenodo.15639519). |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room 8 | Biodiversity, sustainability, envirobioinformatic | TPMM: Three-component Posterior Mixture Model Enables Robust Inverton Detection in Low-Depth Metagenomes and Suggests Potential Viral Invertons | Yi Lu | Yi Lu, Jiaojiao Guan, Yang Shen, Jiayu Shang, Yanni Sun | Bacterial phase variation enables reversible, locus-specific phenotypic switching, often driven by DNA inversion (invertons). To identify these events, researchers commonly rely on sequencing reads that provide orientation-specific support. Metagenomic sequencing, which captures total genetic material independent of cultivation, offers a powerful platform for the comprehensive study of invertons. However, computational inverton calling from metagenomic data is difficult at low sequencing depth: hard read-support cutoffs can miss true events, while sequence-only predictors lack read-backed interpretability and uncertainty quantification. To address this, we present TPMM, a three-component posterior mixture model for inverton calling in metagenomic data. TPMM explicitly incorporates sequencing depth to formulate inverton detection as a probabilistic mixture problem. Starting from candidates flanked by inverted repeats, the model classifies the candidates into noise, low-probability, or high-probability inversion signals using read evidence. Finally, TPMM assigns posterior probabilities as soft labels and applies cumulative Bayesian False Discovery Rate control to robustly identify true invertons. On two real gut metagenomic datasets, TPMM agrees well with PhaseFinder at high depth but recovers substantially more invertons under systematic downsampling, demonstrating superior performance in sparse-data regimes. We further examine potential reversible inversion elements in viral genomes and provide supporting analyses, suggesting a broader scope for inversion-mediated regulation. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | Agent-Based Modelling Identifies many paths for Inflammation Resolution | Hermes Desgrez Dautet | Hermes Desgrez Dautet, David Bernard, Cousin Beatrice, Paul Monsarrat, Sylvain Cussat-Blanc | Sterile inflammation emerges from spatially coordinated interactions between immune recruitment, polarization, signal diffusion, and tissue turnover. Capturing this complexity requires modeling frameworks that integrate stochasticity, spatial heterogeneity, and functional redundancy across immune cell types. We present a spatially explicit agent-based model of sterile inflammation based on a functional mono-agent abstraction, in which immune cells share core capabilities, e.g. polarization, chemotaxis, signal production, and phagocytosis, while differing through parameterization rather than rigid rule sets. Using large-scale global parameter exploration via Latin Hypercube Sampling, we generated 300,000 simulations to characterize the model's dynamical landscape. Dimensionality reduction suggested a continuous manifold of inflammatory trajectories. Applying biologically informed temporal criteria identified a highly constrained subspace consistent with canonical sterile inflammation. Within this region, multiple distinct yet self-consistent kinetic regimes achieved successful resolution. These results indicate that sterile inflammation is not a single dynamical attractor but an emergent coordination constraint in a high-dimensional functional space, providing a systems-level framework for exploring immune regulation and dysregulation. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa | Cyril Malbranke | Cyril Malbranke, Gionata Paolo Zalaffi, Anne-Florence Bitbol, Cecilia Fruet |
Language models trained on biological sequences are advancing inference tasks from the scale of single
proteins to that of genomic neighborhoods. Here, we introduce ProteomeLM, a transformer-based language model
that uniquely operates on entire proteomes from species spanning the tree of life. ProteomeLM is trained to
reconstruct masked protein embeddings using the whole proteomic context, yielding contextualized protein
representations that reflect proteome-scale functional constraints. Notably, ProteomeLM's attention coefficients encode protein-protein interactions (PPI), despite being trained without interaction labels. Furthermore, it enables interactome-wide PPI screening that is substantially more accurate, and orders of magnitude faster, than amino-acid coevolution-based methods. We further develop ProteomeLM-PPI, a supervised model that combines ProteomeLM embeddings and attention coefficients to achieve state-of-the-art PPI prediction across benchmarks and species. Finally, we introduce ProteomeLM-Ess, a supervised gene essentiality predictor that generalizes across diverse taxa. Our results demonstrate the potential of proteome-scale language models for addressing function and interactions at the organism level. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | Relation Extraction for Diet, Non-Communicable Disease and Biomarker Associations (RECoDe): A CoDiet study | Donghee Choi | Donghee Choi, Yajie Gu, Kai Qi Zong, Antoine Lain, Dimitrios Zaikis, Thomas Rowlands, Marek Rei, Tim Beck, Joram Posma |
Diet plays a critical role in human health, with growing evidence linking dietary habits to disease
outcomes. However, extracting structured dietary knowledge from biomedical literature remains challenging
due to the lack of dedicated relation extraction datasets. To address this gap, we introduce RECoDe, a novel
relation extraction (RE) dataset designed specifically for diet, disease, and related biomedical
entities. RECoDe captures a diverse set of relation types, including a broad spectrum of positive association patterns and explicit negative examples, with over 5,000 human-annotated instances validated by up to five independent annotators. Furthermore, we benchmark various natural language processing (NLP) RE models, including BERT-based architectures and enhanced prompting techniques with locally deployed large language models (LLMs) to improve classification performance on underrepresented relation types. The best performing model was gpt-oss-20B, a locally-deployed open-weight LLM, achieving an F1-score of 61% (macro) for multi-class classification and 89% for binary classification using a hierarchical prompting strategy with a separate reflection step built in. To demonstrate the practical utility of RECoDe, we introduce the Contextual Co-occurrence Summarisation (CoCoS) framework, which aggregates sentence-level relation extractions into document-level summaries and further integrates evidence across multiple documents. CoCoS produces effect estimates consistent with established dietary knowledge, demonstrating its validity as a general framework for systematic evidence synthesis. Availability: The code, models, and dataset are publicly available at https://github.com/omicsNLP/RECoDe. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | Coral microbiomes as reservoirs of unknown genomic and biosynthetic diversity | Lucas Paoli | Denis Allemand, Rebecca Vega Thurber, Romain Trouble, Christian Voolstra, Patrick Wincker, Maren Ziegler, Joern Piel, Shinichi Sunagawa, Eric Douville, Chris Bowler, Sylvain Agostini, Olivier Thomas, Serge Planes, Sarah Romac, Julie Poulain, Guillaume Iwankow, Juliette Cailliau, Guillaume Bourdin, Emilie Boissin, Clémentine Moulin, Maggie Reddy, Aude Perdereau, Taylor Priest, Daniel Richter, Lucas Paoli, Dora Racunica, Hans-Joachim Ruscheweyh, Martin Sperfeld, James O'Brien, Samuel Miravet-Verde, Alena Streiff, Jessica Ransome, Clara Chepkirui, Fabienne Wiederkehr, Anna Sintsova, Guillem Salazar, Kalia Bistolas, Teresa Sawyer, Karine Labadie, Kim-Isabelle Mayer, Didier Forcioli, Pierre Galand, Fabien Lombard, Pedro Oliveira | Coral reefs are marine biodiversity hotspots that provide a wide range of ecosystem services. They are reservoirs of bioactive metabolites, many produced by microorganisms associated with reef invertebrate hosts. However, for the keystone species of coral reefs—the reef-building corals—we still lack a systematic assessment of their microbially encoded biosynthetic potential and the molecular resources at stake due to the alarming decline in reef biodiversity. Here we analysed microbial genomes reconstructed from 820 reef-building coral samples of three representative coral genera collected at 99 reefs across 32 islands throughout the Pacific Ocean (Tara Pacific expedition). By contextualizing our analyses with the microbiomes of other reef species, we found that only 10% of the 4,224 microbial species and less than 1% of the 645 species exclusively identified in Tara Pacific samples had genomic information available. Furthermore, the biosynthetic potential of reef-building coral microbiomes rivalled or surpassed that of traditional natural product sources such as sponges. Among the biosynthetically rich bacteria in the reef microbiome, we identified new groups of Acidobacteriota that encode previously unknown enzymology, in turn opening promising avenues for functional protein engineering. Together, this study underscores the importance of conserving coral reefs as vital reservoirs of molecular diversity. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | GiantHost: A Domain-Adaptive and Uncertainty-Aware Framework for Giant Virus Host Prediction | Fuchuan Qu | Fuchuan Qu, Guowei Chen, Yanni Sun |
Motivation: Nucleocytoplasmic large DNA viruses (NCLDVs) play crucial roles in global ecosystems. Although
metagenomics has vastly accelerated the discovery of novel NCLDVs, predicting their hosts from fragmented
contigs remains a critical bottleneck, with no dedicated end-to-end computational tools currently available.
Addressing this gap requires overcoming three fundamental challenges: the extreme scarcity of labeled
reference genomes, the severe domain shift between laboratory isolates and diverse environmental
metagenomes, and the inability of traditional deterministic models to quantify prediction uncertainty—a
crucial requirement for reliable ecological profiling where novel, divergent viruses are prevalent. Results: We present GiantHost, the first NCLDV host prediction tool with domain adaptation and uncertainlty awareness. GiantHost employs a dual-tower neural network to integrate dense genome traits and sparse GVOG profiles, allowing better integration of heterogeneous features. To overcome label scarcity and domain shift, we leverage 1,400 environmental viral genomes (GVMAGs) via semi-supervised multi-task learning and Domain Adversarial Neural Networks (DANN), effectively bridging the distributional gap between RefSeq and environmental data. Additionally, GiantHost incorporates Conformal Prediction (CP) to output statistically guaranteed prediction sets rather than overconfident single labels. Evaluated under rigorous genome-level cross-validation, GiantHost demonstrates robust predictive power. Applied to the Tara Ocean dataset, GiantHost successfully captured the vertical stratification of NCLDV hosts—revealing a depth-dependent decline of phytoplankton-infecting viruses and a relative enrichment of Amoebozoa-infecting viruses in the mesopelagic zone. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room EF | Biodiversity, sustainability, envirobioinformatic | ViralQC: A Tool for Assessing Completeness and Contamination of Predicted Viral Contigs | Cheng Peng | Cheng Peng, Jiayu Shang, Jiaojiao Guan, Yanni Sun |
Viruses represent the most abundant biological entities on Earth, playing vital roles in diverse ecosystems.
Cataloging viruses across various environments is essential for understanding their properties and
functions. Metagenomic sequencing has emerged as the most comprehensive method for virus discovery. However,
distinguishing viral sequences from the vast background of microbial organisms in metagenomic data remains a
significant challenge. Existing tools experience varying degrees of false positive rates due to noise in
sequencing and assembly, and the integration of proviruses into microbial genomes. This highlights the
urgent need for an accurate and efficient method to evaluate the quality of viral contigs. To address these challenges, we introduce ViralQC, a tool designed to assess the quality of viral contigs or bins. ViralQC identifies microbial contamination within putative viral sequences using a multi-modal framework powered by DNA and protein foundation models and estimates completeness by analyzing protein organization. We evaluated ViralQC on multiple datasets and compared its performance against the state-of-the-art tool, CheckV. Leveraging both DNA and protein foundation models, ViralQC achieves higher recall on contamination detection for contigs longer than 10 kbp while maintaining comparable precision. Additionally, ViralQC delivers more accurate estimation on contigs with completeness>50%. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room 8 | Elixir | Highlights from the Rare Disease Community: Improving the Research Service Ecosystem by Integrating FAIR Resources | Emidio Capriotti | Sergi Beltran, Emidio Capriotti, Magda Chegkazi | The ELIXIR Rare Disease Community is dedicated to advancing a sustainable, collaborative, and interoperable research ecosystem to accelerate rare disease research across Europe and support its translation into healthcare. The Community established key service bundles including AMP4RD for molecular variant interpretation, Systems Biology Service Bundles for pathway and network-based analyses, and the ELIXIR Training Platform for Rare Diseases, an eLearning platform supporting training and capacity development. These services are powered by ELIXIR's interoperability resources and core data resources, which provide the foundation for FAIR data management, reproducible analyses, and cross-domain integration. Specifically, the Community utilises UniProt for the annotation of genetic variants, mapping amino acid changes to functional consequences and protein-level effects. Orphadata Science is used for linking resources from Orphanet, providing standardised machine-readable data on rare diseases and associated genes. IntAct enables protein-protein interaction network analysis to uncover molecular mechanisms underlying rare diseases. DisGeNET supports the retrieval of genes and variants associated with rare diseases, facilitating gene-disease association discovery. Looking ahead to 2026–2028, the Community will align with the European Health Data Space and the European Genomic Data Infrastructure, enhancing secure federated access via Beacon technology. Future activities focus on systematic FAIRification of existing tools rather than new tool development, leveraging the ELIXIR Tools Platform for benchmarking, and expanding training for clinical and research communities. Collaboration with ERDERA and GA4GH will ensure alignment and maximise impact, strengthening an integrated ecosystem supporting data-driven rare disease research and improved patient outcomes. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room 8 | Elixir | Automatic MeSH Descriptor Assignment for the ELIXIR FHDPortal | Patrick Ruch | Luc Mottin, Julien Knafou, Michael Baudis, Anais Mottaz, Venkata Satagopam, Teresa D'Altri, Patrick Ruch |
The semantic annotation of biomedical datasets remains a major challenge to support both data discovery and
semantic interoperability. In this work, we present a k-Nearest Neighbors (kNN) approach for the automatic
assignment of MeSH descriptors to studies from the Federated Human Data Portal (FHDportal) using MEDLINE as
a knowledge base. The method relies on a simple pipeline: input text is vectorized, similar biomedical
articles are retrieved, and MeSH terms are aggregated through a voting mechanism. Two retrieval strategies were evaluated: a neural dense index using FAISS and a sparse search service based on the SIB Literature Services. The system was evaluated on EGA studies (~9,890 records) using cross-validation. Preprocessing included removal of EGA-related publications from the index and filtering of non-informative MeSH terms. Performances were assessed using precision, recall, and F1-score at multiple cutoffs (top-5, top-10, top-15) under the assumption that an interactive service should not return more than 15-20 descriptors. Results show that the approach achieves a precision above expected inter-annotator agreement, with respectively P@0 and P@5 at 0.97 and 0.61. While the top returned descriptor is highly reliable, the precision at P@5 suggests that approximately 3 of 5 descriptors are relevant. Response time remains compatible with interactive deployment (~1.2 seconds on average). These results suggest that a scalable classification framework can effectively support semantic enrichment of EGA metadata. The system, available through an API, is intended as a decision-support tool to support the curation workflows of EGA datasets. Availability: https://fhdportal-dev.text-analytics.ch/docs#/ |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room 8 | Elixir | Federated Genomic Variant Discovery and Analysis Using GA4GH Standards | Anaïs Mottaz | Anaïs Mottaz, Worawich Phornsiricharoenphant, Michael Baudis, Valérie Barbié, Sergi Beltran, Venkata Satagopam, David Salgado, Hindrik Kerstens, Friederike Ehrhart, Melissa Cline, Tim Beck, Emidio Capriotti, Jordi Rambla, Patrick Ruch |
Genomic data are increasingly distributed across clinical, research and national infrastructures, making
interoperability a key challenge for their effective reuse. This is particularly critical for rare disease,
cancer and population genomics, where variant interpretation requires integrating both observational data
and interpretative knowledge across sources. GA4GH standards provide a common framework to address these challenges. The Variant Representation Specification (VRS) enables consistent, computable representations of genomic variants across formats and sources, while Categorical VRS (Cat-VRS) extends this to groups of related variants. The Beacon protocol supports federated discovery across distributed datasets without centralising sensitive data, with controlled access and adjustable granularity of query responses. Downstream analyses can be performed within Trusted Research Environments (TREs), where standards such as the Task Execution Service (TES) support federated execution by bringing computation to the data. This work illustrates how these components can be articulated into a federated analysis pipeline, using a rare disease-oriented scenario as an illustrative example. Starting from a candidate variant, consistent representations enable linking observational occurrences with interpretative evidence from databases and literature, including related variants within the same functional class. Federated discovery through Beacon identifies relevant datasets, while subsequent analyses within TREs enable comparison of phenotypic profiles across cohorts. The work highlights current efforts and remaining challenges in interoperability, including variant representation harmonisation, integration of heterogeneous data types and alignment between discovery and analysis layers. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room 8 | Elixir | Stress Knowledge Map and beyond: ELIXIR community knowledge graph efforts in plant science and systems biology | Carissa Bleker | Carissa Bleker, Maja Zagorščak, Mojca Juteršek, Sergej Praček, Anže Županič, Kristina Gruden, Bruno Sá, Augustin Luna, Yağmur Doğay, Jan Zrimec, Adrien Rougny, Ugur Dogrusoz, Marek Ostaszewski, Matti van Welzen, Sebastian Lobentanzer |
Understanding plant stress responses requires integrating complex, dispersed knowledge on molecular
interactions into queryable, reusable resources. Stress Knowledge Map (SKM; skm.nib.si) addresses this by
consolidating curated information on protein-protein interactions, transcriptional regulation, small
RNA-transcript interactions, and enzymatic transformations in Arabidopsis thaliana. An additional workflow
integrating multiple lines of evidence supports translation of this information to crop species including
potato, tomato, and fruit trees. Together with associated analysis and modelling tools, SKM supports
systematic contextualisation of multi-omic data and the development of dynamic systems biology models of
