orthogene: gene mapping made easy

Authors: Brian Schilder

README updated: Jan-25-2026

Intro

orthogene is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across 700+ organisms. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using 1:1, many:1, 1:many or many:many gene mappings, both within- and between-species.

In brief, orthogene lets you easily:

• convert_orthologs between any two species.
• map_species names onto standard taxonomic ontologies.
• report_orthologs between any two species.
• map_genes onto standard ontologies
• aggregate_mapped_genes in a matrix.
• get all_genes from any species.
• infer_species from gene names.
• create_background gene lists based one, two, or more species.
• get_silhouettes of each species from phylopic.
• prepare_tree with evolutionary divergence times across >147,000 species.

Citation

If you use orthogene, please cite:

Brian M. Schilder, Alan E. Murphy, Nathan G. Skene (2026). orthogene: a Bioconductor package to easily map genes within and across hundreds of species. bioRxiv, https://doi.org/10.64898/2026.01.17.700094

Documentation website

PDF manual

Installation

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
# orthogene is only available on Bioconductor>=3.14
if(BiocManager::version()<"3.14") BiocManager::install(update = TRUE, ask = FALSE)

BiocManager::install("orthogene")

Docker

orthogene can also be installed via a Docker or Singularity container with Rstudio pre-installed. Further instructions provided here.

Methods

library(orthogene)

data("exp_mouse")
# Setting to "homologene" for the purposes of quick demonstration.
# We generally recommend using method="gprofiler" (default).
method <- "homologene"  

For most functions, orthogene lets users choose between different methods, each with complementary strengths and weaknesses: "gprofiler", "homologene", and "babelgene"

In general, we recommend you use "gprofiler" when possible, as it tends to be more comprehensive.

While "babelgene" contains less species, it queries a wide variety of orthology databases and can return a column “support_n” that tells you how many databases support each ortholog gene mapping. This can be helpful when you need a semi-quantitative measure of mapping quality.

It’s also worth noting that for smaller gene sets, the speed difference between these methods becomes negligible.

Quick example

Convert orthologs

convert_orthologs is very flexible with what users can supply as gene_df, and can take a data.frame/data.table/tibble, (sparse) matrix, or list/vector containing genes.

Genes, transcripts, proteins, SNPs, or genomic ranges will be recognised in most formats (HGNC, Ensembl, RefSeq, UniProt, etc.) and can even be a mixture of different formats.

All genes will be mapped to gene symbols, unless specified otherwise with the ... arguments (see ?orthogene::convert_orthologs or here for details).

Note on non-1:1 orthologs

A key feature of convert_orthologs is that it handles the issue of genes with many-to-many mappings across species. This can occur due to evolutionary divergence, and the function of these genes tend to be less conserved and less translatable. Users can address this using different strategies via non121_strategy=.

gene_df <- orthogene::convert_orthologs(gene_df = exp_mouse,
                                        gene_input = "rownames", 
                                        gene_output = "rownames", 
                                        input_species = "mouse",
                                        output_species = "human",
                                        non121_strategy = "drop_both_species",
                                        method = method) 

## Preparing gene_df.

## sparseMatrix format detected.

## Extracting genes from rownames.

## 15,259 genes extracted.

## Converting mouse ==> human orthologs using: homologene

## Retrieving all organisms available in homologene.

## Mapping species name: mouse

## Common name mapping found for mouse

## 1 organism identified from search: 10090

## Retrieving all organisms available in homologene.

## Mapping species name: human

## Common name mapping found for human

## 1 organism identified from search: 9606

## Checking for genes without orthologs in human.

## Extracting genes from input_gene.

## 13,416 genes extracted.

## Extracting genes from ortholog_gene.

## 13,416 genes extracted.

## Checking for genes without 1:1 orthologs.

## Dropping 46 genes that have multiple input_gene per ortholog_gene (many:1).

## Dropping 56 genes that have multiple ortholog_gene per input_gene (1:many).

## Filtering gene_df with gene_map

## Setting ortholog_gene to rownames.

## 
## =========== REPORT SUMMARY ===========

## Total genes dropped after convert_orthologs :
##    2,016 / 15,259 (13%)

## Total genes remaining after convert_orthologs :
##    13,243 / 15,259 (87%)

knitr::kable(as.matrix(head(gene_df)))

convert_orthologs is just one of the many useful functions in orthogene. Please see the documentation website for the full vignette.

