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

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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 FastQC"];
  1 -->|output| 4;
  5["Cutadapt"];
  1 -->|output| 5;
  6["Get gene length"];
  2 -->|output| 6;
  077640cc-edbb-4185-9eb1-d11b522774af["Output\nGene length"];
  6 --> 077640cc-edbb-4185-9eb1-d11b522774af;
  style 077640cc-edbb-4185-9eb1-d11b522774af stroke:#2c3143,stroke-width:4px;
  7["convert gtf to bed12"];
  2 -->|output| 7;
  8["STAR: map single reads"];
  2 -->|output| 8;
  3 -->|out1| 8;
  9["Merge fastqs for FastQC"];
  4 -->|output| 9;
  0 -->|output| 9;
  10["Merge Cutadapt reports"];
  5 -->|report| 10;
  3 -->|report| 10;
  11["STAR: map paired reads"];
  2 -->|output| 11;
  5 -->|out_pairs| 11;
  12["count reads per gene for SR"];
  8 -->|mapped_reads| 12;
  2 -->|output| 12;
  13["FastQC check read qualities"];
  9 -->|output| 13;
  14["Combine cutadapt results"];
  10 -->|output| 14;
  cab760db-5c9d-4a3c-b768-998bfbac6b57["Output\nmultiqc_cutadapt_html"];
  14 --> cab760db-5c9d-4a3c-b768-998bfbac6b57;
  style cab760db-5c9d-4a3c-b768-998bfbac6b57 stroke:#2c3143,stroke-width:4px;
  15["Merge STAR logs"];
  11 -->|output_log| 15;
  8 -->|output_log| 15;
  16["Merge STAR counts"];
  8 -->|reads_per_gene| 16;
  11 -->|reads_per_gene| 16;
  17["count fragments per gene for PE"];
  11 -->|mapped_reads| 17;
  2 -->|output| 17;
  1527b5d7-1681-4934-9d9e-3a5f86ae0fee["Output\nfeatureCounts_gene_length"];
  17 --> 1527b5d7-1681-4934-9d9e-3a5f86ae0fee;
  style 1527b5d7-1681-4934-9d9e-3a5f86ae0fee stroke:#2c3143,stroke-width:4px;
  18["Merge STAR BAM"];
  11 -->|mapped_reads| 18;
  8 -->|mapped_reads| 18;
  802017f4-fb1a-4243-b50d-2ed46f746f11["Output\nSTAR_BAM"];
  18 --> 802017f4-fb1a-4243-b50d-2ed46f746f11;
  style 802017f4-fb1a-4243-b50d-2ed46f746f11 stroke:#2c3143,stroke-width:4px;
  19["merge coverage unique strand 1"];
  8 -->|signal_unique_str1| 19;
  11 -->|signal_unique_str1| 19;
  20["merge coverage unique strand 2"];
  8 -->|signal_unique_str2| 20;
  11 -->|signal_unique_str2| 20;
  21["Combine FastQC results"];
  13 -->|text_file| 21;
  8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd["Output\nmultiqc_fastqc_html"];
  21 --> 8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd;
  style 8d0ce9ee-e4e4-4c0c-8261-420ce756ecfd stroke:#2c3143,stroke-width:4px;
  22["Combine STAR Results"];
  15 -->|output| 22;
  204e3f6c-6f54-46f0-b07c-1f31113265e7["Output\nmultiqc_star_html"];
  22 --> 204e3f6c-6f54-46f0-b07c-1f31113265e7;
  style 204e3f6c-6f54-46f0-b07c-1f31113265e7 stroke:#2c3143,stroke-width:4px;
  23["Remove statistics from STAR counts"];
  16 -->|output| 23;
  24["Determine library strandness with STAR"];
  16 -->|output| 24;
  fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a["Output\nmultiqc_star_counts_html"];
  24 --> fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a;
  style fe7b84dd-4466-4fe7-94a8-408f4ac7ed1a stroke:#2c3143,stroke-width:4px;
  25["merge counts from featureCounts"];
  12 -->|output_short| 25;
  17 -->|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"];
  12 -->|output_summary| 26;
  17 -->|output_summary| 26;
  27["Determine library strandness with Infer Experiment"];
  18 -->|output| 27;
