Query a metaplasmidome database to identify and annotate plasmids in metagenomes
microbiome-metaplasmidome_query/metaplasmidome
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flowchart TD 0["ℹ️ Input Dataset\nMetaplasmidome sequences"]; style 0 stroke:#2c3143,stroke-width:4px; 1["ℹ️ Input Dataset\nRaw metagenomics data"]; style 1 stroke:#2c3143,stroke-width:4px; 2["ℹ️ Input Dataset\nMetaplasmidome predicted CDS"]; style 2 stroke:#2c3143,stroke-width:4px; 3["ℹ️ Input Dataset\nKEGG Ortogolog"]; style 3 stroke:#2c3143,stroke-width:4px; 4["ℹ️ Input Dataset\nPFAM"]; style 4 stroke:#2c3143,stroke-width:4px; 5["Map with minimap2"]; 1 -->|output| 5; 0 -->|output| 5; 6["Convert FASTA to Tabular"]; 1 -->|output| 6; 7["Count GFF Features"]; 2 -->|output| 7; 8["Cut"]; 5 -->|alignment_output| 8; 9["Cut"]; 5 -->|alignment_output| 9; 10["Compute"]; 8 -->|out_file1| 10; 11["Histogram with ggplot2"]; 9 -->|out_file1| 11; 12["Cut"]; 10 -->|out_file1| 12; 13["Histogram"]; 10 -->|out_file1| 13; 14["Filter"]; 10 -->|out_file1| 14; 15["Histogram with ggplot2"]; 12 -->|out_file1| 15; 16["Filter"]; 14 -->|out_file1| 16; 17["Cut"]; 14 -->|out_file1| 17; 18["Cut"]; 16 -->|out_file1| 18; 19["Histogram with ggplot2"]; 17 -->|out_file1| 19; 20["Join two Datasets"]; 18 -->|out_file1| 20; 6 -->|output| 20; 21["Cut"]; 20 -->|out_file1| 21; 22["Cut"]; 20 -->|out_file1| 22; 23["Add Header"]; 21 -->|out_file1| 23; a183cd1f-a2f6-459c-a1f7-49ac50ab0df3["Output\nMetagenomes identified as plasmids"]; 23 --> a183cd1f-a2f6-459c-a1f7-49ac50ab0df3; style a183cd1f-a2f6-459c-a1f7-49ac50ab0df3 stroke:#2c3143,stroke-width:4px; 24["Sort"]; 22 -->|out_file1| 24; 25["Join two Datasets"]; 2 -->|output| 25; 23 -->|Data Table| 25; 26["Unique"]; 24 -->|out_file1| 26; 27["Cut"]; 25 -->|out_file1| 27; 28["Tabular-to-FASTA"]; 26 -->|outfile| 28; 7c9e9f30-c340-4738-9f3b-66cbe5bde2c3["Output\nMetagenome sequences identified as plasmids"]; 28 --> 7c9e9f30-c340-4738-9f3b-66cbe5bde2c3; style 7c9e9f30-c340-4738-9f3b-66cbe5bde2c3 stroke:#2c3143,stroke-width:4px; 29["Group"]; 27 -->|out_file1| 29; 30["Filter"]; 27 -->|out_file1| 30; 31["Cut"]; 30 -->|out_file1| 31; 32["Filter by keywords and/or numerical value"]; 31 -->|out_file1| 32; 33["Replace Text"]; 32 -->|kept_lines| 33; 34["Replace Text"]; 32 -->|discarded_lines| 34; 35["Add Header"]; 33 -->|outfile| 35; 36["Concatenate datasets"]; 35 -->|Data Table| 36; 34 -->|outfile| 36; dd7ee5f7-b8a4-45a7-9525-8ae626364e20["Output\nCDS in metagenomes identified as plasmids"]; 36 --> dd7ee5f7-b8a4-45a7-9525-8ae626364e20; style dd7ee5f7-b8a4-45a7-9525-8ae626364e20 stroke:#2c3143,stroke-width:4px; 37["Join two Datasets"]; 36 -->|out_file1| 37; 3 -->|output| 37; 38["Cut"]; 37 -->|out_file1| 38; 39["Join two Datasets"]; 38 -->|out_file1| 39; 4 -->|output| 39; 40["Cut"]; 39 -->|out_file1| 40; 41["Select last"]; 40 -->|out_file1| 41; 42["Add Header"]; 41 -->|outfile| 42; 8d4adc03-cab4-444a-8122-b891f3eca2be["Output\nCDS in metagenomes identified as plasmids + KO + PFAM"]; 42 --> 8d4adc03-cab4-444a-8122-b891f3eca2be; style 8d4adc03-cab4-444a-8122-b891f3eca2be stroke:#2c3143,stroke-width:4px; 43["Filter"]; 42 -->|Data Table| 43; 44["Filter"]; 42 -->|Data Table| 44; 45["Group"]; 42 -->|Data Table| 45; 46["Group"]; 43 -->|out_file1| 46; 47["Group"]; 44 -->|out_file1| 47; 48["Select last"]; 45 -->|out_file1| 48; 49["Sort"]; 46 -->|out_file1| 49; 50["Sort"]; 47 -->|out_file1| 50; 51["Add Header"]; 48 -->|outfile| 51; 39e36bb3-4f8d-46e1-8d26-24619c7ad36d["Output\nCDS annotation overview per metagenomic sequences"]; 51 --> 39e36bb3-4f8d-46e1-8d26-24619c7ad36d; style 39e36bb3-4f8d-46e1-8d26-24619c7ad36d stroke:#2c3143,stroke-width:4px;
Inputs
Input | Label |
---|---|
Input dataset | Metaplasmidome sequences |
Input dataset | Raw metagenomics data |
Input dataset | Metaplasmidome predicted CDS |
Input dataset | KEGG Ortogolog |
Input dataset | PFAM |
Outputs
From | Output | Label |
---|---|---|
toolshed.g2.bx.psu.edu/repos/estrain/add_column_headers/add_column_headers/0.1.3 | Add Header | |
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 | Tabular-to-FASTA | |
cat1 | Concatenate datasets | |
toolshed.g2.bx.psu.edu/repos/estrain/add_column_headers/add_column_headers/0.1.3 | Add Header | |
toolshed.g2.bx.psu.edu/repos/estrain/add_column_headers/add_column_headers/0.1.3 | Add Header |
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 |
---|---|---|---|
3 | 4829220a9 | 2024-12-17 10:04:34 | Add missing creator id |
2 | a1f65030b | 2024-12-16 15:39:45 | Add Zenodo data and missing metadata |
1 | d455a0211 | 2024-08-02 14:08:20 | Create metaplasmidome query tutorial |
For Admins
Installing the workflow tools
wget https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/metaplasmidome_query/workflows/metaplasmidome.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