LOAD TRAINING DATA

For the course "Analyse des Génomes", we need three types of datasets

The reference sequences that will be used to align sequencing reads (full genome, miRNA, transposons, etc.)
libraries of sequencing reads from small RNAs (for analysis of piRNAs)
Librairies of sequencing reads from mRNA (for Gene differential expression analysis)

All these data have been deposited in the storage server Psilo at Sorbonne-Université.

Get data "by URL"¶

As these data are available through a URL (Universal Resource Location) we will use as before the menu Paste/Fetch Data of the Upload Data menu.

There are other methods to upload data in Galaxy !

You can transfer data from your local machine (the one where your keyboard is plugged !) to Galaxy
You can upload data to your Galaxy FTP account and then transfer these data from your Galaxy FTP directory to one of your Galaxy histories.

1. Upload of reference files as a batch of multiple URLs Programmatic file naming¶

As you have already uploaded single files using their url, we are going to use a more powerful procedure which is appropriate when uploading numerous files.

Before all, create a new history by clicking the icon in the history header

and immediately renaming the new history as References.

Click the Upload Data button at the top-left corner of the Galaxy interface.
This time, Click the Rule-based tab !
Leave Upload data as Datasets and Load tabular data from Pasted Table
In the text field Tabular source data to extract collection files and metadata from, paste the following Tabular source data:

URLs of references (genome and RNA classes)

The following list corresponds to the list of genomic features the sequence of the PLacZ transgene, given in your course manual

https://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6.54_FB2023_05/fasta/dmel-all-chromosome-r6.54.fasta.gz   dmel-r6.54-fasta
https://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6.54_FB2023_05/fasta/dmel-all-miRNA-r6.54.fasta.gz    dmel-r6.54-miRNA
https://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6.54_FB2023_05/fasta/dmel-all-miscRNA-r6.54.fasta.gz  dmel-r6.54-miscRNA
https://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6.54_FB2023_05/fasta/dmel-all-tRNA-r6.54.fasta.gz dmel-r6.54-tRNA
https://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6.54_FB2023_05/gtf/dmel-all-r6.54.gtf.gz  dmel-r6.54-gtf
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_GenomicFeatures/download?path=%2F&files=PLacZ.fasta   PLacZ

Click the Build button
In the Build Rules ... pannel that opens, click the and choose Add/Modify Column Definitions
Click a first time on Add Definition and Select URL. Leave the URL column to A
Click a second time on Add Definition, select Name and choose the column B for Name
Now, click the Apply button
And to finish the job, click on the dark-blue button Upload

2. Upload of small RNA sequencing datasets Programmatic dataset naming.¶

Create a new history using the icon of the history menu, and rename it Small RNA sequence datasets
Click the Upload Data button at the top-left corner of the Galaxy interface.
Click the Rule-basedtab as we just did with the reference datasets
Leave Upload data as Datasets and Load tabular data from Pasted Table
In the text field Tabular source data to extract collection files and metadata from, paste the following Tabular source data:

small RNAseq datasets

https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA25.fastqsanger.gz WT-ALBA25
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA26.fastqsanger.gz WT-ALBA26
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA27.fastqsanger.gz WT-ALBA27 
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA28.fastqsanger.gz GLKD-ALBA28
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA29.fastqsanger.gz GLKD-ALBA29
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_smallRNAseqData/download?path=%2F&files=ALBA30.fastqsanger.gz GLKD-ALBA30

Click the Build button
In the Build Rules ... pannel that opened, click the and choose Add/Modify Column Definitions
Click a first time on Add Definition and Select URL. Leave the URL column to A
Click a second time on Add Definition, select Name and choose the column B for Name
Now, click the Apply button
select the Type "fastqsanger.gz" at the bottom of the panel. In the menu, the fastqsanger.gz looks very similar to the fasqcsanger.gz data type, which is obsolete. The extra c makes a big difference and will put your future jobs in error. Alternatively, you can let Galaxy guess the datatype. Nowadays, it is pretty good at this !
To finish the job, click on the dark-blue button Upload

3. RNAseq datasets (for gene differential expression analysis)¶

Create a new history in Galaxy and rename it RNA sequence datasets
Click the Upload Data button at the top-left corner of the Galaxy interface.
Click the Rule-basedtab as we just did with the reference datasets
Leave Upload data as Datasets and Load tabular data from Pasted Table
In the text field Tabular source data to extract collection files and metadata from, paste the following Tabular source data:

RNAseq datasets

https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA4.fastqsanger.gz   WT-ALBA4
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA5.fastqsanger.gz   WT-ALBA5
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA6.fastqsanger.gz   WT-ALBA6
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA1.fastqsanger.gz   GLKD-ALBA1
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA2.fastqsanger.gz   GLKD-ALBA2
https://psilo.sorbonne-universite.fr/index.php/s/Kdm3_RNAseqData/download?path=%2F&files=ALBA3.fastqsanger.gz   GLKD-ALBA3

Click the Build button
In the Build Rules ... pannel that opened, click the and choose Add/Modify Column Definitions
Click a first time on Add Definition and Select URL. Leave the URL column to A
Click a second time on Add Definition, select Name and choose the column B for Name
Click the Apply button
select the Type "fastqsanger.gz" at the bottom of the panel
And to finish the job, click on the dark-blue button Upload

4. Uncompress datasets¶

[Section 4 should be optionnal, see with Stéphane, I do not think it is necessary, but he's the boss !]

At this stage, we have uploaded small RNA and RNA sequencing datasets as fastqsanger.gz. To simplify the subsequent analyzes we are going to uncompress all these datasets, whose datatype will therefore become fastqsanger.

Procedure for a single dataset¶

Go to your small RNA input datasets history (or whatever you named it).
Click on the pencil icon of the first dataset.
Click on the tab datatype .
In the panel Convert to Datatype, select fastqsanger (using 'Convert compressed file to uncompressed.')
Why NOT using the panel ?
- Let's imagine a Galaxy dataset whose name is Hamlet
- the content of this dataset is:
```
To be, or not to be, that is the question:
```
- Would you agree that the datatype of this dataset is english? I think so.
- Let's put it all together in the form of:
```
@name: Hamlet
@datatype: english
@content:
To be, or not to be, that is the question:
```
Now, what if you change the Datatype of this dataset from english to french using the Assign Datatype panel? This →
```
@name: Hamlet
@datatype: french
@content:
To be, or not to be, that is the question:
```
This does not seem correct ! Do you aggree ?

If you Convert instead this dataset from english to french, you will have This →
```
@name: Hamlet
@datatype: french
@content:
Être ou ne pas être, telle est la question
```
It is looking better, isn't it ?

In contrast, if your starting dataset was as this:
```
@name: Hamlet
@datatype: english
@content:
Être ou ne pas être, telle est la question
```
There, you would "just" Assign the Datatype of the dataset from english to french and get:
```
@name: Hamlet
@datatype: french
@content:
Être ou ne pas être, telle est la question
```
Click on

→ A new dataset is created. During the decompression job, its name looks like 5: Convert compressed file to uncompressed. on data 1. But when the job finishes, the name of the dataset changes to more self-explanatory: 5: GRH-103 uncompressed.

Repeat the same procedure for every small RNAseq dataset.¶

Repeat the same procedure for every RNAseq dataset.¶

Naturally, you can launch as many jobs as you need in the same time

When all datasets are decompressed¶

Delete the compressed datasets (by clicking on the cross icon of datasets).
Rename the uncompressed datasets by removing the uncompressed suffix.
Purge the deleted datasets. This is done by clicking the wheel icon of the top history menu, and selecting Purge Deleted Datasets in the Datasets Actions section.
If you do not perform this last action, the deleted datasets remain on your instance disk !

5. Dataset collections ¶

We are going to organize our various datasets using an additional structure layer: the Galaxy Collection.

A Galaxy Collection is a container object which is convenient to treat together multiple equivalent datasets, such as a list of sequencing datasets, of text labels, of fasta sequences, etc.

A. Making collections of RNA sequence datasets.¶

Collections are particularly useful for RNAseq datasets,since these datasets often come as replicates which can be grouped upon a label. Your training is indeed a good example of that, since you are provided with 3 WT datasets (ALBA4, 5 and 6) and 3 GLKD datasets (ALBA1, 2 and 3).

Navigate to you RNAseq inputs history (or whatever you named it) and click the upper left small check box at the top of the dataset stack

You see that check boxes appear for each dataset of the history

Check the 3 RNA datasets WT (-ALBA4, 5 and 6)
In the menu 3 of 6 selected (also in the top area of the history), select Build Dataset List

build list

In the pop-up panel, just type WT in the field Name: Enter a name for your new collection
Reorganize the datasets order by clicking the alphabetic sorting icon.
Press the button Create Collection
Repeat exactly the same operations for the 3 remaining datasets GLKD (-ALBA1, 2 and 3)
When you are done with the creation of collection, you can uncheck the upper left small check box

What do you see when you click on name of the new dataset collections ?

You see the content of the collection, with datasets identified with original names.

Click on the << History link, to come back to the normal history view.

what do you see if you click the crossed eye icon at the right corner

?

You see the actual datasets contained in the Collection. If you click on unhide for each of these datasets, you will actually see permanently both the container collection and the contained datasets !