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_orthologsbetween any two species.map_speciesnames onto standard taxonomic ontologies.report_orthologsbetween any two species.map_genesonto standard ontologiesaggregate_mapped_genesin a matrix.- get
all_genesfrom any species. infer_speciesfrom gene names.create_backgroundgene lists based one, two, or more species.get_silhouettesof each species from phylopic.prepare_treewith evolutionary divergence times across >147,000 species.
If you use orthogene, please cite:
Brian M. Schilder, Nathan G. Skene (2022). orthogene: Interspecies gene mapping. R package version 1.4.0, https://doi.org/doi:10.18129/B9.bioc.orthogene
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")orthogene can also be installed via a
Docker
or
Singularity
container with Rstudio pre-installed. Further instructions provided
here.
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.
| gprofiler | homologene | babelgene | |
|---|---|---|---|
| Reference organisms | 700+ | 20+ | 19 (but cannot convert between pairs of non-human species) |
| Gene mappings | More comprehensive | Less comprehensive | More comprehensive |
| Updates | Frequent | Less frequent | Less frequent |
| Orthology databases | Ensembl, HomoloGene, WormBase | HomoloGene | HGNC Comparison of Orthology Predictions (HCOP), which includes predictions from eggNOG, Ensembl Compara, HGNC, HomoloGene, Inparanoid, NCBI Gene Orthology, OMA, OrthoDB, OrthoMCL, Panther, PhylomeDB, TreeFam and ZFIN |
| Data location | Remote | Local | Local |
| Internet connection | Required | Not required | Not required |
| Speed | Slower | Faster | Medium |
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).
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)))| astrocytes_ependymal | endothelial-mural | interneurons | microglia | oligodendrocytes | pyramidal CA1 | pyramidal SS | |
|---|---|---|---|---|---|---|---|
| TSPAN12 | 0.3303571 | 0.5872340 | 0.6413793 | 0.1428571 | 0.1207317 | 0.2864750 | 0.1453634 |
| TSHZ1 | 0.4285714 | 0.4468085 | 1.1551724 | 0.4387755 | 0.3621951 | 0.0692226 | 0.8320802 |
| ADAMTS15 | 0.0089286 | 0.0978723 | 0.2206897 | 0.0000000 | 0.0231707 | 0.0117146 | 0.0375940 |
| CLDN12 | 0.2232143 | 0.1148936 | 0.5517241 | 0.0510204 | 0.2609756 | 0.4376997 | 0.6842105 |
| RXFP1 | 0.0000000 | 0.0127660 | 0.2551724 | 0.0000000 | 0.0158537 | 0.0511182 | 0.0751880 |
| SEMA3C | 0.1964286 | 0.9957447 | 8.6379310 | 0.2040816 | 0.1853659 | 0.1608094 | 0.2280702 |
convert_orthologs is just one of the many useful functions in
orthogene. Please see the documentation
website
for the full vignette.
utils::sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## 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_1.8.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.4 babelgene_22.9
## [3] xfun_0.41 ggplot2_3.4.4
## [5] htmlwidgets_1.6.4 rstatix_0.7.2
## [7] lattice_0.22-5 vctrs_0.6.5
## [9] tools_4.3.1 generics_0.1.3
## [11] yulab.utils_0.1.1 parallel_4.3.1
## [13] tibble_3.2.1 fansi_1.0.6
## [15] pkgconfig_2.0.3 Matrix_1.6-4
## [17] ggplotify_0.1.2 data.table_1.14.10
## [19] homologene_1.4.68.19.3.27 RColorBrewer_1.1-3
## [21] desc_1.4.3 lifecycle_1.0.4
## [23] compiler_4.3.1 treeio_1.26.0
## [25] dlstats_0.1.7 munsell_0.5.0
## [27] carData_3.0-5 ggtree_3.10.0
## [29] gprofiler2_0.2.2 ggfun_0.1.3
## [31] htmltools_0.5.7 yaml_2.3.8
## [33] lazyeval_0.2.2 plotly_4.10.3
## [35] pillar_1.9.0 car_3.1-2
## [37] ggpubr_0.6.0 tidyr_1.3.0
## [39] cachem_1.0.8 grr_0.9.5
## [41] abind_1.4-5 nlme_3.1-164
## [43] tidyselect_1.2.0 aplot_0.2.2
## [45] digest_0.6.33 dplyr_1.1.4
## [47] purrr_1.0.2 rprojroot_2.0.4
## [49] fastmap_1.1.1 grid_4.3.1
## [51] here_1.0.1 colorspace_2.1-0
## [53] cli_3.6.2 magrittr_2.0.3
## [55] patchwork_1.1.3 utf8_1.2.4
## [57] broom_1.0.5 ape_5.7-1
## [59] withr_2.5.2 scales_1.3.0
## [61] backports_1.4.1 httr_1.4.7
## [63] rmarkdown_2.25 rvcheck_0.2.1
## [65] ggsignif_0.6.4 memoise_2.0.1.9000
## [67] evaluate_0.23 knitr_1.45
## [69] rworkflows_1.0.1 viridisLite_0.4.2
## [71] gridGraphics_0.5-1 rlang_1.1.2
## [73] Rcpp_1.0.11 glue_1.6.2
## [75] tidytree_0.4.6 BiocManager_1.30.22
## [77] renv_1.0.3 rstudioapi_0.15.0
## [79] jsonlite_1.8.8 R6_2.5.1
## [81] badger_0.2.3 fs_1.6.3
-
gprofiler2:orthogeneuses this package.gprofiler2::gorth()pulls from many orthology mapping databases. -
homologene:orthogeneuses this package. Provides API access to NCBI HomoloGene database. -
babelgene:orthogeneuses 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.
-
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.
UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
GitHub
DockerHub
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent