Query a metaplasmidome database to identify and annotate plasmids in metagenomes

microbiome-metaplasmidome_query/metaplasmidome

Author(s)
Bérénice Batut, Nadia Goué
version Version
1
last_modification Last updated
Jan 7, 2025
license License
MIT
galaxy-tags Tags
metagenomics
metaplasmidome
name:microGalaxy

Features
Tutorial
hands_on Query an annotated mobile genetic element database to identify and annotate genetic elements (e.g. plasmids) in metagenomics data

Workflow Testing
Tests: ✅
Results: Not yet automated
FAIRness purl PURL
https://gxy.io/GTN:
RO-Crate logo with flask Download Workflow RO-Crate
Launch in Tutorial Mode question
galaxy-download Download
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

Tool Links
CONVERTER_fasta_to_tabular
Cut1
Filter1
Grouping1
cat1
join1
sort1
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_replace_in_column/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_sorted_uniq/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_tail_tool/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/column_maker/Add_a_column1/2.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/count_gff_features/count_gff_features/0.2 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/histogram/histogram_rpy/1.0.4 View in ToolShed
toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/estrain/add_column_headers/add_column_headers/0.1.3 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_histogram/ggplot2_histogram/3.4.0+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/minimap2/minimap2/2.28+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/proteore/proteore_filter_keywords_values/MQoutputfilter/2021.04.19.1 View in ToolShed

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:

Video: Importing a workflow from URL

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