The current human reference genome, primarily derived from a single individual, does not capture the full spectrum of genetic variation found across human populations. While recent pangenome projects aim to represent this diversity, their application to whole transcriptome RNA-sequencing analysis remains uncertain, particularly regarding accuracy and impact across populations with different genetic ancestries. This project will conduct a benchmarking study comparing RNA-seq mapping outcomes against multiple reference genome constructs: GRCh38, the telomere-to-telomere reference genome (CHM13), individual and combined references from the Human Pangenome Research Project, and de novo assembled individualized genomes generated by long-read sequencing. The study will evaluate tradeoffs in computational efficiency, mapping accuracy, and transcriptome interpretation across these reference frameworks. We hypothesize that using reference genomes with ancestry-related composition will improve RNA-seq alignment accuracy and enhance functional interpretation of gene expression. Through three specific aims, we will (1) compare alignment metrics using GRCh38 and CHM13; (2) assess the utility of ancestry-informed pangenome references for improving transcriptome analyses; and (3) benchmark individualized reference genomes against standard and pangenome-based references. In addition to advancing methods for accurate transcriptome analysis, this project will provide training opportunities for faculty, postdoctoral researchers, and students at Morehouse School of Medicine. The findings will inform whether individualized or population-based reference genomes improve RNA-seq data interpretation, ultimately strengthening the precision and reproducibility of genomics research.