{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:20:13Z","timestamp":1768519213831,"version":"3.49.0"},"posted":{"date-parts":[[2024,11,26]]},"group-title":"Bioinformatics","reference-count":47,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,11,26]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, pre-clinical research often requires\n                  <jats:italic>in vivo<\/jats:italic>\n                  experiments in non-human species, which makes capitalizing on such valuable resources difficult. To facilitate the application of pharmacotranscriptomic databases to pre-clinical research models and to facilitate human orthologous conversion of non-human transcriptomes, we introduce OrthologAL. OrthologAL leverages the BioMart database to access different gene sets from Ensembl, facilitating the interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional gene expression data from any species, and OrthologAL will output a human ortholog-converted dataset for download and use. To demonstrate the utility of this application, we characterized orthologous conversion in single-cell, single-nuclei, and spatial transcriptomic data derived from common pre-clinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. We show that 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 applying pre-clinical, high-dimensional transcriptomics data in functional small molecule predictions using existing human-annotated databases.\n                <\/jats:p>","DOI":"10.1101\/2024.11.25.625000","type":"posted-content","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T20:25:30Z","timestamp":1732652730000},"source":"Crossref","is-referenced-by-count":0,"title":["OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data"],"prefix":"10.64898","author":[{"given":"Rishika","family":"Chowdary","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert K.","family":"Suter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"D\u2019Antuono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Gomes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Stein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ki-Bum","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae K","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nagi G.","family":"Ayad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024112807102925000_2024.11.25.625000v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3456"},{"key":"2024112807102925000_2024.11.25.625000v1.2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-10-22"},{"key":"2024112807102925000_2024.11.25.625000v1.3","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti525"},{"key":"2024112807102925000_2024.11.25.625000v1.4","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad1049"},{"key":"2024112807102925000_2024.11.25.625000v1.5","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkac1052"},{"key":"2024112807102925000_2024.11.25.625000v1.6","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-023-01767-y"},{"key":"2024112807102925000_2024.11.25.625000v1.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2021.04.048"},{"key":"2024112807102925000_2024.11.25.625000v1.8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.05.031"},{"key":"2024112807102925000_2024.11.25.625000v1.9","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.4096"},{"key":"2024112807102925000_2024.11.25.625000v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3192"},{"key":"2024112807102925000_2024.11.25.625000v1.11","doi-asserted-by":"crossref","unstructured":"hao Y. 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