R4AOP

R4AOP gives R users access to the AOP-Wiki through its REST API. Adverse Outcome Pathways, Key Events, Key Event Relationships, stressors, chemicals and genes are retrieved by calling a function with arguments, and are returned as tibbles that fit straight into an R workflow. The AOP-Wiki RDF data are normally queried with SPARQL; for the routine tasks covered here, the package removes that step.

Installation

# install.packages("remotes")
remotes::install_github("SidraAdil/R4AOP")

Example

Find the Adverse Outcome Pathways that describe liver fibrosis, then follow one of them to its Key Events and genes.

library(R4AOP)

fibrosis <- aop_list(q = "liver fibrosis")
fibrosis[, c("id", "title", "oecd_status")]
#> # A tibble: 4 × 3
#>      id title                                                        oecd_status
#>   <int> <chr>                                                        <chr>      
#> 1    38 Protein Alkylation leading to Liver Fibrosis                 WPHA/WNT E…
#> 2   144 Endocytic lysosomal uptake leading to liver fibrosis         Under Revi…
#> 3   383 Inhibition of Angiotensin-converting enzyme 2 leading to li… Under Deve…
#> 4   494 AhR activation leading to liver fibrosis                     <NA>

aop_summary() gives the overview of one AOP: its Key Events in causal order, from the molecular initiating event to the adverse outcome, with the OECD status, the taxonomic applicability and the stressors in the header.

aop_summary(38)
#> AOP 38: Protein Alkylation leading to Liver Fibrosis
#> OECD status:  WPHA/WNT Endorsed
#> Taxa:         Rattus norvegicus (10116)
#> Taxa (text):  WCS_9606
#> Stressors:    Allyl Alcohol; Carbon tetrachloride; Dimethyl nitrosamine; Retinol; Thioacetamide
#> Data version: 2026.10.31-ca48500
#> 
#> # A tibble: 7 × 5
#>    step ke_id ke_title                                   level     role 
#>   <int> <int> <chr>                                      <chr>     <chr>
#> 1     1   244 Alkylation, Protein                        Molecular mie  
#> 2     2    55 Increase, Cell injury/death                Cellular  ke   
#> 3     3  1492 Tissue resident cell activation            Cellular  ke   
#> 4     4  1493 Increased Pro-inflammatory mediators       Tissue    ke   
#> 5     5   265 Increase, Hepatic stellate cell activation Cellular  ke   
#> 6     6    68 Increase, Collagen accumulation            Tissue    ke   
#> 7     7   344 Increase, Liver fibrosis                   Organ     ao

genes <- aop_genes(38)
nrow(genes)
#> [1] 245
table(genes$detected_by)
#> 
#>  both   ner regex 
#>    54   182     9

Genes are linked to individual Key Events, so each Key Event can be treated as a candidate gene set. The molecular initiating event and the adverse outcome are marked in the titles, in green and red, and a Key Event without gene links is shown with an empty bar:

aop_plot_genes(38)

Key Events are also described in ontology terms, as a biological process, an optional biological object and an action. Blank cells mark components that AOP-Wiki does not record:

aop_plot_components(38)

The same data can be entered from the other side. Starting from a stressor, stressor_aops() returns the AOPs it is used in and the adverse outcomes they end in:

stressor_aops("hexachlorobenzene")[, c("aop_id", "aop_title", "ao_title")]
#> # A tibble: 1 × 3
#>   aop_id aop_title                                                    ao_title  
#>    <int> <chr>                                                        <chr>     
#> 1    131 Aryl hydrocarbon receptor activation leading to uroporphyria Uroporphy…

The gene links can also be returned as a GeneSetCollection, one gene set per Key Event, which passes straight to Bioconductor enrichment tools:

gsc <- ke_gene_sets(38)
names(gsc)
#> [1] "KE_55"   "KE_68"   "KE_265"  "KE_344"  "KE_1492" "KE_1493"

Main functions

Topic	Functions
AOPs	aop_list(), aop_get(), aop_key_events(), aop_kers(), aop_stressors(), aop_chemicals(), aop_genes(), aop_methods()
Key Events	ke_list(), mie_list(), ao_list(), ke_get(), ke_aops(), ke_upstream(), ke_downstream(), ke_mies(), ke_adverse_outcomes(), ke_stressors(), ke_chemicals()
Relationships	ker_list(), ker_get()
Stressors and chemicals	stressor_list(), stressor_get(), stressor_aops(), chemical_list(), chemical_get(), chemical_aops(), chemical_key_events(), chemical_pathways()
Genes and search	gene_list(), gene_get(), ke_gene_sets(), aop_search()
Key Event Components	ke_components(), aop_components(), aop_plot_components()
Summaries	aop_summary(), aop_gene_summary(), aop_plot_genes()
Dataset	aop_dataset_info(), aop_data_version(), aop_cache_clear()

Responses are cached for the session, and list endpoints are paginated automatically, so a call such as ke_list() returns all Key Events rather than the first page.

Reproducibility

AOP-Wiki is curated continuously. Every result carries the version of the data it came from, which can be reported alongside an analysis:

aop_data_version(fibrosis)
#> [1] "2026.10.31-ca48500"

Data source and licence

Data come from the AOP-Wiki RDF API, maintained by the Department of Translational Genomics (TGX) at Maastricht University, and are licensed CC BY-SA 4.0. The package code is licensed MIT.

When using R4AOP, please cite both the package and AOP-Wiki.