Evidence map›Paper›PMID 40392208›Full record

ArticleBioinformatics (Oxford, England)2025

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

Rishika Chowdary, Robert K Suter, Matthew D'Antuono, Cynthia Gomes, Joshua Stein, Ki-Bum Lee, Jae K Lee, Nagi G Ayad

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Rishika ChowdaryDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, United States.
Robert K SuterDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, United States.ORCID 0000-0002-3335-8426
Matthew D'AntuonoDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, United States.
Cynthia GomesThe Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, United States.
Joshua SteinDepartment of Chemistry and Chemical Biology, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, United States.
Ki-Bum LeeDepartment of Chemistry and Chemical Biology, Rutgers University, The State University of New Jersey, Piscataway, NJ 08854, United States.
Jae K LeeThe Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL 33136, United States.
Nagi G AyadDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, United States.ORCID 0009-0002-1994-3095

Funding

Targeting Cell-Type Specific Disease Phenotypes to Promote CNS RepairRM1NS133003 · NINDS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI NAGI G AYAD, Jae K Lee · 2023 to 2026
$4.8M
Epigenetic pathways and cell cycle exitR01NS118023 · NINDS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI AYAD, NAGI G · 2020 to 2024
$1.8M
NINDS NIH HHS R01 NS118023NINDS NIH HHS R01NS118023NINDS NIH HHS RM1 NS133003NINDS NIH HHS RM1NS133003
6 · The paper itself

Abstract

motivationSingle-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.

resultsResearchers 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. AVAILABILITY 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. The single-cell and single-nuclei transcriptomics data of rat and mouse spinal-cord injury were acquired from the Gene Expression Omnibus under the identifiers GSE213240 and GSE234774.

Indexed as

Gene Expression ProfilingSoftwareTranscriptomeAnimalsDatabases, GeneticHumansMiceRatsSingle-Cell Analysis

Identifiers

PMID40392208
PMCPMC12158155

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.