Merge branch 'galaxyproject:main' into DASH-1211

faqs/galaxy/histories_copy_dataset.md

@@ -4,26 +4,32 @@ description: Sometimes you may want to use a dataset in multiple histories. You
 area: histories
 box_type: tip
 layout: faq
-contributors: [lecorguille,shiltemann,hexylena,bebatut]
+contributors: [lecorguille,shiltemann,hexylena,bebatut,lldelisle]
 ---
 
 There 3 ways to copy datasets between histories
+
 1. From the original history
 
-    1. Click on the {% icon galaxy-gear %} icon (**History options**) on the top of the history panel
-    2. Click on **Copy Dataset**
+    1. Click on the {% icon galaxy-gear %} icon which is on the top of the list of datasets in the history panel
+    2. Click on **Copy Datasets**
     3. Select the desired files
     {% if include.history_name %}
-    4. "New history name:" `{{ include.history_name }}`
+    4. "New history named:" `{{ include.history_name }}`
     {% else %}
     4. Give a relevant name to the "New history"
     {% endif %}
+    5. Validate by 'Copy History Items'
     5. Click on the new history name in the green box that have just appear to switch to this history
 
-2. From the {% icon galaxy-columns %} **View all histories**
+2. Using the {% icon galaxy-columns %} **Show Histories Side-by-Side**
 
-    1. Click on {% icon galaxy-columns %} **View all histories** on the top right
-    2. Switch to the history in which the dataset should be copied
+    1. Click on the {% icon galaxy-dropdown %} dropdown arrow top right of the history panel (**History options**)
+    2. Click on {% icon galaxy-columns %} **Show Histories Side-by-Side**
+    3. If your target history is not present
+        1. Click on 'Select histories'
+        2. Click on your target history
+        3. Validate by 'Change Selected'
     3. Drag the dataset to copy from its original history
     4. Drop it in the target history
 
@@ -32,5 +38,5 @@ There 3 ways to copy datasets between histories
     1. Click on **User** in the top bar
     2. Click on **Datasets**
     3. Search for the dataset to copy
-    4. Click on it
-    5. Click on **Copy to History**
+    4. Click on its name
+    5. Click on **Copy to current History**

topics/transcriptomics/tutorials/ref-based/tutorial.md

@@ -1729,7 +1729,7 @@ Now we would like to extract the most differentially expressed genes due to the
 >
 >    We will now select only the genes with a fold change (FC) > 2 or FC < 0.5. Note that the DESeq2 output file contains $$log_{2} FC$$, rather than FC itself, so we filter for $$abs(log_{2} FC) > 1$$ (which implies FC > 2 or FC < 0.5).
 >
-> 3. {% tool [Filter](Filter1) %} to extract genes with an $$abs(log_{2} FC) > 1$$:
+> 3. {% tool [Filter data on any column using simple expressions](Filter1) %} to extract genes with an $$abs(log_{2} FC) > 1$$:
 >    - {% icon param-file %} *"Filter"*: `Genes with significant adj p-value`
 >    - *"With following condition"*: `abs(c3)>1`
 >    - *"Number of header lines to skip"*: `1`
@@ -1743,7 +1743,7 @@ Now we would like to extract the most differentially expressed genes due to the
 >    >
 >    > > <solution-title></solution-title>
 >    > >
->    > > 1. 114, or 11.79% of the significantly differentially expressed genes.
+>    > > 1. We get 113 genes (114 lines including a header), or 11.79% of the significantly differentially expressed genes.
 >    > > 2. The *Pasilla* gene can be found with a quick Search (or even using  {% tool [Filter data on any column using simple expressions](Filter1) %} )
 >    > {: .solution}
 >    {: .question}
@@ -1822,7 +1822,7 @@ You should obtain something similar to:
 >
 > > <solution-title></solution-title>
 > >
-> > 1. The X-axis shows the 7 samples, together with a dendrogram representing the similarity between their levels of gene expression. The Y-axis shows the 130 differentially expressed genes, likewise with a dendrogram representing the similarity between the levels of gene expression.
+> > 1. The X-axis shows the 7 samples, together with a dendrogram representing the similarity between their levels of gene expression. The Y-axis shows the 113 differentially expressed genes, likewise with a dendrogram representing the similarity between the levels of gene expression.
 > > 2. The samples are clustering by treatment.
 > > 3. The scale changes and we only see few genes.
 > > 4. Because the normalized expression of the gene `FBgn0013688` in `GSM461180_treat_paired` is at `0`.
@@ -2029,7 +2029,7 @@ We have now the two required input files for goseq.
     > >
     > > 1. 60 GO terms (0.50%) are over-represented and 7 (0.07%) under-represented.
     > >
-    > >    {% tool [Filter](Filter1) %} on c8 (adjusted p-value for over-represented GO terms) and c9 (adjusted p-value for under-represented GO terms)
+    > >    {% tool [Filter data on any column using simple expressions](Filter1) %} on c8 (adjusted p-value for over-represented GO terms) and c9 (adjusted p-value for under-represented GO terms)
     > >
     > > 2. For over-represented, 50 BP, 5 CC and 5 MF and for under-represented, 5 BP, 2 CC and 0 MF
     > >
@@ -2097,9 +2097,9 @@ For example, the pathway `dme00010` represents the glycolysis process (conversio
     > > <solution-title></solution-title>
     > >
     > > 1. The file has 128 lines including an header, so 127 KEGG pathways have been identified.
-    > > 2. 2 KEGG pathways (2.34%) are over-represented, using **Filter** on c6 (adjusted p-value for over-represented KEGG pathways)
+    > > 2. 2 KEGG pathways (2.34%) are over-represented, using {% tool [Filter data on any column using simple expressions](Filter1) %} on c6 (adjusted p-value for over-represented KEGG pathways)
     > > 3. The 2 KEGG pathways over-represented are `01100` and `00010`. By searching on the [KEGG database](https://www.genome.jp/kegg/kegg2.html) for them, we can find more information about these pathways: `01100` corresponds to all metabolic pathways and `00010` to pathway for Glycolysis / Gluconeogenesis.
-    > > 4. No KEGG pathway is under-represented, using **Filter** on c7 (adjusted p-value for under-represented KEGG pathways)
+    > > 4. No KEGG pathway is under-represented, using {% tool [Filter data on any column using simple expressions](Filter1) %} on c7 (adjusted p-value for under-represented KEGG pathways)
     > {: .solution}
     {: .question}
 
@@ -2295,7 +2295,7 @@ Similarly to DESeq2, DEXSeq generates a table with:
 
 > <hands-on-title></hands-on-title>
 >
-> 1. {% tool [Filter](Filter1) %} to extract exons with a significant differential usage (adjusted *p*-value equal or below 0.05) between treated and untreated samples
+> 1. {% tool [Filter data on any column using simple expressions](Filter1) %} to extract exons with a significant differential usage (adjusted *p*-value equal or below 0.05) between treated and untreated samples
 >
 > > <question-title></question-title>
 > >

topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting-test.yml

@@ -31,10 +31,8 @@
             filetype: fastqsanger
     Drosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz:
       class: File
-      # Can be uncommented when https://github.com/galaxyproject/galaxy/pull/16014 is merged
-      # location: https://zenodo.org/record/6457007/files/Drosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz
-      # decompress: true
-      path: test-data/Drosophila_melanogaster.BDGP6.32.109_UCSC.gtf
+      location: https://zenodo.org/record/6457007/files/Drosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz
+      decompress: true
       filetype: gtf
   outputs:
     multiqc_cutadapt_html: