Enhanced RNA-Seq Expression Profiling and Functional Enrichment in Non-model Organisms Using Custom Annotations

Functional enrichment analysis is essential for understanding the biological significance of differentially expressed genes. Commonly used tools such as g:Profiler, DAVID, and GOrilla are effective when applied to well-annotated model organisms. However, for non-model organisms, particularly for bacteria and other microorganisms, curated functional annotations are often scarce. In such cases, researchers often rely on homology-based approaches, using tools like BLAST to transfer annotations from closely related species. Although this strategy can yield some insights, it often introduces annotation errors and overlooks unique species-specific functions. To address this limitation, we present a user-friendly and adaptable method for creating custom annotation R packages using genomic data retrieved from NCBI. These packages can be directly imported as libraries into the R environment and are compatible with the clusterProfiler package, enabling effective gene ontology and pathway enrichment analysis. We demonstrate this approach by constructing an R annotation package for Mycobacterium tuberculosis H37Rv, as an example. The annotation package is then utilized to analyze differentially expressed genes from a subset of RNA-seq dataset (GSE292409), which investigates the transcriptional response of M. tuberculosis H37Rv to rifampicin treatment. The chosen dataset includes six samples, with three serving as untreated controls and three exposed to rifampicin for 1 h. Further, enrichment analysis was performed on genes to demonstrate changes in response to the treatment. This workflow provides a reliable and scalable solution for functional enrichment analysis in organisms with limited annotation resources. It also enhances the accuracy and biological relevance of gene expression interpretation in microbial genomics research.