diff --git a/topics/assembly/tutorials/largegenome/tutorial.md b/topics/assembly/tutorials/largegenome/tutorial.md index fcebb5d88c4a2..0685d89fbc5d1 100644 --- a/topics/assembly/tutorials/largegenome/tutorial.md +++ b/topics/assembly/tutorials/largegenome/tutorial.md @@ -186,11 +186,9 @@ Options: > Run the Data QC workflow > -> 1. **Import the Data QC workflow** into Galaxy: -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-Data_QC.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Data_QC.ga" title="Galaxy Workflow Data QC" %} > > - Click "Expand to full workflow form" > @@ -272,7 +270,9 @@ Options: > - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-kmer_counting.ga) or download it to your computer. > - Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> 1. **Import the workflow** into Galaxy: +> +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-kmer_counting.ga" title="Kmer counting workflow" %} > > - Click "Expand to full workflow form" > @@ -361,11 +361,9 @@ Options: > Run the Trim and Filter Reads workflow > -> 1. **Import the Trim and Filter reads workflow** into Galaxy: -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-Trim_and_filter_reads.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Trim_and_filter_reads.ga" title="Trim and Filter reads" %} > > - Click "Expand to full workflow form" > @@ -461,11 +459,9 @@ Options > Run the Assembly with Flye workflow > -> 1. **Import the Assembly with Flye workflow** into Galaxy: -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-Assembly_with_Flye.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assembly_with_Flye.ga" title="Assembly with Flye" %} > > - Click "Expand to full workflow form" > @@ -574,11 +570,9 @@ Options: > Run the Assembly polishing workflow > -> 1. **Import the Assembly polishing workflow** into Galaxy: -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-Assembly_polishing.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assembly_polishing.ga" title="Assembly polishing workflow" %} > > - Click "Expand to full workflow form" > @@ -675,11 +669,9 @@ Options: > Run the Assess Genome Quality workflow > -> 1. **Import the Assess Genome Quality workflow** into Galaxy: -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/Galaxy-Workflow-Assess_genome_quality.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/assembly/tutorials/largegenome/workflows/Galaxy-Workflow-Assess_genome_quality.ga" title="Assess Genome Quality" %} > > - Click "Expand to full workflow form" > diff --git a/topics/computational-chemistry/tutorials/zauberkugel/tutorial.md b/topics/computational-chemistry/tutorials/zauberkugel/tutorial.md index 7c7adc6cceb54..00d9263c9086e 100644 --- a/topics/computational-chemistry/tutorials/zauberkugel/tutorial.md +++ b/topics/computational-chemistry/tutorials/zauberkugel/tutorial.md @@ -327,11 +327,7 @@ For pharmacophore-based protein target prediction, you can choose to use Galaxy > > Upload the Zauberkugel workflow from the following URL: > -> ``` -> https://github.com/galaxyproject/training-material/blob/main/topics/computational-chemistry/tutorials/zauberkugel/workflows/main_workflow.ga -> ``` -> -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/computational-chemistry/tutorials/zauberkugel/workflows/main_workflow.ga" title="Zauberkugel Workflow" %} > > The Zauberkugel workflow requires only two inputs; the ligand structure file (SMI format) and the ePharmaLib dataset (PHAR format). The output of the prediction of human targets of staurosporine performed with the ePharmaLib human target subset () and this workflow is available as a [Galaxy history](https://usegalaxy.eu/u/aurelien_moumbock/h/zauberkugel). {: .hands_on} diff --git a/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md b/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md index fad16e5f0da44..f8574f7bf4718 100644 --- a/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md +++ b/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md @@ -63,10 +63,8 @@ We will start by importing this workflow into your Galaxy account: > Import the workflow > > 1. **Import the workflow** into Galaxy -> - Copy the URL (e.g. via right-click) of [this workflow](https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/workflow-reports/workflows/galaxy-101-everyone.ga) or download it to your computer. -> - Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/galaxy-interface/tutorials/workflow-reports/workflows/galaxy-101-everyone.ga" title="Galaxy 101 for Everyone" %} > {: .hands_on} diff --git a/topics/galaxy-interface/tutorials/workflow-reports/tutorial.md b/topics/galaxy-interface/tutorials/workflow-reports/tutorial.md index 9d10ab115eaa5..bbe66d54556b8 100644 --- a/topics/galaxy-interface/tutorials/workflow-reports/tutorial.md +++ b/topics/galaxy-interface/tutorials/workflow-reports/tutorial.md @@ -63,10 +63,9 @@ We will start by importing this workflow into your Galaxy account: > Import the workflow > > 1. **Import the workflow** into Galaxy -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/galaxy-101-everyone.ga) or download it to your computer. -> - Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="/topics/galaxy-interface/tutorials/workflow-reports/workflows/galaxy-101-everyone.ga" title="Galaxy 101 for Everyone" %} +> > {: .hands_on} diff --git a/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.md b/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.md index d7991c628d2a7..ae844223816b1 100644 --- a/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.md +++ b/topics/introduction/tutorials/galaxy-intro-101-everyone/tutorial.md @@ -564,7 +564,7 @@ Galaxy makes this very easy with the `Extract workflow` option. This means any t > If you had problems extracting your workflow in the previous step, we provide [a working copy for you]({% link topics/introduction/tutorials/galaxy-intro-101-everyone/workflows/main_workflow.ga %}), > which you can import to Galaxy and use for the next sections (see below how to import a workflow to Galaxy). > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/introduction/tutorials/galaxy-intro-101-everyone/workflows/main_workflow.ga" title="Main Workflow" %} > {: .comment} diff --git a/topics/proteomics/tutorials/metaproteomics/tutorial.md b/topics/proteomics/tutorials/metaproteomics/tutorial.md index faebe249b5862..0152d43be2a39 100644 --- a/topics/proteomics/tutorials/metaproteomics/tutorial.md +++ b/topics/proteomics/tutorials/metaproteomics/tutorial.md @@ -25,9 +25,6 @@ subtopic: multi-omics tags: [microbiome] --- -# Introduction - - In this metaproteomics tutorial we will identify expressed proteins from a complex bacterial community sample. For this MS/MS data will be matched to peptide sequences provided through a FASTA file. @@ -94,11 +91,9 @@ We have a choice to run all these steps using a single workflow, then discuss ea > Pretreatments > -> 1. **Import the workflow** into Galaxy -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/workflow.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/proteomics/tutorials/metaproteomics/workflows/workflow.ga" title="Pretreatments" %} > > 2. Run **Workflow** {% icon workflow %} using the following parameters: > - *"Send results to a new history"*: `No` @@ -120,7 +115,7 @@ will be used to match MS/MS to peptide sequences via a sequence database search. For this, the sequence database-searching program called [SearchGUI](https://compomics.github.io/projects/searchgui.html) will be used. The created dataset collection of the three *MGF files* in the history is used as the MS/MS input. -#### SearchGUI +### SearchGUI > SearchGUI > @@ -186,7 +181,7 @@ SearchGUI archive file) that will serve as an input for the next section, Peptid > {: .comment} -#### PeptideShaker +### PeptideShaker [PeptideShaker](https://compomics.github.io/projects/peptide-shaker.html) is a post-processing software tool that processes data from the SearchGUI software tool. It serves to organize the Peptide-Spectral @@ -265,7 +260,7 @@ proteins and provides a fast matching algorithm for peptides. > or operated on within Galaxy. {: .comment} -#### Recieving the list of peptides: Query Tabular +### Recieving the list of peptides: Query Tabular In order to use *Unipept*, a list containing the peptide sequences has to be generated. The tool **Query Tabular** can load tabular data (the PSM report in this case) into a SQLite data base. @@ -367,7 +362,7 @@ Therefore we can search the database for the peptides and count the occurrence w {: .hands_on} -#### Retrieve taxonomy for peptides: Unipept +### Retrieve taxonomy for peptides: Unipept The generated list of peptides can now be used to search via *Unipept*. We do a taxonomy analysis using the UniPept