Package 'rbioapi'

Description

Run this function to test the internet connectivity of your device and the current status of the supported Services.

Usage

rba_connection_test(print_output = TRUE, diagnostics = FALSE)

Arguments

Details

This function attempts to send a simple query to the supported services. If the service successfully responded, you will be informed with a success message; If not, the content of the error will be reported to you.
Please run this function if you encounter any errors while using rbioapi. Also, if you need to contact support, kindly call this function with 'diagnostic = TRUE' and include the output messages in your support request.

Value

Connection test for the supported servers will be displayed in console and the results will be invisibly returned as a list.

See Also

Other "Helper functions": rba_metadata(), rba_options(), rba_pages()

Examples

rba_connection_test()

Description

This function provides a convenient one-step wrapper for performing enrichment analysis on a given gene list using Enrichr. It simplifies the process by internally calling the necessary functions in the correct order. See the details section for more information.

Usage

rba_enrichr(
  gene_list,
  description = NULL,
  gene_set_library = "all",
  regex_library_name = FALSE,
  organism = "human",
  background_genes = NULL,
  progress_bar = TRUE,
  ...
)

Arguments

Details

This function will call other rba_enrichr_*** functions with the following order:

1. (If necessary) Call rba_enrichr_libs to obtain a list of available libraries in Enrichr for the given organism.

2. Call rba_enrichr_add_list to upload your gene-list and obtain a 'user list ID'.

3. (If necessary) Call rba_enrichr_add_background to upload your background gene-list and obtain a 'background list ID'.

4. Call rba_enrichr_enrich to perform enrichment analysis on the gene-list against one or multiple Enrichr libraries

Value

A list containing data frames of the enrichment results of your supplied gene-list against the selected Enrichr libraries.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/datasetStatistics"
"POST https://maayanlab.cloud/Enrichr/addList"
"POST https://maayanlab.cloud/speedrichr/api/addList"
"POST https://maayanlab.cloud/speedrichr/api/addbackground"
"GET https://maayanlab.cloud/Enrichr/export"
"POST https://maayanlab.cloud/speedrichr/api/backgroundenrich"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

Other "Enrichr": rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_libs(), rba_enrichr_view_list()

Other "Enrichment/Over-representation": rba_mieaa_enrich(), rba_panther_enrich(), rba_reactome_analysis(), rba_string_enrichment(), rba_string_enrichment_image()

Examples

## Not run: 
rba_enrichr(gene_list = c("TP53", "TNF", "EGFR"))

## End(Not run)

rba_enrichr(gene_list = c("TP53", "TNF", "EGFR"),
    gene_set_library = "GO_Molecular_Function_2025",
    regex_library_name = FALSE)


rba_enrichr(gene_list = c("TP53", "TNF", "EGFR"),
    gene_set_library = "^GO_Molecular_Function_(2025|2026)$",
    regex_library_name = TRUE)

Description

In addition to the main gene list, you can also submit a background gene list to Enrichr. This gene list can be used later to compute the statistics of the enrichment analysis.

Usage

rba_enrichr_add_background(background_genes, ...)

Arguments

Details

Please note that rba_enrichr provides a one-step and more convenient way to automatically handle this and other required function calls needed to perform gene set enrichment analysis with Enrichr.

Value

A list with the unique IDs for your uploaded background gene list.

Corresponding API Resources

"POST https://maayanlab.cloud/speedrichr/api/addbackground"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

rba_enrichr

Other "Enrichr": rba_enrichr(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_libs(), rba_enrichr_view_list()

Examples

my_background_genes <- c(
"NSUN3", "POLRMT", "NLRX1", "SFXN5", "ZC3H12C", "SLC25A39", "ARSG",
"DEFB29", "PCMTD2", "ACAA1A", "LRRC1", "2810432D09RIK", "SEPHS2",
"SAC3D1", "TMLHE", "LOC623451", "TSR2", "PLEKHA7", "GYS2", "ARHGEF12",
"HIBCH", "LYRM2", "ZBTB44", "ENTPD5", "RAB11FIP2", "LIPT1",
"INTU", "ANXA13", "KLF12", "SAT2", "GAL3ST2", "VAMP8", "FKBPL",
"AQP11", "TRAP1", "PMPCB", "TM7SF3", "RBM39", "BRI3", "KDR", "ZFP748",
"NAP1L1", "DHRS1", "LRRC56", "WDR20A", "STXBP2", "KLF1", "UFC1",
"CCDC16", "9230114K14RIK", "RWDD3", "2610528K11RIK")

rba_enrichr_add_background(background_genes = my_background_genes)

Description

This function uploads your gene list to Enrichr and retrieves a unique 'user list ID' required for performing enrichment analysis.

Usage

rba_enrichr_add_list(
  gene_list,
  description = NULL,
  organism = "human",
  speedrichr = FALSE,
  ...
)

Arguments

Details

Enrichr uses separate APIs for analysis with or without a background gene list. Set 'speedrichr = TRUE' if this gene list will be used with a background gene list; otherwise, set it to FALSE. Gene lists submitted with 'speedrichr = TRUE' can only be analyzed with a background set, and those submitted with 'speedrichr = FALSE' can only be analyzed without one. Currently, background-based enrichment is supported only for human libraries.

Please note that rba_enrichr provides a one-step and more convenient way to automatically handle this and other required function calls needed to perform gene set enrichment analysis with Enrichr.

Value

A list with the unique IDs for your uploaded gene list. 'userListId' is the ID required for later steps.

Corresponding API Resources

"POST https://maayanlab.cloud/Enrichr/addList"
"POST https://maayanlab.cloud/speedrichr/api/addList"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

rba_enrichr

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_libs(), rba_enrichr_view_list()

Examples

rba_enrichr_add_list(gene_list = c("TP53", "TNF", "EGFR"),
     description = "tumoral genes",
     speedrichr = FALSE)


rba_enrichr_add_list(gene_list = c("RAG1", "RAG2", "DNTT", "LIG4", "ARTEMIS"),
     description = "TCR rearrangment",
     speedrichr = TRUE)

Description

This function retrieves enrichment analysis results for your supplied 'user_list_id' against one or multiple Enrichr libraries.

Usage

rba_enrichr_enrich(
  user_list_id,
  gene_set_library = "all",
  regex_library_name = FALSE,
  organism = "human",
  background_id = NULL,
  progress_bar = TRUE,
  ...
)

Arguments

Details

If 'background_id' is supplied, this function will interact with the speedrichr API. In this case, 'user_list_id' must have been obtained from a rba_enrichr_add_list call with the 'speedrichr' parameter set to 'TRUE'. Additionally, this feature is only available for "human" organism.

Please note that rba_enrichr provides a one-step and more convenient way to automatically handle this and other required function calls needed to perform gene set enrichment analysis with Enrichr.

Value

A list containing data frames of the enrichment results of your supplied gene-list against the selected Enrichr libraries.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/export"
"POST https://maayanlab.cloud/speedrichr/api/backgroundenrich"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

rba_enrichr

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_libs(), rba_enrichr_view_list()

Examples

## Not run: 
rba_enrichr_enrich(user_list_id = 11111)

## End(Not run)
## Not run: 
rba_enrichr_enrich(user_list_id = 11111,
    gene_set_library = "GO_Molecular_Function_2017",
    regex_library_name = FALSE)

## End(Not run)
## Not run: 
rba_enrichr_enrich(user_list_id = 11111,
    gene_set_library = "go",
    regex_library_name = TRUE)

## End(Not run)

Description

This function will search the gene and retrieve a list of Enrichr Terms that contains that gene.

Usage

rba_enrichr_gene_map(gene, categorize = FALSE, organism = "human", ...)

Arguments

Value

A nested list containing the search results of your supplied gene.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/genemap"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_sets(), rba_enrichr_libs(), rba_enrichr_view_list()

Examples

rba_enrichr_gene_map(gene = "p53")


rba_enrichr_gene_map(gene = "p53", categorize = TRUE)

Description

This function retrieves all gene sets in a given Enrichr library. If a term is supplied, only the gene set associated with that term will be returned.

Usage

rba_enrichr_gene_sets(gene_set_library, term = NULL, organism = "human", ...)

Arguments

Details

If the available Enrichr library names are not already stored in the "rba_enrichr_libs" global option, this function will retrieve and store them before validating the supplied library name.

