Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.21.723404

A Transformer-based Multi-omics Model for Translation Efficiency in S. cerevisiae

Sr., D.·Sr., X.·Sr., L.·Sr., Y.·Peng, X.·Lu, H.·Chen, J.
View original source ↗Export metadata ↓

Abstract

Precise regulation of protein synthesis is fundamental to cellular homeostasis and remains a primary target for synthetic biology applications. However, the non-linear relationship between mRNA abundance and protein levels presents complexities that poses challenges for predictive engineering. Here, we present TRIM, a Transformer-based RNA Inference Model that leverages full-length mRNA sequences and multi-omics data to predict translation efficiency. By employing a Parallel Expert Mixer, TRIM achieves robust prediction accuracy (R2 [≥] 0.8,Pearson r [≥] 0.9). Trained on multimodal data from massive Saccharomyces cerevisiae isolates, TRIM demonstrates outstanding biological interpretability, helping to decipher complex translational patterns such as synergistic effects between bases, sequence-dependent codon preference in different stages, and distinct attention on key secondary structures. These results indicate that the integration of multi-omics data with holistic sequence modeling can effectively decode the cis-regulatory grammar of translation as well as providing a scalable and interpretable generative framework for future synthetic biology engineering. Availability and ImplementationThe source code and data used to produce the results and analyses presented in the manuscript are available from Github (https://github.com/ZeusLiu666/TRIM).

Explore related subjects

Molecular BiologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sr., D., Sr., X., Sr., L., Sr., Y., Peng, X., Lu, H., Chen, J.. 2026-06-22. A Transformer-based Multi-omics Model for Translation Efficiency in S. cerevisiae. https://doi.org/10.64898/2026.06.21.723404

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Functional primary human 3D skeletal muscle organoids enable exercise and metabolic research

Human skeletal muscle is the principal site of insulin-stimulated glucose disposal and a major mediator of exercise-induced metabolic benefits, yet human models that preserve metabolic and exercise responsiveness remain limited. We generated primary human skeletal muscle organoids from donor-derived CD56+ myoblasts using a collagen-based extracellular matrix and serum-free IGF1-guided differentiation. The organoids formed aligned contractile tissues containing oxidative and glycolytic fiber type-like myotubes, displayed enhanced mitochondrial respiration, insulin-stimulated glucose uptake, and reproducible force generation. Electrical pulse stimulation induced AMPK activation, increased glucose utilization and lactate production, and upregulated canonical exercise-responsive genes including NR4A3 and PPARGC1A. Notably, transcriptional responses to in vitro exercise overlapped with acute exercise responses observed in skeletal muscle biopsies from the same donors. The organoids further detected functional impairments of skeletal muscle performance induced by TGF-{beta}1 and metformin and increased speed generation by testosterone treatment. These findings establish a donor-specific human skeletal muscle platform that recapitulates key features of insulin action and exercise adaptation and may enable mechanistic studies of skeletal muscle metabolism, exercise responsiveness, and therapeutic interventions relevant to diabetes.

Molecular Biology↗
bioRxiv2026

TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces): An Optimized Algorithm for Vertebrate Taxonomic Assignments in eDNA Metabarcoding, Integrating Molecular, Taxonomic, and Ecological Criteria

Environmental DNA (eDNA) metabarcoding has become a powerful approach for large-scale biodiversity assessment, yet taxonomic assignment remains one of its most critical error-prone steps. Current bioinformatic pipelines rely on molecular similarity searches against reference databases, but assignment accuracy is constrained not only by short marker length and database incompleteness, but also by fundamental limitations, including recent species radiations, incomplete lineage sorting, introgression, NUMTs, and the imperfect correspondence between genetic variation and species boundaries. Here, we present TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces), an automated and simple protocol designed to improve taxonomic assignments in eDNA metabarcoding. Initially developed for marine vertebrates, TRIDENT may be used with any barcode and integrates three complementary sources of evidence: molecular similarity (NCBI/GenBank and BOLD), curated taxonomic information (WoRMS), and ecological plausibility derived from biogeographic occurrence data (GBIF). The workflow sequentially constructs candidate taxon lists based on sequence similarity, expands them through taxonomic hierarchies, and filters them using spatial occurrence constraints. It further identifies possible taxa lacking reference barcodes and evaluates their plausibility through CO1-based similarity if data exist in BOLD. TRIDENT has been implemented as a source-available Python tool and tested using empirical eDNA datasets from marine vertebrates as well as simulated communities. Results demonstrate that the tool produces taxonomic assignments consistent with expert manual curation while substantially reducing processing time and attention errors caused by manual processing of large datasets. By combining molecular, taxonomic, and ecological criteria within a single framework, TRIDENT improves transparency and reproducibility and provides a robust and flexible solution strengthening confidence in taxonomic identifications in eDNA-based biodiversity assessments.

Molecular Biology↗
bioRxiv2026

Identifying and Addressing Systematic Data Leakage in Protein-Ligand Affinity Benchmarks

Accurate prediction of protein-ligand binding affinity is a crucial goal in structure-based drug discovery, with the potential to significantly shorten development timelines. Recently, a new wave of machine learning models based on co-folding, such as Boltz-2 and IsoDDE, has demonstrated performance that matches or exceeds that of gold-standard physics-based methods like Free Energy Perturbation (FEP). This paper provides a critical assessment of these claims, revealing that current benchmarks are heavily influenced by data leakage, and proposes a new benchmark that explicitly controls for data leakage. We demonstrate that splitting by protein-sequence identity is inherently insufficient to prevent data leakage due to "target mirroring," in which homologous proteins with low overall sequence identity still exhibit highly correlated binding profiles. Our meta-analysis of documents in the ChEMBL 36 database identifies more than 6,000 such assay pairs and finds that leakage persists for sequence-identity thresholds as low as 0.2, well below the values commonly used in benchmarks today. Additionally, we show that a ligand-only baseline model, which lacks protein structural information, achieves surprisingly high performance on the FEP+ 4 and OpenFE benchmarks (r = 0.66 and r = 0.36, respectively). Our results indicate that current benchmarks tend to reward models for memorizing training data and exploiting localized leakage rather than truly learning biophysical principles. To address this issue, we propose the Novelty-Tiered Affinity Benchmark, in which the test data is partitioned into ligand novelty tiers. In the most challenging tier (Tanimoto similarity < 0.35), ligand-only models perform notably worse (r = 0.14), offering a clear baseline for evaluating genuine generalization. We argue that the field must move beyond sequence-based splits to ensure that AI-driven discovery translates into successful prospective laboratory research.

Molecular Biology↗