Biology subjects

Haas, N.

Publications and source records attributed to Haas, N..

4 recordsLinked to original sources

bioRxiv2026

GLARE: Genomic Language model for Automated Repetitive Element classification

Reliable classification of transposable elements (TEs) is important for genome annotation and evolutionary analysis, yet remains challenging: TE families differ widely across taxa, reference databases label them inconsistently, and many TEs are represented only by degraded copies. We introduce GLARE (Genomic Language model for Automated Repetitive Element classification), a TE classifier built by fine-tuning a genomic language model on harmonized PanTEon and Repbase reference data to predict TE superfamily across 32 superfamilies in 11 orders. Among the genomic language models compared, Nucleotide Transformer v3 provided the strongest representation. Built on this backbone, GLARE exceeded all nine classifiers in the PanTEon benchmark, reaching 0.966 weighted superfamily F1 on its 30-superfamily task. GLARE also showed stronger generalization to clades absent from training, consistently exceeding state-of-the-art classifiers on holdouts spanning three taxonomic ranks: the order Poales, the phylum Arthropoda, and the kingdom Fungi. Training on both PanTEon and Repbase reduced the performance drop seen when a single-source model was applied to the other database. Under simulated sequence decay, GLARE was the most robust classifier tested, and it performed best on independently curated degraded libraries. These findings show that genomic language models fine-tuned on harmonized multi-source references can classify TEs robustly under the taxonomic, database, and sequence-quality shifts that matter in practice. GLARE is available at https://code.jgi.doe.gov/NHaas/glare.

bioinformatics↗

bioRxiv2025

Soil microbiome predator diversity outperforms nitrogen addition in boosting plant biomass via bacterial community shifts

Nitrogen (N) is crucial for plant growth, but its overuse harms biodiversity. Increasing soil biodiversity might provide the means to reduce N inputs, but experimental evidence for this paradigm-shift is limited. Using microbiome predators (protists and nematodes) that shape microbiome composition and release N, we examined how interactions between their diversity and N addition affect Cannabis sativa growth. Microbiome predator addition overall boosted plant biomass by up to 53%, particularly under low N conditions, primarily by altering bacterial community composition and enriching functions related to carbon and N cycling. In turn, microbiome predator diversity had the strongest effect on biomass production, while N levels played a greater role in determining plant nutrient content. These findings suggest that increased microbiome predator diversity can reduce the plants dependency on added N, showing the pivotal role of soil biodiversity in enhancing plant performance and serving as tools to mitigate N inputs.

ecology↗

bioRxiv2025

TransStop, a genomic language model for the pan-drug prediction of translational readthrough efficacy

MotivationPremature termination codons (PTCs) are a major cause of genetic diseases, but the efficacy of therapeutic readthrough agents is highly context-dependent. While linear models have shown promise in predicting readthrough efficiency, they may not fully capture the complex, non-linear interactions between sequence context and drug activity. MethodsWe developed TransStop, a transformer-based pan-drug model, trained on a dataset of [~]5,400 PTCs and eight readthrough compounds. The model learns sequence representations and incorporates learnable embeddings to capture drug-specific effects, allowing a single model to predict efficacy for multiple drugs. ResultsOur model achieved a global R2=0.94 on a held-out test set. Visualizations of the learned embeddings revealed a deep understanding of biological principles, including the distinct clustering of stop codon types and grouping of drugs by mechanism of action. In silico saturation mutagenesis and epistasis analyses uncovered complex, non-additive sequence determinants of readthrough. We generated 32.7 million predictions across the human genome, covering all possible PTCs. Analysis of these genome-wide predictions revealed strong drug specializations for specific stop codon contexts and identified key areas of disagreement with previous models, particularly for UGA codons, where our model predicts a more effective drug in thousands of cases. The TransStop model represents a significant advancement in the prediction of translational readthrough efficiency. Its superior accuracy and the biological insights derived from its applications provide a powerful tool for guiding clinical trial design, drug development, and personalized patient treatment. Availability and ImplementationSource code: https://github.com/Dichopsis/TransStop. Model: https://huggingface.co/Dichopsis/TransStop.Genome-wide predictions: https://doi.org/10.5281/zenodo.16918476.

bioinformatics↗

bioRxiv2025

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition

In this report, we summarize and analyze the 2024 Adaptyv protein design competition. Participants used computational and Machine Learning (ML) methods of their choice to design proteins that bind the Epidermal Growth Factor Receptor (EGFR), a key drug target involved in cell growth, differentiation, and cancer development. Over 1,800 designs were submitted across two rounds. Of these, 601 proteins were selected and characterized for expression and binding affinity to EGFR, with competitors both optimizing existing binders (KD = 1.21 nM) and creating de novo binders (KD = 82 nM). All selected designs were experimentally validated using Adaptyvs automated Bio-Layer Interferometry (BLI) pipeline. This competition illustrates the potential of crowdsourcing to drive creativity and innovation in protein design. However, it also exposed key challenges, such as the lack of standardized benchmarks, experimental design targets, and robust computational metrics for method comparison. We anticipate that future competitions will address these gaps and further motivate progress in computational protein design.

bioengineering↗