stress signalling. ELIXIR community activities have been central to SKM's growth and dissemination. The implementation study "Increasing Plant data findability and reuse beyond ELIXIR" improved the accessibility and interoperability of plant knowledge resources across the plant sciences. At the de.NBI BioHackathon Germany 2023, SKM was indexed in FAIDARE, increasing its findability within the ELIXIR plant data infrastructure. An ELIXIR Plant Sciences Community staff exchange extended SKM further, enabling a BioChatter-based prototype for LLM-accessibility to lower barriers for experimental biologists. These developments revealed a broader need: standardised, reusable infrastructure for transforming systems biology models (in SBGN-ML and SBML formats) into human- and AI-accessible knowledge graphs. This motivated the ELIXIR Systems Biology Community's project at the 4th BioHackathon Germany (December 2025), which produced an initial common labelled property graph schema grounded in a standard ontology (SBO), SBGN and SBML BioCypher adapters, and initial support for SBML export - laying the groundwork for a community-wide framework and future AI integration across systems biology. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room 8 | Elixir | Advancing FAIR systems biology: from principles to AI-enhanced practice | Miguel Rocha | Carissa Bleker, Miguel Rocha, Rahuman Sheriff, John Hancock, Vitor Martins dos Santos |
The ELIXIR Systems Biology Community has contributed to making Systems Biology (SB) research more Findable,
Accessible, Interoperable, Reusable (FAIR). Through the SYBEL project, we mapped connections between
researchers and essential tools/data resources, demonstrating that access mechanisms are as vital as the
resources themselves for enabling reproducible research. Building on this foundation, we are synthesizing FAIR and CURE (Credible, Understandable, Reproducible, Extensible) principles into actionable guidelines for the community. This translates into curated FAIRsharing collections documenting standards and databases, bio.tools collections cataloging essential software, and a RDMKit page providing practical implementation guidance. These resources directly leverage ELIXIR Recommended Interoperability Resources and Core Data Resources, exemplifying how Community activities enhance the value of ELIXIR. Looking forward, we are exploring how artificial intelligence (AI) and knowledge-graphs (KGs) can transform SB workflows. AI-assisted curation can accelerate development and annotation of models and datasets, improving efficiency and consistency. KGs enhance data integration across heterogeneous sources, making complex data more accessible. For modeling workflows, AI tools can ease accessibility to datasets, suggest appropriate formalisms, validate model structure, propose mechanistic hypotheses grounded in literature, and contribute to enhanced interpretability and explainability of SB models. In a broader sense, AI-enabled data-driven models provide a synergistic complement to fundamental knowledge-based SB models. This evolution from merely exploring FAIR resources to AI-enhanced models/data resources represents the Community's commitment to reducing barriers in SB research. By combining robust data management practices with emerging AI capabilities, we aim to accelerate discovery, while maintaining the rigor essential for reproducible SB. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room 8 | Elixir | Investigating the principles of plant growth-defence trade-offs and biotic interactions using metabolic modelling | Jan Zrimec | Jan Zrimec, Sandra Correa, Maja ZagorÅ¡Äak, Carissa Bleker, Marko Petek, Katja Stare, Kristina Gruden, Zoran Nikoloski |
Plants enter a defensive state in response to environmental stresses or pathogens, rerouting many resources
from their primary growth objective and thus performing well below their maximum potential. Understanding
such trade-offs can help us develop improved crop varieties with increased stress tolerance, improving
yields and quality. As potato is among the most important food crops and a model tuber crop species, it is
of interest to establish it also as a model system for studying growth-defence trade-offs. To investigate growth-defence trade-offs in the context of metabolism, we developed a compartmentalised genome-scale metabolic model of potato metabolism, Potato-GEM. It contains information on the stoichiometry of over 7,000 biochemical reactions, connecting them to the genes encoding the enzymes that catalyse them. We also curated and expanded the Potato-GEM secondary metabolism, capturing its full extent according to the MetaCyc database. The potato leaf biomass was quantified using in-house and published experimental data. We employed the model to explore growth-defence trade-offs using transcriptomics data of biotic stress responses to the pathogens Potato virus Y and pest Colorado potato beetle. This revealed the general principles of stress induced growth-defence trade-offs and enabled the comparison of micro- and macro-biotic stress response mechanisms. We thus demonstrate how the curated model can be used to investigate multi-reaction trade-offs across primary and secondary metabolism, by integrating and interpreting omics data. This provides the basis for a metabolic perspective on trade-offs and biotic interactions typical across many plant species. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room 4 | Elixir | Bottom up interoperability using semantics in ELIXIR resources | Jerven Bolleman | Jerven Bolleman, Gos Micklem, Julia Koblitz, Pierre Larmande, Karel Berka, Sebastien Moretti, Jakub Galgonek, Adrian Altenhoff, Dmitry Kuznetsov, Pierre-Andre Michel, Deepak Unni, Sabine Oesterle, Nicole Redaschi, Alan Bridge, Tarcisio Mendes |
We would like to demonstrate how interoperability between ELIXIR Core Data, Interoperability and Services of
9 ELIXIR nodes, is possible using the already deployed and supported Semantic Web based solutions. These
resources connected using FAIR respecting technologies enable scientific research and interoperate with
Global Core Biodata Resources, EU infrastructure and NFDI4Chem today. This demonstrates that FAIR data has
no borders and that interoperability is not just a dream but a practical reality. TESS and training data are
also a part of this interoperability success story, and due to its use of BioSchemas markup are part of the
wider ELIXIR Semantic Web community. Core Data Interoperability Service OMABrowser (CH) Identifiers.org (EMBL-EBI) WikiPathways.org (NL) BGee (CH) humanmine (UK) idsm/sachem (CZ) OrthoDB (CH) BridgeDb (NL) MolMeDB (CZ) UniProt (CH) InterMine (UK) Cellosaurus (CH) AgroLD (FR) Rhea (CH) datacatalogue (LU) BRENDA (DE) HAMAP (CH) LipidMaps (UK) SIBiLS (CH) ChEBI (EMBL-EBI) SwissLipids (CH) CHEMBL (EMBL-EBI) Bacdive (DE) |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room 4 | Elixir | The ELIXIR Research Software Ecosystem | Hervé Ménager | Olivier Sallou, Steffen Möller, Stuart Owen, Magnus Palmblad, Perrine Paul-Gilloteaux, Yasset Perrez-Riverol, Hedi Peterson, Manthos Pithoulias, Dmitri Repchevsky, Claire Rioualen, Kota Miura, Veit Schwämmle, Mariia Steeghs-Turchina, Stian Soiland-Reyes, Marco-Antonio Tangaro, Jonathan Tedds, Andreas Tille, Federico Zambelli, Oleg Zharkov, Hervé Ménager, Björn Grüning, Finn Bacall, Bérénice Batut, Salvador Capella-Gutiérrez, Frederik Coppens, José Mª Fernández, Alban Gaignard, Claire Garrard, Josep Ll. Gelpi, Carole Goble, Mihail Anton, Ove Johan Ragnar Gustafsson, Jennifer Harrow, Alireza Heidari, Hans Ienasescu, Arash Kadkhodaei Elyaderani, Matus Kalas, Peter Maccallum, Steven Manos, Ana Mendes |
Research software underpins contemporary science, yet the metadata needed to discover, evaluate, deploy, and
reuse software remains fragmented across registries, packaging systems, workflow platforms, and execution
environments. This talk introduces the ELIXIR Research Software Ecosystem (RSEc), an open, federated, and
version-controlled metadata commons designed to reduce that fragmentation without imposing a single metadata
standard. Instead, RSEc synchronises, validates, and cross-links heterogeneous metadata from multiple
upstream services, creating a shared infrastructure that supports interoperability while respecting
community-specific schemas and governance models. I will outline the architectural principles behind RSEc, including its central public Git-based repository, continuous integration workflows for automated import and validation, explicit equivalence mappings across repositories, and export into machine-actionable formats such as JSON-LD and RDF. I will also show how this infrastructure already supports practical downstream use cases: unified software discovery through the RSEc Atlas, enriched metadata delivery to services such as ToolFinder and WorkflowHub, and stronger integration with Galaxy, BioContainers, and other components of the ELIXIR tools landscape. Beyond the technical implementation, the talk will argue for RSEc as a sustainability mechanism for research software metadata. By centralising curation effort, improving consistency across platforms, and making metadata openly reusable, the ecosystem benefits developers, users, and curators alike. The broader claim is that a federated metadata backbone is essential if research software is to become more discoverable, FAIR, and reusable at ecosystem scale. Finally, I will discuss current limitations, including dependence on upstream metadata quality, the continuing need for community-led curation and governance, and equivalence mapping across services. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room 4 | Elixir | Developing Structured Learning Paths for the ELIXIR Biodiversity Community: A collaborative Approach | Robert Waterhouse | Valeria Di Cola, Patricia M. Palagi, Anne-Françoise Adam-Blondon, Anthony Bretaudeau, Robert Waterhouse |
Learning Paths (LPs) serve as curated sequences of learning topics designed to guide learners through the
mastery of specific subjects. Within ELIXIR, the Learning Path Focus Group collaborates across communities
to standardize the development of these structured curricula. Here we outline the methodology and results
the ELIXIR Biodiversity Community used to establish an initial set of three Learning Paths developed under
the Implementation Study. The development process used a systematic approach to map skills and stakeholder roles within the ELIXIR Biodiversity Community. Through a series of face-to-face and online consultations (2023-2024), we benchmarked existing LPs in Research Data Management and Plant Sciences to identify core competencies and domains. We successfully defined and populated three primary domains: Eukaryotic Genome Sequence Assembly and Annotation BiiGLE: Species annotation of images and videos PLAZI: Accessing digital taxonomic literature For each path, we mapped target audiences, entry points, and comprehensive learning outcomes. By indexing available training materials - ranging from synchronous short courses to asynchronous tutorials - we utilized the TeSS (Training e-Support System) interface to visualize these pathways. The Learning Paths also served as a diagnostic tool to identify training gaps and set priorities for the development of new training materials. The collaborative approach, combining internal analysis with external community input, ensures that forthcoming training resources will be both relevant and impactful. These LPs provide a scalable framework for biodiversity training, ensuring that the community's evolving needs are met through continuous, structured knowledge transfer. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room 4 | Elixir | Connecting data and compute for AI-driven health innovation | Peter Maccallum, Becky Upton, César Velasco |
This panel will discuss how Europe can accelerate responsible AI-driven health innovation by better
connecting life-science data resources, advanced computing infrastructures, and public–private
collaboration. A central question is how to ensure that Europe’s major investment in AI factories and antennas translates into real impact for life-science and health applications. The discussion will focus on the role of trusted, high-value biological and biomedical datasets, domain expertise, interoperable standards, benchmarking, and secure access to sensitive data. The panel will also address practical roadblocks: what is still missing to make AI factories genuinely useful for life science, and how can ELIXIR, computing infrastructures, industry, and public institutions work together to close these gaps? |
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| 2026-08-31 | 13:30:00 | 14:30:00 | Room AD | Genomics, epigenomics, and genome editing | Leveraging ONT move table values for signal aware variant calling | Xian Yu | Xian Yu, Zhenxian Zheng, Lei Chen, Zilan Qin, Minggao He, Ruibang Luo | Oxford Nanopore Technologies (ONT) sequencing enables long-range haplotype phasing and contiguous genome assembly but still exhibits elevated error rates that challenge small variant calling, particularly for insertions and deletions (Indels). While raw electrical signals contain rich information, existing signal-aware methods require computationally intensive processing of large signal files. Here, we present Clair3 v2, a method that leverages the ONT move table—a lightweight byproduct of basecalling that maps signal events to nucleotide positions—to improve variant calling accuracy. Clair3 v2 builds upon Clair3 and integrates signal-level dwelling time to significantly enhance variant calling performance. We also propose a genome position based circular buffer to incorporate dwelling time with minimal computational overhead. Benchmarking across six Genome in a Bottle samples demonstrates substantial improvements in variant calling accuracy. With HAC basecalling, Clair3 v2 achieves a mean SNP F1-score of 97.69% at 10× depth (compared to 96.45% for baseline Clair3), and Indel F1 scores improved from 64.27% to 76.70%, while gains persisted at higher depths. The benefits were most pronounced for longer Indels and in complex genomic regions, where Indel F1 scores in long homopolymer regions improved from 14.3% to 45.2%. Benchmark results across various basecalling modes, samples, and coverage settings outperformed Clair3 baselines and other methods, including DeepVariant and Dorado Variant, and demonstrate the significant benefits of Clair3 v2. Furthermore, Clair3 v2 incurs negligible runtime compared to standard Clair3, making it practical for routine use. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room AD | Genomics, epigenomics, and genome editing | GUANinE v1.1 Reveals Complementarity of Supervised and Genomic Language Models | Eyes Robson | Eyes Robson, Nilah Ioannidis | Summary: There has been much debate about the benefits of supervised versus unsupervised learning on genomes. Determining which is better in what contexts requires developing comprehensive benchmarks spanning functional and evolutionary tasks. Importantly, such benchmarks need large sample sizes to enable well-powered ranking of models. Having developed and applied such a benchmark here (GUANinE v1.1), we conclusively demonstrate each paradigm offers key advantages and outperforms on certain tasks. In accordance with training, supervised sequence-to-function models exhibit strong performance when annotating functional states characterized by chromatin accessibility or histone marks, while self-supervised language models outperform on evolutionary conservation. Our hundreds of new evaluations in this v1.1 expansion provide evidence for a tradeoff between input context size and model parameter count for a fixed compute budget, which we depict with new metrics such as kiloparameters/base pair. We also construct two new large-scale variant interpretation tasks in v1.1: cadd-snv measuring deleteriousness, and clinvar-snv measuring clinical pathogenicity. We find that conservation scores, and by extension, genomic language models, predict deleteriousness well, but successfully translating deleteriousness to pathogenicity remains challenging. GUANinE v1.1 newly evaluates dozens of pretrained genomic models. We identify uniquely performant models, and we conclude by showing hybrid or post-trained language models may define the next era of machine learning in genomics. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room AD | Genomics, epigenomics, and genome editing | Locityper enables biobank-scale genotyping of structurally complex loci | Timofey Prodanov | Timofey Prodanov, Tobias Marschall |
The human genome harbors many structural variants (SVs)–long and complex genomic differences that are
difficult to characterize and genotype, with >50% of all SVs missed by short read callers. SV-rich loci
generate numerous diverse haplotypes, poorly represented in a single reference genome, making read
alignments ambiguous and unreliable, and hundreds of disease-associated genes inaccessible to accurate
variant calling. To tackle this problem we developed Locityper (Nature Genetics, Oct. 2025), a targeted genotyper that searches pangenome assemblies for two locus haplotypes that best explain a given whole genome sequencing dataset. Specifically, Locityper recruits and maps reads to all haplotypes and identifies the haplotype pair with optimal alignment likelihood and coverage profile. Leveraging an ever-growing collection of high-quality genome assemblies, Locityper predicts diploid locus sequence more accurately than state-of-the-art phased variant calling pipelines across 265 challenging medically relevant genes, raising median QV (Phred-scaled sequence divergence) from 24.4 to 35.3 (short reads) and 35.2 to 36.9 (long reads). Subsequently, Locityper was used for genotyping tandem repeat-rich mucin genes; for evaluation of the new HPRC2 and HGSVC3 pangenomes; and for validation of putative disease-relevant SVs in the All-of-Us cohort. Most recently, we genotyped 50 SVs across 424k UK Biobank short read datasets and identified disease associations for 4 of them (FDR-corrected p-value <0.05). To facilitate this analysis we extended and optimized the Locityper pipeline, reducing compute cost to just 0.007¢/sample/SV. This low genotyping cost, together with high accuracy, underscores Locityper's value for large-scale diagnostic and discovery analyses across SV-rich loci. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room BC | Genomics, epigenomics, and genome editing | RSHash: A fast and space-efficient hash table for k-mers | Jonas Schulte-Mattler | Jonas Schulte-Mattler, Knut Reinert |
Large genomic data collections can be viewed as a continuous string of DNA characters. The essential
operations for data structures indexing the $k$-mer content of such a string are {\tt lookup} and {\tt
locate}. {\tt Lookup} determines whether a query $k$-mer $q$ exists in the string and {\tt locate} returns
all locations in the string where $q$ is present. High-throughput DNA sequencing generates very many k-mer
sets of size exceeding billions of characters. In such scenarios, memory consumption and query efficiency
pose significant challenges to a data structure supporting the above mentioned queries. To address this problem, we describe a simple, compressed, static data structure for k-mers that answers {\tt lookup} and can be extended for supporting {\tt locate}. The general scheme follows the use of minimizers like the state-of-the art SSHash. However, instead of using minimum perfect hash functions our solution (RSHash for Rank-Select Hash) relies on bitvectors with rank and select support, a multiple layered minimizer scheme, and a clever buffering strategy. We can show that RSHash is on average $40\%$ and in some cases up to two times faster than SSHash while having the same memory requirements. Indeed we can go as low as $8$~bits per canonical $31$-mer on a human dataset. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room BC | Genomics, epigenomics, and genome editing | Ultrafast and accurate sequence alignment and clustering of viral genomes | Adam Gudys | Andrzej Zielezinski, Adam Gudys, Jakub Barylski, Krzysztof Siminski, Piotr Rozwalak, Bas E. Dutilh, Sebastian Deorowicz |
An essential challenge in clustering massive datasets of viral genomes is efficient calculation of average
nucleotide identity (ANI). Since gold-standard, alignment-based approaches for calculating ANI (e.g., BLAST)
are computationally unsuitable for large-scale analyses, more efficient k-mer-based methods employing
sketching (FastANI, RabbitTClust) or approximate alignment (skani) have been developed. These tools,
however, have lower accuracy than alignment-based methods and/or do not provide clustering capabilities.
