diff --git a/topics/sequence-analysis/tutorials/quality-control/tutorial.md b/topics/sequence-analysis/tutorials/quality-control/tutorial.md
index 2e1d2131178dce..51d8629eac4fef 100644
--- a/topics/sequence-analysis/tutorials/quality-control/tutorial.md
+++ b/topics/sequence-analysis/tutorials/quality-control/tutorial.md
@@ -1,44 +1,41 @@
 ---
 layout: tutorial_hands_on
-
-title: "Quality Control"
-zenodo_link: "https://zenodo.org/records/61771"
+title: Quality Control
+zenodo_link: https://zenodo.org/records/61771
 questions:
-  - How to perform quality control of NGS raw data?
-  - What are the quality parameters to check for a dataset?
-  - How to improve the quality of a dataset?
+- How to perform quality control of NGS raw data?
+- What are the quality parameters to check for a dataset?
+- How to improve the quality of a dataset?
 objectives:
-  - Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
-  - Assess long reads FASTQ quality using Nanoplot and PycoQC
-  - Perform quality correction with Cutadapt (short reads)
-  - Summarise quality metrics MultiQC
-  - Process single-end and paired-end data
+- "Assess short reads FASTQ quality using FASTQE \U0001F9EC\U0001F60E and FastQC"
+- Assess long reads FASTQ quality using Nanoplot and PycoQC
+- Perform quality correction with Cutadapt (short reads)
+- Summarise quality metrics MultiQC
+- Process single-end and paired-end data
 follow_up_training:
-  -
-    type: "internal"
-    topic_name: sequence-analysis
-    tutorials:
-      - mapping
-time_estimation: "1H30M"
+- type: internal
+  topic_name: sequence-analysis
+  tutorials:
+  - mapping
+time_estimation: 1H30M
 level: Introductory
 key_points:
-  - Perform quality control on every dataset before running any other bioinformatics analysis
-  - Assess the quality metrics and improve quality if necessary
-  - Check the impact of the quality control
-  - Different tools are available to provide additional quality metrics
-  - For paired-end reads analyze the forward and reverse reads together
+- Perform quality control on every dataset before running any other bioinformatics
+  analysis
+- Assess the quality metrics and improve quality if necessary
+- Check the impact of the quality control
+- Different tools are available to provide additional quality metrics
+- For paired-end reads analyze the forward and reverse reads together
 contributors:
-  - bebatut
-  - mblue9
-  - alexcorm
-  - abretaud
-  - lleroi
-  - r1corre
-  - stephanierobin
-  - gallantries
-  - neoformit
-
-
+- bebatut
+- mblue9
+- alexcorm
+- abretaud
+- lleroi
+- r1corre
+- stephanierobin
+- gallantries
+- neoformit
 recordings:
 - youtube_id: coaMGvZazoc
   length: 50M
@@ -57,10 +54,21 @@ recordings:
   youtube_id: QJRlX2hWDKM
   speakers:
   - heylf
+- youtube_id: uiWZea53QIA
+  length: 51M
+  galaxy_version: 24.1.2.dev0
+  date: '2024-09-30'
+  speakers: 
+  - dianichj
+  captioners: 
+  - dianichj
+  bot-timestamp: 1727710795
+
 
 ---
 
 
+
 During sequencing, the nucleotide bases in a DNA or RNA sample (library) are determined by the sequencer. For each fragment in the library, a sequence is generated, also called a **read**, which is simply a succession of nucleotides.
 
 Modern sequencing technologies can generate a massive number of sequence reads in a single experiment. However, no sequencing technology is perfect, and each instrument will generate different types and amount of errors, such as incorrect nucleotides being called. These wrongly called bases are due to the technical limitations of each sequencing platform.