Value

A named list of character vectors. Each element name is an Enrichr term and the corresponding character vector contains the genes in that gene set. If the save_file option was set to TRUE or a valid path, the raw GMT file will also be saved to the requested path.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/geneSetLibrary"

Saving Gene Sets as a GMT File

To save the raw server's response as a GMT file, supply the save_file rbioapi option through .... Set it to TRUE to automatically generate a proper file path, or supply a character string with a valid file or directory path. See rba_options's arguments manual for more information on available options. The organism will be included in the automatically-generated file name. If a term is supplied, its name will also be included.

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

rba_enrichr

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_libs(), rba_enrichr_view_list()

Examples

rba_enrichr_gene_sets(
    gene_set_library = "Reactome_Pathways_2024",
    term = "Signaling by NOTCH")


rba_enrichr_gene_sets(gene_set_library = "Reactome_Pathways_2024")

## Not run: 
rba_enrichr_gene_sets(
    gene_set_library = "Reactome_Pathways_2024",
    save_file = "Reactome_Pathways_2024.gmt")

## End(Not run)

Description

This function retrieves a list of libraries available in Enrichr along with their associated statistics. Each library represents a collection of gene sets that can be used for enrichment analysis.

Usage

rba_enrichr_libs(organism = "human", store_in_options = TRUE, ...)

Arguments

Details

By default, this function will save the library names as a global option ("rba_enrichr_libs") for other Enrichr functions that internally require the names of Enrichr libraries. You should call this function once per R session with the argument 'store_in_options = TRUE' before using rba_enrichr_gene_sets, rba_enrichr or rba_enrichr_enrich. However, if you do not explicitly call it, rbioapi will automatically execute this function in the background the when it is needed.

Please note that rba_enrichr provides a one-step and more convenient way to automatically handle this and other required function calls needed to perform gene set enrichment analysis with Enrichr.

Value

A data frame with the names of available library in Enrichr and their statistics.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/datasetStatistics"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

rba_enrichr

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_view_list()

Examples

rba_enrichr_libs()

Description

Retrieve the list of uploaded genes with a given 'user list ID'.

Usage

rba_enrichr_view_list(
  user_list_id,
  organism = "human",
  speedrichr = FALSE,
  ...
)

Arguments

Value

A list containing the genes and description associated to the supplied user_list_id.

Corresponding API Resources

"GET https://maayanlab.cloud/Enrichr/view"
"GET https://maayanlab.cloud/speedrichr/api/view"

References

• Chen, E.Y., Tan, C.M., Kou, Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. Bioinformatics 14, 128 (2013). https://doi.org/10.1186/1471-2105-14-128

• Maxim V. Kuleshov, Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, Sherry L. Jenkins, Kathleen M. Jagodnik, Alexander Lachmann, Michael G. McDermott, Caroline D. Monteiro, Gregory W. Gundersen, Avi Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Research, Volume 44, Issue W1, 8 July 2016, Pages W90–W97, https://doi.org/10.1093/nar/gkw377

• Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma’ayan, A. (2021). Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. doi: 10.1002/cpz1.90

• Enrichr API Documentation

• Citations note on Enrichr website

See Also

Other "Enrichr": rba_enrichr(), rba_enrichr_add_background(), rba_enrichr_add_list(), rba_enrichr_enrich(), rba_enrichr_gene_map(), rba_enrichr_gene_sets(), rba_enrichr_libs()

Examples

## Not run: 
rba_enrichr_view_list(user_list_id = 11111)

## End(Not run)

Description

JASPAR organizes matrix profiles into collections. Using this function, you can retrieve a list of available collections in a JASPAR release.

Usage

rba_jaspar_collections(release = 2026, ...)

Arguments

Value

A data frame with collections' names and URLs.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/collections/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_collections(release = 2026)

Description

Using this function you can list all matrix profiles that are available in a collection from a JASPAR release.

Usage

rba_jaspar_collections_matrices(
  collection,
  release = 2026,
  only_last_version = FALSE,
  search = NULL,
  order = NULL,
  page_size = 1000,
  page = 1,
  ...
)

Arguments

Details

The results are paginated. You can control the page's size number with the function's arguments. Also, you can use rba_pages to automatically iterate over multiple pages.

Value

A list that contains a data frame with information on matrix profiles available in the collection.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/collections/{collection}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_collections_matrices(collection = "CORE",
  release = 2026,
  page_size = 100,
  page = 2)

Description

Using this function, you can retrieve the position frequency matrix (PFM) associated with a matrix profile identifier, along with its details and annotations. If a base ID (i.e., without a version suffix) is supplied, the latest version will be returned.

Usage

rba_jaspar_matrix(matrix_id, file_format = NULL, save_to = NULL, ...)

Arguments

Value

A list containing the PFM, details, and annotations. If file_format is supplied, the function returns the unparsed file content as a character string.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/matrix/{matrix_id}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_matrix("MA0600.2")

## Not run: 
rba_jaspar_matrix(matrix_id = "MA0600.2",
                  file_format = "meme",
                  save_to = "my_matrix.meme")

## End(Not run)

Description

You can use this function to list the JASPAR matrix profiles that match your search query, or run the function without any arguments to return a list of every matrix profile available in the latest release.

Usage

rba_jaspar_matrix_search(
  term = NULL,
  tf_name = NULL,
  tf_class = NULL,
  tf_family = NULL,
  tax_group = NULL,
  tax_id = NULL,
  data_type = NULL,
  collection = NULL,
  release = 2026,
  only_last_version = FALSE,
  order = NULL,
  page_size = 1000,
  page = 1,
  ...
)

Arguments

Details

Note that this is a search function. Thus, you are not required to fill every argument; you may use whatever combinations of arguments you see fit for your query.
The results are paginated. You can control the page's size number with the function's arguments. Also, you can use rba_pages to automatically iterate over multiple pages.

Value

A list that contains a data frame of matrix profiles' information.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/matrix/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_matrix_search(term = "FOX")
rba_jaspar_matrix_search(tf_name = "FOXP3")
rba_jaspar_matrix_search(tf_name = "FOXP3", only_last_version = TRUE)
rba_jaspar_matrix_search(tf_class = "Zipper-Type")
rba_jaspar_matrix_search(tax_group = "insects")
rba_jaspar_matrix_search(page_size = 100)

Description

Since JASPAR release 2010, matrix profiles have been versioned. A matrix profile identifier follows a "base_id.version" naming scheme. Using this function, you can retrieve a list of matrix profiles associated with a base (stable) ID.

Usage

rba_jaspar_matrix_versions(base_id, order = NULL, ...)

Arguments

Value

A data frame with information on the matrix profile versions.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/matrix/{base_id}/versions/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_matrix_versions("MA0600")

Description

If a release number is supplied, this function returns the details of that release. Otherwise, when called without the release_number argument, it returns a list of all JASPAR database releases.

Usage

rba_jaspar_releases(release_number = NULL, ...)

Arguments

Value

A list that contains all JASPAR database releases' information or details of a particular release.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/releases/"
"GET https://jaspar.elixir.no/api/v1/releases/{release_number}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_releases()
rba_jaspar_releases(11)

Description

Use this function to retrieve a list of transcription factor binding sites associated with a matrix profile.

Usage

rba_jaspar_sites(matrix_id, ...)

Arguments

Value

A list containing a data frame with binding-site information.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/sites/{matrix_id}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_sites("MA0600.1")

Description

JASPAR organizes matrix profiles from multiple species in multiple taxonomic groups. Use this function to retrieve a list of available species in a JASPAR database release.

Usage

rba_jaspar_species(release = 2026, search = NULL, order = NULL, ...)

Arguments

Value

A data frame with information on available species.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/species/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_species(release = 2026)

Description

JASPAR curates matrix profiles from multiple species in multiple taxonomic groups. Using this function you can list all matrix profiles that are available in a JASPAR release for a species.

Usage

rba_jaspar_species_matrices(
  tax_id,
  release = 2026,
  only_last_version = FALSE,
  search = NULL,
  order = NULL,
  page_size = 1000,
  page = 1,
  ...
)

Arguments

Details

The results are paginated. You can control the page's size number with the function's arguments. Also, you can use rba_pages to automatically iterate over multiple pages.

