diff --git a/inst/content/api.md b/inst/content/api.md
index baeac3f..d574dda 100644
--- a/inst/content/api.md
+++ b/inst/content/api.md
@@ -1,3 +1,11 @@
+## API access
+
+Ubigen provides programmatic access via an HTTP API. Please see the
+documentation below which includes usage examples using the commonly available
+command [cURL](https://curl.se/). There is also an exemplary
+[Python script](https://code.johrpan.de/johrpan/ubigen/src/branch/main/scripts/api_example.py)
+to show how to use the API in your own applications.
+
 ## Using the API for retrieving the data
 
 You can use the API endpoint `/ranking` to download the dataset formatted in
diff --git a/scripts/api_example.py b/scripts/api_example.py
new file mode 100644
index 0000000..c7680de
--- /dev/null
+++ b/scripts/api_example.py
@@ -0,0 +1,75 @@
+from io import StringIO
+import json
+import pandas
+import requests
+
+# Genes of interest (in this case, genes involved in glycolysis according to
+# the KEGG pathways database [KEGG:hsa00010+M00001]).
+genes = [
+    "ENSG00000111640",
+    "ENSG00000111669",
+    "ENSG00000149925",
+    "ENSG00000074800",
+    "ENSG00000105220",
+    "ENSG00000067225",
+    "ENSG00000102144",
+    "ENSG00000141959",
+    "ENSG00000156515",
+    "ENSG00000171314",
+    "ENSG00000067057",
+    "ENSG00000111674",
+    "ENSG00000159322",
+    "ENSG00000152556",
+    "ENSG00000109107",
+    "ENSG00000159399",
+    "ENSG00000108515",
+    "ENSG00000160883",
+    "ENSG00000226784",
+    "ENSG00000188316",
+    "ENSG00000106633",
+    "ENSG00000136872",
+    "ENSG00000156510",
+    "ENSG00000143627",
+    "ENSG00000170950",
+]
+
+# Get a summary on the ubiquity of the above gene set:
+
+response_summary = requests.post(
+    "https://ubigen.uni-rostock.de/api/summary",
+    headers={"Content-Type": "text/plain"},
+    data=" ".join(genes),
+)
+
+summary = json.loads(response_summary.content)
+median_percentile = summary["median_percentile"]
+median_percentile_rounded = round(median_percentile, 3)
+estimated_change = summary["change"]
+p_value = summary["p_value"]
+
+# Example information: Median percentile of the genes and p-value for difference
+# in scores in comparison with other genes.
+
+print(f"Median percentile: {median_percentile_rounded * 100}%")
+print(f"Estimated score difference: {estimated_change} (p = {p_value})")
+
+# Retrieve detailed information on all parameters and the ranking of the gene
+# set defined above:
+
+response_ranking = requests.post(
+    "https://ubigen.uni-rostock.de/api/ranking",
+    headers={"Content-Type": "text/plain"},
+    data=" ".join(genes),
+)
+
+# Parse the data using pandas. This gives lots of opportunity for further
+# analyses.
+
+data = pandas.read_csv(StringIO(response_ranking.text))
+
+# Example analysis: Recompute the mean percentile using pandas.
+
+median_percentile_new = data["percentile"].median()
+median_percentile_new_rounded = round(median_percentile_new, 3)
+
+print(f"Recomputed median percentile: {median_percentile_new_rounded * 100}%")