Análisis de microARN utilizando Galaxy: superando barreras bioinformáticas en la formación estudiantil

Abstract

Omics data analysis has become a cornerstone of modern biology, yet the technical complexity of bioinformatics tools remains a significant barrier for students and researchers without programming expertise. This study aimed to demonstrate that a complete microRNA workflow can be carried out entirely within the Galaxy platform, as a pedagogical strategy to make bioinformatics more accessible in the life sciences. Six human brain tissue samples—three fetal and three adult—were obtained from a public repository and analyzed. The workflow included quality control with FastQC, adapter trimming with Cutadapt, alignment to the human genome using HISAT2, and read quantification with featureCounts. Differential expression analysis was conducted with DESeq2. The pipeline achieved high mapping rates (87–93%) and consistent assignment of reads to known miRNAs. Principal component analysis revealed clear separation between fetal and adult groups, while heatmaps confirmed the reproducibility of biological replicates and differences across brain regions. Additional outputs, including p-value distributions, dispersion estimates, and MA-plots, reflected typical RNA-seq patterns and highlighted sets of miRNAs with significant differential expression. By leveraging Galaxy, the entire analysis was completed without the need for programming skills or advanced computing infrastructure, underscoring its value as a teaching tool for omics data analysis. In conclusion, this study demonstrates that a reproducible and accessible workflow for microRNA profiling can be implemented in Galaxy, offering a practical educational resource for bioinformatics training.