Identifier Translation and Mapping in OmnipathR
Zaoqu Liu
Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou Universityliuzaoqu@163.com
Dénes Türei
turei.denes@gmail.comJulio Saez-Rodriguez
Institute for Computational Biomedicine, Heidelberg UniversitySource:
vignettes/id_translation.Rmd
id_translation.Rmd1 Introduction
Biological databases use diverse identifier systems to reference genes, proteins, and other molecular entities. OmnipathR provides a unified framework for translating between these identifier systems, supporting seamless data integration across multiple resources.
1.1 Supported Identifier Types
OmnipathR supports translation between numerous identifier systems:
| Category | Identifier Types |
|---|---|
| Protein | UniProt AC, UniProt ID, RefSeq |
| Gene | HGNC Symbol, Entrez Gene ID, Ensembl Gene ID |
| Transcript | Ensembl Transcript ID, RefSeq mRNA |
| Peptide | Ensembl Peptide ID |
| External | KEGG, Reactome, PDB, InterPro |
2 Theoretical Framework
2.1 Identifier Mapping Architecture
The identifier translation in OmnipathR follows a hub-and-spoke model:
┌─────────────┐
│ UniProt │
│ (Hub) │
└──────┬──────┘
┌───────────────┼───────────────┐
│ │ │
┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
│ Ensembl │ │ HGNC │ │ Entrez │
│ Gene ID │ │ Symbol │ │ Gene ID │
└─────────────┘ └─────────────┘ └─────────────┘2.2 Handling Ambiguous Mappings
Identifier mappings can be:
- One-to-one: Single source maps to single target
- One-to-many: Single source maps to multiple targets (e.g., gene with multiple isoforms)
- Many-to-one: Multiple sources map to single target (e.g., synonyms)
- Many-to-many: Complex relationships
OmnipathR provides tools to quantify and handle these ambiguities.
3 Basic Usage
3.1 Simple Vector Translation
# Translate UniProt IDs to gene symbols
uniprot_ids <- c("P00533", "P04637", "P31749", "P42345")
# Using translate_ids with vector input
result <- translate_ids(
uniprot_ids,
uniprot,
genesymbol
)
print(result)
#> [1] "EGFR" "TP53" "AKT1" "MTOR"3.2 Data Frame Translation
# Create example data frame
expression_data <- tibble(
uniprot = c("P00533", "P04637", "P31749", "P42345", "P06400"),
log2fc = c(2.5, -1.8, 1.2, 0.8, -2.1),
pvalue = c(0.001, 0.005, 0.02, 0.15, 0.003)
)
# Add gene symbol column
expression_annotated <- translate_ids(
expression_data,
uniprot,
genesymbol
)
print(expression_annotated)
#> # A tibble: 5 x 4
#> uniprot log2fc pvalue genesymbol
#> <chr> <dbl> <dbl> <chr>
#> 1 P00533 2.5 0.001 EGFR
#> 2 P04637 -1.8 0.005 TP53
#> 3 P31749 1.2 0.02 AKT1
#> 4 P42345 0.8 0.15 MTOR
#> 5 P06400 -2.1 0.003 RB14 Visualization of Translation Results
# Visualize translated data
if(nrow(expression_annotated) > 0 && "genesymbol" %in% colnames(expression_annotated)) {
plot_data <- expression_annotated %>%
filter(!is.na(genesymbol))
if(nrow(plot_data) > 0) {
ggplot(plot_data, aes(x = reorder(genesymbol, log2fc), y = log2fc, fill = log2fc > 0)) +
geom_col(alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
scale_fill_manual(values = c("TRUE" = "#E74C3C", "FALSE" = "#3498DB"), guide = "none") +
coord_flip() +
labs(
title = "Differential Expression",
subtitle = "UniProt IDs translated to gene symbols",
x = "Gene Symbol",
y = "Log2 Fold Change"
) +
theme_minimal() +
theme(plot.title = element_text(face = "bold"))
}
}
Figure 4.1: Expression data with translated gene symbols showing log2 fold change values.
5 Advanced Translation Features
5.1 Organism-Specific Translation
# Human protein translation (default)
human_result <- translate_ids(
c("P00533", "P04637"),
uniprot,
genesymbol,
organism = 9606 # Human NCBI Taxonomy ID
)
print(human_result)
#> [1] "EGFR" "TP53"6 Integration with OmniPath Data
6.1 Annotating Interaction Data
# Get interactions
interactions <- omnipath(resources = "SIGNOR")
# Check columns
cat("Interaction columns:", paste(head(colnames(interactions), 10), collapse = ", "), "...\n")
#> Interaction columns: source, target, source_genesymbol, target_genesymbol, is_directed, is_stimulation, is_inhibition, consensus_direction, consensus_stimulation, consensus_inhibition ...