We present Vclust, a package for clustering millions of viral genomes. The method consists of three steps. The first one is a k-mer-based filtering of sequence pairs performed with Kmer-db 2, an updated version of Kmer-db, equipped with algorithms for producing sparse distance matrices. This is followed by an accurate estimation of ANI for selected sequence pairs using LZ-ANI, an alignment method grounded in Lempel-Ziv parsing. The final step is clustering genomes with Clusty, a package providing efficient implementations of multiple clustering algorithms suited for sparse distance matrices. When assigning 90,000 contigs from IMG/VR database to virus OTUs, Vclust exhibited the highest agreement with BLAST (99%) compared to MegaBLAST (97%), skani (96%) and FastANI (93%), being noticeably faster than the competitors. MMSeqs2, the only method comparable in terms of execution times, was significantly less accurate (70% BLAST agreement). The ability to process the entire IMG/VR database (over 15 million contigs) in 2.4 hours and 92GB of RAM, confirms Vclust to be ready for future challenges in the viral genomics. With over 18 thousand Bioconda/GitHub downloads, Vclust has already proven its usefulness in the community. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room BC | Genomics, epigenomics, and genome editing | k-mer manifold approximation and projection for visualizing DNA sequences | Lu Cheng | Chengbo Fu, Einari Niskanen, Gong Hong Wei, Zhirong Yang, Marta Sanvicente-GarcÃa, Marc Güell, Lu Cheng | Identifying and illustrating patterns in DNA sequences are crucial tasks in various biological data analyses. In this task, patterns are often represented by sets of k-mers, the fundamental building blocks of DNA sequences. To visually unveil these patterns, one could project each k-mer onto a point in two-dimensional (2D) space. However, this projection poses challenges owing to the high-dimensional nature of k-mers and their unique mathematical properties. Here, we establish a mathematical system to address the peculiarities of the k-mer manifold. Leveraging this k-mer manifold theory, we develop a statistical method named KMAP for detecting k-mer patterns and visualizing them in 2D space. We applied KMAP to three distinct data sets to showcase its utility. KMAP achieves a comparable performance to the classical method MEME, with ∼90% similarity in motif discovery from HT-SELEX data. In the analysis of H3K27ac ChIP-seq data from Ewing sarcoma (EWS), we find that BACH1, OTX2, and KNCH2 might affect EWS prognosis by binding to promoter and enhancer regions across the genome. We also observe potential colocalization of BACH1, OTX2, and the motif CCCAGGCTGGAGTGC in ∼70 bp windows in the enhancer regions. Furthermore, we find that FLI1 binds to the enhancer regions after ETV6 degradation, indicating competitive binding between ETV6 and FLI1. Moreover, KMAP identifies four prevalent patterns in gene editing data of the AAVS1 locus, aligning with findings reported in the literature. These applications underscore that KMAP can be a valuable tool across various biological contexts. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room AD | Genomics, epigenomics, and genome editing | Improved RNA-DNA interaction calling suggests RNA-based gene regulation of phenotypic transitions | Timothy Warwick | Simonida Zehr, Ralf Brandes, Marcel Schulz, Timothy Warwick | Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA–DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA–DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA–DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background, which considers RNA binding site–TSS distance and genomic region bias, and outperforms previously proposed methods in the accurate recall of genome-wide RNA–DNA interactions. Accurate RNA–DNA interaction calling enables the analysis of gene regulatory RNAs in dynamic biological contexts. Here, dynamically chromatin-associated RNAs were identified in the physiologically- and pathologically relevant process of endothelial-to-mesenchymal transition. By depleting candidate chromatin-associated lncRNAs, their gene regulatory behavior at bound target genes important to endothelial phenotype maintenance could be validated. These data demonstrate how effective RNA–DNA interaction calling can help to place RNAs at key points in gene regulatory networks governing cellular behavior. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room AD | Genomics, epigenomics, and genome editing | From Transcripts to Cells: Dissecting Sensitivity, Signal Contamination, and Specificity in Xenium Spatial Transcriptomics | Mariia Bilous | Mariia Bilous, Daria Buszta, Jonathan Bac, Senbai Kang, Yixing Dong, Stephanie Tissot, Sylvie Andre, Marina Alexandre-Gaveta, Christel Voize, Solange Peters, Krisztian Homicsko, Raphael Gottardo |
Spatial transcriptomics enables high-resolution gene expression mapping in intact tissues, yet the technical
properties and limitations of widely adopted platforms remain poorly characterized. We present one of the
most comprehensive Xenium datasets to date, encompassing over 40 breast and lung tumor sections profiled
across diverse gene panels. Leveraging this resource, we systematically dissect technical noise - including
transcript spillover - along with assay specificity, panel performance, and segmentation strategies,
providing a critical benchmark for Xenium performance in complex tissues. To our knowledge, we are the first to systematically characterize and quantify transcript spillover from neighboring cells at scale, identifying it as a dominant source of noise that generates mixed signals, reduces specificity, and obscures biologically meaningful programs, particularly within immune populations in cancer tissues. We demonstrate that single-nucleus RNA-seq enables precise quantification of this phenomenon, establishing a rigorous framework for its assessment. Building on these insights, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a novel computational method that models and corrects transcript spillover using cell type deconvolution outputs within an interpretable linear modeling framework. SPLIT is implemented as an open-source R package, scales to large spatial datasets, and is applicable in principle to other spatial omics modalities where signal mixing occurs. SPLIT substantially improves background correction and cell-type resolution, enabling detection of T-cell exhaustion signatures associated with malignant cell colocalization - signals otherwise obscured in uncorrected data - demonstrating its value for spatially informed biological inference. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room AD | Genomics, epigenomics, and genome editing | CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species | Niklas Kempynck | Niklas Kempynck, Seppe De Winter, Cas Blaauw, Vasilieios Konstantakos, Eren Can EkÅŸi, Sam Dieltiens, Darina Abaffyová, Valérie Bercier, Ibrahim Taskiran, Gert Hulselmans, Valerie Christiaens, Ludo Van Den Bosch, Lukas Mahieu, Stein Aerts | Sequence-based deep learning models have become the state of the art for analyzing the genomic regulatory code. Particularly for enhancers, these models excel at deciphering sequence grammar that underlies their activity. To enable end-to-end enhancer modeling and design, we developed a software package called CREsted (cis-regulatory element sequence training, explanation and design). It combines preprocessing and analysis of single-cell assay for transposase-accessible chromatin using sequencing data, modeling chromatin accessibility from sequence, sequence design and downstream analysis to decipher enhancer grammar. We demonstrate CREsted's functionality on a mouse cortex and a human peripheral blood mononuclear cell dataset. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types, and we investigate different fine-tuning strategies of genomic foundation models within CREsted. Finally, we train a model on a zebrafish development atlas and use this to design and in vivo validate cell-type-specific enhancers. For varying datasets, we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room AD | Genomics, epigenomics, and genome editing | Liquid biopsies reveal dual compartments of cancer risk from tumor and host-derived mutations | Federica Malighetti | Federica Malighetti, Ivan Civettini, Andrea Aroldi, Alberto Maria Villa, Matteo Villa, Luca Sala, Nicoletta Cordani, Massimiliano Cadamuro, Diego Cortinovis, Rocco Piazza, Luca Mologni, Daniele Ramazzotti |
Circulating tumor DNA (ctDNA) and clonal hematopoiesis of indeterminate potential (CHIP) are two
biologically distinct sources of somatic mutations detectable in blood. While ctDNA captures tumor-intrinsic
alterations, CHIP arises from age-related hematopoietic clones and is often considered back-ground noise.
Here, we conduct a large-scale, tumor-type-resolved analysis of over 9,000 patients with CHIP data and 1,500
patients with ctDNA data across solid tumors profiled at Memorial Sloan Kettering Cancer Center. Our results reveal that CHIP and ctDNA mutations exhibit non-overlapping, clinically meaningful signals. CHIP mutations, particularly in DNA damage response and epigenetic regulators (e.g., PPM1D, CHEK2, ATM, TP53, ASXL1), are associated with worse overall survival, increased metastatic potential, and site-specific dissemination. ctDNA mutations in canonical oncogenic drivers (e.g., TP53, EGFR, KRAS, STK11) reflect tumor aggressiveness and correlate with poor prognosis and metastasis across multiple cancer types. Joint modeling in lung adenocarcinoma confirms the independent prognostic contributions of both compartments. Additionally, longitudinal clonal analysis links specific CHIP mutations to the emergence of hematologic malignancies under therapeutic pressure. These findings support a dual-compartment model of liquid biopsy, in which tumor- and host-derived mutations jointly inform on cancer risk, progression, and metastatic behavior. Integrating both compartments may enhance the clinical utility of blood-based biomarkers in oncology. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room AD | Genomics, epigenomics, and genome editing | Micropeptides encoded by lncRNAs associated with cancer progression reveal novel immunogenic epitopes | Stav Zok | Stav Zok, Michal Linial |
Motivation: Long non-coding RNAs (lncRNAs) regulate gene expression, chromatin organization, and cellular signaling. Recent studies indicate that ~20% of the ~36,000 human lncRNA genes harbor small open reading frames (sORFs) capable of producing micropeptides (MPs), whose functions remain largely unknown. Whether these peptides contribute to the cancer immunopeptidome is largely unexplored. Results: We systematically analyzed lncRNAs with strong experimental and computational evidence of MP-encoding potential (~13% of the initial MP collection). Using The Cancer Genome Atlas (TCGA), we identified 2,606 high-confidence lncRNA-derived MPs encoded by 647 genes across 16 cancer types. We then focused on 501 MPs from 124 lncRNA genes whose expression changes significantly across tumor stages and metastatic transitions, representing cancer transitional lncRNAs (Tr-lncRNAs). Dipeptide composition and conservation analyses showed that these MPs differ from a size-matched human coding proteome, supporting their potential as neoantigens. All possible 9-mer peptides were evaluated for predicted binding to prevalent European HLA class I alleles. Approximately 60% of Tr-lncRNA genes and 184 (37%) of derived peptides exhibited strong predicted HLA binding. Peptides from XIST, PCAT7, PVT1, HAND2-AS1 showed broad HLA coverage. Notably, TTN-AS1, encoded an MP (79 aa) generated 33 predicted distinct epitopes spanning all 27 HLA alleles. Our analysis identifies lncRNA-derived MPs as a previously underexplored source of potential cancer neoantigens, highlighting their promise as biomarkers and targets for immunotherapy. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room AD | Genomics, epigenomics, and genome editing | Modeling time-varying genetic effects on binary disease risk via functional Mendelian Randomization | Nicole Fontana | Nicole Fontana, Piercesare Secchi, Emanuele Di Angelantonio, Francesca Ieva |
Motivation: Genome-wide association studies have identified thousands of genetic variants associated with
complex traits, establishing Mendelian Randomization (MR) as a powerful framework for causal inference using
variants as natural experiments. However, existing MR methods treat causal effects as static, rely on
cross-sectional exposure measurements, and ignore how genetic predispositions to disease operate dynamically
across the life course. Recovering age-specific causal effect functions from longitudinal data requires
combining functional data representations of exposure trajectories with instrumental variable estimation
strategies suitable for binary disease endpoints, a methodological gap that has remained unaddressed. Results: We develop a functional MR framework for binary outcomes that integrates Functional Principal Component Analysis with Two-Stage Residual Inclusion (2SRI), ensuring consistent estimation under the nonlinear logistic link function that renders standard instrumental variable estimators inconsistent. Simulations across different causal effect trajectory shapes, varying measurement densities, and varying instrument strengths demonstrate accurate recovery of time-varying genetically predicted effects with minimal bias. Applied to UK Biobank data, the framework identifies an age-specific causal effect of genetically predicted body mass index on type 2 diabetes risk concentrated in early mid-adulthood and progressively attenuating thereafter. Concordance between the proposed 2SRI estimator applied to type 2 diabetes and the established continuous-outcome functional MR estimator applied to the paired glycated haemoglobin marker in the same cohort provides indirect empirical support for the validity of the proposed approach. Availability and implementation: The method is implemented in the R package mvfmr, with a full tutorial vignette. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room BC | Genomics, epigenomics, and genome editing | BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data | Marta Lemanczyk | Marta Lemanczyk, Lucas Kock, Johanna Schlimme, Nadja Klein, Bernhard Renard |
Biological data is often high-dimensional, noisy, and governed by complex interactions among sparse signals.
This poses major challenges for interpretability and reliable feature selection. Tasks such as identifying
motif interactions in genomics exemplify these difficulties, as only a small subset of biologically relevant
features are typically active. While statistical approaches often result in more interpretable models, deep
learning models are effective at modeling complex interactions and accurate predictions, yet their black-box
nature limits interpretability. We introduce BaGGLS, a flexible and interpretable probabilistic binary regression model designed for high-dimensional biological inference involving feature interactions. BaGGLS incorporates a Bayesian group global-local shrinkage prior, aligned with the group structure of interaction terms. This prior encourages sparsity while retaining interpretability, helping to isolate meaningful signals and suppress noise. To enable scalable inference, we employ a partially factorized variational approximation that captures posterior skewness and supports efficient learning even in large feature spaces. In simulations, we compare BaGGLS to frequentist probit regressions (unconstrained and with L1-penalty) as well as a probit model with Markov Chain Monte Carlo (MCMC) sampling under a horseshoe prior. We can show that BaGGLS outperforms the other methods in interaction detection and is much faster than MCMC sampling under a horseshoe prior. We also demonstrate the usefulness of BaGGLS for interaction discovery from motif scanner outputs (e.g., FIMO) and noisy attribution scores from deep learning models. This shows that BaGGLS is a promising approach for uncovering biologically relevant interaction patterns, with potential applicability across various high-dimensional tasks in computational biology. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room BC | Genomics, epigenomics, and genome editing | PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data | Alejandro Correa Rojo | Alejandro Correa Rojo, Yves Moreau, Gökhan Ertaylan |
Synthetic genomic data promises broader data access, but unresolved privacy risks remain a major concern.
Existing evaluations often rely on similarity-based metrics that measure proximity between real and
synthetic genomes, overlooking additional mechanisms through which genomic information may leak. We
introduce PRISM-G, a model-agnostic framework that quantifies privacy exposure in synthetic genomic data
across three complementary components: proximity to real genomes in genetic-coordinate space, replay of
familial or population-structure patterns, and trait-linked exposure through rare variants and
membership-inference signals. These components are normalized and combined through a risk-averse aggregation
into a single 0–100 PRISM-G score. By pairing PRISM-G with downstream utility metrics, the framework also
enables analysis of privacy–utility trade-offs across generative models. We evaluated PRISM-G on synthetic
cohorts generated by a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), and a
logic-based SAT solver (Genomator). Our results show that privacy vulnerabilities arise along different axes
across models and marker densities, demonstrating that a single similarity-based metric is insufficient to
characterize genomic privacy risk. The source code of PRISM-G is available at
https://github.com/alejocrojo09/prismg . |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room BC | Genomics, epigenomics, and genome editing | Knowledge-Guided Learning with Curated Prior Genetic Biomarkers for Robust Model Interpretation | Beomsu Baek | Beomsu Baek, Eunyoung Jang, Sai Phani Parsa, Youngsoon Kim, Mingon Kang |
Motivation: Knowledge-guided learning offers effective and robust model training strategies in data-scarce
settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and
interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning,
which can limit robust model interpretability, particularly in high-dimensional settings with limited size
samples. In computational biology, knowledge-guided learning has primarily leveraged network- and
structural-based knowledge, leading to biologically interpretable representations and enhanced predictive
performance compared to conventional approaches. However, curated biomarkers, one of the most accessible
forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results: In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation: The open-source is publicly available at: https://github.com/datax-lab/BioExPL. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room BC | Genomics, epigenomics, and genome editing | Nerpa 2: probabilistic linking of biosynthetic gene clusters to nonribosomal peptides | Ilia Olkhovskii | Ilia Olkhovskii, Aleksandra Kushnareva, Azat Tagirdzhanov, Alexey Gurevich |
Motivation: Nonribosomal peptides (NRPs) are bioactive microbial metabolites with high pharmaceutical
potential. Although genome mining enables large-scale detection of biosynthetic gene clusters (BGCs)
predicted to encode NRPs, reliably linking these clusters to their chemical products remains challenging due
to the flexible and heterogeneous organization of NRP assembly pathways. Results: We present Nerpa 2, a probabilistic framework for accurate and scalable linking of NRP BGCs to candidate chemical structures. The method represents assembly lines as hidden Markov models (HMMs) that capture uncertainty and alternative biosynthetic routes. On curated datasets of experimentally validated BGC–product pairs, our tool outperforms existing methods in linking accuracy and pathway reconstruction. When applied to large genome mining datasets, Nerpa 2 efficiently identifies BGCs likely associated with known compounds and highlights potential producers of novel chemistry. Availability and implementation: Nerpa 2 is freely available at https://github.com/gurevichlab/nerpa. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room BC | Genomics, epigenomics, and genome editing | Episode Clustering in Phylogenetic Networks | Pawel Gorecki | Pawel Gorecki, Agnieszka Mykowiecka, Jarosław Paszek |
The classical duplication episode clustering (EC) model introduced by Guigo et al. in the 1990s provides a
foundational approach for inferring genomic duplication events crucial to understanding genome evolution.
