OrthologAL: a Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40392208", "kind": "science-record", "revision": 2, "content_hash": "sha256:8c05a8fb2d3f665751a166eeda76ae257d58a60da749059fba4a7ad83b3f5687", "created_at": "2026-11-01T20:30:32Z", "updated_at": "2026-11-01T21:35:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "OrthologAL: a Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data.", "doi": "10.1093/bioinformatics/btaf311", "pmid": "40392208", "authors": ["Rishika Chowdary", "Robert K Suter", "Matthew D'Antuono", "Cynthia Gomes", "Joshua Stein", "Ki-Bum Lee", "Jae K Lee", "Nagi G Ayad"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Single-cell and spatial transcriptomics provide unprecedented insight into diseases. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and discovery. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, preclinical research often requires in vivo experiments in non-human species, which makes utilizing such valuable resources difficult. To facilitate both human orthologous conversion of non-human transcriptomes and the application of pharmacotranscriptomic databases to pre-clinical research models, we introduce OrthologAL. OrthologAL interfaces with BioMart to access different gene sets from the Ensembl database, allowing for ortholog conversion without the need for user-generated code.\nRESULTS: Researchers can input their single-cell or other high-dimensional gene expression data from any species as a Seurat object, and OrthologAL will output a human ortholog-converted Seurat object for download and use. To demonstrate the utility of this application, we tested OrthologAL using single-cell, single-nuclei, and spatial transcriptomic data derived from common preclinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for the simple conversion of Seurat objects and for applying preclinical, high-dimensional transcriptomics data to functional human-derived small molecule predictions.\nAVAILABILITY AND IMPLEMENTATION: OrthologAL is available for download as an R package with functions to launch the Shiny GUI at https://github.com/AyadLab/OrthologAL or via Zenodo at https://doi.org/10.5281/zenodo.15225041. The medulloblastoma single-cell transcriptomics data were downloaded from the NCBI Gene Expression Omnibus with the identifier GSE129730. 10X Visium data of medulloblastoma PDX mouse models from Vo et al. were acquired by contacting the authors, and the raw data are available from ArrayExpress under the identifier E-MTAB-11720. 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