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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%. |