Biology subjects

De Donno, C.

Publications and source records attributed to De Donno, C..

3 recordsLinked to original sources

bioRxiv2026

Assessing Codon Language Models for Context-Aware Codon Optimization in Nucleic Acid-Based Medicines

Codon optimization uses synonymous sequence changes to improve the expression and therapeutic performance of nucleic acid-based medicines. Masked language models (MLMs) have recently been proposed as alternatives to traditional, frequency-based codon optimization approaches, yet whether they offer a meaningful advantage over such simpler methods remains unclear. Here we benchmark three prominent MLMs - CaLM, EnCodon and CodonTransformer - across backtranslation fidelity, sequence generation and nine molecular phenotype prediction tasks, and experimentally evaluate model-designed sequences using a secreted embryonic alkaline phosphatase (SEAP) reporter. The models differed markedly in amino-acid fidelity and generated distinct synonymous sequence variants. However, no single model performed best across all benchmark tasks and simple sequence features remained competitive in several settings. Our interpretability analysis revealed that the models integrate a large window of codon context for making predictions, as opposed to frequency-based approaches. Our in vitro data showed that MLM-designed variants outperformed conventional and commercial-vendor-derived sequences in both transient and stably integrated expression, supporting the models ability to capture translational context beyond codon frequency. Together, our results establish MLMs as effective and complementary tools for codon optimization and suggest that sampling across multiple models may improve the likelihood of identifying high-performing therapeutic sequences.

bioinformatics↗

bioRxiv2022

Population-level integration of single-cell datasets enables multi-scale analysis across samples

The increasing generation of population-level single-cell atlases with hundreds or thousands of samples has the potential to link demographic and technical metadata with high-resolution cellular and tissue data in homeostasis and disease. Constructing such comprehensive references requires large-scale integration of heterogeneous cohorts with varying metadata capturing demographic and technical information. Here, we present single-cell population level integration (scPoli), a semi-supervised conditional deep generative model for data integration, label transfer and query-to-reference mapping. Unlike other models, scPoli learns both sample and cell representations, is aware of cell-type annotations and can integrate and annotate newly generated query datasets while providing an uncertainty mechanism to identify unknown populations. We extensively evaluated the method and showed its advantages over existing approaches. We applied scPoli to two population-level atlases of lung and peripheral blood mononuclear cells (PBMCs), the latter consisting of roughly 8 million cells across 2,375 samples. We demonstrate that scPoli allows atlas-level integration and automatic reference mapping with label transfer. It can explain sample-level biological and technical variations such as disease, anatomical location and assay by means of its novel sample embeddings. We use these embeddings to explore sample-level metadata, enable automatic sample classification and guide a data integration workflow. scPoli also enables simultaneous sample-level and cell-level analysis of gene expression patterns, revealing genes associated with batch effects and the main axes of between-sample variation. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

genomics↗

bioRxiv2021

Compositional perturbation autoencoder for single-cell response modeling

Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.

bioinformatics↗