Biology subjects

Testa, L.

Publications and source records attributed to Testa, L..

3 recordsLinked to original sources

bioRxiv2026

TIDEST: post-imputation differential expression testing for spatial transcriptomics data

Spatial transcriptomics is increasingly extended by computational reconstruction of unmeasured genes from matched single-cell RNA-sequencing references, enabling transcriptome-wide analyses from targeted or sparse assays. Yet downstream analyses typically treat reconstructed expression as experimentally observed, overlooking prediction error and latent spatial variation that can misattribute tissue architecture to biological regulation. Here we introduce TIDEST, a framework for statistically valid inference on reconstructed spatial transcriptomes. TIDEST calibrates reconstructed expression using measured genes and adjusts for latent spatial variation before differential expression analysis. Across realistic spatial tissue simulations, TIDEST controls false discoveries where existing approaches fail, while preserving power. Across mouse and human brain, glioblastoma and breast cancer, TIDEST changes biological interpretation by correcting misleading differential-expression calls and revealing disease-associated transcriptional programs obscured by spatial confounding. Our results show that prediction accuracy alone is insufficient for reliable biological discovery and establish valid statistical inference as an essential component of reconstructed spatial transcriptomics.

genomics↗

bioRxiv2026

Estimating protein isoform abundances with PAQu

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce PAQu, a novel Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. PAQu offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that PAQu consistently outperforms competing methods in detecting differentially expressed protein isoforms and estimating their abundances. We use PAQu to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that PAQu can identify significant variations in isoform abundance levels not previously possible.

genomics↗

bioRxiv2025

BioChemAIgent: An AI-driven Protein Modeling and Docking Framework for Structure-Based Drug Discovery

Recent advances in AI have substantially accelerated in silico drug discovery, yet most computational approaches remain task-specific and require expert knowledge. We present BioChemAIgent, an agentic framework that orchestrates state-of-the-art AI models and established computational chemistry tools to support end-to-end small-molecule analysis, protein modeling, molecular docking, and interaction analysis through a unified interface. BioChemAIgent emphasizes transparent reasoning, reproducible workflows, and community-oriented extensibility for structural biology and drug discovery applications.

bioinformatics↗