Value

A list that contains a data frame with information on matrix profiles available for the species.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/species/{tax_id}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_species_matrices(tax_id = 9606, page_size = 100)

Description

JASPAR organizes matrix profiles from multiple species in multiple taxonomic groups. Use this function to retrieve a list of available taxonomic groups in a JASPAR database release.

Usage

rba_jaspar_taxons(release = 2026, ...)

Arguments

Value

A data frame with information on available taxonomic groups.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/taxon/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_taxons(release = 2026)

Description

JASPAR organizes matrix profiles from multiple species in multiple taxonomic groups. Using this function you can list all matrix profiles that are available in a JASPAR release from a taxonomic group.

Usage

rba_jaspar_taxons_matrices(
  tax_group,
  release = 2026,
  only_last_version = FALSE,
  search = NULL,
  order = NULL,
  page_size = 1000,
  page = 1,
  ...
)

Arguments

Details

The results are paginated. You can control the page's size number with the function's arguments. Also, you can use rba_pages to automatically iterate over multiple pages.

Value

A list that contains a data frame with information on matrix profiles available for the taxonomic group.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/taxon/{tax_group}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_tffm(), rba_jaspar_tffm_search()

Examples

rba_jaspar_taxons_matrices(tax_group = "plants", page_size = 100)

Description

Using this function, you can retrieve details and annotations for the transcription factor flexible model (TFFM) associated with a TFFM ID. If a base ID (i.e., without a version suffix) is supplied, the latest version will be returned.

Usage

rba_jaspar_tffm(tffm_id, ...)

Arguments

Value

A list that contains the TFFM's information and annotations.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/tffm/{tffm_id}/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm_search()

Examples

rba_jaspar_tffm("TFFM0056.3")

Description

You can use this function to list the JASPAR TF flexible models (TFFMs) that match your search query, or run the function without any arguments to return a list of every TFFM profile available in the latest release.

Usage

rba_jaspar_tffm_search(
  term = NULL,
  release = 2026,
  tax_group = NULL,
  search = NULL,
  order = NULL,
  page_size = 1000,
  page = 1,
  ...
)

Arguments

Details

Note that this is a search function. Thus, you are not required to fill every argument; you may use whatever combinations of arguments you see fit for your query.
The results are paginated. You can control the page's size number with the function's arguments. Also, you can use rba_pages to automatically iterate over multiple pages.

Value

A list that contains a data frame with information on matching TFFMs.

Corresponding API Resources

"GET https://jaspar.elixir.no/api/v1/tffm/"

References

• Baydar Ovek D, et al. JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Res. 2026;54(D1):D184-D193; doi: 10.1093/nar/gkaf1209

• Khan, A. and Mathelier, A. JASPAR RESTful API: accessing JASPAR data from any programming language. Bioinformatics, 2017, doi: 10.1093/bioinformatics/btx804

• JASPAR API Documentation

• Citations note on JASPAR website

See Also

Other "JASPAR": rba_jaspar_collections(), rba_jaspar_collections_matrices(), rba_jaspar_matrix(), rba_jaspar_matrix_search(), rba_jaspar_matrix_versions(), rba_jaspar_releases(), rba_jaspar_sites(), rba_jaspar_species(), rba_jaspar_species_matrices(), rba_jaspar_taxons(), rba_jaspar_taxons_matrices(), rba_jaspar_tffm()

Examples

rba_jaspar_tffm_search(term = "FOX")
rba_jaspar_tffm_search(tax_group = "insects")
rba_jaspar_tffm_search(page_size = 100)

Description

Retrieve API request metadata saved with an rbioapi result. Metadata is off by default. Set metadata = TRUE on one call to save metadata for that result, or use rba_options(metadata = TRUE) to save it for all later calls.

Usage

rba_metadata(result)

Arguments

Details

Saving metadata does not change the result's class. The returned rba_metadata object is a list. Printing it shows a short summary; use $ or [[ to access its elements. Using rba_metadata(result) is equivalent to retrieving attributes(result)$rbioapi_metadata.

The list contains:

• rbioapi_version: the rbioapi version used to create the result.

• requests: request entries in the order they were made. Each entry contains:

  • timestamp: the date value from the original httr response.

  • call: the API call used for the request.

  • response: the original httr response object.

  • parsers: the exact parser functions used, in the order they ran.

Functions that use several requests to create one result save their entries in the order the requests were made. Each result returned by rba_pages() keeps its own metadata. Retry attempts are included when they receive an HTTP response. If a response was not parsed, its parsers list is empty.

Saving the complete httr responses and parser functions can make results and saved files much larger.

Value

An object of class rba_metadata containing saved API request metadata, or NULL if result has no metadata.

See Also

Other "Helper functions": rba_connection_test(), rba_options(), rba_pages()

Examples

## Not run: 
## Save metadata with one result:
result <- rba_reactome_species(metadata = TRUE)
request_metadata <- rba_metadata(result)

## Print a short summary:
request_metadata

## Check the rbioapi version saved with the result:
request_metadata$rbioapi_version

## View the requests without printing full functions and responses:
str(request_metadata$requests, max.level = 2)

## View one original httr response in more detail:
str(request_metadata$requests[[1]]$response, max.level = 1)

## End(Not run)

Description

Each combination of species and miRNA type supports a predefined set of enrichment categories. This function retrieves the categories available for a given combination.

Usage

rba_mieaa_cats(mirna_type, species, mode = "all", ...)

Arguments

Value

A named character vector whose values are the supported category identifiers and whose names are their descriptions. If the selected subset has no categories, returns character(0).

Corresponding API Resources

"GET https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/enrichment_categories/{species}/{mirna_type}/{mode}"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_convert_type(), rba_mieaa_convert_version(), rba_mieaa_enrich(), rba_mieaa_enrich_results(), rba_mieaa_enrich_status(), rba_mieaa_enrich_submit()

Examples

rba_mieaa_cats("mature", "Homo sapiens")

Description

miRBase identifiers can refer to either mature or precursor miRNAs. (see: A uniform system for microRNA annotation). Use this function to convert mature miRNA identifiers to precursor identifiers or vice versa.

Usage

rba_mieaa_convert_type(
  mirna,
  input_type,
  only_unique = FALSE,
  simple_output = FALSE,
  ...
)

Arguments

Value

Depending on simple_output, a data frame or character vector containing the mappings returned by miEAA. Unrecognized or unmapped supplied identifiers can be omitted from the output.

Corresponding API Resources

"POST https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/mirna_precursor_converter/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_version(), rba_mieaa_enrich(), rba_mieaa_enrich_results(), rba_mieaa_enrich_status(), rba_mieaa_enrich_submit()

Examples

Sys.sleep(1) # to prevent 429 error during R CMD check
rba_mieaa_convert_type(mirna = c("hsa-miR-20b-5p", "hsa-miR-144-5p"),
    input_type = "mature")

Description

miEAA uses miRBase v22 identifiers. This function converts a set of mature or precursor miRNA identifiers between two supported miRBase versions.

Usage

rba_mieaa_convert_version(
  mirna,
  mirna_type,
  input_version,
  output_version,
  simple_output = FALSE,
  ...
)

Arguments

Value

Depending on simple_output, a data frame or character vector containing the mappings returned by miEAA. Unrecognized or unmapped supplied identifiers can be omitted from the output.

Corresponding API Resources

"POST https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/mirbase_converter/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_type(), rba_mieaa_enrich(), rba_mieaa_enrich_results(), rba_mieaa_enrich_status(), rba_mieaa_enrich_submit()

Examples

Sys.sleep(1) # to prevent 429 error during R CMD check
rba_mieaa_convert_version(mirna = c("hsa-miR-20b-5p", "hsa-miR-144-5p"),
    mirna_type = "mature", input_version = 22, output_version =  16)

Description

This function is a wrapper for the multiple function calls necessary to perform enrichment analysis on a given miRNA list using miEAA. See Details section for more information.

Usage

rba_mieaa_enrich(
  test_set,
  mirna_type,
  test_type,
  species,
  categories = NULL,
  p_adj_method = "fdr",
  independent_p_adj = TRUE,
  sig_level = 0.05,
  min_hits = 2,
  ref_set = NULL,
  sort_by = "p_adjusted",
  sort_asc = TRUE,
  poll_interval = 5,
  poll_timeout = 300,
  ...
)

Arguments

Details

This function will call other rba_mieaa_*** functions with the following order:

1. Call rba_mieaa_enrich_submit to submit an enrichment analysis request to the miEAA server using the supplied miRNA list and other arguments.

2. Once the job is successfully submitted, call rba_mieaa_enrich_status at the selected polling interval to check whether the server-side analysis has finished.

3. Call rba_mieaa_enrich_results to retrieve the results of your enrichment analysis.

See each function's manual for more details. The save_file rbioapi option applies only to the final enrichment results and not to the intermediate submission or status responses.

Value

A data frame with your enrichment analysis results.

Corresponding API Resources

"https://ccb-compute2.cs.uni-saarland.de/mieaa/api/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_type(), rba_mieaa_convert_version(), rba_mieaa_enrich_results(), rba_mieaa_enrich_status(), rba_mieaa_enrich_submit()

Other "Enrichment/Over-representation": rba_enrichr(), rba_panther_enrich(), rba_reactome_analysis(), rba_string_enrichment(), rba_string_enrichment_image()

Examples

## Not run: 
rba_mieaa_enrich(test_set = c("hsa-miR-20b-5p", "hsa-miR-144-5p",
 "hsa-miR-17-5p", "hsa-miR-20a-5p"),
     mirna_type = "mature",
     test_type = "ORA",
     species = 9606,
     categories = "miRPathDB_GO_Biological_process_mature")

## End(Not run)

Description

After your submitted enrichment analysis request has finished (check using rba_mieaa_enrich_status), you can retrieve the results using this function.

Usage

rba_mieaa_enrich_results(job_id, sort_by = "p_adjusted", sort_asc = TRUE, ...)

Arguments

Details

Note that using rba_mieaa_enrich is a more convenient way to automatically perform this and other required function calls to perform enrichment analysis on your input miRNA-set using miEAA.

Value

A data frame with your enrichment analysis results.

Corresponding API Resources

"GET https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/enrichment_analysis/results/{job_id}/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_type(), rba_mieaa_convert_version(), rba_mieaa_enrich(), rba_mieaa_enrich_status(), rba_mieaa_enrich_submit()

Examples

## Not run: 
rba_mieaa_enrich_results("f52d1aef-6d3d-4d51-9020-82e68fe99012")

## End(Not run)

Description

After you have submitted your enrichment analysis (using rba_mieaa_enrich_submit) and retrieved a job-id, you can use this function to check the status of the job. The status is either a numeric completion percentage or "FAILED". A status value equal to 100 means that the requested analysis has finished and you may retrieve the results using rba_mieaa_enrich_results.

Usage

rba_mieaa_enrich_status(job_id, ...)

Arguments

Details

Note that using rba_mieaa_enrich is a more convenient way to automatically perform this and other required function calls to perform enrichment analysis on your input miRNA-set using miEAA.

Value

A list containing a status element with either the numeric completion percentage or "FAILED" for the supplied job ID. A completed job also includes its results URL.

Corresponding API Resources

"GET https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/job_status/{job_id}/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_type(), rba_mieaa_convert_version(), rba_mieaa_enrich(), rba_mieaa_enrich_results(), rba_mieaa_enrich_submit()

Examples

## Not run: 
Sys.sleep(1) # to prevent 429 error during R CMD check
rba_mieaa_enrich_status("f52d1aef-6d3d-4d51-9020-82e68fe99012")

## End(Not run)

Description

Submit a request to the miEAA server to perform over-representation analysis or gene set enrichment analysis for a set of miRNA identifiers.

Usage

rba_mieaa_enrich_submit(
  test_set,
  mirna_type,
  test_type,
  species,
  categories = NULL,
  p_adj_method = "fdr",
  independent_p_adj = TRUE,
  sig_level = 0.05,
  min_hits = 2,
  ref_set = NULL,
  ...
)

Arguments

Details

Note that using rba_mieaa_enrich is a more convenient way to automatically perform this and other required function calls to perform enrichment analysis on your input miRNA-set using miEAA.

Value

A list that contains your submitted job's ID and a URL to manually check for your job status.

Corresponding API Resources

"POST https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/enrichment_analysis/{species}/{type}/{test}/"

References

• Ernesto Aparicio-Puerta, Pascal Hirsch, Georges P. Schmartz, Fabian Kern, Tobias Fehlmann, Andreas Keller, miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations, Nucleic Acids Research, Volume 51, Issue W1, 5 July 2023, Pages W319–W325, https://doi.org/10.1093/nar/gkad392

• miEAA browsable API tutorial

• Citation note on miEAA website

See Also

Other "miEAA": rba_mieaa_cats(), rba_mieaa_convert_type(), rba_mieaa_convert_version(), rba_mieaa_enrich(), rba_mieaa_enrich_results(), rba_mieaa_enrich_status()

Examples

Sys.sleep(1) # to prevent 429 error during R CMD check
rba_mieaa_enrich_submit(test_set = c("hsa-miR-20b-5p", "hsa-miR-144-5p"),
    mirna_type = "mature",
    test_type = "GSEA",
    species = 9606,
    categories = NULL)

Description

A safe way to change rbioapi's global options and behavior. see "arguments" section for available options.
Note that you are not limited to changing the options globally, you can include the option names and values in the '...' argument of any rbioapi function to alter the option(s) only in that function call; e.g. example_function(x, diagnostics = TRUE, timeout = 300).
Alternatively, you can call this function with no arguments, i.e. rba_options(), to retrieve a data frame of available rbioapi options and their current values.

Usage

rba_options(
  diagnostics = NULL,
  dir_name = NULL,
  retry_max = NULL,
  retry_wait = NULL,
  progress = NULL,
  save_file = NULL,
  skip_error = NULL,
  timeout = NULL,
  verbose = NULL,
  metadata = NULL
)

Arguments

Details

Because this function validates your supplied changes, please only change rbioapi options using this function and avoid directly editing them.

Value

If called without any argument, a Data frame with available options and their information; If Called with an argument, will Return NULL but Alters that option globally.

See Also

Other "Helper functions": rba_connection_test(), rba_metadata(), rba_pages()

Examples

rba_options()
## Not run: 
rba_options(verbose = FALSE)

## End(Not run)
## Not run: 
rba_options(save_file = TRUE)

## End(Not run)
## Not run: 
rba_options(diagnostics = TRUE, progress = TRUE)

## End(Not run)
## Not run: 
## Save metadata with all later rbioapi calls:
rba_options(metadata = TRUE)

## Turn it off again:
rba_options(metadata = FALSE)

## End(Not run)

Description

Evaluate a quoted call to an exported rbioapi function for multiple page numbers. Calls are made sequentially, and their results are returned in the requested order.

Usage

rba_pages(
  input_call,
  page_arg = NULL,
  pages = NULL,
  sleep_time = 2,
  skip_error = TRUE,
  progress = FALSE,
  verbose = getOption("rba_verbose")
)

Arguments

Details

Pagination can be specified in either of two ways. To request an inclusive range, set the named page argument in input_call to a character string of the form "pages:start:end". Alternatively, omit the page argument from input_call, supply its exact name through page_arg, and supply the desired page numbers through pages. The range may run in either direction, and the two forms cannot be combined. The page argument must exactly match a formal function argument; partial and positional matching are not used for pagination.

Page numbers must be unique positive whole numbers, and no more than 100 pages can be requested in one call. sleep_time seconds are inserted between successive calls.

The value of skip_error is passed to every page call, allowing rbioapi's standard error-handling mechanism to determine whether a failed request stops the operation. If progress = TRUE, one progress bar is displayed and verbose = FALSE and progress = FALSE are passed to the individual rbioapi calls.

Value

A named list containing one element per requested page. Element names have the form page_<number>.

See Also

Other "Helper functions": rba_connection_test(), rba_metadata(), rba_options()

Examples

rba_pages(
  input_call = quote(
    rba_uniprot_taxonomy_name(
      name = "adenovirus",
      search_type = "contain",
      page_size = 20,
      page_number = "pages:1:2"
    )
  )
)


rba_pages(
  input_call = quote(
    rba_uniprot_taxonomy_name(
      name = "adenovirus",
      search_type = "contain",
      page_size = 20
    )
  ),
  page_arg = "page_number",
  pages = c(1, 3)
)

Description

Use PANTHER services to perform over-representation enrichment analysis. You can either provide a character vector of gene IDs for over-representation analysis, or a data frame of gene IDs and expression analysis.
Please refer to the details section for more information on the statistical analysis.

