Allele-based Pathogen Identification

microbiome-pathogen-detection-from-nanopore-foodborne-data/allele-based-pathogen-identification

Author(s)
Engy Nasr, Bérénice Batut, Paul Zierep
version Version
1
last_modification Last updated
Jun 20, 2024
license License
MIT
galaxy-tags Tags
name:Collection
name:microGalaxy
name:PathoGFAIR
name:IWC

Features
Tutorial
hands_on Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition
workflow Other workflows associated with this material
Workflow Testing
Tests: ✅
Results: Not yet automated
FAIRness purl PURL
https://gxy.io/GTN:W00141
RO-Crate logo with flask Download Workflow RO-Crate Workflowhub cloud with gears logo View on (Dev) WorkflowHub
Launch in Tutorial Mode question
galaxy-download Download
flowchart TD
  0["ℹ️ Input Collection\ncollection_of_preprocessed_samples"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Parameter\nsamples_profile"];
  style 1 fill:#ded,stroke:#393,stroke-width:4px;
  2["ℹ️ Input Dataset\nreference_genome_of_tested_strain"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["Convert compressed file to uncompressed."];
  2 -->|output| 3;
  9d6bde4a-7180-4097-9ffd-6992111a617c["Output\ndecompressed_rg_file"];
  3 --> 9d6bde4a-7180-4097-9ffd-6992111a617c;
  style 9d6bde4a-7180-4097-9ffd-6992111a617c stroke:#2c3143,stroke-width:4px;
  4["Map with minimap2"];
  1 -->|output| 4;
  0 -->|output| 4;
  3 -->|output1| 4;
  31779c23-4f26-418e-9418-2551e883dbe7["Output\nmap_with_minimap2"];
  4 --> 31779c23-4f26-418e-9418-2551e883dbe7;
  style 31779c23-4f26-418e-9418-2551e883dbe7 stroke:#2c3143,stroke-width:4px;
  5["Clair3"];
  4 -->|alignment_output| 5;
  3 -->|output1| 5;
  1cf1ee6c-4cb7-45e3-9c7b-88a1b678dd53["Output\nclair3_pileup_vcf"];
  5 --> 1cf1ee6c-4cb7-45e3-9c7b-88a1b678dd53;
  style 1cf1ee6c-4cb7-45e3-9c7b-88a1b678dd53 stroke:#2c3143,stroke-width:4px;
  987f9145-83ab-44f1-801a-b0d527ecbce8["Output\nclair3_full_alignment_vcf"];
  5 --> 987f9145-83ab-44f1-801a-b0d527ecbce8;
  style 987f9145-83ab-44f1-801a-b0d527ecbce8 stroke:#2c3143,stroke-width:4px;
  fab82215-f59a-43b1-92d7-37629a1fdb65["Output\nclair3_merged_output"];
  5 --> fab82215-f59a-43b1-92d7-37629a1fdb65;
  style fab82215-f59a-43b1-92d7-37629a1fdb65 stroke:#2c3143,stroke-width:4px;
  6["Samtools depth"];
  4 -->|alignment_output| 6;
  7["Samtools coverage"];
  4 -->|alignment_output| 7;
  8["bcftools norm"];
  5 -->|merge_output| 8;
  3 -->|output1| 8;
  22553aa1-f5db-4d37-87c4-1164dbb3d2d5["Output\nnormalized_vcf_output"];
  8 --> 22553aa1-f5db-4d37-87c4-1164dbb3d2d5;
  style 22553aa1-f5db-4d37-87c4-1164dbb3d2d5 stroke:#2c3143,stroke-width:4px;
  9["Advanced Cut"];
  6 -->|output| 9;
  10["Remove beginning"];
  7 -->|output| 10;
  11["SnpSift Filter"];
  8 -->|output_file| 11;
  3e7981ec-2205-4571-93eb-10c0dd14b288["Output\nquality_filtered_vcf_output"];
  11 --> 3e7981ec-2205-4571-93eb-10c0dd14b288;
  style 3e7981ec-2205-4571-93eb-10c0dd14b288 stroke:#2c3143,stroke-width:4px;
  12["Table Compute"];
  9 -->|output| 12;
  13["Cut"];
  10 -->|out_file1| 13;
  14["SnpSift Extract Fields"];
  11 -->|output| 14;
  1b0f4f2c-4717-45be-b580-fe10cba78c35["Output\nextracted_fields_from_the_vcf_output"];
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  style 1b0f4f2c-4717-45be-b580-fe10cba78c35 stroke:#2c3143,stroke-width:4px;
  15["bcftools consensus"];
  11 -->|output| 15;
  3 -->|output1| 15;
  49497f3c-9332-4924-bcfd-cb71788ad2c2["Output\nbcftools_consensus"];
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  16["Select first"];
  13 -->|out_file1| 16;
  17["Remove beginning"];
  14 -->|output| 17;
  18["Collapse Collection"];
  16 -->|outfile| 18;
  5ee1c158-1a85-45ca-9abd-b30632b3092c["Output\nmapping_coverage_percentage_per_sample"];
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  style 5ee1c158-1a85-45ca-9abd-b30632b3092c stroke:#2c3143,stroke-width:4px;
  19["Count"];
  17 -->|out_file1| 19;
  20["Advanced Cut"];
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  21["Cut"];
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  22["Paste"];
  20 -->|output| 22;
  12 -->|table| 22;
  25b7b87b-5ef1-487f-90cc-81351a2f81ce["Output\nmapping_mean_depth_per_sample"];
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  23["Select first"];
  21 -->|out_file1| 23;
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  23 -->|outfile| 24;
  25["Column Regex Find And Replace"];
  24 -->|output| 25;
  2852c4ee-24ea-4df7-b59e-54eb6e2f470b["Output\nnumber_of_variants_per_sample"];
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Inputs

