diff --git a/topics/transcriptomics/tutorials/minerva-pathways/tutorial.md b/topics/transcriptomics/tutorials/minerva-pathways/tutorial.md
index 6a25266ae5eca7..a834fce37d3809 100644
--- a/topics/transcriptomics/tutorials/minerva-pathways/tutorial.md
+++ b/topics/transcriptomics/tutorials/minerva-pathways/tutorial.md
@@ -242,6 +242,19 @@ MultiQC report. Looking at the report we see generally reasonable quality data.
> - `Add Definition` → `List Identifier(s)` → Select Column `B`
> - `Add Definition` → `URL` → Column `A`
>
+> > Trouble entering?
+> > 1. Press the {% icon tool %} button by **Rules**
+> > 1. Paste the following JSON into the dialog:
+> > ```json
+> > {"rules":[],"mapping":[{"type":"collection_name","columns":[2]},{"type":"list_identifiers","columns":[1],"editing":false},{"type":"url","columns":[0]}],"genome":"hg19"}
+> > ```
+> > 1. Click Apply
+> {: .tip}
+>
+> 1. At the bottom of the dialog set `Genome` to `hg19` (it is probably something like "Human Feb 2009 (GRCh37/hg19) (hg19)" but we are focused on that last parenthetical portion).
+>
+> 1. Click **Upload**
+>
{: .hands_on}
@@ -250,17 +263,6 @@ Now we're ready to analyse the counts files. Here we'll take the feature counts
With this result in hand we're ready to do two further steps: preparing the dataset for goseq, and for analysis in MINERVA. Goseq is a tool for gene ontology enrichment analysis, and MINERVA is a tool for visualising pathway analysis.
-The MINERVA dataset must be correctly formatted as a tabular dataset (`\t` separated values) like the following:
-
-```
-SYMBOL logFC P.Value adj.P.Val
-TRIM25 2.07376444684004 1.2610025125617e-18 3.57368112059986e-15
-ACSL1 2.90647033200259 2.71976234791064e-16 3.85390324698937e-13
-NBEAL2 2.45952426389725 2.71787290816654e-14 2.56748394058132e-11
-MIR150 -2.55304226607428 9.55912390273625e-14 6.74866152827879e-11
-SLC2A3 2.95861349227708 1.19066011437523e-13 6.74866152827879e-11
-```
-