Over the course of my career, I have contributed to 10+ transcriptomics projects, spanning a range of clinical research areas including glioblastoma, Alzheimer’s disease, Parkinson’s disease, and metabotranscriptomic analyses. I have gained hands-on experience with a diverse set of computational tools tailored to various transcriptomic analyses. These include CIRIquant for circular RNA quantification, Bowtie for small RNA alignment and quantification, STAR for splice-aware RNA-seq alignment, LeafCutter for detecting differential splicing events, and TEtranscripts/TEcount for quantifying transposable elements such as human endogenous retroviruses (HERVs). In addition to these specialized workflows, I have routinely performed standard RNA-seq data processing steps, including raw data quality control, post-alignment QC, normalization, differential expression analysis, and functional enrichment. These analyses are primarily implemented using R-based packages such as limma, edgeR, and clusterProfiler, alongside GSEA from the Broad Institute. This combined experience demonstrates both my technical versatility and my deep understanding of transcriptomic data analysis in a biomedical context.