This model clusters single gene duplications from a collection of gene trees at locations in the species
tree to minimize the total number of such locations, called duplication episodes. Here, we introduce NetEC, a novel extension of this problem to phylogenetic networks. To solve NetEC, we first develop a polynomial-time dynamic programming (DP) algorithm for testing whether a given set of network nodes can serve as episode locations. We then propose a main inference algorithm that utilizes this DP component to optimize the episode count; while the feasibility test runs in polynomial time, the full optimization has exponential worst-case complexity, and an optional heuristic mode is provided for larger instances. We also propose an extended episode analysis procedure that identifies additional genomic duplication candidates below reticulation nodes, complementing the main algorithm by resolving potential upward clustering of duplications induced by reticulation. We evaluate our method on simulated data and on an empirical Pandanales dataset comprising over 29,000 gene trees, demonstrating exact and accurate inference of genomic duplication events even in the presence of multiple reticulations. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room BC | Genomics, epigenomics, and genome editing | ARGformer: learning on ancestral recombination graphs with transformers | David Bonet | David Bonet, Cole Shanks, Marçal Comajoan Cara, Jordi Abante, Alexander Ioannidis | Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through recombination and shared lineages, have made it possible to reconstruct genome-wide genealogies for large cohorts, but it remains difficult to summarize and use this information for population genetic analyses. We present ARGformer, an encoder-only transformer that learns context-dependent embeddings with a self-supervised masked objective finetuned with contrastive learning for downstream retrieval tasks. We train ARGformer on genealogies from coalescent simulations and on genealogies inferred from ancient and present-day Homo sapiens genomes. Using only these learned embeddings, without access to genotype matrices, ARGformer captures patterns of global population structure and supports ancestry inference through clustering and nearest-neighbor retrieval. On genealogies that include archaic hominins, ARGformer can highlight Denisovan-derived segments in Oceanian genomes and reveals Oceanian-like ancestry in South American Indigenous populations. |
| 2026-08-31 | 10:00:00 | 11:00:00 | Room ABCD | Distinguished keynotes | Opening ceremony | Christophe Dessimoz, Simone de Montmollin, Audrey Leuba, Anne Hiltpold, Guy Parmelin |
10h-10h05: Christophe Dessimoz, Executive Director, SIB Swiss Institute of Bioinformatics 10h06-10h11: Simone de Montmollin, National Councillor, President of the Foundation Council of the SIB 10h12-10h17: Audrey Leuba, Rector of the University of Geneva 10h18-10h25: Anne Hiltpold, President of the Geneva Canton, State Councillor 10h26-10h36: Guy Parmelin, President of the Swiss government 10h37-11h00: Dance performance "If data could breathe” |
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| 2026-08-31 | 11:00:00 | 12:00:00 | Room ABCD | Distinguished keynotes | Jeremy Farrar | Jeremy Farrar | Jeremy Farrar is the Assistant Director-General for Health Promotion, Disease Prevention and Care at the World Health Organization (WHO). He previously served as WHO Chief Scientist (2023–2025) and was Director of the Wellcome Trust (2013–2023). Before that, he led the Oxford University Clinical Research Unit at the Hospital for Tropical Diseases in Ho Chi Minh City (1996–2013). Trained in neurology and infectious diseases, he holds a PhD in immunology from the University of Oxford and is a Fellow of the UK Academy of Medical Sciences, the US National Academies, and the European Molecular Biology Organization. He has received major international honours, including the Queen’s 2018 New Year knighthood and the Japanese Order of the Rising Sun (2020). | |
| 2026-08-31 | 11:00:00 | 12:00:00 | Distinguished keynotes | Introducing the ELIXIR Programme | Peter Maccallum | |||
| 2026-09-01 | 08:45:00 | 09:00:00 | Room AD | Distinguished keynotes | In Memoriam | Frédérique Lisacek, Knut Reinert, Christian von Mering, Paul Thomas |
A tribute to members of our scientific community Honouring the memory of: Amos Bairoch Peer Bork Phil Bourne Craig Venter |
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| 2026-09-01 | 09:00:00 | 10:00:00 | Room AD | Distinguished keynotes | Anna-Sapfo Malaspinas - Tracing Human Migrations with Ancient DNA | Anna-Sapfo Malaspinas | Anna-Sapfo Malaspinas | Anna-Sapfo Malaspinas is an associate professor in the Department of Computational Biology at the University of Lausanne and a Group Leader at the SIB Swiss Institute of Bioinformatics, Switzerland. She studied biology and physics at the University of Geneva and obtained her PhD in population genetics and computational biology from UC Berkeley under Montgomery Slatkin. After a postdoc with Eske Willerslev in Copenhagen, she returned to Switzerland as an Assistant Professor at the University of Bern. Her research focuses on theoretical population genetics and the analysis of ancient DNA. Her lab develops computational tools and applies large-scale genomic analyses to understand how humans colonized the world and adapted to new environments. |
| 2026-09-01 | 14:00:00 | 15:00:00 | Room AD | Distinguished keynotes | Stephen Quake - Understanding the Mysteries of the Cell: How Do Many Cell Types Arise from One Genome? | Stephen Quake | Stephen Quake | Stephen Quake is the Lee Otterson Professor of Bioengineering and Applied Physics at Stanford University. He has received numerous awards for his contributions to science and is one of only two dozen scientists elected to all three National Academies. Previously he was the Head of Science at Chan Zuckerberg Initiative (2022-2025), founding co-president of the Chan Zuckerberg Biohub (2016-2022), investigator of the Howard Hughes Medical Institute (2006-2016), and Professor at the California Institute of Technology (1996-2005). |
| 2026-09-02 | 14:30:00 | 15:30:00 | Room AD | Distinguished keynotes | Aleksandra Walczak - Learning About Immune Response from Large-Scale Data | Aleksandra Walczak | Aleksandra Walczak | Aleksandra Walczak received her PhD in physics at the University of California, San Diego, working on models of stochastic gene expression. After a graduate fellowship at Kavli Institute for Theoretical Physics (KITP), she was a Princeton Center for Theoretical Science Fellow, focusing on applying information theory to signal processing. Currently she is a Centre National de la Recherche Scientifique (CNRS) research director at the Ecole Normale Superieure in Paris, interested in collective behavior, fly development and statistical descriptions of the immune system. She was awarded the “Grand Prix Jacques Herbrand de l’Académie des sciences" in 2014, the bronze medal of CNRS in 2015, the American Physical Society Fellowship, the Prix Jean Ricard of the French Physics Society in 2021 and the silver medal of CNRS in 2024. She was elected to the French National Academy of Sciences in 2024. |
| 2026-09-02 | 15:30:00 | 16:00:00 | Room AD | Distinguished keynotes | Conference closing | Christophe Dessimoz | ||
| 2026-08-31 | 13:30:00 | 14:30:00 | Room BC | Proteins and structural biology | Stoic: Fast and accurate protein stoichiometry prediction | Daniil Litvinov | Daniil Litvinov, Lorenzo Pantolini, Peter Å krinjar, Gerardo Tauriello, Caitlyn McCafferty, Benjamin Engel, Torsten Schwede, Janani Durairaj |
Motivation: Protein complexes are central to cellular function, but experimental determination of their
structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry - the number of copies of each protein entity within a complex. Current approaches rely on computationally expensive brute-force methods that run structure prediction on multiple stoichiometry combinations, often with limited accuracy. Results: We introduce Stoic, a method that uses protein language model embeddings to predict protein complex stoichiometry. Our approach learns to identify interface residues that participate in protein-protein interactions, rather than relying on global sequence features. By integrating these interface-aware embeddings into a graph neural network, Stoic achieves fast and accurate stoichiometry prediction for both homomeric and heteromeric targets. Availability: Source code for inference and training along with web versions are available in the repository at https://github.com/PickyBinders/stoic. Contact: janani.durairaj@unibas.ch |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room BC | Proteins and structural biology | Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS | Luiz Felipe Piochi | Luiz Felipe Piochi, Di Tang, Johan Malmström, Yasaman Karami, Hamed Khakzad | Protein-protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. Here, we introduce ppIRIS (protein-protein Interaction Regression via Iterative Siamese networks), a lightweight deep learning framework that integrates evolutionary and structural embeddings to predict PPIs directly from sequence. Evaluated on multi-species benchmarks, ppIRIS achieves state-of-the-art accuracy while enabling proteome-wide screening in minutes. Trained on curated bacterial datasets and applied to the Group A Streptococcus (GAS) proteome, ppIRIS identified functional clusters associated with virulence pathways, such as nutrient transport, stress response, and metal scavenging. Extending to cross-species prediction, ppIRIS recovered 56.2% of known GAS-human plasma interactions with enrichment in complement, coagulation, and protease inhibition pathways. Experimental validation confirmed novel predictions, demonstrating the applicability of ppIRIS for systematic discovery of bacterial and cross-species PPIs. The model together with a Google Colaboratory is freely available at github.com/lupiochi/ppIRIS. |
| 2026-08-31 | 13:30:00 | 14:30:00 | Room BC | Proteins and structural biology | PreFold-dG: estimating binding affinity of protein–protein interaction from intermediate representations of protein folding model | Sungjoon Park | Sungjoon Park, Soorin Yim, Dongyun Kim, Kiwoong Yoo, Doyeong Hwang, Kyungwook Lee, Jongseong Jang, Kiyoung Kim |
Motivation: Binding affinity governs how proteins interact and underlies essential biological processes.
Computational approaches have been developed to simulate and predict protein binding, but the scarcity of
high-quality data has imposed significant constraints. One consequence is that most methods focus on
predicting mutational changes in binding affinity (ΔΔG), rather than binding affinity (ΔG) itself. This
practice risks overfitting to skewed data distributions, limiting the generalizability of predictions.
Recent advances in protein structure prediction have enabled computational modeling of protein conformations
in mass, providing rich structural information from which binding interactions can be largely explained.
However, leveraging these advances for effective prediction of binding affinity has yet to translate into
reliable predictions. Results: We present PreFold-dG, a model that estimates binding affinities of protein complexes utilizing intermediate embeddings from Boltz-2, an open-source foundation model for protein structure prediction. Our approach aggregates residue-level information weighted by interresidue distance, and predicts ΔG directly rather than its derivative, ΔΔG. PreFold-dG achieved state-of-the-art performance on well-established binding affinity prediction benchmarks and demonstrated robustness on independent test sets. Ablation studies suggest that all intermediate embeddings are utilized in the prediction, whereas their contributions to modeling ΔΔG and ΔG vary. We further validated our model through case studies on real-world broadly neutralizing antibody data with evolutionary relevance. Availability: https://github.com/LGAI-Research/PreFold-dG. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room EF | Proteins and structural biology | From transporter to motor: Evolutionary and structural insights into the emergence of prestin's area-motor activity in mammals | Raul Araya-Secchi | Nicolas Fuentes-Ugarte, Tiaren Ruiz-Rojas, Felipe GarcÃa-Olave, Alvaro Ruiz-Fernandez, Jose Antonio Gatare, Victor Castro-Fernandez, Raul Araya-Secchi | Prestin, a member of the SLC26A family, is essential for the electromotility of mammalian outer hair cells, converting voltage changes into mechanical work. In contrast, nonmammalian orthologues function as anion transporters. To investigate the molecular and structural basis of this functional divergence, we performed ancestral sequence reconstruction (ASR) of prestin across vertebrates, followed by structural modeling using AlphaFold2-multimer and molecular dynamics simulations. We identified more than 200 amino acid substitutions along the lineage that lead to placental mammals, with early substitutions concentrated in the transmembrane domain (TMD) and late substitutions clustering in the STAS domain, particularly in the intervening sequence (IVS). Structural modeling and simulation revealed that early substitutions modulate protein–lipid interactions and interhelical contacts. In placental mammals, the IVS-loop adopts a distinct conformation that places a negatively charged patch near the chloride access pathway, potentially affecting the ion dynamics and voltage responsiveness. These structural transitions occurred without major rearrangements of the global fold of prestin, supporting a notion in which the novel function evolved through distributed substitutions within a conserved scaffold. Our findings illustrate how molecular exaptation, and incremental structural remodeling enabled the repurposing of an ancestral anion transporter into a voltage-sensitive area-motor, providing a framework for understanding the molecular evolution of complex biophysical traits central to auditory neuroscience. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room EF | Proteins and structural biology | usiGrabber: Automating the curation of proteomics spectra data at scale, making large datasets ready for use in machine learning systems | Konstantin Ketterer | Georg Auge, Matthis Clausen, Konstantin Ketterer, Jacob Schaefer, Nils Schmitt, Tom Altenburg, Yannick Hartmaring, Hendrik Raetz, Christoph N. Schlaffner, Bernhard Y. Renard |
Motivation: An unprecedented amount of mass spectrometry-based proteomics data is publicly available through
repositories such as the PRoteomics IDEntifications Database (PRIDE), and the field is increasingly
leveraging machine learning approaches. However, the available data is not ready to be reused in a scalable
way beyond the original acquisition purpose. Existing machine learning models commonly rely on a few
manually curated datasets that require deep domain expertise and tedious technical work to construct.
Importantly, these datasets have not been updated in recent years, so that newly published data remains
inaccessible. We present usiGrabber, a scalable framework for assembling large proteomic datasets.
usiGrabber is designed around portability and extensibility. It extracts spectra identification data from
mzIdentML files, stores additional project-level metadata retrieved through the PRIDE API, indexes raw
spectra using Universal Spectrum Identifiers (USIs), and offers download utilities to retrieve spectra data
at scale. Results: Within 49 hours, we parsed over 800 million peptide spectrum matches and corresponding USIs from over 1,200 projects. As a proof of concept, we used usiGrabber to construct a phosphorylation-specific training dataset of nearly 11 million spectra in under two days and used it to retrain a binary phosphorylation classifier based on the AHLF model architecture. With a balanced accuracy of 0.78, our model achieves comparable performance to the original model on an independent test set, showing that automated data extraction is an alternative to manual curation of static datasets. Availability: All code is available at https://github.com/usiGrabber/usiGrabber; the data is available at https://zenodo.org/records/18853258. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room EF | Proteins and structural biology | Structure-Conditioned Self-Supervised Learning of Residue Interaction Constraints in Protein Kinases for Variant Interpretation | Shakiba Fadaei | Shakiba Fadaei, Fanny Krebs, Vincent Zoete |
Motivation: Protein kinases are key regulators of cellular signaling and are frequently implicated in human
diseases. Although kinase domains are structurally conserved, predicting the effects of amino acid
substitutions remains challenging as mutations often introduce subtle structural pertur-bations that are not
captured by sequence-based or evolutionary methods. Existing supervised approaches further rely on
pathogenicity annotations that are inconsistent across databases, thereby motivating the development of
structure-based, label-independent frameworks for mutation effect prediction. Results: We present a structure-based method using SE(3)-transformers to learn residue compati-bility with the local structural environment from experimentally resolved kinase 3D structures. Pro-teins are represented as atom-level graphs with physicochemical descriptors derived from the CHARMM force field and spatial connectivity. The model is trained on two self-supervised tasks given local structural context: masked residue atom reconstruction and masked residue classifica-tion. This formulation enables learning of geometric and physicochemical constraints without relying on pathogenicity labels. Evaluation using reconstruction loss, residue prediction accuracy, and comparison with BLOSUM substitution patterns indicate that the model captures biologically mean-ingful relationships between residue identity and 3D structural context. We interpret the scores as-signed to alternative amino acids as measures of structural fitness, where low-scoring residues are hypothesized to be less compatible with the local environment and more likely to induce deleterious effects on protein structure and activity. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room BC | Proteins and structural biology | De Novo Epitope-Specific Antibody Design via Time-Dependent Guidance | Yunji Kim | Yunji Kim, Minkyung Baek |
Motivation: De novo antibody design requires jointly determining the global binding orientation and shaping
flexible CDR loops to engage a target epitope. Diffusion-based approaches such as RFantibody are capable of
this joint task but frequently produce severe steric clashes requiring extensive post-hoc filtering.