pept2lca function to return the taxonomic lowest common ancestor for each peptide: @@ -499,7 +494,7 @@ This allows to get an insight of the **biological process**, the **molecular fun > {: .comment} -#### Data upload +### Data upload For this tutorial, a tabular file containing the relevant GO terms has been created. It contains the GO aspect, the ID and the name. It is available at Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.839701.svg)](https://doi.org/10.5281/zenodo.839701). @@ -536,7 +531,7 @@ It is available at Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8 > {: .details} -#### Retrieve GO IDs for peptides: Unipept +### Retrieve GO IDs for peptides: Unipept The **UniPept** application `pept2prot` can be used to return the list of proteins containing each peptide. The option `retrieve extra information` option is set to `yes` so that we retrieve Gene Ontology assignments (`go_references`) @@ -562,7 +557,7 @@ for each protein. {: .hands_on} -#### Combine all information to quantify the GO results +### Combine all information to quantify the GO results As a final step we will use **Query Tabular** in a more sophisticated way to combine all information to quantify the GO analysis. The three used file and the extracted information are: diff --git a/topics/proteomics/tutorials/metaquantome-data-creation/tutorial.md b/topics/proteomics/tutorials/metaquantome-data-creation/tutorial.md index 39c40cc021b85..c17056a47cfcf 100644 --- a/topics/proteomics/tutorials/metaquantome-data-creation/tutorial.md +++ b/topics/proteomics/tutorials/metaquantome-data-creation/tutorial.md @@ -120,11 +120,10 @@ We have a choice to run all these steps using a single workflow, then discuss ea > Pretreatments > -> 1. **Import the workflow** into Galaxy -> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/main_workflow.ga) or download it to your computer. -> - Import the workflow into Galaxy +> 1. **Import the workflow** into Galaxy: +> +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/proteomics/tutorials/metaquantome-data-creation/workflows/main_workflow.ga" title="Pretreatments" %} > -> {% snippet faqs/galaxy/workflows_import.md %} > > 2. Run **Workflow** {% icon workflow %} using the following parameters: > - *"Send results to a new history"*: `No` diff --git a/topics/synthetic-biology/tutorials/basic_assembly_analysis/tutorial.md b/topics/synthetic-biology/tutorials/basic_assembly_analysis/tutorial.md index d50509e2d4ae3..fde4f6ace0879 100644 --- a/topics/synthetic-biology/tutorials/basic_assembly_analysis/tutorial.md +++ b/topics/synthetic-biology/tutorials/basic_assembly_analysis/tutorial.md @@ -199,9 +199,9 @@ In this section, you can run the Genetic Design - BASIC Assembly Workflow more e > Execute the entire workflow in one go. > -> 1. Import your **Genetic Design - Basic Assembly Workflow** by uploading the [**workflow file**](https://training.galaxyproject.org/training-material/topics/synthetic-biology/tutorials/basic_assembly_analysis/workflows/Genetic_Design_BASIC_Assembly.ga). +> 1. Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/synthetic-biology/tutorials/basic_assembly_analysis/workflows/Genetic_Design_BASIC_Assembly.ga" title="Genetic Design - Basic Assembly Workflow" %} > > 2. Click on *Workflow* on the top menu bar of Galaxy. You will see **Genetic Design - Basic Assembly Workflow** > 3. Click on the {% icon workflow-run %} (*Run workflow*) button next to your workflow diff --git a/topics/synthetic-biology/tutorials/pathway_analysis/tutorial.md b/topics/synthetic-biology/tutorials/pathway_analysis/tutorial.md index f67f5825bf1a6..63c81c5db0baf 100644 --- a/topics/synthetic-biology/tutorials/pathway_analysis/tutorial.md +++ b/topics/synthetic-biology/tutorials/pathway_analysis/tutorial.md @@ -245,9 +245,9 @@ In this section, you can run the Pathway Analysis Workflow more easily and fastl > Execute the entire workflow in one go. > -> 1. Import your **Pathway Analysis Workflow** by uploading the [**workflow file**](https://training.galaxyproject.org/training-material/topics/synthetic-biology/tutorials/pathway_analysis/workflows/main_workflow.ga). +> 1. Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/synthetic-biology/tutorials/pathway_analysis/workflows/main_workflow.ga" title="Pathway Analysis Workflow" %} > > 2. Click on *Workflow* on the top menu bar of Galaxy. You will see **Pathway Analysis Workflow** > 3. Click on the {% icon workflow-run %} (*Run workflow*) button next to your workflow @@ -268,4 +268,4 @@ In this section, you can run the Pathway Analysis Workflow more easily and fastl To select the best pathways for producing the lycopene in *E. coli*, some metrics have to be estimated, namely production flux of the target and pathway thermodynamics. A global score is then computed by combining these criteria with others (pathway length, enzyme availability score, reaction SMARTS) using a machine learning model. These steps achieved using the tools of the presented Pathway Analysis workflow. -![This scheme represents the pathway analysis workflow enabling the identification of the best pathways for producing a molecule of interest. To do that the workflow takes as input the collection of pathways to be scored and the metabolic model of the chassis (all files are SBMLs). Iteratively, each pathway is merged with the chassis model, and several metrics are evaluated such as the target production flux (using the rpFBA tool) and the thermodynamics of the pathway (using the rpThermo tool). A global score of each pathway combining these metrics with others is computed using a machine learning method (with the rpScore tool). Finally, the pathways are ranked from best to worst according to their global score (using the rpRanker tool). During the workflow, all metrics are stored as SBML annotations. The final output is a CSV file which contains the pathway IDs and their corresponding global score.](../../images/pathway_analysis_scheme.png) \ No newline at end of file +![This scheme represents the pathway analysis workflow enabling the identification of the best pathways for producing a molecule of interest. To do that the workflow takes as input the collection of pathways to be scored and the metabolic model of the chassis (all files are SBMLs). Iteratively, each pathway is merged with the chassis model, and several metrics are evaluated such as the target production flux (using the rpFBA tool) and the thermodynamics of the pathway (using the rpThermo tool). A global score of each pathway combining these metrics with others is computed using a machine learning method (with the rpScore tool). Finally, the pathways are ranked from best to worst according to their global score (using the rpRanker tool). During the workflow, all metrics are stored as SBML annotations. The final output is a CSV file which contains the pathway IDs and their corresponding global score.](../../images/pathway_analysis_scheme.png) diff --git a/topics/synthetic-biology/tutorials/retrosynthesis_analysis/tutorial.md b/topics/synthetic-biology/tutorials/retrosynthesis_analysis/tutorial.md index 1d06192de2ac3..09e76c5978869 100644 --- a/topics/synthetic-biology/tutorials/retrosynthesis_analysis/tutorial.md +++ b/topics/synthetic-biology/tutorials/retrosynthesis_analysis/tutorial.md @@ -257,9 +257,9 @@ In this section, you can run the RetroSynthesis Workflow more easily and fastly > Execute the entire workflow in one go. > -> 1. Import your **RetroSynthesis workflow** by uploading the [**workflow file**](https://training.galaxyproject.org/training-material/topics/synthetic-biology/tutorials/basic_assembly_analysis/workflows/RetroSynthesis.ga). +> 1. Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/synthetic-biology/tutorials/basic_assembly_analysis/workflows/RetroSynthesis.ga" title="RetroSynthesis workflow" %} > > 2. Click on *Workflow* on the top menu bar of Galaxy. You will see **RetroSynthesis** workflow. > 3. Click on the {% icon workflow-run %} (*Run workflow*) button next to your workflow diff --git a/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.md b/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.md index 9abce58432739..6b5ad758da4f6 100644 --- a/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.md +++ b/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.md @@ -487,7 +487,7 @@ We'll use a prepared workflow to run the first few of the QCs below. This will a > - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/qc_report.ga) or download it to your computer. > - Import the workflow into Galaxy > -> {% snippet faqs/galaxy/workflows_import.md %} +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.ga" title="QC Report" %} > > 2. Import this file as type BED file: > ```