Usage

rba_panther_enrich(
  genes,
  organism,
  annot_dataset,
  test_type = NULL,
  correction = "FDR",
  cutoff = NULL,
  ref_genes = NULL,
  ref_organism = NULL,
  request_mapped_genes = "input",
  ...
)

Arguments

Details

Over-representation Test: It assesses whether specific gene sets are represented in your input gene list differently from what is expected by chance. It uses Fisher's exact test or Binomial test to calculate p-values. Fisher's exact test determines the probability of observing the gene counts in a category based on a hypergeometric distribution; the binomial test compares the observed proportion of genes in a category to the expected proportion based on the reference list. A significant p-value indicates over-representation or under-representation of a gene set.

Statistical Enrichment Test: The statistical enrichment test uses the Mann-Whitney U (Wilcoxon Rank-Sum) test to assess if the expression values associated with genes in a specific category differ significantly from the overall distribution in the input list. This non-parametric test first ranks the numerical values and computes whether the expression values were randomly drawn from the overall distribution of values. A small p-value indicates that the numerical values for the genes in the category are significantly different from the background distribution, thus non-random patterns.

Please note that starting from rbioapi version 0.8.2, you can supply a gene expression data frame to perform statistical enrichment analysis. In earlier versions, only a character vector of gene IDs was possible, thus only over-representation analysis.

Value

For a successful analysis, a list. The "result" element is a data frame with one row per returned annotation term and columns describing the term, observed counts, enrichment direction, and statistical significance. The remaining elements contain input and reference mapping summaries, when applicable, and PANTHER analysis and release metadata.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/enrich/overrep"
"POST https://www.pantherdb.org/services/oai/pantherdb/enrich/statenrich"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• Mi H, Muruganujan A, Huang X, Ebert D, Mills C, Guo X, Thomas PD. (2019) Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nature Protocols, 14, 703–721. https://doi.org/10.1038/s41596-019-0128-8

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_family(), rba_panther_genome(), rba_panther_homolog(), rba_panther_info(), rba_panther_mapping(), rba_panther_ortholog(), rba_panther_tree_grafter()

Other "Enrichment/Over-representation": rba_enrichr(), rba_mieaa_enrich(), rba_reactome_analysis(), rba_string_enrichment(), rba_string_enrichment_image()

Examples

rba_panther_enrich(
  genes = c("TP53", "BRCA1", "CDK2", "Q99835", "CDC42"),
  organism = 9606, annot_dataset = "GO:0008150",
  cutoff = 0.01
  )



expression_df <- data.frame(
  genes = c("TP53", "BRCA1", "CDK2", "CDC42", "CDK1"),
  expr = c(10, 8, 6, 4, 2)
  )

rba_panther_enrich(
  genes = expression_df,
  organism = 9606,
  annot_dataset = "GO:0008150"
  )

Description

Using this function, you can retrieve Orthologs, MSA or Tree topology information of a given PANTHER family.

Usage

rba_panther_family(id, what, target_organisms = NULL, ...)

Arguments

Value

For trees a list and otherwise a data frame with the requested family's information.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/familyortholog"
"POST https://www.pantherdb.org/services/oai/pantherdb/familymsa"
"POST https://www.pantherdb.org/services/oai/pantherdb/treeinfo"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_genome(), rba_panther_homolog(), rba_panther_info(), rba_panther_mapping(), rba_panther_ortholog(), rba_panther_tree_grafter()

Examples

rba_panther_family("PTHR10000", what = "ortholog")

Description

Retrieve one page of genes and their associated information for a genome supported by PANTHER. Each page contains up to 1,000 genes.

Usage

rba_panther_genome(organism, page, ...)

Arguments

Value

A list with the following elements:

gene

A data frame with one row per returned gene. Fields are kept as returned by PANTHER, with annotation information in nested columns.

page

The retrieved page.

pages_count

The total number of available pages.

number_of_genes_in_genome

The total number of genes in the genome.

product

PANTHER product source and version information.

search_type

The search type reported by PANTHER.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/downloadgenome"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_homolog(), rba_panther_info(), rba_panther_mapping(), rba_panther_ortholog(), rba_panther_tree_grafter()

Examples

rba_panther_genome(organism = 243273, page = 1)

Description

Using this function you can search and retrieve homolog of given gene(s).

Usage

rba_panther_homolog(genes, organism, type = "P", target_organisms = NULL, ...)

Arguments

Value

A data frame with homolog information.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/ortholog/homologOther"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_genome(), rba_panther_info(), rba_panther_mapping(), rba_panther_ortholog(), rba_panther_tree_grafter()

Examples

rba_panther_homolog("OR4F5", organism = 9606, type = "P")

Description

Using this function you can retrieve a list of available organisms, annotation datasets, families, and pathways which are supported in PANTHER.

Usage

rba_panther_info(what, organism_chr_loc = FALSE, families_page = 1, ...)

Arguments

Value

For families, a list containing family information, the requested page, and the total number of pages. For the species tree, a list; otherwise a data frame with pertinent information.

Corresponding API Resources

"GET https://www.pantherdb.org/services/oai/pantherdb/supportedgenomes"
"GET https://www.pantherdb.org/services/oai/pantherdb/supportedannotdatasets"
"GET https://www.pantherdb.org/services/oai/pantherdb/supportedpantherfamilies"
"GET https://www.pantherdb.org/services/oai/pantherdb/supportedpantherpathways"
"GET https://www.pantherdb.org/services/oai/pantherdb/speciestree"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_genome(), rba_panther_homolog(), rba_panther_mapping(), rba_panther_ortholog(), rba_panther_tree_grafter()

Examples

rba_panther_info(what = "organisms")


rba_panther_info(what = "families", families_page = 4)

Description

Using this function, you can search your genes in PANTHER database and retrieve attributes and annotations associated to your genes.

Usage

rba_panther_mapping(genes, organism, ...)

Arguments

Value

A list containing your unmapped inputs and mapped genes with pertinent information.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/geneinfo"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_genome(), rba_panther_homolog(), rba_panther_info(), rba_panther_ortholog(), rba_panther_tree_grafter()

Examples

rba_panther_mapping(genes = c("Cd40", 7124, "ENSG00000203747", "P33681"),
    organism = 9606)

Description

Using this function you can search and retrieve orthologs of given gene(s), and optionally return the corresponding position in the target organisms' protein sequences.

Usage

rba_panther_ortholog(
  genes,
  organism,
  type = "all",
  target_organisms = NULL,
  seq_pos = NULL,
  include_msa = NULL,
  ...
)

Arguments

Value

A data frame with Orthologs information.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/ortholog/matchortho"
"POST https://www.pantherdb.org/services/oai/pantherdb/ortholog/homologpos"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_genome(), rba_panther_homolog(), rba_panther_info(), rba_panther_mapping(), rba_panther_tree_grafter()

Examples

rba_panther_ortholog("CD40", organism = 9606, type = "LDO")

Description

Use this function to retrieve a PANTHER family's tree topology information with a node corresponding to your sequence grafted in the best location in that tree.

Usage

rba_panther_tree_grafter(protein_seq, target_organisms = NULL, ...)

Arguments

Details

For more information, see: Haiming Tang, Robert D Finn, Paul D Thomas, TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations, Bioinformatics, Volume 35, Issue 3, February 2019, Pages 518–520, doi:10.1093/bioinformatics/bty625

Value

A list containing PANTHER tree topology information.

Corresponding API Resources

"POST https://www.pantherdb.org/services/oai/pantherdb/graftsequence"

References

• Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou L-P, Mi H. (2022) PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22. https://doi.org/10.1002/pro.4218

• PANTHER Services Details

• Citations note on PANTHER website

See Also

Other "PANTHER": rba_panther_enrich(), rba_panther_family(), rba_panther_genome(), rba_panther_homolog(), rba_panther_info(), rba_panther_mapping(), rba_panther_ortholog()

Examples

rba_panther_tree_grafter("MKVLWAALLVTFLAGCQAKVEQAVETE")

Description

Using this function, you can perform Reactome Analysis In a convenient way. The Analysis Type will be chosen depending on your supplied input:

1. If you supply a vector or a single-columned table, "Over-Representation" analysis will be performed.

2. If you supply a multi-column table, with the first column being molecules identifiers and the rest being numeral expression values, "Expression" analysis will be performed.