Flow-based methods such as IgFlow and FlowDesign offer more stable generation but remain restricted to
pre-aligned frames, precluding true de novo design. Achieving structural integrity and epitope specificity
simultaneously under this setting remains an open challenge. Results: We propose TiDE-Ab, a conditional
SE(3) Flow Matching framework for de novo epitope-specific antibody design. By conditioning on unpaired
antigen and antibody structures without any pre-aligned frame, TiDE-Ab inherits the structural stability of
flow matching while enabling global binding pose search from scratch. To further improve epitope targeting,
we introduce Time-Dependent Classifier-Free Guidance (TD-CFG), which replaces static conditioning with an
adaptive schedule: strong guidance early to establish the global binding pose, followed by gradual
relaxation for precise local CDR refinement. On 55 non-redundant benchmark complexes, TiDE-Ab outperforms
RFantibody with higher epitope recall (0.93 vs. 0.88) and over 95% fewer steric clashes, with TD-CFG further
improving backbone designability as measured by scRMSD and iPAE. In therapeutic case studies on TGF- and
IL-17A, TiDE-Ab reproduced the binding profiles of clinical antibodies across isoform-selective and
cross-reactive epitopes, where RFantibody consistently failed to produce viable candidates. Availability: Source code, data, and pre-trained models are available at https://github.com/SNU-CSSB/TiDE-Ab |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room BC | Proteins and structural biology | ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains | Joseph Ng | Dongjun Guo, Deborah Dunn-Walters, Franca Fraternali, Joseph Ng | The development of stable antibodies formed by compatible heavy (H) and light (L) chain pairs is crucial in both in vivo maturation of antibody-producing cells and ex vivo designs of therapeutic antibodies. We present ImmunoMatch, a machine-learning framework trained on paired H and L sequences from human B cells to identify molecular features underlying chain compatibility. ImmunoMatch distinguishes cognate from random H–L pairs and captures differences associated with lambda and kappa light chains, reflecting B cell selection mechanisms in the bone marrow. We apply ImmunoMatch to reconstruct paired antibodies from spatial VDJ sequencing data and study the refinement of H–L pairing across B cell maturation stages in health and disease. We find further that ImmunoMatch is sensitive to sequence differences at the H-L interface. These insights provide a computational lens into the broader biological principles governing antibody assembly and stability. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room BC | Proteins and structural biology | Mitigating Goodhart's Law in Epitope-Conditioned TCR Generation Using Plug-and-Play Reward Designs | Fredo Guan | Pengfei Zhang, Xiaoyi He, Fredo Guan, Hao Mei, Gloria Grama, Seojin Bang, Heewook Lee | Epitope-conditioned T cell receptor (TCR) generation extends protein language modeling to therapeutic design of TCRs, with recent works demonstrating improved performance using reinforcement learning (RL) post-training utilizing surrogate models. However, such frameworks suffer from Goodhart's Law: optimizing imperfect surrogate rewards leads to reward hacking and biologically invalid sequences. We present a plug-and-play reward design framework that mitigates reward hacking without altering the generator and its training pipeline. The framework integrates multiple complementary strategies: heuristic biological priors to reject degenerate sequences, model ensembling to reduce per-model biases, and binding-specificity objectives to suppress cross-epitope overfitting and promote target-focused binding behavior. When applied to the RL-based fine-tuning process of TCR generators, these rewards stabilize optimization, preserve sequence diversity, and yield generations that are more biologically aligned with real TCR sequences. The resulting models produce more authentic epitope-specific receptors, demonstrating that our Goodhart-resistant reward design substantially improves reliability and controllability in biologically grounded sequence generation. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room EF | Proteins and structural biology | Optimizing Protein Design Through Uncertainty-weighted Steering of Protein Language Models | Byung-Jun Yoon | Alif Bin Abdul Qayyum, Yingtong Zhou, Xiaoning Qian, Byung-Jun Yoon |
Motivation: Protein Language Models (PLMs) have revolutionized protein engineering by capturing the evolutionary constraints inherent in natural protein sequences. However, precisely steering these models to engineer novel proteins with targeted functionalities remains challenging due to the inherent difficulty in modifying their latent representations considering the design objectives. Recently, activation steering of PLMs has emerged as a potent, training-free intervention for directing PLM outputs. However, the requirement for high-quality labeled datasets limits its application. In data-scarce or out-of-distribution (OOD) regimes, researchers must rely on surrogate models for label prediction; however, deterministic surrogates fail to account for the underlying uncertainty, often yielding steering vectors that result in suboptimal protein design. Results: To address this, we propose PROSOUNDS (PROtein Sequence Optimization through UNcertainty-weighteD Steering), a PLM-based protein design framework that integrates uncertainty quantification into the activation steering logic. By weighting the steering activation calculation process based on uncertainty estimates of the surrogate predictions, PROSOUNDS enables robust protein optimization through precise mutational design even in the absence of ground-truth labels. Comprehensive performance evaluation reveals that PROSOUNDS consistently outperforms deterministic alternatives across three different protein property optimization tasks. Availability and Implementation: The datasets and implementation code for PROSOUNDS are available at https://github.com/TeresaZhouTamu/PRO-SOUNDS. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room EF | Proteins and structural biology | STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings | Mehmet Efe Akça, Gökçe UludoÄŸan, Arzucan Ozgur, Inci BaytaÅŸ |
Motivation: Accurate prediction of protein function is essential for elucidating molecular mechanisms and
advancing biological and therapeutic discovery. Yet experimental annotation lags far behind the rapid growth
of protein sequence data. Computational approaches address this gap by associating proteins with Gene
Ontology (GO) terms, which encode functional knowledge through hierarchical relations and textual
definitions. However, existing models often emphasize one modality over the other, limiting their ability to
generalize, particularly to unseen or newly introduced GO terms that frequently arise as the ontology
evolves, and making the previously trained models outdated. Results: We present STAR-GO, a Transformer-based framework that jointly models the semantic and structural characteristics of GO terms to enhance zero-shot protein function prediction. STAR-GO integrates textual definitions with ontology graph structure to learn unified GO representations, which are processed in hierarchical order to propagate information from general to specific terms. These representations are then aligned with protein sequence embeddings to capture sequence–function relationships. STAR-GO achieves state-of-the-art performance and superior zero-shot generalization, demonstrating the utility of integrating semantics and structure for robust and adaptable protein function prediction. Availability: Code and pre-trained models are available at https://github.com/boun-tabi-lifelu/stargo |
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| 2026-09-01 | 11:45:00 | 12:45:00 | Room EF | Proteins and structural biology | RIBEX: Predicting and Explaining RNA Binding Across Structured and Intrinsically Disordered Regions (IDR)-rich Proteins | Samuele Firmani | Samuele Firmani, Felix Steinbauer, Gjergji Kasneci, Marc Horlacher, Annalisa Marsico |
Motivation: RNA-binding proteins (RBPs) regulate post-transcriptional processes, yet many remain
undiscovered because RNA-binding activity often occurs outside canonical RNA-binding domains (RBDs),
including within intrinsically disordered regions (IDRs) or through protein complexes. Computational methods can help identify novel RBPs, but approaches relying solely on sequence-derived features or ignoring the cellular interaction context are limited in capturing the complexity of RNA-binding behavior. To date, no framework rigorously integrates both sequence information and protein interaction context for RBP prediction. Results: We introduce RIBEX, a multimodal framework that combines protein language model (pLM) embeddings with protein interactome topology to improve RBP prediction and interpretation. Specifically, we integrate sequence representations with graph-derived positional encodings (PE) from the human STRING protein-protein interaction (PPI) network. PE are computed using Personalized PageRank, reduced with principal component analysis, and fused with pooled sequence embeddings through FiLM conditioning, while Low-Rank Adaptation (LoRA) enables parameter-efficient task adaptation. Across both an annotation-based benchmark and experimental RNA Interactome Capture (RIC) dataset, PE consistently improves predictive performance, indicating that interactome topology provides complementary information beyond sequence features. LoRA adaptation of ESM2-650M further yields larger gains than simply scaling frozen backbone size. RIBEX outperforms state-of-the-art methods such as RBP-TSTL and HydRA, particularly on challenging subsets including proteins lacking canonical RBDs and those enriched in IDRs. For interpretability, we combine sequence-level computational alanine scanning with network-level positional-encoding ablation and inverse-PCA mapping, recovering known RNA-binding domains, IDR-associated contributions, and functional interactome communities linked to RBP predictions. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room EF | Proteins and structural biology | Reconstructing Atomistic Biomolecular Dynamics from High-Speed Atomic Force Microscopy Data | Florence Tama | Florence Tama |
Understanding biomolecular function requires insight into both structure and dynamics. High-speed atomic
force microscopy (HS-AFM) enables direct visualization of biomolecular motions at the single-molecule level
under near-physiological conditions, producing two-dimensional (2D) topographical images in real time.
However, its limited resolution prevents direct determination of three-dimensional (3D) atomic structures,
necessitating computational approaches to bridge this gap. We present NMFF-AFM, a flexible fitting method that integrates normal mode analysis with AFM image fitting to reconstruct 3D atomic models from HS-AFM data. The method leverages low-frequency normal modes to capture large-scale, functionally relevant conformational changes while deforming a known protein structure to match AFM images. Validation using simulated AFM data from proteins with known dynamics demonstrated robustness and reliability. To facilitate broader use, NMFF-AFM was implemented in the BioAFMviewer platform, creating a streamlined workflow from raw HS-AFM movies to atomistic modeling and visualization. This user-friendly integration enables direct analysis of experimental data across diverse systems. Applications to a single protein domain, a multi-protein complex, and a megadalton-scale filament highlight the method's versatility. Furthermore, applying NMFF-AFM to time-resolved HS-AFM data allowed reconstruction of atomistic molecular movies. Overall, NMFF-AFM and BioAFMviewer provide an efficient framework for extracting atomistic dynamics from HS-AFM data, enabling large-scale analysis and advancing mechanistic understanding of biomolecular function. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room EF | Proteins and structural biology | Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes | Domenico Scaramozzino | Domenico Scaramozzino, Byung Ho Lee, Laura Orellana | Protein conformational changes are the cornerstone of biological function. While conformers captured experimentally represent metastable states, the pathways connecting them have been elusive for experiments and simulations alike. Nowadays, cryogenic Electron Microscopy is providing rich structural data on proteins trapped in different states for increasingly large systems, but these are out of scope for most computational methods which exhibit an N^2 dependence on size. Based on our previous eBDIMS algorithm, here we present eBDIMS2, an optimized version with quasi-linear size dependence, able to simulate on a desktop computer particularly complex transitions for megadalton protein assemblies, like the rotary motion of ATP synthases. Not only eBDIMS2 pathways spontaneously visit experimental intermediates but also overlap with enhanced and microsecond Molecular Dynamics simulations requiring extensive supercomputing resources. By integrating Elastic Networks with Brownian Dynamics, eBDIMS2 allows an unprecedented exploration of conformational changes of sub-mesoscopic systems previously inaccessible. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room EF | Proteins and structural biology | Accurate identification and mechanistic evaluation of pathogenic missense variants with Rhapsody-2 | Ivet Bahar | Anupam Banerjee, Anthony Bogetti, Ivet Bahar | Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basis of diseases/disorders and designing rational therapies. We introduce here Rhapsody-2, a machine learning tool for discriminating pathogenic and neutral SAVs, significantly expanding on a precursor limited by the availability of structural data. With the advent of AlphaFold2 as a powerful tool for structure prediction, Rhapsody-2 is trained on a significantly expanded dataset of 117,525 SAVs corresponding to 12,094 human proteins reported in the ClinVar database. Adopting a broad set of descriptors composed of sequence evolutionary, structural, dynamic, and energetics features in the training algorithm, Rhapsody-2 achieved an AUROC of 0.94 in 10-fold cross-validation when all SAVs of a particular test protein (mutant) were excluded from the training set. Benchmarking against a variety of testing datasets demonstrated the high performance of Rhapsody-2. While sequence evolutionary descriptors play a dominant role in pathogenicity prediction, those based on structural dynamics provide a mechanistic interpretation. Notably, residues involved in allosteric communication and those distinguished by pronounced fluctuations in the high-frequency modes of motion or subject to spatial constraints in soft modes usually give rise to pathogenicity when mutated. Overall, Rhapsody-2 provides an efficient and transparent tool for accurately predicting the pathogenicity of SAVs and unraveling the mechanistic basis of the observed behavior, thus advancing our understanding of genotype-to-phenotype relations. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room EF | Proteins and structural biology | Attracting Cavities 3.0: Faster and More Versatile Molecular Docking for the SwissDock Webserver | Ute F. Röhrig | Ute F. Röhrig, Marine Mathieu-Bugnon, Vincent Zoete |
Motivation: Molecular docking is a pillar of structure-based drug design and shows advantages in structure
prediction of small-molecule ligand–protein complexes over co-folding methods for novel ligands and novel
binding pockets. Here, we describe substantial improvements of our physics-based docking algorithm
Attracting Cavities, which is widely used through the SwissDock webserver. Results: AC 3.0 includes enhanced sampling features, new functionalities, and technical improvements. These lead to better sampling at lower execution times and higher versatility. Comparison with AutoDock Vina demonstrates better docking results on multiple test sets. Availability: AC 3.0 will be made freely accessible through the SwissDock webserver (www.swissdock.ch). |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room EF | Proteins and structural biology | PepGen: Conditional generation of peptides for MHC binding | Dani Korpela | Dani Korpela, Alexandru Dumitrescu, Martin Strazar, Rui Li, Ramnik Xavier, Daniel Graham, Harri Lähdesmäki |
Motivation: Peptide-MHC II binding drives adaptive immunity, yet discovery of novel binder peptides remains
challenging due to open binding grooves of MHC-II that accommodate variable-length peptides. While
discriminative models perform well, they are unfeasible for generation via enumeration due to vast peptide
space (20^13 ~ 8 * 10^16 for peptides of length 13 amino acids). Generative AI approaches could accelerate
binder design to enable vaccines targeted to particular MHC-II alleles or optimize other peptide chemical
properties. Results: We introduce PepGen, the first protein language model for MHC II peptide generation building on Generalized Language Modeling. PepGen conditions on alleles, arbitrary partial peptides including putative TCR- interacting motifs, and continuous binding affinity. Across multiple benchmarks including infilling and de novo generation, PepGen outperformed frequency sampling, Gibbs clustering, and autoregressive baselines. Adjusted log-probabilities enable good classification performance. Experimental validation confirmed that the SARS-CoV-2 peptide TEGALNTPKDHIGTR binding the HLA-DQA101:03-DQB106:03 allele can be redesigned to bind the HLA-DQA101:02-DQB105:02 allele. PepGen generated three TCR-motif-preserving binders gaining up to 70% of original MFI. Overall, PepGen provides scalable, motif-constrained MHC II peptide re-design and de novo generation, validated through thorough benchmarks and functional assays. Availability and implementation: Code and data is available at https://github.com/DaniTheOrange/PepGen |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room EF | Proteins and structural biology | Pocket-PROTACs: An Interpretable Pocket-Aware Deep Learning Framework for Predicting PROTAC-Induced Protein Degradation | Kai Chen | Kai Chen, Zhijian Huang, Yinbo Wang, Siyuan Shen, Jinmiao Song, Lei Deng |
Motivation: Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation by recruiting an E3
ubiquitin ligase to a protein of interest (POI) and forming a ternary complex. Despite their therapeutic
promise, rational PROTAC design remains challenging, as degradation efficacy depends on subtle and highly
structure-dependent interactions among the POI, the E3 ligase, and the bifunctional molecule. Results: We propose Pocket-PROTACs, a pocket-aware attention-based framework for predicting PROTAC-induced protein degradation from a triplet of POI, E3 ligase, and PROTAC. Pocket-PROTACs encodes protein sequences using a pre-trained protein language model and represents PROTACs with a geometry-aware graph neural network over an ensemble of three-dimensional conformers. Both POI–PROTAC and E3 ligase–PROTAC interactions are explicitly modeled through a residue–atom cross-attention mechanism that captures fine-grained interaction patterns. To improve model interpretability, we introduce a pocket-aware module that incorporates structural context to guide residue-level relevance estimation, enabling multi-level attribution analysis. Experiments on two benchmark datasets show that Pocket-PROTACs consistently outperforms fingerprint-based baselines and recent deep learning methods. The learned relevance maps highlight localized interaction patterns on both the POI and the E3 ligase that are qualitatively consistent with known pocket-level features. A case study on KLHDC2-engaging BET PROTACs further demonstrates that our model accurately predicts degradation behavior and provides biologically meaningful, attention-based interpretations, offering practical support for PROTAC design and experimental investigation. Availability: Source code and datasets are available at https://github.com/Adochew/Pocket-PROTACs. Contact: leideng@csu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room EF | Special track | Industry and innovation session | Sean Hill, Thomas Collin, Moustafa Houmani, Jon Manning, Nir Orpaz | Join a dedicated session showcasing scientific innovations and research advancements from industry. Invited speakers present recent technological developments and share perspectives on how industry is shaping the future of computational biology and the life sciences. | |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room 4 | Special track | Focus session: From data to discovery: expanding the frontiers of computational biology | Theo Sanderson, Angie Hinrichs, Nick Loman | Advances pioneered by computational biologists are redefining what is possible in data analysis, visualization, and collaboration. This session will highlight transformative approaches that have enabled analyses at unprecedented scale, from phylogenetics, to population-level genomics, and federated data infrastructures. Beyond their technical accomplishment, these tools can catalyze conceptual and cultural shifts: how can they reshape the questions we ask, the expectations of what we can achieve, and the next challenges we face. | |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room 4 | Special track | Focus session: International women in computational biology: stories & journeys | Shilpa Garg, Ana Conesa |
Computational biology has been shaped by the contributions of women across academia, healthcare, industry,
and entrepreneurship. This focus session will bring together leading scientists and innovators to share
their personal and professional journeys, discuss career development, leadership, mentorship, and explore
opportunities and challenges within the field. The session aims to foster mentorship, strengthen professional networks, increase the visibility of women role models, and promote a more inclusive computational biology community. Through scientific perspectives and personal experiences, speakers will highlight diverse pathways to success and leadership in computational biology. |
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| 2026-09-02 | 10:30:00 | 11:30:00 | Room 4 | Special track | Entrepreneurs' stories session | Samir Ounzain, Jurgi Camblong |
Join a dedicated session showcasing Samir Ounzain | CEO and co-founder of Haya Therapeutics Jurgi Camblong | CEO and co-founder of Sophia Genetics |
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| 2026-09-02 | 11:30:00 | 13:00:00 | Room 4 | Special track | Career workshop | Valérie Barbié, Fabian Birzele, Moustafa Houmani, Livia Parodi, Erik Studer , Sandra Reinders, Dala Egger, Vanessa Monteiro, Andrea Cavallini, Thomas Collin, Emma Duncan | Organized by the SIB People & Culture, this career workshop offers an open forum for early-career researchers to engage with professionals working across academia, industry, and the public sector. Through expert talks and facilitated discussions, participants gain first-hand insights into hybrid career paths within and beyond academic research. | |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room 4 | Special track | Focus session: Digital earth, living planet: biodiversity and climate in the age of big data | This session will explore how large-scale data infrastructures are transforming our understanding of biodiversity and climate systems. It will highlight advances in aggregating, harmonising, and analysing massive environmental datasets to support evidence-based research and policy. Complementary perspectives will connect biodiversity informatics with emerging digital twin and Earth system modelling approaches. Together, the talks will emphasise the power of integrated big data frameworks to address interconnected ecological and climate challenges. | ||
| 2026-08-31 | 14:45:00 | 15:45:00 | Room AD | Systems biology, multi-omics integration, modeling | Multimodal contrastive learning for integrating molecular representations and cellular phenotypes in drug-target interaction prediction | Yu-Chiao Chiu | Ying-Ju Lai, Tianyuzhou Liang, Po-Yuan Chen, Yu-Che Tsai, George C. Tseng, Yufei Huang, Yu-Chiao Chiu |
Motivation: Accurate prediction of drug-target interactions (DTIs) is fundamental to drug discovery and
mechanistic understanding. While deep learning has advanced computational DTI prediction, most existing
methods rely primarily on molecular structural representations, including drug structures and protein
sequences, while overlooking cellular phenotypes that reflect downstream biological effects. Cell Painting
enables high-content morphological profiling that captures systems-level responses to chemical and genetic
perturbations but remains underutilized in DTI modeling. Integrating molecular information with cellular
phenotypes offers an opportunity to improve both predictive performance and biological interpretability. Results: We propose a two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space. Stage 1 learns modality-specific representations independently from structure-based and image-based data; Stage 2 aligns these via multi-positive contrastive learning to bridge molecular structural information with cellular phenotypes. Cross-modal retrieval achieves median Recall@10 values of 0.77 (random split) and 0.33 (scaffold split), outperforming bilinear and random baselines. In external DTI prediction on the BIOSNAP dataset, our model achieves an AUC of 0.92 with image-based representations and 0.90 under structure-only settings, surpassing existing methods. Model interpretation via integrated gradients reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room AD | Systems biology, multi-omics integration, modeling | Large-scale simulation of coverage and error rate trade-offs for cancer detection in cell-free DNA whole-genome sequencing | Li-Ting Chen | Li-Ting Chen, Jeroen de Ridder, Myrthe Jager |
Motivation: Cell-free DNA (cfDNA) whole-genome sequencing (WGS) is a promising approach for detecting cancer
recurrence. It enables cancer detection by identifying all tumor-derived cfDNA (ctDNA) molecules carrying
somatic single nucleotide variants (sSNVs). While, ideally, a sequencing platform should be highly accurate
for reliable ctDNA detection, in reality, all sequencing platforms introduce sequencing errors that generate
false positives indistinguishable from true SNVs. Understanding how sequencing parameters influence ctDNA
detection sensitivity at low tumor fractions (TFs) in cfDNA samples is essential for guiding sequencing
strategies in clinical contexts. To model cfDNA sequencing for tumor detection which contains asymmetric
noise, and multiple interacting parameters, analytical modeling is intractable, motivating large-scale
parallelized simulation. Results: We developed a simulation framework to generate in silico cfDNA data across ten cancer types. In total, 480 million cfDNA samples were simulated from tumor WGS profiles. Overall, the lowest detectable TF differs substantially between cancer types under identical sequencing conditions due to variations in mutational load. For cancers with high mutational load, 3x coverage with low-error techniques reliably detects TFs below 0.1%. In contrast, cancers with low mutational load require at least six-fold higher coverage to achieve comparable detection thresholds. Increasing sequencing quality scores from Q30 to Q55 at 30x coverage further enhances sensitivity, enabling detection of TFs as low as 1x10â»âµ. This study provides a comprehensive framework for optimizing sequencing parameters, offering valuable guidance for tailoring future technology development for specific cancer types and clinical applications. |
| 2026-08-31 | 14:45:00 | 15:45:00 | Room AD | Systems biology, multi-omics integration, modeling | A linear interpretable model for Drug Target Prediction | Santiago Ferreyra | Santiago Noto, Santiago Ferreyra, Ruben Jimenez, Diego Galeano, Alberto Paccanaro |
Motivation: Identifying drug targets is fundamental in drug development, both for discovering new therapies
and for ensuring effective and safe treatments. Drug-target interactions (DTIs) have been predicted using
machine learning approaches that integrate heterogeneous data; however, often these models are complex and
lack interpretability. Results: We investigate whether a simple, fully interpretable linear model can achieve competitive performance for DTI prediction. We propose LI-DTI (Linear Interpretable Drug-Target Interaction), a prediction model inspired by recommender systems. LI-DTI learns from different drug-drug and target-target similarity matrices and provides interpretable predictions as a linear combination of these similarity measures. We show that LI-DTI can recover drug-target interactions even when drugs or targets have no previously known interactions, across multiple cross-validation settings. We further evaluate performance while mitigating potential bias arising from high chemical similarity between drugs or sequence similarity between targets. Finally, we assess LI-DTI in a prospective evaluation, training on DTIs present in DrugBank from 2011 and testing on interactions added through 2022. Across all evaluations, LI-DTI achieves state-of-the-art performance while producing interpretable predictions. For practical use, we provide a web-based tool that enables users to visualize individual LI-DTI predictions for DrugBank (2025) and inspect the biological evidence underlying them. Our results indicate that simple linear models with well-curated similarity features can deliver robust and interpretable DTI predictions, facilitating hypothesis generation and downstream experimental prioritization. Availability and Implementation: Code and data available at https://github.com/paccanarolab/LI-DTI. Web tool available at https://paccanarolab.org/lidtiweb/. Contact: alberto.paccanaro@rhul.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room AD | Systems biology, multi-omics integration, modeling | Shortcomings of silhouette in single-cell integration benchmarking | Pia Rautenstrauch | Pia Rautenstrauch, Uwe Ohler |
Single-cell data integration is a central challenge in modern genomics, enabling the joint analysis of
datasets across experiments. Integration methods aim to preserve genuine biological variation while removing
technical batch effects. As downstream conclusions depend on integration methods, rigorous evaluation and
independent benchmarking are essential. Quantitative metrics are central to these efforts. Among these metrics, adaptations of the silhouette score have emerged as a popular choice. Originally developed to assess unsupervised clustering results by comparing within-cluster cohesion to between-cluster separation, it is now frequently applied to evaluate integration performance. However, we show that silhouette-based metrics suffer from fundamental and largely overlooked limitations in this context. We formalize the silhouette score and its adaptations for single-cell integration and demonstrate through simple simulations that their underlying assumptions are violated under basic conditions. As a result, these metrics can misleadingly reward poor integration outcomes. We further corroborate these findings across multiple real-world datasets, where silhouette-based metrics fail to reliably assess batch effect removal and biological signal conservation. To address these limitations, we outline alternative evaluation strategies that enable more robust assessment of integration success. This is critically important, as it guides method selection, which directly impacts analysis outcomes. More broadly, our results not only demonstrate shortcomings of silhouette-based metrics in single-cell integration benchmarking but also highlight the need for metric validation in computational biology. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room AD | Systems biology, multi-omics integration, modeling | Discovering Reference-missing Cell Types from Bulk Transcriptomics | Yimin Fan | Yimin Fan, Yixuan Liu, Yunhua Zhong, Yue Wang, Kin Hei Lee, Yixuan Wang, Xinyuan Liu, Jiayi Li, Xuesong Wang, Ziqian Lin, Lei Li, Yu Li | Bulk RNA-seq deconvolution methods rely on single-cell reference data to estimate cell-type proportions in heterogeneous tissues, but certain cell types may be systematically absent from single-cell references due to technical limitations such as poor dissociation efficiency, low capture rates, or cell fragility. While recent studies have shown that signatures of missing cell types persist in deconvolution residuals, existing approaches cannot automatically determine how many cell types are missing, estimate their specific proportions, or reconstruct their expression signatures. Here, we present DeconX, a computational framework that addresses these limitations by generating ``pseudo-cells" from deconvolution residuals, enabling estimation of the number, proportions, and expression signatures of missing cell types. Through comprehensive evaluation on simulated datasets, we demonstrate that DeconX accurately recovers missing cell-type proportions and expression profiles across varying conditions, and systematically identifies key factors affecting performance, including expression similarity between missing and reference cell types and missing cell-type proportions. Application to high-grade serous ovarian cancer (HGSOC) samples successfully identifies and quantifies adipocyte populations that are absent from matched single-cell references, recovering biologically interpretable expression signatures consistent with known adipocyte markers. We further demonstrate that DeconX can determine the number of missing cell types in an unsupervised manner, enabling fully automated analysis of unknown tissue compositions. DeconX transforms residual-based missing cell-type inference from exploratory analysis into a complete computational framework, enabling accurate characterization of tissue heterogeneity from archival bulk RNA-seq data even when single-cell references are incomplete. Codebase is available at \url{https://github.com/Seniorious123/DeconX/}. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room AD | Systems biology, multi-omics integration, modeling | Charting spatial ligand-target activity using Renoir | Hamim Zafar | Narein Rao, Tanush Kumar, Dina Kazemi, Shaozhi Hou, Atefeh Khakpoor, Merrin Mary Eapen, Rhea Pai, Liang Qiao, Archita Mishra, Florent Ginhoux, Jerry Chan, Jacob George, Ankur Sharma, Hamim Zafar |
The advancement of single-cell RNA sequencing and spatial transcriptomics has enabled the inference of
cellular interactions in a tissue microenvironment. Despite advances in cell-cell interaction inference,
methods capable of mapping the influence of ligands on downstream target genes across spatial niches
harboring specific cell type composition, crucial for resolving niche-specific relationship between ligands
and their downstream targets are still lacking. Here, we present Renoir for charting the ligand-target activities across a spatial topology, delineating spatial communication niches harboring specific ligand-target activities and spatially mapping pathway-level activity of genesets. Across spatial datasets with varying resolution (spot to single-cell) ranging from development to disease, Renoir infers cellular niches with distinct ligand-target interactions, spatially maps pathway activities, and identifies context-specific cell-cell interactions, including hepatocyte-macrophage interactions in fetal liver and interactions between onco-fetal and bi-potent cells in hepatocellular carcinoma. Renoir uncovers biological insights and therapeutically-relevant cellular crosstalk from spatial transcriptomics data. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks | Maria Rodriguez Martinez | Aurelien Pelissier, Miroslav Phan, Didier Le Bail, Niko Beerenwinkel, Maria Rodriguez Martinez |
Stochastic simulation is foundational to computational biology, yet existing frameworks face a fundamental
tension: classical algorithms such as the Gillespie stochastic simulation algorithm (SSA) are efficient but
assume memoryless, homogeneous agents, while agent-based approaches capture biological diversity at
prohibitive computational cost. Real biological systems often exhibit both memory effects and heterogeneity
at the level of individual agents, features that existing methods cannot handle efficiently. We introduce MOSAIC (Modeling of Stochastic Agents with Individual Complexity), a rejection-based simulation framework that extends the Gillespie SSA to systems with agent-specific dynamics and arbitrary non-Markovian waiting-time distributions. Rather than maintaining an exhaustive list of reaction propensities, MOSAIC tracks a single global maximum rate and evaluates individual rates on demand, yielding constant-time updates per accepted event, independent of system size. This design preserves Gillespie-like scalability while enabling each agent to follow distinct inter-event distributions (Gamma, Pareto, Weibull, log-normal) with rates that adapt dynamically to the evolving system state. We demonstrate MOSAIC across three biological domains. First, in a model of clonal B-cell affinity maturation, MOSAIC reproduces experimentally observed clonal dominance patterns with near-linear runtime scaling where standard Gillespie methods become prohibitive. Second, in a Hes1 transcription model with delayed negative feedback, MOSAIC captures state-dependent, non-exponential elongation times that classical delay-based algorithms cannot dynamically accommodate. Third, applied to empirical face-to-face interaction data, MOSAIC-TN reproduces heavy-tailed interaction statistics and higher-order network features beyond the reach of existing temporal network models. MOSAIC is openly available and establishes a practical, unified framework for heterogeneous stochastic simulation in biology. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics | Kevin Hoffer-Hawlik | Bar Rozenman, Kevin Hoffer-Hawlik, Elham Azizi, Nicholas Djedjos |
Motivation: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing
(scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture
limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate
how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level
correspondence such as gene–protein linkage or cell-level barcode pairing, which is often unavailable. Results: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, i.e., convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. Availability and Implementation: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility. |
| 2026-09-01 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | Informing agent-based models with spatial data using convolutional autoencoders | Bi-Rong Wang | Bi-Rong Wang, Chen-Yi Liao, Erik Danen, Elsa Neubert, Federica Eduati | Spatial computational models such as agent-based models (ABMs) offer powerful in silico tools to study tumor dynamics, yet imaging data are still rarely used to inform these models directly. We present an ABM optimization framework that leverages convolutional encoders to compare spatial patterns between experimental imaging data and ABM-generated outputs within a shared latent space. This quantitative comparison was used to estimate ABM parameters across three datasets, ranging from synthetic data to 3D tumoroid-T cell co-culture microscopy and histopathology images from The Cancer Genome Atlas skin cutaneous melanoma samples. Estimated parameters were evaluated using data-derived features and experimental knowledge, including experimental conditions and gene expressions. Simulations using optimized parameters reproduced key spatial features of the training images, such as tumor boundary complexity and tumor-tumor neighborhood structure. Together, these results demonstrate a flexible framework for ABM parameter optimization using spatial data across modalities, enabling systematic investigation of how spatial architecture influences tumor progression and immune interactions. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room BC | Systems biology, multi-omics integration, modeling | Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging | Jiahang Li | Jiahang Li, Steffen Waldherr, Wolfram Weckwerth |
Physical inactivity and low fitness have become global health concerns. Metabolomics, as an integrative
approach, may link fitness to molecular changes. In this study, we analyzed blood metabolomes from elderly
individuals under different treatments. By defining two fitness groups and their corresponding metabolite
profiles, we applied several machine learning classifiers to identify key metabolite biomarkers. Aspartate
consistently emerged as a dominant fitness marker. We further defined a body activity index (BAI) and
analyzed two cohorts with high and low BAI using COVRECON, a novel method for metabolic network interaction
analysis. The method can automatically construct a simplified model of the metabolic network and calculate
the changes in the biochemical regulations using a novel algorithm. COVRECON can help to understand the
complex dynamics and mechanisms of biological systems and diseases (1,2). Through this approach，we
identified causal molecular dynamics in multi-omics data. Aspartate-amino transferase (AST) was among the
dominant processes distinguishing the groups. Routine blood tests confirmed significant differences in AST
and ALT. Aspartate is also a known biomarker in dementia, related to physical fitness. In summary, we
combine machine learning and COVRECON to identify metabolic biomarkers and molecular dynamics supporting
active aging. References 1，Li J, Waldherr S, Weckwerth W. COVRECON. Bioinformatics, 2023, 39(7): btad397. 2，Li J, Weckwerth W, Waldherr S. NPJ Systems Biology and Applications, 10(1), 137. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room BC | Systems biology, multi-omics integration, modeling | Reusable Generative Models for Mechanistic Studies of Metabolic Dynamics | Ljubisa Miskovic | Ljubisa Miskovic, Subham Choudhury, Ilias Toumpe, Ousaama Gabouj, Jakob Behler, Vassily Hatzimanikatis |
Dynamic metabolic models provide a mechanistic framework for studying how cellular systems respond over time
to genetic and environmental perturbations. By linking biochemical mechanisms to transient cellular
behavior, such models are becoming increasingly important in computational biology for predictive analysis,
hypothesis generation, and integration of heterogeneous biological data. Recent generative machine-learning
approaches have substantially accelerated the parameterization of large-scale and near-genome-scale kinetic
models, making high-throughput dynamic studies increasingly feasible. However, adapting trained generators
to new physiological regimes or organisms often still requires retraining, and the biological meaning of
their latent representations remains poorly understood. Here, we present a latent-space exploration framework that repurposes pretrained generative networks to construct kinetic models with targeted dynamic properties without retraining from scratch. Rather than modifying network parameters, the approach operates directly in latent space to identify directions that systematically tune dynamic behavior while preserving biochemical feasibility and consistency with the underlying mechanistic model. We demonstrate the framework in Escherichia coli by tuning response times, identifying enzyme-level dynamic bottlenecks, and repurposing generators trained in one setting to produce models with distinct dynamic properties under anaerobic growth. We further test robustness and generalizability in Saccharomyces cerevisiae, showing that latent-space control remains effective across generators at different training stages and across different latent-space regions. Overall, this work establishes latent-space control as an interpretable and computationally efficient strategy for reusing generative models in mechanistic systems biology, enabling scalable analysis of metabolic dynamics across physiological states and supporting more reusable, data-constrained dynamic modelling workflows in computational biology. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room BC | Systems biology, multi-omics integration, modeling | MAAMOUL: Metabolic network-based discovery of microbiome-metabolome shifts in disease | Efrat Muller | Elhanan Borenstein, Efrat Muller, Shiri Baum |