  7 -->|bed_file| 27;
  940ec3ec-dd2e-4d50-bbc4-756945eb16b2["Output\ninferexperiment"];
  27 --> 940ec3ec-dd2e-4d50-bbc4-756945eb16b2;
  style 940ec3ec-dd2e-4d50-bbc4-756945eb16b2 stroke:#2c3143,stroke-width:4px;
  28["Read Distribution"];
  18 -->|output| 28;
  7 -->|bed_file| 28;
  29["Compute read distribution statistics"];
  18 -->|output| 29;
  7 -->|bed_file| 29;
  30["sample BAM"];
  18 -->|output| 30;
  31["Get reads number per chromosome"];
  18 -->|output| 31;
  32["Remove duplicates"];
  18 -->|output| 32;
  33["Determine library strandness with STAR coverage"];
  19 -->|output| 33;
  20 -->|output| 33;
  2 -->|output| 33;
  89e1b053-03c2-467a-95a0-d2dc404670ec["Output\npgt"];
  33 --> 89e1b053-03c2-467a-95a0-d2dc404670ec;
  style 89e1b053-03c2-467a-95a0-d2dc404670ec stroke:#2c3143,stroke-width:4px;
  34["Select unstranded counts"];
  23 -->|outfile| 34;
  bce755be-ac3b-4346-9ac5-1128a287bf00["Output\ncounts_from_star"];
  34 --> bce755be-ac3b-4346-9ac5-1128a287bf00;
  style bce755be-ac3b-4346-9ac5-1128a287bf00 stroke:#2c3143,stroke-width:4px;
  35["Sort counts to get gene with highest count on feature Counts"];
  25 -->|output| 35;
  6aeb4dd1-445f-4c66-b1ce-4bb8faac53db["Output\nfeatureCounts_sorted"];
  35 --> 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db;
  style 6aeb4dd1-445f-4c66-b1ce-4bb8faac53db stroke:#2c3143,stroke-width:4px;
  36["Combine read asignments statistics"];
  26 -->|output| 36;
  fc72242a-f23c-4ceb-9a8b-5280343ea5d6["Output\nmultiqc_featureCounts_html"];
  36 --> fc72242a-f23c-4ceb-9a8b-5280343ea5d6;
  style fc72242a-f23c-4ceb-9a8b-5280343ea5d6 stroke:#2c3143,stroke-width:4px;
  37["Combine read distribution on known features"];
  29 -->|output| 37;
  07dca732-0ac7-432e-9e61-2b77f921a23b["Output\nmultiqc_read_distrib"];
  37 --> 07dca732-0ac7-432e-9e61-2b77f921a23b;
  style 07dca732-0ac7-432e-9e61-2b77f921a23b stroke:#2c3143,stroke-width:4px;
  38["Get gene body coverage"];
  30 -->|outputsam| 38;
  7 -->|bed_file| 38;
  39["Combine results on reads per chromosome"];
  31 -->|output| 39;
  7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf["Output\nmultiqc_reads_per_chrom"];
  39 --> 7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf;
  style 7bfa8ae7-8ffd-46a1-a56e-815ed2c9f1cf stroke:#2c3143,stroke-width:4px;
  40["Combine results of duplicate reads"];
  32 -->|metrics_file| 40;
  66553d0f-e851-458b-82c2-f9b30e394bac["Output\nmultiqc_dup"];
  40 --> 66553d0f-e851-458b-82c2-f9b30e394bac;
  style 66553d0f-e851-458b-82c2-f9b30e394bac stroke:#2c3143,stroke-width:4px;
  41["Sort counts to get gene with highest count on STAR"];
  34 -->|out_file1| 41;
  383df008-0ccb-4d67-98dd-33fa5e2db81e["Output\ncounts_from_star_sorted"];
  41 --> 383df008-0ccb-4d67-98dd-33fa5e2db81e;
  style 383df008-0ccb-4d67-98dd-33fa5e2db81e stroke:#2c3143,stroke-width:4px;
  42["Combine gene body coverage"];
  38 -->|outputtxt| 42;
  8544ea5c-faf2-44c9-85d6-40658fc9b9eb["Output\nmultiqc_gene_body_cov"];
  42 --> 8544ea5c-faf2-44c9-85d6-40658fc9b9eb;
  style 8544ea5c-faf2-44c9-85d6-40658fc9b9eb stroke:#2c3143,stroke-width:4px;

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

• 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

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