See the details section for the accepted input types and format.

Usage

rba_reactome_analysis(
  input,
  input_format = NULL,
  projection = TRUE,
  interactors = FALSE,
  species = NULL,
  sort_by = "ENTITIES_PVALUE",
  order = "ASC",
  resource = "TOTAL",
  p_value = 1,
  include_disease = TRUE,
  min = NULL,
  max = NULL,
  ...
)

Arguments

Details

You can supply your table or vector input in numerous formats:

1. An R object which can be a data frame, matrix, or simple vector.

2. A path to a local text file in your device that contains the molecules data. (The file should be formatted correctly, see below.)

3. An HTTP or HTTPS URL pointing to a text file on the web that contains the molecules data. (The file should be formatted correctly, see below.)

If you supply a text file (as a local file path or URL), it should be in TSV (Tab-Separated Values) format; the first column name should start with "#". Note that if you are providing the file for "Over-Representation" analysis (i.e. Single columned-data) this header line is optional and will be used as your 'Sample Name', otherwise it is required.
Also, form the "summary" element in the function's output, you can see how Reactome Interpreted your input and subsequently the type of analysis that has been performed.
There is no strict criteria about the type of your molecules Identifiers, Reactome will Map the IDs to it's internal database entities. Nevertheless, You can check if all your identifiers has been found in "identifiersNotFound" element in the function's output.
After any analysis, Reactome will associate a token with your analysis. It can later be used in functions that require the token (e.g. to retrieve the analysis results, download pdf).
Note that Reactome will store your token for only 7 days. You can download your full results with rba_reactome_analysis_download, and re-import it anytime to reactome (using rba_reactome_analysis_import) to generate a new token.

Value

A list containing the results and information about the analysis. The pathways element is a data frame with information about each pathway expanded into columns; it is an empty data frame when no pathways match. The token in results$summary$token can be used to retrieve the results later or in other Reactome analysis functions.

Corresponding API Resources

"POST https://reactome.org/AnalysisService/identifiers/form"
"POST https://reactome.org/AnalysisService/identifiers/form/projection"
"POST https://reactome.org/AnalysisService/identifiers/url"
"POST https://reactome.org/AnalysisService/identifiers/url/projection"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Analysis Service": rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Other "Enrichment/Over-representation": rba_enrichr(), rba_mieaa_enrich(), rba_panther_enrich(), rba_string_enrichment(), rba_string_enrichment_image()

Examples

## Not run: 
rba_reactome_analysis(input = c("p53", "BRCA1", "cdk2", "Q99835", "CDC42"))

## End(Not run)
## Not run: 
rba_reactome_analysis(input = "c:/rbioapi/genes.txt")

## End(Not run)
## Not run: 
rba_reactome_analysis(input = "https://example.com/genes.txt")

## End(Not run)

Description

Based on the "request" argument, you can download different analysis results data associated with a given token.

Usage

rba_reactome_analysis_download(
  token,
  request,
  save_to = NULL,
  resource = "TOTAL",
  ...
)

Arguments

Details

Token is associated to each Reactome analysis results and kept by Reactome for at least 7 days. You can locate it in rba_reactome_analysis's output, under a sub-list named "summary" (i.e. results$summary$token).
Use rba_reactome_analysis_pdf to save a full report in PDF format.

Value

NULL, a CSV,JSON or Gzipped JSON file will be saved to disk based on your input.

Corresponding API Resources

GET https://reactome.org/AnalysisService/download/{token}/entities/ found/{resource}/{filename}.csv"
GET https://reactome.org/AnalysisService/download/{token}/entities/ notfound/{filename}.csv"
GET https://reactome.org/AnalysisService/download/{token}/pathways/ {resource}/{filename}.csv"
GET https://reactome.org/AnalysisService/download/{token}/result.json"
GET https://reactome.org/AnalysisService/download/{token}/result.json.gz"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_analysis_pdf rba_reactome_analysis

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Examples

## Not run: 
rba_reactome_analysis_download(token = "MjAyMDEwMTYwMTI3MTNfMjY1MjM",
    request = "pathways", save_to = "found_ids.csv")

## End(Not run)
## Not run: 
rba_reactome_analysis_download(token = "MjAyMDEwMTYwMTI3MTNfMjY1MjM",
    request = "found_ids", save_to = "found_ids.csv")

## End(Not run)

Description

If you have a JSON file of analysis results (only obtained via rba_reactome_analysis_download with the result argument set to "results", or "results_gz"), you can import the results back to Reactome and retrieve a token.
This is useful when you want to use other Reactome services which require a token but you do not have a token or your token has been expired (i.e. more than 7 days passed from your analysis).

Usage

rba_reactome_analysis_import(input, input_format = NULL, ...)

Arguments

Value

A list containing the new token and other information of your imported results.

Corresponding API Resources

"POST https://reactome.org/AnalysisService/import/form"
"POST https://reactome.org/AnalysisService/import/url"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_download(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Examples

## Not run: 
rba_reactome_analysis_import("c:/rbioapi/res.json")

## End(Not run)
## Not run: 
rba_reactome_analysis_import("https://qaz.com/res.json.gz")

## End(Not run)

Description

Use this function to map molecule identifiers of different species to Reactome Identifiers.

Usage

rba_reactome_analysis_mapping(
  input,
  input_format = NULL,
  projection = TRUE,
  interactors = FALSE,
  ...
)

Arguments

Value

List containing your identifiers and the IDS and resources they are mapped to.

Corresponding API Resources

"POST https://reactome.org/AnalysisService/mapping/form"
"POST https://reactome.org/AnalysisService/mapping/form/projection"
"POST https://reactome.org/AnalysisService/mapping/url"
"POST https://reactome.org/AnalysisService/mapping/url/projection"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Examples

## Not run: 
rba_reactome_analysis_mapping(c("Q8SQ34", "cd40"))

## End(Not run)

Description

Use this function to save a detailed report of your previous analysis (That you have done with rba_reactome_analysis). You need to supply a 'token' associated to your previous analysis.

Usage

rba_reactome_analysis_pdf(
  token,
  species,
  save_to = NULL,
  number = 25,
  resource = "TOTAL",
  diagram_profile = "Modern",
  analysis_profile = "Standard",
  fireworks_profile = "Barium Lithium",
  ...
)

Arguments

Details

Token is associated to each Reactome analysis results and kept by Reactome for at least 7 days. You can locate it in rba_reactome_analysis's output, under a sub-list named "summary" (i.e. results$summary$token).
Note that Reactome will store your token for only 7 days. You can download your full results with rba_reactome_analysis_download, and re-import it anytime to reactome (using rba_reactome_analysis_import) to generate a new token. Use rba_reactome_analysis_download to save your results in other formats.

Value

NULL, a PDF file will be saved to disk.

Corresponding API Resources

"GET https://reactome.org/AnalysisService/report/{token}/{species}/ {filename}.pdf"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_analysis_download rba_reactome_analysis

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_species(), rba_reactome_analysis_token()

Examples

## Not run: 
rba_reactome_analysis_pdf(token = "MjAyMDEwMTYwMTI3MTNfMjY1MjM%3D",
    species = 9606, save_to = "my_analysis.pdf")

## End(Not run)

Description

Use This function to Compare human's manually-curated pathways and computationally inferred pathways (orthologous) in other species.

Usage

rba_reactome_analysis_species(
  species_dbid,
  sort_by = "ENTITIES_PVALUE",
  order = "ASC",
  resource = "TOTAL",
  p_value = 1,
  min = NULL,
  max = NULL,
  ...
)

Arguments

Details

Reactome incorporate manually curated human reactions and PANTHER's protein homology data to Computationally infer events in other eukaryotic species.
Reactome uses an orthology-based approach to project curated human events to supported non-human species. See Reactome Computationally Inferred Events for more information.

Value

A list containing the comparison results. The pathways element is a data frame with information about each pathway expanded into columns; it is an empty data frame when no pathways match.

Corresponding API Resources

"GET https://reactome.org/AnalysisService/species/homoSapiens/{species}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_orthology

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_token()

Examples

rba_reactome_analysis_species(species_dbid = 48892)

Description

Use a token generated After a Reactome analysis (via rba_reactome_analysis) to Retrieve the analysis results. The output format is identical to the returned object of rba_reactome_analysis.

Usage

rba_reactome_analysis_token(
  token,
  species = NULL,
  sort_by = "ENTITIES_PVALUE",
  order = "ASC",
  resource = "TOTAL",
  p_value = 1,
  include_disease = TRUE,
  min = NULL,
  max = NULL,
  ...
)

Arguments

Details

After any analysis, Reactome will associate a token with your analysis. It can later be used in functions that require the token (e.g. to retrieve the analysis results, download pdf).
Note that Reactome will store your token for only 7 days. You can download your full results with rba_reactome_analysis_download, and re-import it anytime to reactome (using rba_reactome_analysis_import) to generate a new token.

Value

A list containing the results and information about the analysis. The pathways element is a data frame with information about each pathway expanded into columns; it is an empty data frame when no pathways match. Its structure is the same as the output from rba_reactome_analysis.

Corresponding API Resources

"GET https://reactome.org/AnalysisService/token/{token}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Analysis Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_analysis

Other "Reactome Analysis Service": rba_reactome_analysis(), rba_reactome_analysis_download(), rba_reactome_analysis_import(), rba_reactome_analysis_mapping(), rba_reactome_analysis_pdf(), rba_reactome_analysis_species()

Examples

## Not run: 
rba_reactome_analysis_token(token = "MjAyMDEwMTYwMTI3MTNfMjY1MjM",
    species = 9606)

## End(Not run)

Description

This function will retrieve a list of complexes that include your supplied molecule as a component.

Usage

rba_reactome_complex_list(id, resource, ...)

Arguments

Value

Data frame where each row is a complex containing your supplied molecule and columns are pertinent information.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/complexes/{resource}/ {identifier}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Physical Entity Queries": rba_reactome_complex_subunits(), rba_reactome_entity_other_forms(), rba_reactome_participant_of()

Examples

rba_reactome_complex_list(id = "3845", resource = "NCBI Gene")


rba_reactome_complex_list(id = "P00533", resource = "UniProt")

Description

This function will return a list of subunits which are participants of your supplied complex.

Usage

rba_reactome_complex_subunits(complex_id, exclude_structures = FALSE, ...)

Arguments

Details

Subunits will be returned recursively; Which means that if a subunit was itself a complex, subunit of that complex will be also returned in the results.

Value

Data frame which each row is a subunit of your supplied complex and the columns are pertinent information of that subunit.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/complex/{id}/subunits"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Physical Entity Queries": rba_reactome_complex_list(), rba_reactome_entity_other_forms(), rba_reactome_participant_of()

Examples

rba_reactome_complex_subunits(complex_id = "R-HSA-5674003",
    exclude_structures = FALSE)

Description

This function Retrieve a list of all diseases or disease DOIDs annotated in Reactome.

Usage

rba_reactome_diseases(doid = FALSE, ...)

Arguments

Value

Data frame containing all the disease annotation available at Reactome. If doid was set to TRUE, DOID info will be returned instead.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/diseases"
"GET https://reactome.org/ContentService/data/diseases/doid"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

Examples

rba_reactome_diseases()


rba_reactome_diseases(doid = TRUE)

Description

This function retrieve a list containing all other forms of your supplied Physical Entity ID.

Usage

rba_reactome_entity_other_forms(entity_id, ...)

Arguments

Details

According to Reactome API documentation, "These other forms are Physical Entities that share the same Reference Entity identifier, e.g. PTEN H93R R-HSA-2318524 and PTEN C124R R-HSA-2317439 are two forms of PTEN."

Value

Data frame where each row is other forms of your supplied Entity ID and columns are pertinent information.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/entity/{id}/otherForms"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Physical Entity Queries": rba_reactome_complex_list(), rba_reactome_complex_subunits(), rba_reactome_participant_of()

Examples

rba_reactome_entity_other_forms("R-HSA-199420")

Description

Along with Reactome's events hierarchy, This function will retrieve all the events beginning from your supplied event up to the "Top level Pathway". see "Details section" for more information.

Usage

rba_reactome_event_ancestors(event_id, ...)

Arguments

Details

By Reactome's definition, Events are the building blocks of biological processes and could be of two main classes: "Pathway" or "Reaction-like events". The events are organized in a hierarchical structure; and each event could be child or parent to another event; The hierarchy will always begin with a "Top level pathway" event. Also note that a given event could be part of more that one hierarchies.

Value

List which every element is a Data frame listing your supplied event along with it's ancestor events. Because any given event can be part of more than one pathway hierarchy, the list may contain multiple data frames.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/event/{id}/ancestors"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Queries Related to Events": rba_reactome_event_hierarchy()

Examples

rba_reactome_event_ancestors("R-HSA-5673001")

Description

Retrieve the full hierarchy of pathways and reaction-like events for a species, or restrict the hierarchy to pathways. The hierarchy begins with top-level pathways and can be traversed through the children field. Because an event can participate in more than one biological process, it may occur in more than one place in the hierarchy.

Usage

rba_reactome_event_hierarchy(
  species,
  pathways_only = FALSE,
  token = NULL,
  resource = "TOTAL",
  interactors = FALSE,
  importable_only = FALSE,
  ...
)

Arguments

Details

An analysis token can be supplied to add the corresponding analysis results to the hierarchy. The analysis resource can then be selected, and interactors or only importable resources can optionally be included.

Value

A nested list representing the species's event hierarchy. When an analysis token is supplied, the corresponding analysis information is included with the pathway entries.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/eventsHierarchy/{species}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Queries Related to Events": rba_reactome_event_ancestors()

Examples

## Not run: 
# Very large response
rba_reactome_event_hierarchy("Homo sapiens")

## End(Not run)
## Not run: 
# Restrict the hierarchy to pathways
rba_reactome_event_hierarchy(9606, pathways_only = TRUE)

## End(Not run)

Description

This function could be called in two scenarios:

1. With create_document = FALSE: To retrieve an image of that event's Diagram.

2. With create_document = TRUE: To retrieve a PDF document with the event's diagram image and additional information.

see "Details section" for more information

Usage

rba_reactome_exporter_diagram(
  event_id,
  save_to = NULL,
  create_document = FALSE,
  resource = "TOTAL",
  diagram_profile = "Modern",
  analysis_profile = "Standard",
  token = NULL,
  exp_column = NULL,
  document_level = 1,
  output_format = "png",
  image_quality = 5,
  flag_element = NULL,
  flg_interactors = TRUE,
  sel = NULL,
  title = TRUE,
  margin = 15,
  ehld = TRUE,
  ...
)

Arguments

Details

If the function is called with create_document = FALSE:
The result will be an image with the format supplied in "output_format" argument. If the supplied event ID refers to a pathway, the image's content will be the that pathways diagram. If the supplied event ID refers to a sub-pathway or reaction event, the parent pathway's diagram will be exported, with that reaction or sub-pathway's events highlighted.
Note that to export an image of reaction-like event separately, you should use rba_reactome_exporter_reaction.
If the function is called with create_document = TRUE:
A PDF document will contain an image of the event's diagram and the following information of that events: Summation, Literature references, Edit history type, location, compartments and diseases. note that if you call the function with "document level = 1", information of your supplied event's children will also be included.

Value

NULL, Based to the inputs, an image or PDF file will be saved to disk.

Corresponding API Resources

"GET https://reactome.org/ContentService/exporter/diagram/{identifier} .{ext}"
"GET https://reactome.org/ContentService/exporter/document/event/ {identifier}.pdf"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_exporter_reaction rba_reactome_analysis

Other "Reactome Content Service - Format Exporter": rba_reactome_exporter_event(), rba_reactome_exporter_overview(), rba_reactome_exporter_reaction()

Examples

## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-177929",
  create_document = FALSE)

## End(Not run)
## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-6787403",
    create_document = FALSE)

## End(Not run)
## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-177929",
    create_document = TRUE)

## End(Not run)
## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-177929",
    output_format = "svg",
    save_to = "reactome_event_diagram.svg")

## End(Not run)

Description

This function will export a supplied Reactome Event (Pathway or Reaction) to a SBGN (Systems Biology Graphical Notation) or SBML (Systems Biology Markup Language)

Usage

rba_reactome_exporter_event(event_id, output_format, save_to = NULL, ...)

