diff --git a/docs/bulk_RNAseq-IOC/41_workflow_intro.md b/docs/bulk_RNAseq-IOC/41_workflow_intro.md index 9540b0e9..7d438f9a 100644 --- a/docs/bulk_RNAseq-IOC/41_workflow_intro.md +++ b/docs/bulk_RNAseq-IOC/41_workflow_intro.md @@ -80,12 +80,20 @@ The beauty of workflows lies in their reusability. You can: This allows you to export your workflows and import them into other Galaxy servers. As long as the new server has the required data and tools, the analysis will run identically. +### Workflow reports +Another essential aspect of Galaxy workflows is that their invocations are logged and +accessible in the menu `User` --> `Workflow invocations` + +In addition, a report is automatically generated for each workflow invocation. A minimal +default report is generated for each workflow invocation and give access to inputs, outputs +and the workflow ==in its runtime version==. You can customize and enrich this automated +report using the Galaxy workflow editor. + +:warning: Reports cannot still be considered as a Material and Methods section for your +scientific manuscripts with computational analyses but they clearly make this section more +accurate and easier to write ! Moreover, the goal of reports is clearly to generate this +section in a fully automated manner, and Galaxy development is happening at a rapid pace ! + ### Key Takeaway Advanced Galaxy users leverage workflows to capture their analyses, ensuring transparency, reproducibility, and reusability of their computational protocols. - -### Looking Ahead: -The next section will explore... (insert what the next section covers). - -, you will test 2 workflows that are available in your -Galaxy server and recapitulate most of the analyses you have performed today. \ No newline at end of file diff --git a/docs/bulk_RNAseq-IOC/42_workflow_use_1.md b/docs/bulk_RNAseq-IOC/42_workflow_use_1.md index cfb04022..20c4fb7d 100644 --- a/docs/bulk_RNAseq-IOC/42_workflow_use_1.md +++ b/docs/bulk_RNAseq-IOC/42_workflow_use_1.md @@ -1,102 +1,69 @@ -# Workflow upload +# A workflow of your use-case -Same as data libraries, you can import workflows, from shared data that has been pre-set in your Galaxy server for this training session. +The exercise of this week is difficult: -To access these workflows : +You are going to prepare a complete workflow of your analysis. ----- - ![](images/tool_small.png) - - 1. Click the menu `Données partagées` (`Shared data`) and select the submenu - `Workflows`. You should see two workflows : `paired-data-STAR-RNAseq` and `paired-data-HISAT2-RNAseq` - - 2. For each workflow, click on the arrow and select `Import`. - - -Now, you'll be able to see these workflows in the `Workflow` menu. - ----- - -# Running workflows +Depending on your model organisms, you may not have been able to perform all of the +analyses covered in this training. This is not a problem: you are expected to create a +workflow from what you have actually been able to do. -You need to return to our first galaxy history `Inputs`, to do so : +In order to make a sustainable, reproducible and transparent workflow, you should meet the +following requirements: ----- - ![](images/tool_small.png) - - 1. Click the menu `Utilisateur` and select the submenu - `Historiques sauvegardés`. - - 2. Click on `Inputs`. Its status is now **current history**. - ----- +## Workflow inputs -## Prepare inputs +Best inputs are -These workflows use data collection as inputs, one per condition `treat` and `untreat`. Let's create our two data collections ! - ----- - ![](images/tool_small.png) - - 1. Click on the checked box. ![](images/checked-box.png) - - 2. Select all treated datasets in pair ends : - - `GSM461180_1_treat_paired.fastq.gz` - - `GSM461181_1_treat_paired.fastq.gz` - - `GSM461180_2_treat_paired.fastq.gz` - - `GSM461181_2_treat_paired.fastq.gz` +- [x] Completely unprocessed data (i.e. fastq files) +- [x] Preferably accessible through a sustainable URL. If it is not possible, they should + be at least easily accessible (i.e. gathered in a single folder, whose location is + precisely described) +- [x] reference data (GTF, bed, etc...) should be precisely annotated, date, organisation, + version, etc... Importantly, a **direct** URL to the original reference should be included +- [x] :warning: Unless impossible to do, do not use processed data as inputs of your + workflow. If you think this is impossible to do, **let's discuss it** ! +- A lot of good workflows stand on a metadata table, which describes input data, their + names, labels if required, replicate status, etc. This metadata table may be considered + as a genuine dataset which can be used by the workflow to perform some operations. - 3. Then click on the button `Pour toute la sélection...