Background

Gene enrichment analysis is a method to understand the functional pattern of a group of genes that are differentially expressed. While primary transcriptome analysis gives an overview of gene expression by quantifying RNA-Seq reads mapping to different genomic regions, gene enrichment analysis adds functional significance [1]. By aggregating signals from groups of genes, this method becomes critically important, as individual genes alone do not adequately reflect the functional dynamics of gene expression [2]. To date, there is a plethora of free resources available for defining the ontology and function based on statistical thresholds. These tools enable users to provide a list of genes and select the suitable organism and certain statistical parameters to design an ontology and pathway enrichment map. However, these tools often lack support for comprehensive, broad-spectrum analyses. Moreover, most do not allow seamless integration or reuse of results across different analytical workflows [3]. For larger datasets, issues pertaining to computational time and result accuracy also persist. Additionally, the need to rely on external tools for gene name conversion and result customization presents a further limitation.

Most gene ontology and pathway enrichment tools, such as g: Profiler, DAVID, GOrilla, BINGO, Enrichr, GOnet, ShinyGO, and KOBAS, provide enrichment analysis for well-annotated model organisms [4–11]. However, these tools offer limited support for organisms that are not commonly used as models, primarily due to the absence of comprehensive functional annotations. As a result, conducting enrichment analysis for such organisms remains a significant challenge. In many cases, when gene lists from these organisms are submitted, the tools may either reject the input or fail to return meaningful results. To overcome this limitation, researchers often follow a multi-step approach. This involves extracting the sequences of differentially expressed genes, identifying homologous sequences through similarity searches against reference databases of closely related species, and assigning gene ontology terms based on the best matching hits. The assigned ontology terms are then subjected to enrichment analysis using tools that accept ontology term input, since many platforms depend on gene identifiers from well-characterized species. This method has become a commonly used strategy to perform functional profiling in organisms for which detailed annotations are not available in standard gene ontology resources. Despite its practical use, there is no widely accepted protocol or standardized method for functional annotation and enrichment analysis in organisms that lack sufficient reference data. In an effort to address this gap, we explored open-source platforms, community discussions, and public repositories to identify reliable and accessible methods capable of producing biologically relevant results that are consistent with those obtained for model organisms. This search led us to the in-house development of a custom annotation strategy. We constructed organism-specific annotation packages using publicly available genomic data from NCBI and Expasy. These annotation packages can be imported as libraries into the R environment and are fully compatible with other established packages from Bioconductor. They allow researchers to perform gene ontology and pathway enrichment analysis with ease and flexibility [12,13]. We found this approach to enhance reproducibility and analytical depth, offering a dependable framework for functional analysis using well-established tools and customizable workflows.