Arguments

Value

NULL, According to the inputs, a SBGN or SBML file will be saved to disk.

Corresponding API Resources

"GET https://reactome.org/ContentService/exporter/event/ {identifier}.sbgn"
"GET https://reactome.org/ContentService/exporter/event/ {identifier}.sbml"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Format Exporter": rba_reactome_exporter_diagram(), rba_reactome_exporter_overview(), rba_reactome_exporter_reaction()

Examples

## Not run: 
rba_reactome_exporter_event(event_id = "R-HSA-177929",
    output_format = "sbgn",
    save_to = "R-HSA-177929.sbgn")

## End(Not run)
## Not run: 
rba_reactome_exporter_event(event_id = "R-HSA-177929",
    output_format = "sbgn")

## End(Not run)

Description

This function will save a pathway overview of the supplied species as an image file.

Usage

rba_reactome_exporter_overview(
  species,
  output_format = "png",
  save_to = NULL,
  image_quality = 5,
  flag_element = NULL,
  flg_interactors = TRUE,
  sel = NULL,
  title = TRUE,
  margin = 15,
  diagram_profile = "Copper",
  token = NULL,
  resource = "TOTAL",
  exp_column = NULL,
  coverage = FALSE,
  ...
)

Arguments

Value

NULL, Based to the inputs, an image file will be saved to disk.

Corresponding API Resources

"GET https://reactome.org/ContentService/exporter/fireworks/{species}.{ext}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_analysis

Other "Reactome Content Service - Format Exporter": rba_reactome_exporter_diagram(), rba_reactome_exporter_event(), rba_reactome_exporter_reaction()

Examples

## Not run: 
rba_reactome_exporter_overview(species = 9606,
    output_format = "svg",
    save_to = "human_pathways.svg")

## End(Not run)
## Not run: 
rba_reactome_exporter_overview(species = 9606,
    token = 123456789)

## End(Not run)

Description

This function will Save a Reactome event of class "ReactionLikeEvent" as an image file.

Usage

rba_reactome_exporter_reaction(
  event_id,
  save_to = NULL,
  output_format = "png",
  resource = "TOTAL",
  diagram_profile = "Modern",
  analysis_profile = "Standard",
  token = NULL,
  exp_column = NULL,
  image_quality = 5,
  flag_element = NULL,
  flg_interactors = TRUE,
  sel = NULL,
  title = TRUE,
  margin = 15,
  ...
)

Arguments

Details

Note that this function will save Reaction-like event separately and out of it's parent pathway context. To overlay a Reaction on it's parent pathway, use rba_reactome_exporter_diagram.

Value

NULL, Based to the inputs, an image file will be saved to disk.

Corresponding API Resources

"GET https://reactome.org/ContentService/exporter/reaction/ {identifier}.{ext}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_exporter_diagram rba_reactome_analysis

Other "Reactome Content Service - Format Exporter": rba_reactome_exporter_diagram(), rba_reactome_exporter_event(), rba_reactome_exporter_overview()

Examples

## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-6787403",
    create_document = FALSE)

## End(Not run)
## Not run: 
rba_reactome_exporter_diagram(event_id = "R-HSA-6787403",
     output_format = "svg",
     save_to = "reactome_reacion_image.svg")

## End(Not run)

Description

You can call this function in two scenarios: 1- To retrieve information of all available PSICQUIC resources, call the function without providing any argument; i.e rba_reactome_interactors_psicquic(). 2-To retrieve a list of interactors of specific protein(s), fill out the function's arguments.

Usage

rba_reactome_interactors_psicquic(
  proteins = NULL,
  resource = NULL,
  details = TRUE,
  ...
)

Arguments

Value

Depending your input, a list containing the detailed or summary of PSICQUIC interactions or a data frame of all registered PSICQUIC resources.

Corresponding API Resources

"POST https://reactome.org/ContentService/interactors/psicquic/molecules/ {resource}/details"
"POST https://reactome.org/ContentService/interactors/psicquic/molecules/ {resource}/summary"
"GET https://reactome.org/ContentService/interactors/psicquic/resources"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Molecule Interactors": rba_reactome_interactors_static()

Examples

rba_reactome_interactors_psicquic()


rba_reactome_interactors_psicquic(proteins = c("TP53", "MYC"),
    resource = "BioGrid",
    details = FALSE)


rba_reactome_interactors_psicquic(proteins = c("TP53", "MYC"),
    resource = "BioGrid",
    details = TRUE)

Description

Reactome maintains a locally hosted snapshot of the IntAct interactions database. Using this function, you can retrieve IntAct information for one or more proteins in two scenarios:

1. If endpoint = "details" or endpoint = "summary", retrieve detailed or summary information for the supplied accessions.

2. If endpoint = "pathways", retrieve Reactome pathways which include your supplied protein accession. Pathways with the class "TopLevelPathway" will be excluded.

Results depend on Reactome's current static interaction snapshot; a valid accession can therefore have no mapped pathways.

Usage

rba_reactome_interactors_static(
  proteins,
  endpoint = "details",
  only_diagrammed = FALSE,
  species = NULL,
  ...
)

Arguments

Value

An R object containing the requested interaction information. Detailed and summary queries return information for each supplied protein; pathway queries return a data frame of matching Reactome pathways.

Corresponding API Resources

"POST https://reactome.org/ContentService/interactors/static/ molecules/details"
"POST https://reactome.org/ContentService/interactors/static/ molecules/summary"
"GET https://reactome.org/ContentService/interactors/static/molecule/ {identifier}/pathways"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

Other "Reactome Content Service - Molecule Interactors": rba_reactome_interactors_psicquic()

Examples

## Not run: 
rba_reactome_interactors_static(proteins = "Q9BXM7-1",
    endpoint = "pathways", species = "Homo sapiens")

## End(Not run)

rba_reactome_interactors_static(proteins = c("Q9BXM7-1", "Q13501"),
    endpoint = "details")


rba_reactome_interactors_static(proteins = c("Q9BXM7-1", "Q13501"),
    endpoint = "summary")

Description

By providing an external identifier from a given resource, you can retrieve a list of pathways/reactions that include your supplied ID.

Usage

rba_reactome_mapping(id, resource, map_to, species = "Homo sapiens", ...)

Arguments

Value

Data frame where each row is a pathway/reaction and columns are pertinent information.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/mapping/{resource}/ {identifier}/pathways"
"GET https://reactome.org/ContentService/data/mapping/{resource}/ {identifier}/reactions"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

Examples

rba_reactome_mapping(id = "PTEN", resource =  "UniProt",
    map_to = "reactions", species = 9606)

Description

Reactome incorporate manually curated human reactions and PANTHER's protein homology data to Computationally infer events in other eukaryotic species.

Usage

rba_reactome_orthology(event_ids, species_dbid, ...)

Arguments

Details

Reactome uses an orthology-based approach to project curated human events to supported non-human species. See Reactome Computationally Inferred Events for more information.

Value

List containing found Orthologous event(s) in your supplied species and their pertinent information.

Corresponding API Resources

"POST https://reactome.org/ContentService/data/orthologies/ids/ species/{speciesId}"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website

See Also

rba_reactome_analysis_species

Examples

rba_reactome_orthology(event_ids = c("R-HSA-6799198", " R-HSA-72764"),
    species_dbid = 49633)

Description

This function will retrieve a list of complexes and sets that Your supplied entity ID participates in (e.g. as a complex component, reaction output).

Usage

rba_reactome_participant_of(entity_id, ...)

Arguments

Value

List of Reactome database Entities which Your supplied ID is a participant in them.

Corresponding API Resources

"GET https://reactome.org/ContentService/data/entity/{id}/componentOf"

References

• Ragueneau, E., Gong, C., Sinquin, P., Sevilla, C., Beavers, D., Grentner, A., ... D’Eustachio, P. (2026). The Reactome Knowledgebase 2026. Nucleic Acids Res., 54(D1), D673–D681. doi: 10.1093/nar/gkaf1223

• Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020). ReactomeGSA—Efficient Multi-Omics Comparative Pathway Analysis. Molecular & Cellular Proteomics, 19(12), 2115–2125. doi: 10.1074/mcp.TIR120.002155

• Reactome Content Services API Documentation

• Citations note on Reactome website