` and `Build List of Dataset Pairs`. - - 4. Enter a name for your dataset collection. `Name`: Treat data pairs. - - 5. `Create list` - ----- -![](images/redo.png) +## Computational steps - Redo a data collections for untreated datasets. +- [x] Whenever a computational step applies to multiple sample, think "**Collections**" +- [x] A good clue that you should switch to collections is when your workflow contains + twice or more the same step with the same parameters (or almost the same) +- [x] Take the time, for each step, to carefully fill the tool form at the right hand-side + of the workflow editor. +- [x] There are several fields in this tool form that *must* be used to clarify the step: + The `Label` field at the top of the tool form, the `Step Annotation` field, and the + `Configure Output: xxx` fields and their sub-fields `Label`, `Rename dataset` and `Change + datatype` - 1. Unchecked the previous datasets. + Experiment theses fields with your workflow ! - 2. Select all untreated datasets in pair ends : - - `GSM461177_1_untreat_paired.fastq.gz` - - `GSM461178_1_untreat_paired.fastq.gz` - - `GSM461177_2_untreat_paired.fastq.gz` - - `GSM461178_2_untreat_paired.fastq.gz` - - 3. Then click on the button `Pour toute la sélection...` and `Build List of Dataset Pairs`. - - 4. Enter a name for your dataset collection. `Name`: Untreat data pairs. +- [x] Workflow **can use parameters** at their runtime. If you are interested by this functionality, + let's discuss it ! - 5. `Create list` - ----- +## Workflow outputs -You are now the happy owner of two dataset paired collections ! - -It's time to test the worflows ! - ----- - ![](images/tool_small.png) - - 1. Go to Menu `Workflow`. - - 2. For the workflow `imported: paired-data-HISAT2-RNAseq`, click on the arrow and then `Run`. - - 3. `History Options` - - `Send results to a new history`: Yes - - 4. `1: treated data pairs`: Treat data pairs +- [x] You can hide some output datasets for better readability of the workflow by + unchecking this outputs in the tool items of the workflow. + + :warning: By default all outputs are visible although unchecked. This is only when you + check a first output that unchecked outputs become hidden. + + :warning: Hidden does not mean deleted: all workflow outputs are still there and you can + reveal them in the Galaxy history. - 5. `2:GTF`: Drosophila_melanogaster.BDGP6.95.gtf.gz - - 6. `3: un-treated data pairs`: Untreat data pairs +- [x] Whenever possible, rename your datasets in the workflow using the `Configure Output: xxx` + fields in the tool forms - 7. `Run workflow` +## Your objective: ----- +Is that you generate the complete analysis in a **single** workflow run, with the minimal +number of inputs. -![](images/redo.png) +This way, you can even loose/trash your Galaxy history : +Just having the inputs plus the workflow should be enough to regenerate the analysis. - Redo the same for the workflow `imported: paired-data-STAR-RNAseq`. +Consider that it is also a **huge** gain in term of data storage. diff --git a/mkdocs.yml b/mkdocs.yml index 8ddcae01..56af72d5 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -110,9 +110,7 @@ nav: - Galaxy Workflows: - Introduction: bulk_RNAseq-IOC/41_workflow_intro.md - Week 7 exercices: - - Workflows part 1: bulk_RNAseq-IOC/42_workflow_use_1.md - - Workflows part 2: bulk_RNAseq-IOC/43_workflow_use_2.md - - Workflows part 2: bulk_RNAseq-IOC/44_workflow_use_3.md + - Build your workflow: bulk_RNAseq-IOC/42_workflow_use_1.md - Week 8: - Review on week-7 work: bulk_RNAseq-IOC/50_exercices_week_07_review.md