diff --git a/topics/assembly/tutorials/mrsa-nanopore/tutorial.md b/topics/assembly/tutorials/mrsa-nanopore/tutorial.md
index eb9f2e0b43cc58..f87381237916d3 100644
--- a/topics/assembly/tutorials/mrsa-nanopore/tutorial.md
+++ b/topics/assembly/tutorials/mrsa-nanopore/tutorial.md
@@ -22,8 +22,6 @@ tags:
 - nanopore
 - assembly
 - amr
-- gmod
-- jbrowse1
 - microgalaxy
 edam_ontology:
 - topic_0196 # Sequence Assembly
@@ -153,7 +151,9 @@ The dataset is a FASTQ file.
 
 {% include _includes/cyoa-choices.html option1="Without Illumina MiSeq data" option2="With Illumina MiSeq data" default="Without Illumina MiSeq data" text="Do you have associated Illumina MiSeq data?" disambiguation="miseq"%}
 
-<div class="Existing-Illumina-MiSeq-data" markdown="1">
+<span class="Without-Illumina-MiSeq-data"></span>
+
+<div class="With-Illumina-MiSeq-data" markdown="1">
 
 > <hands-on-title>Illumina Data upload</hands-on-title>
 > 1. {% tool [Import](upload1) %} the files from [Zenodo]({{ page.zenodo_link }}) or from the shared data library
@@ -197,7 +197,7 @@ FastQC combines quality statistics from all separate reads and combines them in
 
 ![FastQC plot showing reads that mostly stay in the read](./images/fastqc.png)
 
-<div class="Existing-Illumina-MiSeq-data" markdown="1">
+<div class="With-Illumina-MiSeq-data" markdown="1">
 
 Here, we are going to trim the Illumina data using **fastp** ({% cite Chen2018 %}):
 
@@ -216,7 +216,7 @@ Here, we are going to trim the Illumina data using **fastp** ({% cite Chen2018 %
 >    - In *"Read Modification Options"*:
 >        - In *"Per read cuitting by quality options"*:
 >            - *Cut by quality in front (5')*: `Yes`
->            - *Cut by quality in front (3')*: `Yes`
+>            - *Cut by quality in tail (3')*: `Yes`
 >            - *Cutting window size*: `4`
 >            - *Cutting mean quality*: `20`
 >    - In *"Output Options"*:
@@ -228,7 +228,7 @@ Here, we are going to trim the Illumina data using **fastp** ({% cite Chen2018 %
 
 Depending on the analysis it could be possible that a certain quality or length is needed. The reads can be filtered using the [Filtlong](https://github.com/rrwick/Filtlong) tool. In this training all reads below 1000bp will be filtered.
 
-<div class="Existing-Illumina-MiSeq-data" markdown="1">
+<div class="With-Illumina-MiSeq-data" markdown="1">
 
 When Illumina reads are available, we can use them **if they are good Illumina reads (high depth and complete coverage)**  as external reference. In this case, Filtlong ignores the Phred quality scores and instead judges read quality using k-mer matches to the reference (a more accurate gauge of quality).
 
@@ -240,7 +240,7 @@ When Illumina reads are available, we can use them **if they are good Illumina r
 >    - In *"Output thresholds"*:
 >        - *"Min. length"*: `1000`
 >
->    <div class="Existing-Illumina-MiSeq-data" markdown="1">
+>    <div class="With-Illumina-MiSeq-data" markdown="1">
 >    - In *"External references"*:
 >        - {% icon param-file %} *"Reference Illumina read"*: **fastp** `Read 1 output`
 >        - {% icon param-file %} *"Reference Illumina read"*: **fastp** `Read 2 output` 
@@ -431,7 +431,7 @@ QUAST outputs assembly metrics as an HTML file with metrics and graphs.
 > {: .solution}
 {: .question}
 
-<div class="Existing-Illumina-MiSeq-data" markdown="1">
+<div class="With-Illumina-MiSeq-data" markdown="1">
 
 ## Assembly Polishing
 
@@ -513,4 +513,4 @@ GC (%) | 32.91 | 32.84
 
 # Conclusion
 
-In this tutorial, we prepared long reads (using short reads if we had some) assembled them, inspect the produced assembly for its quality, and polished it (if short reads where provided). The assembly, even if uncomplete, is reasonable good to be used in downstream analysis, like [AMR gene detection]({% link topics/genome-annotation/tutorials/amr-gene-detection/tutorial.md %})
\ No newline at end of file
+In this tutorial, we prepared long reads (using short reads if we had some) assembled them, inspect the produced assembly for its quality, and polished it (if short reads where provided). The assembly, even if uncomplete, is reasonable good to be used in downstream analysis, like [AMR gene detection]({% link topics/genome-annotation/tutorials/amr-gene-detection/tutorial.md %})
diff --git a/topics/microbiome/tutorials/beer-data-analysis/tutorial.md b/topics/microbiome/tutorials/beer-data-analysis/tutorial.md
index 651349a30989ad..3f5b45aba42d31 100644
--- a/topics/microbiome/tutorials/beer-data-analysis/tutorial.md
+++ b/topics/microbiome/tutorials/beer-data-analysis/tutorial.md
@@ -578,7 +578,6 @@ The species identified for Chimay beers are (from the most abundant to the least
 - *Saccharomyces cerevisiae*
 - *Saccharomyces mikatea*: a species generally used in winemaking ({% cite bellon2013introducing %})
 - *Kazachstania martiniae*: *Kazachstania* is a genus from the family Saccharomycetaceaethe.
-
 - *Saccharomyces kudriavzevii*
 - *Brettanomyces bruxellensis*