User-friendly tool streamlines transcriptomic data analysis for precision medicine applications

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The Barcelona Institute for Global Health (ISGlobal) has launched HTGAnalyzer, a new, easy-to-use, fast and reproducible bioinformatics tool for advanced transcriptomic data analysis. Designed within the R statistical environment, this package simplifies complex analytical processes, making them accessible to professionals without specific expertise in bioinformatics.

Transcriptomic analysis examines all messenger RNA molecules present in a cell at a given time, revealing which genes are expressed and in what quantity. This information is crucial in precision medicine, an approach that seeks to tailor treatments to each patient’s genetic and molecular profile.

Despite its clinical potential, the implementation of transcriptomic techniques has been limited not by data generation, but by the complexity of the required bioinformatics analysis.

A user-friendly tool for clinicians and researchers

To address this challenge, the ISGlobal team has developed HTGAnalyzer: an automated, free and user-friendly tool for clinicians of various specialties. Laia Díez-Ahijado was the lead author of the tool, which was jointly conceptualized by Natalia Rakislova and Robert Albero.

The study, published in Computers in Biology and Medicine, demonstrates how HTGAnalyzer simplifies complex transcriptomic workflows, including data import and normalization, sample quality control, differential gene expression analysis, functional enrichment, tumor microenvironment profiling and survival analysis. The tool uses patients’ molecular data to identify biological and clinical differences, thereby supporting the clinical interpretation of results.

Validation using real-world oncology data

HTGAnalyzer was validated using multiple datasets, including RNA-seq data from The Cancer Genome Atlas (TCGA) and a cohort of patients with vulvar cancer—a rare and sometimes aggressive malignancy.

In the latter case, the tool identified differentially expressed genes, described their immunological profiles, and revealed genes and cellular pathways linked to this cancer and to patient survival. These findings highlight the clinical potential of HTGAnalyzer for generating knowledge applicable to cancer diagnosis, treatment, and prognosis.

With this development, the research team has achieved its goal of bridging the gap between complex bioinformatic analysis and the practical needs of clinical and research environments. HTGAnalyzer enables professionals without advanced bioinformatics expertise to perform essential transcriptomic analyses that are increasingly being used in personalized medicine to inform diagnosis, prognosis, and personalized treatments based on the patient’s molecular biology.

More information:
Laia Díez-Ahijado et al, HTGAnalyzer: An accessible R package with a web interface for enhanced transcriptomic analysis in precision medicine, Computers in Biology and Medicine (2025). DOI: 10.1016/j.compbiomed.2025.110772