Introduction
The miRNA Enrichment Analysis and Annotation Tool (miEAA) is a service provided by the Chair for Clinical Bioinformatics at Saarland University. Basically, miEAA is a multi-species microRNA enrichment analysis tool. For more information, see their website or published paper.
First, find enrichment categories
Before Performing enrichment analysis on a miRNA set, note that based on your input miRNA type (either all mature or precursor, not a mixture of both!) and the species, there will be different sets of supported enrichment categories.
Thus, it is recommended to retrieve a list of possible enrichment categories that you may use:
## A list of available enrichment categories for:
## mature human miRNA:
rba_mieaa_cats(mirna_type = "mature", species = 9606)
## precursor human miRNA
rba_mieaa_cats(mirna_type = "precursor", species = 9606)
## precursor zebrafish miRNA
rba_mieaa_cats(mirna_type = "mature", species = "Danio rerio")
Submit Enrichment analysis request to miEAA
There are two approaches to do this, we will start with the simpler one.
Approach 1: Using the Wrapper function
Just fill the arguments of rba_mieaa_enrich()
according
to the function’s manual; As you can see in the function’s arguments,
you have a lot of controls over your enrichment request, but you need to
provide test_set
, mirna_type
,
test_type
, and species
:
## 1 We create a variable with our miRNAs' mature IDs
mirs <- c("hsa-miR-20b-5p", "hsa-miR-144-5p", "hsa-miR-17-5p", "hsa-miR-20a-5p",
"hsa-miR-222-3p", "hsa-miR-106a-5p", "hsa-miR-93-5p", "hsa-miR-126-3p",
"hsa-miR-363-3p", "hsa-miR-302c-3p", "hsa-miR-374b-5p", "hsa-miR-18a-5p",
"hsa-miR-548d-3p", "hsa-miR-135a-3p", "hsa-miR-558", "hsa-miR-130b-5p",
"hsa-miR-148a-3p")
## 2a We can perform enrichment analysis on our miRNA set without limiting the analysis to any categories
mieaa_all <- rba_mieaa_enrich(test_set = mirs,
mirna_type = "mature",
test_type = "ORA",
species = 9606)
#> -- Step 1/3: Submitting Enrichment analysis request:
#> No categories were supplied, Requesting enrichment using all of the 32 available categories for species 'Homo sapiens'.
#> Submitting ORA enrichment request for 17 miRNA IDs of species Homo sapiens to miEAA servers.
#>
#> -- Step 2/3: Checking for Submitted enrichment analysis's status every 5 seconds.
#> Your submitted job ID is: e83f73b1-6fd5-47e4-8afb-ca3936074b1c
#> .......
#>
#> -- Step 3/3: Retrieving the results.
#> Retrieving results of submitted enrichment request with ID: e83f73b1-6fd5-47e4-8afb-ca3936074b1c
## 2b Or, We can limit the enrichment to certain datasets (enrichment categories)
mieaa_kegg <- rba_mieaa_enrich(test_set = mirs,
mirna_type = "mature",
test_type = "ORA",
species = 9606,
categories = "KEGG_mature"
)
#> -- Step 1/3: Submitting Enrichment analysis request:
#> Submitting ORA enrichment request for 17 miRNA IDs of species Homo sapiens to miEAA servers.
#>
#> -- Step 2/3: Checking for Submitted enrichment analysis's status every 5 seconds.
#> Your submitted job ID is: 8e55645b-3bc3-4790-9f84-c7aaf63e30c7
#> .
#>
#> -- Step 3/3: Retrieving the results.
#> Retrieving results of submitted enrichment request with ID: 8e55645b-3bc3-4790-9f84-c7aaf63e30c7
Approach 2: Going step-by-step
As stated before, rba_mieaa_enrich()
is a wrapper
function, meaning that it executes the following sequence of
functions:
## 1 Submit enrichment request to miEAA
request <- rba_mieaa_enrich_submit(test_set = mirs,
mirna_type = "mature",
test_type = "ORA",
species = 9606,
categories = c("miRWalk_Diseases_mature",
"miRWalk_Organs_mature")
)
## 2 check for job's running status
rba_mieaa_enrich_status(job_id = request$job_id)
## 3 If the job has completed, retrieve the results
results <- rba_mieaa_enrich_results(job_id = request$job_id)
Please Note: Other services supported by rbioapi also provide Over-representation analysis tools. Please see the vignette article Do with rbioapi: Over-Representation (Enrichment) Analysis in R (link to the documentation site) for an in-depth review.
Convert miRNA accessions
miEAA only recognizes miRBASE version 22 accessions. You can use
rba_mieaa_convert_version()
to convert miRNA accession
between different miRBASE versions. Also, as stated before, miEAA
differentiate between precursor and mature miRNA accessions, to convert
between these 2 accession types, use
rba_mieaa_convert_type()
.
How to Cite?
To cite miEAA (Please see https://ccb-compute2.cs.uni-saarland.de/mieaa2/):
- Fabian Kern, Tobias Fehlmann, Jeffrey Solomon, Louisa Schwed, Nadja Grammes, Christina Backes, Kendall Van Keuren-Jensen, David Wesley Craig, Eckart Meese, Andreas Keller, miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W521–W528, https://doi.org/10.1093/nar/gkaa309
To cite rbioapi:
- Moosa Rezwani, Ali Akbar Pourfathollah, Farshid Noorbakhsh, rbioapi: user-friendly R interface to biologic web services’ API, Bioinformatics, Volume 38, Issue 10, 15 May 2022, Pages 2952–2953, https://doi.org/10.1093/bioinformatics/btac172
Session info
#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] rbioapi_0.8.0
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.6.5 httr_1.4.7 cli_3.6.2 knitr_1.45
#> [5] rlang_1.1.3 xfun_0.43 purrr_1.0.2 textshaping_0.3.7
#> [9] jsonlite_1.8.8 DT_0.32 htmltools_0.5.8 ragg_1.3.0
#> [13] sass_0.4.9 rmarkdown_2.26 crosstalk_1.2.1 evaluate_0.23
#> [17] jquerylib_0.1.4 fastmap_1.1.1 yaml_2.3.8 lifecycle_1.0.4
#> [21] memoise_2.0.1 compiler_4.3.3 fs_1.6.3 htmlwidgets_1.6.4
#> [25] systemfonts_1.0.6 digest_0.6.35 R6_2.5.1 curl_5.2.1
#> [29] magrittr_2.0.3 bslib_0.7.0 tools_4.3.3 pkgdown_2.0.7
#> [33] cachem_1.0.8 desc_1.4.3