Motivation: A central goal in human gut microbiome research is to identify disease-associated functional
shifts, an objective increasingly pursued through metagenomic and metabolomic assays. However, common
differential abun-dance analyses of genes or metabolites often yield long and difficult-to-interpret feature
lists. Aggregating features into predefined pathways can improve interpretability but relies on fixed
pathway boundaries that may not reflect context-specific functional changes. Moreover, even when paired
metagenomic-metabolomic data are available, they are often analyzed separately or linked only through simple
statistical associations. Results: We introduce MAAMOUL, a knowledge-based computational framework that integrates metagenomic and metabolomic data to identify disease-associated, data-driven microbial metabolic modules. Leveraging prior knowledge of bacterial metabolism, MAAMOUL maps disease-association scores onto a global microbiome-wide metabolic network and identifies custom modules enriched for altered genes and metabolites. Applying MAAMOUL to inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) datasets revealed significant disease-associated modules not detected by conventional pathway-level analysis. In IBD, modules reflected disrupted sulfur and aromatic amino acid metabolism and enhanced microbial nucleotide salvage, whereas in IBS they linked purine and nicotinate/nicotinamide metabolism. These results demonstrate that network-guided multi-omic integration can uncover coherent functional shifts in the gut microbiome overlooked by single-omic or purely statistical approaches. Availability: MAAMOUL is available as an R package at https://github.com/borenstein-lab/MAAMOUL. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room BC | Systems biology, multi-omics integration, modeling | PatientProfiler, from multi-omic data to clinical insight: patient-specific models to derive signalling-based biomarkers | Veronica Lombardi | Veronica Lombardi, Lorenzo Di Rocco, Eleonora Meo, Veronica Venafra, Elena Di Nisio, Velerio Perticaroli, Mihail Lorentz Nicolaeasa, Chiara Cencioni, Francesco Spallotta, Rodolfo Negri, Francesca Sacco, Livia Perfetto |
A crucial challenge in oncology is deciphering the intricate mechanisms underlying reprogramming in cancer
cells to advance the ability of diagnosis and treatment of cancer patients. Consequently, whole-patient
multi-omic analysis has become increasingly common in clinical practice, but, thus far, no efficient
strategy for the identification of molecular pathways dysregulated at the patient level has emerged, nor
have these advances been effectively employed to define personalized therapeutic regimens. The main shortcoming is the lack of a robust computational framework to exploit such information by integrating and interpreting the available multidimensional data to drive translational solutions. To fill this gap, we developed PatientProfiler, a computational workflow that allows for multi-omic data analysis and standardization, generation of patient-specific mechanistic models of signal transduction, and extraction of network-based prognostic biomarkers. To benchmark it, we retrieved genomics, transcriptomics and (phospho)proteomics derived from 122 treatment-naïve breast cancer biopsies and identified patient-specific mechanistic models that recapitulate oncogenic signaling pathways. Network topology-based stratification revealed seven subgroups of patients associated with unique transcriptomic signatures and distinct prognostic values. The workflow successfully recovered established Basal-like 1 and Basal-like 2 breast cancer subtypes, while also highlighting distinct mechanistic drivers with potential clinical relevance. In conclusion, PatientProfiler provides a generalizable workflow for decoding cohort-level multi-omic data into interpretable mechanistic models to identify deregulated signaling pathways and prognostic biomarkers. It can be applicable across different cancer types and other diseases, leading to the optimization of personalized therapies based on dysregulated signaling. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room BC | Systems biology, multi-omics integration, modeling | A Novel ILP Framework to Identify Compensatory Pathways in Genetic Interaction Networks with GIDEON | Jocelyn Garcia | Jocelyn Garcia, Kevin Yu, Catherine Freudenreich, Lenore Cowen |
In Baker's yeast, there exists a comprehensive collection of pairwise epistasis experiments that, for nearly
every pair of non-essential genes, measure the growth of the double-knockout strain as compared to its
component single knockouts. This data can be represented as a weighted signed graph termed the genetic
interaction network, and we introduce a new ILP-based method named GIDEON to search for a diverse collection
of Between-Pathway Models (BPMs) in this network, where BPMs are a graph motif signature that indicates
potential compensatory pathways in the genetic interaction network. With both an improved distribution-informed edge weighting scheme and an improved ILP method, GIDEON produces BPM collections that are substantially larger and with better functional enrichment compared to previous methods. We find some interesting new BPM gene sets including one with potential insights into antifungal drug targets through ties between ergosterol and aromatic amino acid biosynthesis. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room BC | Systems biology, multi-omics integration, modeling | Decoding non-coding SNPs: systems genomics modelling dissects the heterogeneity of IBD | Dezso Modos | Azedine Zoufir, Tamas Korcsmaros, Andreas Bender, Bram Verstockt, Nicholas Powell, Simon R Carding, Shamith Samarajiwa, David Fazekas, Domenico Cozzetto, Padhmanand Sudhakar, Dezso Modos, Benjamin Alexander-Dann, Wen-Xin Kang, Matthew Madgwick, Yufan Liu, Balazs Bohar, Martina Poletti, Johanne Brooks-Warburton, John Thomas | Genome-wide association studies have identified numerous susceptibility loci in complex diseases, such as chronic immune-mediated inflammatory disorders (IMIDs), yet their impact on pathomechanisms remains poorly understood. Low effect sizes, polygenicity, and predominance within non-coding genomic regions remain major challenges to the functional interpretation of IMID-associated single-nucleotide polymorphisms (SNPs). To address this, we present a novel systems genomics approach which models the cumulative impact of non-coding SNPs on downstream cellular signalling and gene regulatory networks. Applying this to the prototypical chronic IMIDs of Crohn's disease (CD) and ulcerative colitis (UC), both forms of inflammatory bowel disease (IBD), we individually analysed 2,636 patient genomes. Signals from non-coding SNPs were found to propagate towards well-established and novel CD- and UC-associated pathogenic pathways through the signalling and gene regulatory layers. The SNP-propagated gene regulatory networks stratified CD and UC patients into distinct clusters corresponding to cell type-specific gene dysregulation and potential therapeutic response. This approach bridges the gap between genotype and phenotype, laying the foundations for accelerating precision medicine in complex diseases. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | Universal differential equations for quantifying NF-κΒ - p53 signaling crosstalk | Umur Kaya | Umur Kaya, Xhemal Kodragjini, Samuel Zambrano, Katharina Baum | Universal differential equations (UDEs) have emerged as a powerful tool for scientific discovery, uniting differential equations and deep learning. Yet, their application to the noisy and complex data of systems biology remains limited. Here, we demonstrate that UDEs, paired with symbolic regression, can quantify the crosstalk between NF-κB and p53 signaling pathways based on experimental data exhibiting complex, oscillatory dynamics. We validate the framework on synthetic benchmarks, showing that crosstalk recovery is robust to measurement noise and improves markedly with the number of available time series. We then apply the framework to 106 simultaneously measured single-cell p53 and NF-κB time series following DNA damage and NF-κB activation, constituting the largest-scale UDE application in systems biology so far. UDEs with both a detailed and a minimal mechanistic p53 model consistently identify a monotonically increasing crosstalk function in which elevated NF-κB levels enhance p53 synthesis. Symbolic regression distills the learned neural network output into a compact, interpretable closed-form expression, providing a quantitative, time-resolved characterization of NF-κB-driven amplification of p53 at the single-cell level. Our work thus delivers a dual contribution: methodologically, it establishes UDEs as a viable tool for gaining quantitative insights in complex, noisy biological systems at scale; biologically, it provides a data-driven characterization of NF-κB-p53 pathway crosstalk from single-cell dynamics. Source code is available at https://github.com/DILiS-lab/ude-crosstalk-discovery. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | SELFormerMM: multimodal molecular representation learning via SELFIES, structure, text, and knowledge graph integration | Tunca Dogan | Erva Ulusoy, Sevval Bostanci, Bora Engin Deniz, Tunca Dogan |
Motivation: Molecular representation learning is central to computational drug discovery. However, most existing models rely on single-modality inputs, such as molecular sequences or graphs, which capture only limited aspects of molecular behaviour. Yet unifying these modalities with complementary resources such as textual descriptions and biological interaction networks into a coherent multimodal framework remains non-trivial, hindering more informative and biologically grounded representations. Results: We introduce SELFormerMM, a multimodal molecular representation learning framework that integrates SELFIES notations with structural graphs, textual descriptions, and knowledge graph–derived biological interaction data. By aligning these heterogeneous views, SELFormerMM effectively captures complementary signals that unimodal approaches often overlook. Our performance evaluation has revealed that SELFormerMM outperforms structure-, sequence-, and knowledge-based models on multiple molecular property prediction tasks. Ablation analyses further indicate that effective cross-modal alignment and modality coverage improve the model's ability to exploit complementary information. Overall, integrating SELFIES with structural, textual, and biological context enables richer molecular representations and provides a promising framework for hypothesis-driven drug discovery. Availability: SELFormerMM is available as a programmatic tool, together with datasets, pretrained models, and precomputed embeddings at https://github.com/HUBioDataLab/SELFormerMM. |
| 2026-09-02 | 10:30:00 | 11:30:00 | Room AD | Systems biology, multi-omics integration, modeling | Semi-Supervised Learning for Automated Perineural Invasion Detection in Multi-Organ H&E Whole Slide Images | Ahmad Alkhan | Ahmad Alkhan, Michael Lynch, Maire Lavelle, Aedin Culhane, Elizabeth Ryan, Elizabeth Ryan, Elizabeth Ryan |
Motivation: Perineural invasion (PNI) is an important pathological phenotype associated with poor prognosis
in multiple malignancies. The primary detection method is visual inspection of whole slide images (WSIs),
which is labor-intensive, time-consuming, subjective, and prone to high inter-observer variability.
Developing reliable, accurate deep learning models for PNI detection is con-strained by the lack of
pixel-level annotated WSIs. Results: We evaluated three backbone architectures and two different approaches to improve PNI detection in a multi-organ dataset of colon, prostate, and pancreatic adenocarcinomas. We report three key findings. First, two pathology-pretrained foundation models, Virchow-2 and UNI, sub-stantially outperformed ImageNet-pretrained CNNs (EfficientNet-B3, ConvNeXt-2), with distinct baseline error profiles reflecting differences in pretraining-data composition. Second, a data cura-tion strategy driven by confidence-based pseudo-labelling (threshold p > 0.9) with human-in-the-loop review expanded the dataset from 262 to 352 WSIs, yielding a 12.4% relative F1 improve-ment (0.740 to 0.832) and a 55.5% reduction in false positives per slide; an ablation attributed 70% of the F1 gain to data volume and 41% of the FP reduction to benign-class curation. Third, per-organ analysis revealed that the primary driver of this improvement was not data volume alone but the targeted annotation enrichment of underrepresented morphologies in adjacent-normal and benign tissue, including desmoplastic stroma, crypts, and small blood vessels, that had been a systematic source of false positive predictions across tissue types. Availability: Our implementation is available at https://github.com/AhmadAlkhan/PNI_SSL. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room BC | Transcriptomics and gene regulation | SlotDeconv: Spatial Transcriptomics Deconvolution via Diversity Constrained Prototype Learning and Spatial Refinement | Zhi Wei | Hanzhang Fang, Cong Qi, Yuanjie Zou, Yeqing Chen, Zhi Wei |
Motivation: Spatial transcriptomics (ST) measures gene expression in intact tissues, but each spot typically
contains mixtures of multiple cell types. Deconvolution is particularly challenging when closely related
cell subtypes share highly similar expression profiles and when spatial context is underutilized during
proportion estimation. Results: We present SlotDeconv, a method for ST deconvolution consisting of a single-cell reference module and a spatial inference module. The reference module learns discriminative cell-type signatures using slot based prototype vectors decoded into a reference matrix, trained with a negative binomial reconstruction loss and a max-margin diversity constraint that discourages similar cell-type signatures. Ablation studies confirm that both components are essential: removing the diversity constraint reduces spot-wise Pearson correlation by 49%, and replacing learned prototypes with cell-type mean expression reduces it to near zero. The spatial inference module initializes spot level proportions via gene weighted nonnegative least squares, then refines them by minimizing KL divergence between observed and reconstructed spot expression under a spatial neighborhood consistency regularizer. Benchmarked against CARD, RCTD, Cell2location, and Spotiphy on a 27 cell type mouse brain dataset, SlotDeconv achieves the highest spot wise Pearson correlation (0.561) and cosine similarity (0.633), outperforming the next best method by 7.5% in spot-wise correlation, with particularly strong gains on transcriptionally similar cortical neuronal subtypes. Biological validation on human pancreatic cancer and mouse olfactory bulb datasets further confirms spatial specificity. |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room BC | Transcriptomics and gene regulation | SPIDER: Spatially Integrated Denoising via Embedding Regularization with Single Cell Supervision | Md Istiaq Ansari | Md Istiaq Ansari, Muhtasim Noor Alif, Wei Zhang |
Motivation: Spatial transcriptomics (ST) technologies profile gene expression while preserving tissue
architecture, enabling the study of spatial cellular organization and microenvironmental interactions.
However, raw ST data are heavily affected by technical noise, sparsity, and dropout events, which obscure
true biological signals and hinder downstream analyses. While recent denoising methods incorporate spatial
neighborhood information, they lack explicit supervision due to missing cell-type annotations in ST data. To
address this challenge, we introduce SPIDER, a semi-supervised framework that leverages independently
generated, annotated single-cell RNA-seq (scRNA-seq) references to guide ST denoising. SPIDER synthesizes
pseudo-ST data from scRNA-seq to inject cell-type information without requiring paired measurements. The
method constructs three graphs capturing spatial proximity, transcriptional similarity in real-ST data, and
transcriptional structure in pseudo-ST data. Graph encoders map these representations into a shared latent
space, and a domain-alignment module transfers biologically meaningful structure from pseudo-ST to real-ST
embeddings. A graph-attention decoder with a zero-inflated negative binomial objective reconstructs denoised
ST expression profiles. Results: We benchmark SPIDER on human dorsolateral prefrontal cortex and breast cancer datasets. SPIDER consistently enhances spatial gene expression patterns, recovers known tissue structures, and achieves superior clustering performance compared to existing approaches. Marker gene analyses demonstrate improved spatial continuity and clearer anatomical organization. By directly producing denoised expression matrices, SPIDER improves both accuracy and interpretability, providing a generalizable solution for robust ST data denoising. Availability and Implementation: The source code is available at: https://github.com/compbiolabucf/SPIDER |
| 2026-08-31 | 16:15:00 | 17:15:00 | Room BC | Transcriptomics and gene regulation | DESpace2: detection of differential spatial patterns in spatial omics data | Peiying Cai | Peiying Cai, Mark Robinson, Simone Tiberi |
Motivation: Spatially resolved transcriptomics (SRT) enables the investigation of mRNA expression in a
spatial context. While several SRT analysis frameworks have been developed, the vast majority of them focus on analyzing individual samples, and do not allow for comparisons of spatial gene expression patterns across experimental conditions, such as healthy vs. diseased states. Results: Here, we present an approach to identify so-called differential spatial patterns (DSP), i.e., genes that exhibit changes in spatial expression between groups of samples across conditions. Our framework processes diverse SRT data types and detects DSP by performing differential gene expression testing across conditions. Notably, this comparison is not currently available in any other spatial omics method. In addition to detecting DSP across conditions, our framework includes two key features. First, it can identify tissue regions where expression changes across conditions. Second, it can detect spatial gene expression pattern changes across more than two experimental conditions, with flexible models. With ad hoc simulations, we demonstrate that our approach has good true positive rates and well-calibrated false discovery rates. Applied to experimental data, our method identifies biologically relevant DSP genes while maintaining computational efficiency. Availability: Our framework has been implemented within DESpace Bioconductor R package (from version 2.0.0). |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room BC | Transcriptomics and gene regulation | Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models | Lisa Fournier | Lisa Fournier, Garance Haefliger, Albin Vernhes, Vincent Jung, Lena Loye, Valentine Du Bois, Intidhar Labidi-Galy, Pascal Frossard, Igor Letovanec, Cédric Vincent-Cuaz, Raphaëlle Luisier |
Intra-tumor heterogeneity (ITH) is a major driver of cancer progression and therapeutic resistance. Although
ITH has been extensively profiled at the molecular level, revealing the coexistence of tumor subpopulations
with distinct molecular identities, how these programs organize spatially within tumors, forming recurrent
regions with unique molecular profiles, tissue architecture, and cellular composition, remains poorly
understood. In this study, we push the boundaries of histopathology foundation models (hFMs) to reveal
molecularly distinct tumor regions that are invisible to human experts, offering new insights into the
molecular organization of invasive breast tumors. We specialized generalist hFMs through extended pre-training on invasive tumor tissue, enhancing their ability to encode richer, tumor-specific biological concepts. Based solely on these image-derived features, the model identified distinct tumor regions, termed tumor archetypes, that subsequently mapped to unique molecular signatures. Characterizing these tumor archetypes by integrating spatial transcriptomics data, we uncovered distinct patterns of RNA splicing dysregulation alongside other pathways such as TGF-β signaling. Notably, aberrant RNA splicing emerged as the main driver of one archetype across both HER2+ breast cancer and triple-negative breast cancer (TNBC). Strikingly, these archetypes coexist as spatially distinct regions within the same tumors and recur across patients. Survival analysis revealed that enrichment of the RNA splicing–dysregulated archetype is associated with poorer overall survival, highlighting its clinical relevance. This work provides a scalable method to uncover tumor archetypes from H&E images, linking tissue morphology to RNA programs and opening new avenues for precision oncology and RNA-focused therapeutic stratification. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room BC | Transcriptomics and gene regulation | ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning | Jonathan Mandl | Jonathan Mandl, Yaron Orenstein |
Motivation: Predicting the RNA-seq coverage of native and exogenous sequences is central to many molecular-
and synthetic-biology applications. Substantial progress has been made in developing methods to predict the
RNA-seq coverage of native genomic sequences, with the recently developed Shorkie achieving state-of-the-art
performance in yeast. However, prediction performance of these methods over exogenous DNA is still unknown.
