ArticleMethods and protocols2026
Biological Functional Class Enrichment Analysis with R, an Annotated Tutorial for Bench Scientists.
Article in Methods and protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- BRD2 impedes iPSC reprogramming by regulating lipid biosynthesis and the matrisome via its ET tail.Communications biology · 2026Article
- Network-guided transcriptomics identifies drug-linked host-response biomarkers in tuberculosis disease-state transition.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
High-throughput sequencing generally results in a list of genes. Which functional groups of genes among the DEGs are meaningful underlying factors to the differential biological/biomedical conditions under investigation? The process to find answers to this question can be called biological functional class enrichment analysis (FunCEA). R is a robust platform for FunCEA due to its accessibility by general users and availability of well-developed R packages for enrichment analysis and visualization, as well as for knowledge databases. Bench scientists in biomedical sciences need accessible and easy-to-understand protocols for FunCEA. This R tutorial provides detailed R scripts or command lines for FunCEA, as well as for data processing and visualization of the enrichment results. It keeps bench scientists in mind and provides supportive and apprehensible descriptions of the R scripts for each task (enrichment analysis, enrichment data processing, and visualization). It describes detailed procedures for the two popular FunCEA methods, the so-called over-representation analysis (ORA) and functional class scoring (FCS). The introduced FunCEA here uses three basic knowledge databases: gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome. R codes for various visualizations (dot plot, term-gene network plot, enrichment map plot, ridge plot, and GSEA plot) are presented and annotated. Since all analyses are conducted in R, no commercial software is needed, yet
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.