diff --git a/libcodonusage/__init__.py b/libcodonusage/__init__.py
index e82dd57bf57002a12cea186a416fcbba98636161..b1e7b7767a58c41b8b08dfeae821fe996af61736 100644
--- a/libcodonusage/__init__.py
+++ b/libcodonusage/__init__.py
@@ -1,6 +1,6 @@
 __copyright__ = "Copyright (C) 2022-2023 Blaise Li"
 __licence__ = "GNU GPLv3"
-__version__ = "0.28.0"
+__version__ = "0.28.1"
 from .libcodonusage import (
     aa2colour,
     aa_usage,
diff --git a/libcodonusage/libcodonusage.py b/libcodonusage/libcodonusage.py
index f63e15b6a5328b99c024fe94332ae03ff3b6481f..bf629e2157bfc78a87dbb051c7158d7442abbfc1 100644
--- a/libcodonusage/libcodonusage.py
+++ b/libcodonusage/libcodonusage.py
@@ -667,6 +667,12 @@ We compute the global usage, as the sum of the counts for a given codon,
 across genes.
 """)
     global_usage = counts_for_global.sum(axis=0)
+    if return_more:
+        # Same computation as for individual genes,
+        # but based on the total counts.
+        global_max_counts = global_usage.T.groupby("aa").max().T
+        global_r4 = global_usage.div(
+            global_max_counts)
     render_md("Then we sum over codons corresponding to the same amino-acid.")
     global_summed_by_aa = global_usage.groupby(level=0).sum()
     render_md("""
@@ -702,7 +708,8 @@ across genes) so that they are more comparable between codons.
             "rscu": rscu,
             "r4_table": r4_table,
             "global_proportions": global_proportions,
-            "global_rscu": global_proportions.mul(degeneracy)}
+            "global_rscu": global_proportions.mul(degeneracy),
+            "global_r4": global_r4}
     return standardized_codon_usage_biases