Input Label
Input dataset collection collection_of_preprocessed_samples
Input parameter samples_profile
Input dataset reference_genome_of_tested_strain

Outputs

From Output Label
CONVERTER_gz_to_uncompressed Convert compressed file to uncompressed.
toolshed.g2.bx.psu.edu/repos/iuc/minimap2/minimap2/2.24+galaxy0 Map with minimap2
toolshed.g2.bx.psu.edu/repos/iuc/clair3/clair3/0.1.12+galaxy0 Clair3
toolshed.g2.bx.psu.edu/repos/iuc/bcftools_norm/bcftools_norm/1.9+galaxy1 bcftools norm
toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_filter/4.3+t.galaxy1 SnpSift Filter
toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_extractFields/4.3+t.galaxy0 SnpSift Extract Fields
toolshed.g2.bx.psu.edu/repos/iuc/bcftools_consensus/bcftools_consensus/1.9+galaxy1 bcftools consensus
toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0 Collapse Collection
Paste1 Paste
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 Column Regex Find And Replace

Tools

Tool Links
CONVERTER_gz_to_uncompressed
Count1
Cut1
Paste1
Remove beginning1
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_head_tool/9.3+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/galaxyp/regex_find_replace/regexColumn1/1.0.3 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/bcftools_consensus/bcftools_consensus/1.9+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/bcftools_norm/bcftools_norm/1.9+galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/clair3/clair3/0.1.12+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/minimap2/minimap2/2.24+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/samtools_coverage/samtools_coverage/1.15.1+galaxy2 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/samtools_depth/samtools_depth/1.15.1+galaxy2 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_extractFields/4.3+t.galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_filter/4.3+t.galaxy1 View in ToolShed
toolshed.g2.bx.psu.edu/repos/iuc/table_compute/table_compute/1.2.4+galaxy0 View in ToolShed
toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0 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
1 fea16c694 2024-06-19 21:47:55 updating to include all previous comments

For Admins

Installing the workflow tools

wget https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/pathogen-detection-from-nanopore-foodborne-data/workflows/allele_based_pathogen_identification.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