Gigascience_Fusions_demonstration_STS26T-Gent_Workflow
proteomics-neoantigen-1-fusion-database-generation/main-workflow
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flowchart TD 0["ℹ️ Input Dataset\nRNA-Seq_Reads_1"]; style 0 stroke:#2c3143,stroke-width:4px; 1["ℹ️ Input Dataset\nRNA-Seq_Reads_2"]; style 1 stroke:#2c3143,stroke-width:4px; 2["ℹ️ Input Dataset\nhuman_reference_genome_annotation.gtf"]; style 2 stroke:#2c3143,stroke-width:4px; 3["ℹ️ Input Dataset\nhuman_reference_genome.fasta"]; style 3 stroke:#2c3143,stroke-width:4px; 4["Arriba-Get-Filters"]; bdb7b8fe-ad19-44b0-8824-c930809543eb["Output\nArriba-Get-Filters_protein_domains"]; 4 --> bdb7b8fe-ad19-44b0-8824-c930809543eb; style bdb7b8fe-ad19-44b0-8824-c930809543eb stroke:#2c3143,stroke-width:4px; d19553ea-c199-47b1-ad18-f7197de66fc7["Output\nArriba-Get-Filters_cytobands"]; 4 --> d19553ea-c199-47b1-ad18-f7197de66fc7; style d19553ea-c199-47b1-ad18-f7197de66fc7 stroke:#2c3143,stroke-width:4px; 02a9718d-0153-4bf8-b7b9-17826109ede7["Output\nArriba-Get-Filters_blacklist"]; 4 --> 02a9718d-0153-4bf8-b7b9-17826109ede7; style 02a9718d-0153-4bf8-b7b9-17826109ede7 stroke:#2c3143,stroke-width:4px; 75ae6bd6-5b30-4af1-bc91-2f2c798a44ef["Output\nArriba-Get-Filters_known_fusions"]; 4 --> 75ae6bd6-5b30-4af1-bc91-2f2c798a44ef; style 75ae6bd6-5b30-4af1-bc91-2f2c798a44ef stroke:#2c3143,stroke-width:4px; 5["Uncompressed-RNA-Seq-forward-reads"]; 0 -->|output| 5; ad7de653-861f-4fe5-961b-0ec2bd2485dc["Output\nUncompressed-RNA-Seq-forward-reads"]; 5 --> ad7de653-861f-4fe5-961b-0ec2bd2485dc; style ad7de653-861f-4fe5-961b-0ec2bd2485dc stroke:#2c3143,stroke-width:4px; 6["Uncompressed-RNA-Seq-reverse-reads"]; 1 -->|output| 6; 30fe35ba-5af2-4f36-ab8a-7f0099253882["Output\nUncompressed-RNA-Seq-reverse-reads"]; 6 --> 30fe35ba-5af2-4f36-ab8a-7f0099253882; style 30fe35ba-5af2-4f36-ab8a-7f0099253882 stroke:#2c3143,stroke-width:4px; 7["RNA_STAR"]; 2 -->|output| 7; 5 -->|output1| 7; 6 -->|output1| 7; 8002eb14-9ce0-4767-8e64-ad5d8af17f25["Output\nRNA_STAR_mapped_reads"]; 7 --> 8002eb14-9ce0-4767-8e64-ad5d8af17f25; style 8002eb14-9ce0-4767-8e64-ad5d8af17f25 stroke:#2c3143,stroke-width:4px; 8["Arriba"]; 4 -->|blacklist| 8; 2 -->|output| 8; 3 -->|output| 8; 7 -->|mapped_reads| 8; 4 -->|known_fusions| 8; 4 -->|protein_domains| 8; 4 -->|cytobands| 8; 4cb6fdb6-b4c5-47b4-9dca-3a05a7a26c74["Output\nArriba-Fusions-tsv"]; 8 --> 4cb6fdb6-b4c5-47b4-9dca-3a05a7a26c74; style 4cb6fdb6-b4c5-47b4-9dca-3a05a7a26c74 stroke:#2c3143,stroke-width:4px; 9["Reformating Fusion data"]; 8 -->|fusions_tsv| 9; a8817c07-800d-4588-8c87-18ab611f85a8["Output\nReformated_Fusion_data"]; 9 --> a8817c07-800d-4588-8c87-18ab611f85a8; style a8817c07-800d-4588-8c87-18ab611f85a8 stroke:#2c3143,stroke-width:4px; 10["Table_generation_from_Fusion_data"]; 9 -->|outfile| 10; ab3ad7b0-6f43-4f0f-9e0f-758c721a03f0["Output\nTable_generation_from_Fusion_data"]; 