Workflows

These workflows are associated with Exome sequencing data analysis for diagnosing a genetic disease

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.

Exome Seq Training Pre-Mapped W Cached Ref
Wolfgang Maier

Last updated Jul 17, 2023

Launch in Tutorial Mode question
License: MIT
Tests: ✅ Results: Not yet automated

flowchart TD
  0["ℹ️ Input Dataset\nFather data"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nMother data"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nProband data"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["ℹ️ Input Dataset\nPEDigree data"];
  style 3 stroke:#2c3143,stroke-width:4px;
  4["Samtools view"];
  0 -->|output| 4;
  5["Samtools view"];
  1 -->|output| 5;
  6["Samtools view"];
  2 -->|output| 6;
  7["RmDup"];
  4 -->|outputsam| 7;
  8["RmDup"];
  5 -->|outputsam| 8;
  9["RmDup"];
  6 -->|outputsam| 9;
  10["FreeBayes"];
  7 -->|output1| 10;
  8 -->|output1| 10;
  9 -->|output1| 10;
  11["bcftools norm"];
  10 -->|output_vcf| 11;
  12["SnpEff eff:"];
  11 -->|output_file| 12;
  974b6b15-ccf7-4fe8-8a71-2b3fe8b45272["Output\nsnpeff_variant_stats"];
  12 --> 974b6b15-ccf7-4fe8-8a71-2b3fe8b45272;
  style 974b6b15-ccf7-4fe8-8a71-2b3fe8b45272 stroke:#2c3143,stroke-width:4px;
  083b9686-5c6d-495f-9257-7243896e64d7["Output\nnormalized_snpeff_annotated_variants"];
  12 --> 083b9686-5c6d-495f-9257-7243896e64d7;
  style 083b9686-5c6d-495f-9257-7243896e64d7 stroke:#2c3143,stroke-width:4px;
  13["GEMINI load"];
  12 -->|snpeff_output| 13;
  3 -->|output| 13;
  14["GEMINI inheritance pattern"];
  13 -->|outfile| 14;
  c5a64822-3c68-495e-b2fd-4642b1aabb5a["Output\ncandidate_mutations"];
  14 --> c5a64822-3c68-495e-b2fd-4642b1aabb5a;
  style c5a64822-3c68-495e-b2fd-4642b1aabb5a stroke:#2c3143,stroke-width:4px;
	
Exome Seq Training Full W Cached Ref
Wolfgang Maier

Last updated Jul 17, 2023

Launch in Tutorial Mode question
License: MIT
Tests: ✅ Results: Not yet automated

flowchart TD
  0["ℹ️ Input Dataset\nfather_R1"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nfather_R2"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nmother_R1"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["ℹ️ Input Dataset\nmother_R2"];
  style 3 stroke:#2c3143,stroke-width:4px;
  4["ℹ️ Input Dataset\nproband_R1"];
  style 4 stroke:#2c3143,stroke-width:4px;
  5["ℹ️ Input Dataset\nproband_R2"];
  style 5 stroke:#2c3143,stroke-width:4px;
  6["ℹ️ Input Dataset\nPEDigree data"];
  style 6 stroke:#2c3143,stroke-width:4px;
  7["FastQC"];
  0 -->|output| 7;
  8["FastQC"];
  1 -->|output| 8;
  9["Map with BWA-MEM"];
  0 -->|output| 9;
  1 -->|output| 9;
  10["FastQC"];
  2 -->|output| 10;
  11["FastQC"];
  3 -->|output| 11;
  12["Map with BWA-MEM"];
  2 -->|output| 12;
  3 -->|output| 12;
  13["FastQC"];
  4 -->|output| 13;
  14["FastQC"];
  5 -->|output| 14;
  15["Map with BWA-MEM"];
  4 -->|output| 15;
  5 -->|output| 15;
  16["Samtools view"];
  9 -->|bam_output| 16;
  17["Samtools view"];
  12 -->|bam_output| 17;
  18["MultiQC"];
  7 -->|text_file| 18;
  8 -->|text_file| 18;
  10 -->|text_file| 18;
  11 -->|text_file| 18;
  13 -->|text_file| 18;
  14 -->|text_file| 18;
  de867748-890a-454c-baf3-6a9313269975["Output\nmultiqc_input_data"];
  18 --> de867748-890a-454c-baf3-6a9313269975;
  style de867748-890a-454c-baf3-6a9313269975 stroke:#2c3143,stroke-width:4px;
  19["Samtools view"];
  15 -->|bam_output| 19;
  20["RmDup"];
  16 -->|outputsam| 20;
  21["RmDup"];
  17 -->|outputsam| 21;
  22["RmDup"];
  19 -->|outputsam| 22;
  23["FreeBayes"];
  20 -->|output1| 23;
  21 -->|output1| 23;
  22 -->|output1| 23;
  24["bcftools norm"];
  23 -->|output_vcf| 24;
  25["SnpEff eff:"];
  24 -->|output_file| 25;
  4aab5884-4099-4e41-a82d-c4e1ff19609f["Output\nsnpeff_variant_stats"];
  25 --> 4aab5884-4099-4e41-a82d-c4e1ff19609f;
  style 4aab5884-4099-4e41-a82d-c4e1ff19609f stroke:#2c3143,stroke-width:4px;
  e5a2fbc7-acdd-4782-822a-91568cb8d5b8["Output\nnormalized_snpeff_annotated_variants"];
  25 --> e5a2fbc7-acdd-4782-822a-91568cb8d5b8;
  style e5a2fbc7-acdd-4782-822a-91568cb8d5b8 stroke:#2c3143,stroke-width:4px;
  26["GEMINI load"];
  25 -->|snpeff_output| 26;
  6 -->|output| 26;
  27["GEMINI inheritance pattern"];
  26 -->|outfile| 27;
  07bd4bba-e7d9-48a8-b812-5dc2b07712bd["Output\ncandidate_mutations"];
  27 --> 07bd4bba-e7d9-48a8-b812-5dc2b07712bd;
  style 07bd4bba-e7d9-48a8-b812-5dc2b07712bd stroke:#2c3143,stroke-width:4px;
	

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: