QC + Mapping + Counting (single+paired) - Ref Based RNA Seq - Transcriptomics - GTN

Run Workflow in Galaxy

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• Galaxy Europe
• Galaxy France
• Import to another server (≥23.0+ only!)

flowchart TD
  0["ℹ️ Input Collection\nsingle fastqs"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Collection\npaired fastqs"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nDrosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["Cutadapt: remove bad quality bp"];
  0 -->|output| 3;
  4["Flatten paired collection for Falco"];
  1 -->|output| 4;
  5["Cutadapt"];
  1 -->|output| 5;
  6["STAR: map single reads"];
  2 -->|output| 6;
  3 -->|out1| 6;
  7["Merge fastqs for Falco"];
  4 -->|output| 7;
  0 -->|output| 7;
  8["Merge Cutadapt reports"];
  5 -->|report| 8;
  3 -->|report| 8;
  9["STAR: map paired reads"];
  2 -->|output| 9;
  5 -->|out_pairs| 9;
  10["count reads per gene for SR"];
  6 -->|mapped_reads| 10;
  2 -->|output| 10;
  11["Falco"];
  7 -->|output| 11;
  12["Combine cutadapt results"];
  8 -->|output| 12;
  cab760db-5c9d-4a3c-b768-998bfbac6b57["Output\nmultiqc_cutadapt_html"];
  12 --> cab760db-5c9d-4a3c-b768-998bfbac6b57;
  style cab760db-5c9d-4a3c-b768-998bfbac6b57 stroke:#2c3143,stroke-width:4px;
  13["Merge STAR logs"];
  9 -->|output_log| 13;
  6 -->|output_log| 13;
  14["Merge STAR counts"];
  6 -->|reads_per_gene| 14;
  9 -->|reads_per_gene| 14;
  15["Merge STAR BAM"];
  6 -->|mapped_reads| 15;
  9 -->|mapped_reads| 15;
  802017f4-fb1a-4243-b50d-2ed46f746f11["Output\nSTAR_BAM"];
  15 --> 802017f4-fb1a-4243-b50d-2ed46f746f11;
  style 802017f4-fb1a-4243-b50d-2ed46f746f11 stroke:#2c3143,stroke-width:4px;
  16["merge coverage unique strand 1"];
  6 -->|signal_unique_str1| 16;
  9 -->|signal_unique_str1| 16;
  17["merge coverage unique strand 2"];
  6 -->|signal_unique_str2| 17;
  9 -->|signal_unique_str2| 17;
  18["count fragments per gene for PE"];
  9 -->|mapped_reads| 18;
  2 -->|output| 18;
  1527b5d7-1681-4934-9d9e-3a5f86ae0fee["Output\nfeatureCounts_gene_length"];
  18 --> 1527b5d7-1681-4934-9d9e-3a5f86ae0fee;
  style 1527b5d7-1681-4934-9d9e-3a5f86ae0fee stroke:#2c3143,stroke-width:4px;
  19["Combine FastQC results"];
  11 -->|text_file| 19;
  791893e6-6e65-49fe-b71f-4a7b6482e0ce["Output\nmultiqc_falco_html"];
  19 --> 791893e6-6e65-49fe-b71f-4a7b6482e0ce;
  style 791893e6-6e65-49fe-b71f-4a7b6482e0ce stroke:#2c3143,stroke-width:4px;
  20["Combine STAR Results"];
  13 -->|output| 20;
  204e3f6c-6f54-46f0-b07c-1f31113265e7["Output\nmultiqc_star_html"];
  20 --> 204e3f6c-6f54-46f0-b07c-1f31113265e7;
  style 204e3f6c-6f54-46f0-b07c-1f31113265e7 stroke:#2c3143,stroke-width:4px;
  21["🛠️ Subworkflow\ncount STAR"];
  style 21 fill:#edd,stroke:#900,stroke-width:4px;
  2 -->|output| 21;
  14 -->|output| 21;
  7b7c698b-4808-4b45-adf1-686f8d273d18["Output\nGene length"];
  21 --> 7b7c698b-4808-4b45-adf1-686f8d273d18;
  style 7b7c698b-4808-4b45-adf1-686f8d273d18 stroke:#2c3143,stroke-width:4px;
  5fee8aff-4023-43f1-a653-f5af5357d798["Output\ncounts_from_star"];
  21 --> 5fee8aff-4023-43f1-a653-f5af5357d798;
  style 5fee8aff-4023-43f1-a653-f5af5357d798 stroke:#2c3143,stroke-width:4px;
  bd3388e6-5b45-4fdc-9780-3efd1c34ebf8["Output\ncounts_from_star_sorted"];
  21 --> bd3388e6-5b45-4fdc-9780-3efd1c34ebf8;
  style bd3388e6-5b45-4fdc-9780-3efd1c34ebf8 stroke:#2c3143,stroke-width:4px;
  22["Run JBrowse2 to check BAM"];
  15 -->|output| 22;
  2 -->|output| 22;
  23["🛠️ Subworkflow\nmore QC"];
  style 23 fill:#edd,stroke:#900,stroke-width:4px;
  2 -->|output| 23;
  15 -->|output| 23;
  3ea82568-5698-49a7-88fe-91381070aac2["Output\nmultiqc_dup_html"];
  23 --> 3ea82568-5698-49a7-88fe-91381070aac2;
  style 3ea82568-5698-49a7-88fe-91381070aac2 stroke:#2c3143,stroke-width:4px;
  b306cb12-a275-4c6d-b609-47fdc208864b["Output\nmultiqc_reads_per_chrom_html"];
  23 --> b306cb12-a275-4c6d-b609-47fdc208864b;
  style b306cb12-a275-4c6d-b609-47fdc208864b stroke:#2c3143,stroke-width:4px;
  3375d63c-cdc3-4fbb-8a55-6f504c934918["Output\nmultiqc_gene_body_cov_html"];
  23 --> 3375d63c-cdc3-4fbb-8a55-6f504c934918;
  style 3375d63c-cdc3-4fbb-8a55-6f504c934918 stroke:#2c3143,stroke-width:4px;
  f2eed352-ca21-4d65-8810-f5a1d3c282b4["Output\nmultiqc_read_distrib_html"];
  23 --> f2eed352-ca21-4d65-8810-f5a1d3c282b4;
  style f2eed352-ca21-4d65-8810-f5a1d3c282b4 stroke:#2c3143,stroke-width:4px;
  24["🛠️ Subworkflow\nDetermine strandness"];
  style 24 fill:#edd,stroke:#900,stroke-width:4px;
  15 -->|output| 24;
  2 -->|output| 24;
  16 -->|output| 24;
  17 -->|output| 24;
  14 -->|output| 24;
  9727824a-3eb2-4430-92d1-b3c40c3041d1["Output\npgt"];
  24 --> 9727824a-3eb2-4430-92d1-b3c40c3041d1;
  style 9727824a-3eb2-4430-92d1-b3c40c3041d1 stroke:#2c3143,stroke-width:4px;
  105313d8-e31a-405d-8fcd-cc5fd93275e2["Output\nmultiqc_star_counts_html"];
  24 --> 105313d8-e31a-405d-8fcd-cc5fd93275e2;
  style 105313d8-e31a-405d-8fcd-cc5fd93275e2 stroke:#2c3143,stroke-width:4px;
  fb810859-f2d0-43f8-ac7c-5c714c5c6805["Output\ninferexperiment"];
  24 --> fb810859-f2d0-43f8-ac7c-5c714c5c6805;
  style fb810859-f2d0-43f8-ac7c-5c714c5c6805 stroke:#2c3143,stroke-width:4px;
  25["merge counts from featureCounts"];
  10 -->|output_short| 25;
  18 -->|output_short| 25;
  c82388f8-cb09-4fdf-8a0e-03cdad579f37["Output\nfeatureCounts"];
  25 --> c82388f8-cb09-4fdf-8a0e-03cdad579f37;
  style c82388f8-cb09-4fdf-8a0e-03cdad579f37 stroke:#2c3143,stroke-width:4px;
  26["merge featureCounts summary"];
  10 -->|output_summary| 26;
  18 -->|output_summary| 26;
  27["Sort counts to get gene with highest count on feature Counts"];
  25 -->|output| 27;
  6aeb4dd1-445f-4c66-b1ce-4bb8faac53db["Output\nfeatureCounts_sorted"];
  27 --> 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db;
  style 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db stroke:#2c3143,stroke-width:4px;
  28["Combine read asignments statistics"];
  26 -->|output| 28;
  fc72242a-f23c-4ceb-9a8b-5280343ea5d6["Output\nmultiqc_featureCounts_html"];
  28 --> fc72242a-f23c-4ceb-9a8b-5280343ea5d6;
  style fc72242a-f23c-4ceb-9a8b-5280343ea5d6 stroke:#2c3143,stroke-width:4px;

Inputs

Outputs

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

Hands On: Importing a workflow

1. Click on galaxy-workflows-activity Workflows in the Galaxy activity bar (on the left side of the screen, or in the top menu bar of older Galaxy instances). You will see a list of all your workflows
2. Click on galaxy-upload Import at the top-right of the screen
3. 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”
4. Click the Import workflow button

Below is a short video demonstrating how to import a workflow from GitHub using this procedure:

Version History

For Admins

Installing the workflow tools

wget https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting-paired-and-single.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