10 --> ab3ad7b0-6f43-4f0f-9e0f-758c721a03f0; style ab3ad7b0-6f43-4f0f-9e0f-758c721a03f0 stroke:#2c3143,stroke-width:4px; 11["Converting_Tabular_to_Fasta"]; 10 -->|output| 11; e21178f2-6a4a-4174-9660-1af15ff7dbb4["Output\nConverting_Tabular_to_Fasta"]; 11 --> e21178f2-6a4a-4174-9660-1af15ff7dbb4; style e21178f2-6a4a-4174-9660-1af15ff7dbb4 stroke:#2c3143,stroke-width:4px; 12["Arriba-Fusion-Database"]; 11 -->|output| 12; be2983fd-4ddf-4593-8b8d-933393eac469["Output\nArriba-Fusion-Database"]; 12 --> be2983fd-4ddf-4593-8b8d-933393eac469; style be2983fd-4ddf-4593-8b8d-933393eac469 stroke:#2c3143,stroke-width:4px;
Inputs
Input | Label |
---|---|
Input dataset | RNA-Seq_Reads_1 |
Input dataset | RNA-Seq_Reads_2 |
Input dataset | human_reference_genome_annotation.gtf |
Input dataset | human_reference_genome.fasta |
Outputs
From | Output | Label |
---|---|---|
toolshed.g2.bx.psu.edu/repos/iuc/arriba_get_filters/arriba_get_filters/2.4.0+galaxy1 | Arriba Get Filters | Arriba-Get-Filters |
CONVERTER_gz_to_uncompressed | Convert compressed file to uncompressed. | Uncompressed-RNA-Seq-forward-reads |
CONVERTER_gz_to_uncompressed | Convert compressed file to uncompressed. | Uncompressed-RNA-Seq-reverse-reads |
toolshed.g2.bx.psu.edu/repos/iuc/rgrnastar/rna_star/2.7.10b+galaxy4 | RNA STAR | RNA_STAR |
toolshed.g2.bx.psu.edu/repos/iuc/arriba/arriba/2.4.0+galaxy1 | Arriba | Arriba |
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_awk_tool/1.1.2 | Text reformatting | Reformating Fusion data |
toolshed.g2.bx.psu.edu/repos/iuc/query_tabular/query_tabular/3.3.1 | Query Tabular | Table_generation_from_Fusion_data |
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 | Tabular-to-FASTA | Converting_Tabular_to_Fasta |
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regex1/1.0.3 | Regex Find And Replace | Arriba-Fusion-Database |
Tools
To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.
Importing into Galaxy
Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!Hands-on: Importing a workflow
- Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
- Click on galaxy-upload Import at the top-right of the screen
- Provide your workflow
- Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
- Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
- Click the Import workflow button
Below is a short video demonstrating how to import a workflow from GitHub using this procedure:
Version History
Version | Commit | Time | Comments |
---|---|---|---|
1 | 6e4ce4916 | 2024-11-14 15:41:21 | Made sure the order is right |
For Admins
Installing the workflow tools
wget https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/neoantigen-1-fusion-database-generation/workflows/main_workflow.ga -O workflow.ga workflow-to-tools -w workflow.ga -o tools.yaml shed-tools install -g GALAXY -a API_KEY -t tools.yaml workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows