Interspecies gene mapping (original) (raw)

Installation

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.

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

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](reference/convert%5Forthologs.html) 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%)
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.

Session Info

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] orthogene_1.13.01 rmarkdown_2.29   
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6              babelgene_22.9           
##  [3] xfun_0.51                 ggplot2_3.5.1            
##  [5] htmlwidgets_1.6.4         rstatix_0.7.2            
##  [7] lattice_0.22-6            vctrs_0.6.5              
##  [9] tools_4.4.2               generics_0.1.3           
## [11] yulab.utils_0.2.0         parallel_4.4.2           
## [13] tibble_3.2.1              pkgconfig_2.0.3          
## [15] Matrix_1.7-1              data.table_1.17.0        
## [17] homologene_1.4.68.19.3.27 ggplotify_0.1.2          
## [19] RColorBrewer_1.1-3        desc_1.4.3               
## [21] lifecycle_1.0.4           compiler_4.4.2           
## [23] farver_2.1.2              treeio_1.30.0            
## [25] dlstats_0.1.7             munsell_0.5.1            
## [27] carData_3.0-5             ggtree_3.14.0            
## [29] gprofiler2_0.2.3          ggfun_0.1.8              
## [31] htmltools_0.5.8.1         yaml_2.3.10              
## [33] lazyeval_0.2.2            plotly_4.10.4            
## [35] Formula_1.2-5             pillar_1.10.1            
## [37] car_3.1-3                 ggpubr_0.6.0             
## [39] tidyr_1.3.1               grr_0.9.5                
## [41] abind_1.4-8               nlme_3.1-166             
## [43] tidyselect_1.2.1          aplot_0.2.5              
## [45] digest_0.6.37             dplyr_1.1.4              
## [47] purrr_1.0.4               rprojroot_2.0.4          
## [49] fastmap_1.2.0             grid_4.4.2               
## [51] here_1.0.1                colorspace_2.1-1         
## [53] cli_3.6.4                 magrittr_2.0.3           
## [55] patchwork_1.3.0           broom_1.0.7              
## [57] ape_5.8-1                 withr_3.0.2              
## [59] scales_1.3.0              backports_1.5.0          
## [61] httr_1.4.7                rvcheck_0.2.1            
## [63] ggsignif_0.6.4            evaluate_1.0.3           
## [65] knitr_1.49                rworkflows_1.0.6         
## [67] viridisLite_0.4.2         gridGraphics_0.5-1       
## [69] rlang_1.1.5               Rcpp_1.0.14              
## [71] glue_1.8.0                tidytree_0.4.6           
## [73] BiocManager_1.30.25       renv_1.1.2               
## [75] jsonlite_1.9.1            R6_2.6.1                 
## [77] badger_0.2.4              fs_1.6.5

Databases