Impact of Gene Annotation Choices on Spatial Transcriptomics Data Analysis

RNA sequencing technologies enable the simultaneous detection of tens of thousands of genes, providing researchers with a powerful tool for identifying novel genes involved in biological processes and potential targets for new treatments. A recent advancement in this field is spatial transcriptomics, which allows for the analysis of gene expression within the context of tissue architecture. The quantification of gene expression in RNA sequencing data depends on gene annotation models that specify the chromosomal coordinates of genes and their exons. This project aims to assess how gene annotation models from various sources impact the quantification and interpretation of spatial transcriptomics data. We will collect spatial transcriptomics datasets and apply multiple gene annotation sources (e.g., Ensembl, RefSeq) to re-annotate the data. By comparing gene expression patterns and spatial mappings across these annotations, we will assess how variations in gene annotation affect data interpretation, particularly in identifying gene expression hotspots and spatial patterns.