Recent studies measured RNA-seq coverage of large exogenous genomes in yeast, providing a unique opportunity
to train machine-learning models on a large exogenous sequence space and to improve both prediction
performance and our understanding of regulatory mechanisms. Results: We introduce ExoShorkie, a method we developed by extending Shorkie through transfer learning across multiple exogenous RNA-seq datasets. We demonstrate that ExoShorkie significantly improves prediction performance on held-out exogenous genomes and outperforms both a native-genome-trained Shorkie baseline and Yorzoi, the only competing method in predicting exogenous RNA-seq coverage in yeast, in cross-validation and in leave-one-genome-out evaluations. Furthermore, through interpretability analyses we reveal biologically meaningful regulatory motifs and distinct regulatory rules in exogenous genomes in yeast, providing new insights into transcriptional regulation. |
| 2026-09-01 | 11:45:00 | 12:45:00 | Room BC | Transcriptomics and gene regulation | miRBind2 enables sequence-only prediction of miRNA binding and transcript repression. | David Cechak | David Cechak, Dimosthenis Tzimotoudis, Stephanie Sammut, Katarina Gresova, Eva Marsalkova, David Farrugia, Panagiotis Alexiou |
Motivation: MicroRNAs (miRNAs) regulate gene expression by guiding Argonaute proteins to partially
complementary sites on target RNAs. While classical prediction methods rely on engineered features such as
seed match categories, evolutionary conservation, and site context, recent advances in deep learning offer
the potential to learn targeting rules directly from sequence. We developed a sequence-based deep learning
model that improves miRNA target site prediction, and further validated the learned target site
representations by extending the model to gene-level functional repression prediction. Results: We introduce miRBind2, a deep learning method for miRNA target site prediction that incorporates a novel pairwise nucleotide representation capturing all possible miRNA-target nucleotide interactions, with a CNN-based architecture. miRBind2 outperforms previous SotA models across four independent datasets from the debiased miRBench benchmark, while using 92% fewer parameters. We show that the convolutional features and weights learned by miRBind2 can be transferred to transcript-level prediction by extending the miRBind2 architecture and fine-tuning it on miRNA perturbation experiments. This miRBind2-3UTR model predicts gene repression from sequence alone. On a dataset of 50,549 miRNA-gene pairs, miRBind2-3UTR significantly outperforms TargetScan. These results show that deep models pretrained on target site data can capture regulatory signals and predict functional repression without requiring conventional engineered biological features. Availability: Models and source code are freely available via GitHub (https://github.com/BioGeMT/miRBind_2.0). A publicly available web-tool for novel predictions and visualization is available at : (https://huggingface.co/spaces/dimostzim/BioGeMT-miRBind2) |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room EF | Transcriptomics and gene regulation | Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping | Sebastian Hoenig | Sebastian Hoenig, Aayush Grover, Piero Neri, Didier Surdez, Valentina Boeva |
Motivation: Three-dimensional folding of the genome into structures such as chromatin loops is essential for
gene regulation. Current experimental methods for mapping these structures, like Hi-C and HiChIP, are
labor-intensive and require repeated assays to test hypothesized mutation effects. This motivates the need
for predictive approaches that reveal the sequence determinants of chromatin loops. Results: In this work, we present a novel and interpretable computational pipeline for predicting CTCF-mediated chromatin loops. We propose Chiron3D, a DNA-only model trained in a cell-type-specific manner to predict CTCF HiChIP contact maps. By leveraging pre-trained embeddings from a foundation model, our approach is competitive with baselines that take CTCF ChIP-seq as additional input, while enabling nucleotide-level attribution to the input DNA sequence. Using our framework, we provide likely mechanistic insights into the physical control of loop dynamics. Specifically, we find that the strength of the loop extrusion anchorage site is largely governed by the amount and binding affinity of CTCF sites at the boundaries. Furthermore, we reveal that loop stability is regulated by the amount of intra-loop CTCF binding sites, where fewer sites within the loop lead to a more stable domain. Using targeted, single-nucleotide edit simulations with Chiron3D, we show that both loop strength and stability can be precisely controlled. Together, these results provide novel mechanistic insights into the physical control of genome organization and highlight the potential of decoding the DNA sequence logic in silico. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room EF | Transcriptomics and gene regulation | Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease | Aishwarya Iyer | Aishwarya Iyer, Daan van Beek, Friederike Ehrhart, Chris Evelo, Theo de Kok, Ilja Arts, Michiel Adriaens, Martina Summer-Kutmon |
Motivation: Huntington's disease (HD) exhibits substantial variability in age of onset and disease
progression that is not fully explained by CAG repeat length alone. Part of this residual variation is
heritable, implicating additional genetic mechanisms. Cis-regulatory variation, genetic variants that alter
transcription and splicing of nearby genes, represents one such mechanism that can be quantified through
allele-specific expression (ASE) analysis. However, methods for integrating ASE profiles into patient
stratification frameworks remain underdeveloped, particularly for rare diseases with small cohorts and
sparse data. Results: We adapt a network-based stratification algorithm, originally developed for somatic tumor mutations, to ASE data. By propagating gene-level ASE imbalance profiles through a protein-protein interaction network, we stratified 20 HD patients into three distinct biological patient subgroups. Differential ASE and gene expression analyses both highlight neuroinflammatory pathways, including IL6 signaling, microglial activation, and cytokine regulation, supporting their role as key sources of inter-patient heterogeneity. Intersection of differentially imbalanced and expressed genes identified four candidate genes with potential eQTL-mediated regulation, MAGI3, HLA-B, CCND3, and PSMB1. These genes implicate synaptic organization, immune modulation, and proteasomal dysfunction as potential drivers of molecular heterogeneity between patient subgroups. Differences in cortical and striatal neuropathological scores between clusters, even when adjusted for CAG repeat length, provide clinical support for the biological relevance of the identified subgroups. Availability: All analysis code, Docker containers, and conda environments are available at https://github.com/macsbio/HD-ASE-NBS. Contact: martina.kutmon@maastrichtuniversity.nl Supplementary information: Supplementary data are available at Bioinformatics online. |
| 2026-09-01 | 15:15:00 | 16:15:00 | Room EF | Transcriptomics and gene regulation | An End-to-End Computational Framework for 'Record-seq' Transcriptional Recording Data | Florian Hugi | Florian Hugi, Tanmay Tanna, Randall J Platt |
Motivation: Record-seq captures transcriptional activity over time in engineered E. coli by integrating
cellular RNA-derived spacer sequences into CRISPR arrays, which are subsequently read out by sequencing.
Unlike the approximately uniform transcript sampling of RNA-seq, Record-seq records biological signal as
spacers sampled by the CRISPR spacer acquisition machinery. Consequently, standard RNA-seq analysis
strategies are not directly applicable, limiting sensitivity and interpretability. Our previous pipeline
addressed these challenges only partially, retained inherited RNA-seq assumptions, and had limited
algorithmic efficiency. Results: Here, we present an end-to-end computational framework for Record-seq data. To address the primary computational bottleneck – spacer sequence extraction – we implemented a wavefront-alignment approach for efficient quasi-local pattern matching, achieving a 30-fold speedup. We also introduce transcription unit-based feature counting as an alternative to gene-body quantification to better represent prokaryotic transcription and increase statistical power by capturing signal from untranslated regions, which are spacer acquisition hotspots. For downstream analyses, we incorporate multiple normalization strategies and a nonparametric differential expression testing framework specifically designed for sparse datasets. Further, we analyze spacer acquisition patterns and train sequence-based neural models that predict acquisition propensity from genomic sequence and annotations, providing a framework to assess whether acquisition rules generalize as Record-seq is extended to new microbial hosts. Availability and Implementation: The primary analysis workflow, recoRdseq package, acquisition modelling repository and relevant data are all linked at https://github.com/plattlab/Record-seq-Framework. Acquisition models and training data are on Zenodo at https://doi.org/10.5281/zenodo.18891434. Supplementary Information: Supplementary data are available online. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room AD | Transcriptomics and gene regulation | Unexpectedly high intrinsic sequence specificity and complex DNA binding of human transcription factors | Hamed Najafabadi | Arttu Jolma, Aldo Hernandez-Corchado, Ally Yang, Ali Fathi, Kaitlin Laverty, Alexander Brechalov, Rozita Razavi, Mihai Albu, Hong Zheng, Ivan Kulakovskiy, Hamed Najafabadi, Tim Hughes |
There is ongoing controversy regarding the degree to which transcription factors (TFs) independently specify
genomic binding: TF binding motifs are short and degenerate, producing excessive binding site predictions.
Here, we present a computational framework, developed in conjunction with large-scale genomic HT-SELEX
(GHT-SELEX), to examine intrinsic sequence-specificities of human TFs. The first component of this framework is MAGIX, a hierarchical Bayesian model that explicitly captures the exponential enrichment dynamics of TF-bound genomic DNA fragments across SELEX cycles, producing quantitative enrichment scores and statistical confidence estimates for genomic binding regions. Applied to in vitro GHT-SELEX data for 179 TFs across 25 families, we find that genomic binding regions discovered by MAGIX often display surprisingly high overlap with ChIP-seq peaks for the same TF, suggesting much higher intrinsic sequence specificity than anticipated. The second component, RCADEEM, leverages a protein sequence-derived recognition code to model alternative DNA-binding modes of C2H2 zinc finger (C2H2-ZF) TFs. RCADEEM integrates machine learning-based prediction of C2H2-ZF sequence preferences with GHT-SELEX/MAGIX binding profiles to infer which subsets of ZFs are engaged at individual loci. Applied to 86 C2H2-ZF proteins, RCADEEM reveals that modular, alternative engagement of C2H2-ZF domains is the norm, leading to recognition of multiple distinct motifs by the same TF. These alternative motifs often evolve by internal duplication and divergence within the C2H2-ZF array. Together, this framework reveals that it is common for TFs to delineate a large fraction of their in vivo genomic binding sites independently of other cellular factors, often through complex, context-dependent DNA-binding behaviours. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room AD | Transcriptomics and gene regulation | STAN, a computational framework for inferring spatially informed transcription factor activity | Hatice Ulku Osmanbeyoglu | Linan Zhang, April Sagan, Bin Qin, Haoyu Wang, Elena Kim, Baoli Hu, Hatice Ulku Osmanbeyoglu | Transcription factors (TFs) orchestrate cellular responses to environmental signals and intercellular communication. The activity of TFs is influenced by neighboring cells, impacting cellular fate and function. Spatial transcriptomics (ST) allows for the mapping of mRNA expression across tissue samples, providing insights into the local microenvironment. However, the potential of ST data to systematically infer TF activity and its role in cell identity has not been fully exploited. We introduce STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects computational approach that predicts spatially informed, spot-specific TF activities by integrating curated TF–target gene priors, mRNA expression, spatial coordinates, and histological features. We demonstrate the utility of STAN on lymph node, dorsolateral prefrontal cortex, breast cancer, and glioblastoma ST datasets, identifying TFs associated with specific cell types, spatial regions, pathological zones, and ligand–receptor pairs. STAN enhances the utility of ST data, revealing the intricate interplay between TFs and spatial organization in diverse biological contexts. |
| 2026-09-02 | 09:00:00 | 10:00:00 | Room AD | Transcriptomics and gene regulation | From enhancer-scale to locus-scale: modelling gene regulation with sequence-to-function models | Casper H. Blaauw | Ibrahim Taskiran, Stein Aerts, David R. Kelley, Oliver Hobert, Alexander van Oudenaarden, Lukas Mahieu, Ludo Van Den Bosch, Valerie Christiaens, Gert Hulselmans, Casper H. Blaauw, Valérie Bercier, Darina Abaffyová, Sam Dieltiens, Eren Can EkÅŸi, Vasilieios Konstantakos, Seppe De Winter, Niklas Kempynck |
The main goal of regulatory genomics is to decipher how spatiotemporal regulation of genes is encoded in the
genome. Sequence-to-function models, which learn to link sequence to readouts like chromatin accessibility
or gene expression, are the state-of-the-art for this task. These models primarily operate at two levels:
predicting the scATAC-seq-based chromatin accessibility for individual enhancers and predicting
scRNA-seq-based expression genes based on their surrounding locus. Here, we present CREsted, a user-friendly package for enhancer modelling covering all steps from preprocessing to interpretation. Starting from scATAC-seq data, CREsted provides tools to preprocess the data, train sequence-to-function models, decipher the learned sequence grammar, and design new cell type-specific enhancers. We showcase CREsted's performance on a variety of tissues and species, ranging from mouse brain cortex and human cancer states to a whole-organism zebrafish developmental atlas. Furthermore, we discuss the evolution of the field towards modelling cell type-specific gene expression-predicting models. Using the toolkit provided in CREsted, we trained a model to predict gene expression across every cell type of an entire animal, the nematode C. elegans. Using this model, we extract and cluster high-importance sequence motifs from the genome, which we use to build an organism-wide motif grammar across tissues. Furthermore, we use in-silico perturbations to link regulatory elements to genes, and fine-map eQTLs by scoring naturally occurring variants. Finally, we use atlases from two related nematode species to for cross-species comparison and augmentation. Altogether, this provides a view into genome regulation at a unique scale. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room AD | Transcriptomics and gene regulation | scTimeBench: A streamlined benchmarking platform for single-cell time-series analysis | Eric Haoran Huang | Adrien Osakwe, Eric Haoran Huang, Yue Li | Temporal modelling of single-cell gene expression is essential for capturing dynamic cellular processes, yet a systematic framework for evaluating time-aware trajectory inference methods has not yet been established. Here, we present a modular and scalable benchmark designed to assess methods across three critical tasks: forecast accuracy (temporal cell alignment) for projecting cells to unseen time points, embedding coherence between original and projected data, and cell-type lineage fidelity. We evaluated ten state-of-the-art methods, which are broadly categorized into 8 forecasting-based and 2 optimal transport (OT)-based methods across eight diverse datasets spanning four species. Our results show that while several methods achieve high forecast accuracy, they often fail to preserve biological signals, both in their latent spaces and in cell lineage reconstruction. Notably, most methods confer low lineage fidelity and often underperform compared to a correlation baseline. We further demonstrate that integrating pseudotime can effectively denoise trajectories by aligning the data snapshots with the intrinsic biological clock in each cell. Finally, to streamline benchmarking for temporal single-cell analysis, we built one of the first self-contained Python packages for the research community: https://github.com/li-lab-mcgill/scTimeBench. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room AD | Transcriptomics and gene regulation | CellTypeAI: cell annotation for scRNA-seq using local generative-AI | Rufus Daw | Rufus Daw, Harry Deijnen, Magnus Rattray, John Grainger | Single-cell RNA sequencing (scRNA-seq) cell annotation techniques rely on the matching of known defining marker genes to a given cell population. However, these methods may lack robustness to dynamic fluctuations in cell marker expression between patients, samples and pathologies. The advent of easy-to-implement predictive technologies, like generative-AI (gen-AI), has facilitated the introduction of computational workflows that improve otherwise inaccurate context-dependent cell type annotation. Here, we introduce CellTypeAI, a streamlined, scalable program developed for tissue context-dependent cell annotation of scRNA-seq datasets using modern gen-AI models, enhanced by retrieval augmented generation methods. Our implementation builds upon local gen-AI hosting technologies and directly integrates into scRNA-seq analysis pipelines. We show that CellTypeAI provides improved annotation accuracy compared to current conventional annotation methods and nascent cloud-based gen-AI approaches. As CellTypeAI leverages locally-run AI models, it can be applied to sensitive datasets, unlike approaches utilising online gen-AI tools such as ChatGPT, DeepSeek, or Claude. CellTypeAI presents a novel solution for tissue-specific cell type identification, overcoming traditional marker-based limitations via locally-deployed gen-AI models. |
| 2026-09-02 | 13:15:00 | 14:15:00 | Room AD | Transcriptomics and gene regulation | Sensitivity Analysis of Cell Fate Trajectories from Single-Cell Transcriptomics | Abdullah Al Noman | Abdullah Al Noman, Palash Sashittal | Cell differentiation is a dynamic process in which cells traverse through high-dimensional gene expression space under the influence of gene regulatory networks and environmental cues. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled us to measure high-resolution snapshots of this dynamic process. Several computational methods have been developed to reconstruct cellular flow maps from these snapshots, revealing the trajectories taken by cells in gene expression space. While existing methods provide increasingly detailed descriptions of cellular trajectories, the stability of these trajectories to perturbations is largely unexplored. As such, it remains unclear how robust inferred trajectories are to perturbations, which genes most strongly influence long-term fate outcomes, and where instability arises between competing fate commitments. While sensitivity and stability analysis tools from dynamical systems theory provide a principled way to study the stability of differentiation trajectories, existing approaches are not designed for the high-dimensionality and sparsity of scRNA-seq data. Here, we introduce FateSens, a sensitivity-based computational framework for analyzing gene regulatory dynamics using flow maps derived from scRNA-seq data. FateSens performs sensitivity analysis of differentiation trajectories derived from scRNA-seq data to identify regulatory genes and fate boundaries. To demonstrate its utility, we applied FateSens to study neutrophil-monocyte differentiation using scRNA-seq data of mouse hematopoiesis. While FateSens relies only on transcriptomic measurements, this dataset also contains lineage tracing barcodes that provide ground-truth fate relationships. Our results show that FateSens accurately recovers regulators consistent with known biology and identifies fate boundaries that are supported by lineage tracing data. |