cat("Total interactions:", nrow(interactions), "\n")
#> Total interactions: 65532
# Show sample with gene symbols
interactions %>%
select(source, source_genesymbol, target, target_genesymbol) %>%
head(10)
#> # A tibble: 10 x 4
#> source source_genesymbol target target_genesymbol
#> <chr> <chr> <chr> <chr>
#> 1 Q13976 PRKG1 Q13507 TRPC3
#> 2 P06241 FYN Q9Y210 TRPC6
#> 3 Q13976 PRKG1 Q9Y210 TRPC6
#> 4 P12931 SRC Q9Y210 TRPC6
#> 5 Q13976 PRKG1 Q9HCX4 TRPC7
#> 6 Q00535 CDK5 Q8NER1 TRPV1
#> 7 Q13438 OS9 Q9HBA0 TRPV4
#> 8 P18031 PTPN1 Q9H1D0 TRPV6
#> 9 P63244 RACK1 Q9BX84 TRPM6
#> 10 Q9BX84 TRPM6 Q96QT4 TRPM77 Mapping Statistics
# Get all UniProts from interactions
all_uniprots <- unique(c(interactions$source, interactions$target))
cat("Unique proteins in network:", length(all_uniprots), "\n")
#> Unique proteins in network: 6258
# Sample translation
sample_uniprots <- head(all_uniprots, 100)
sample_translated <- translate_ids(
data.frame(uniprot = sample_uniprots),
uniprot,
genesymbol
)
# Calculate success rate
success_rate <- sum(!is.na(sample_translated$genesymbol)) / nrow(sample_translated) * 100
cat("Translation success rate:", round(success_rate, 1), "%\n")
#> Translation success rate: 100 %
# Visualize
mapping_summary <- data.frame(
Status = c("Translated", "Not Found"),
Count = c(sum(!is.na(sample_translated$genesymbol)),
sum(is.na(sample_translated$genesymbol)))
)
ggplot(mapping_summary, aes(x = Status, y = Count, fill = Status)) +
geom_col(alpha = 0.8) +
geom_text(aes(label = Count), vjust = -0.5) +
scale_fill_manual(values = c("Translated" = "#27AE60", "Not Found" = "#E74C3C")) +
labs(
title = "ID Translation Results",
subtitle = paste("Sample of", length(sample_uniprots), "UniProt IDs"),
y = "Number of IDs"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold"),
legend.position = "none"
) +
expand_limits(y = max(mapping_summary$Count) * 1.1)
Figure 7.1: Distribution of identifier mapping success rates across different resources.
8 Session Information
sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 15.6.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] C
#>
#> time zone: Asia/Shanghai
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.1 tibble_3.3.1 dplyr_1.1.4 OmnipathR_3.19.1
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 xfun_0.56 bslib_0.9.0 httr2_1.2.2
#> [5] htmlwidgets_1.6.4 websocket_1.4.4 processx_3.8.6 tzdb_0.5.0
#> [9] vctrs_0.7.0 tools_4.4.0 ps_1.9.1 generics_0.1.4
#> [13] curl_7.0.0 RSQLite_2.4.5 blob_1.2.4 pkgconfig_2.0.3
#> [17] R.oo_1.27.1 checkmate_2.3.3 RColorBrewer_1.1-3 S7_0.2.1
#> [21] readxl_1.4.5 lifecycle_1.0.5 farver_2.1.2 compiler_4.4.0
#> [25] stringr_1.6.0 progress_1.2.3 chromote_0.5.1 htmltools_0.5.9
#> [29] sass_0.4.10 yaml_2.3.12 later_1.4.5 pillar_1.11.1
#> [33] crayon_1.5.3 jquerylib_0.1.4 tidyr_1.3.2 R.utils_2.13.0
#> [37] cachem_1.1.0 sessioninfo_1.2.3 zip_2.3.3 tidyselect_1.2.1
#> [41] rvest_1.0.5 digest_0.6.39 stringi_1.8.7 purrr_1.2.1
#> [45] bookdown_0.44 labeling_0.4.3 grid_4.4.0 fastmap_1.2.0
#> [49] cli_3.6.5 logger_0.4.1 magrittr_2.0.4 utf8_1.2.6
#> [53] dichromat_2.0-0.1 XML_3.99-0.20 withr_3.0.2 readr_2.1.6
#> [57] scales_1.4.0 prettyunits_1.2.0 promises_1.5.0 backports_1.5.0
#> [61] rappdirs_0.3.4 bit64_4.6.0-1 lubridate_1.9.4 timechange_0.3.0
#> [65] rmarkdown_2.30 httr_1.4.7 igraph_2.2.1 bit_4.6.0
#> [69] otel_0.2.0 cellranger_1.1.0 R.methodsS3_1.8.2 hms_1.1.4
#> [73] memoise_2.0.1 evaluate_1.0.5 knitr_1.51 tcltk_4.4.0
#> [77] rlang_1.1.7 Rcpp_1.1.1 glue_1.8.0 DBI_1.2.3
#> [81] selectr_0.5-1 xml2_1.5.2 vroom_1.6.7 jsonlite_2.0.0
#> [85] R6_2.6.1 fs_1.6.69 References
Türei D, et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Molecular Systems Biology 2021;17:e9923.
The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research 2021;49:D480-D489.
Cunningham F, et al. Ensembl 2022. Nucleic Acids Research 2022;50:D988-D995.