Additional resources

Hex sticker creation

Benchmarking methods

Session Info

utils::sessionInfo()

## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] orthogene_1.17.2
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6              babelgene_22.9           
##  [3] xfun_0.55                 ggplot2_4.0.1            
##  [5] htmlwidgets_1.6.4         rstatix_0.7.3            
##  [7] lattice_0.22-7            vctrs_0.7.0              
##  [9] tools_4.5.1               generics_0.1.4           
## [11] yulab.utils_0.2.3         parallel_4.5.1           
## [13] tibble_3.3.1              pkgconfig_2.0.3          
## [15] Matrix_1.7-4              ggplotify_0.1.3          
## [17] data.table_1.18.0         homologene_1.4.68.19.3.27
## [19] RColorBrewer_1.1-3        S7_0.2.1                 
## [21] desc_1.4.3                lifecycle_1.0.5          
## [23] compiler_4.5.1            farver_2.1.2             
## [25] treeio_1.34.0             dlstats_0.1.7            
## [27] carData_3.0-5             ggtree_4.0.4             
## [29] gprofiler2_0.2.4          fontLiberation_0.1.0     
## [31] fontquiver_0.2.1          ggfun_0.2.0              
## [33] htmltools_0.5.9           yaml_2.3.12              
## [35] lazyeval_0.2.2            plotly_4.11.0            
## [37] Formula_1.2-5             pillar_1.11.1            
## [39] car_3.1-3                 ggpubr_0.6.2             
## [41] tidyr_1.3.2               abind_1.4-8              
## [43] fontBitstreamVera_0.1.1   nlme_3.1-168             
## [45] tidyselect_1.2.1          aplot_0.2.9              
## [47] digest_0.6.39             dplyr_1.1.4              
## [49] purrr_1.2.1               rprojroot_2.1.1          
## [51] fastmap_1.2.0             grid_4.5.1               
## [53] here_1.0.2                cli_3.6.5                
## [55] magrittr_2.0.4            patchwork_1.3.2          
## [57] dichromat_2.0-0.1         broom_1.0.11             
## [59] ape_5.8-1                 withr_3.0.2              
## [61] gdtools_0.4.4             scales_1.4.0             
## [63] backports_1.5.0           rappdirs_0.3.4           
## [65] httr_1.4.7                rmarkdown_2.30           
## [67] rvcheck_0.2.1             otel_0.2.0               
## [69] ggsignif_0.6.4            evaluate_1.0.5           
## [71] knitr_1.51                rworkflows_1.0.8         
## [73] viridisLite_0.4.2         gridGraphics_0.5-1       
## [75] rlang_1.1.7               ggiraph_0.9.2            
## [77] Rcpp_1.1.1                glue_1.8.0               
## [79] tidytree_0.4.6            BiocManager_1.30.27      
## [81] renv_1.1.6                rstudioapi_0.18.0        
## [83] jsonlite_2.0.0            R6_2.6.1                 
## [85] badger_0.2.5              systemfonts_1.3.1        
## [87] fs_1.6.6

Related projects

Tools

• gprofiler2: orthogene uses this package. gprofiler2::gorth() pulls from many orthology mapping databases.

• homologene: orthogene uses this package. Provides API access to NCBI HomoloGene database.

• babelgene: orthogene uses this package. babelgene::orthologs() pulls from many orthology mapping databases.

• annotationTools: For interspecies microarray data.

• orthology: R package for ortholog mapping (deprecated?).

• hpgltools::load_biomart_orthologs(): Helper function to get orthologs from biomart.

• JustOrthologs: Ortholog inference from multi-species genomic sequences.

• orthologr: Ortholog inference from multi-species genomic sequences.

• OrthoFinder: Gene duplication event inference from multi-species genomics.

Databases

• HomoloGene: NCBI database that the R package homologene pulls from.

• gProfiler: Web server for functional enrichment analysis and conversions of gene lists.

• OrtholoGene: Compiled list of gene orthology resources.

Contact

Neurogenomics Lab

UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub
DockerHub