Workflows

These workflows are associated with Reference-based RNA-Seq data analysis

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.

flowchart TD
  0["ℹ️ Input Collection\nInput Dataset Collection"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nDrosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Dataset\nheader"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["ℹ️ Input Dataset\nKEGG pathways to plot"];
  style 3 stroke:#2c3143,stroke-width:4px;
  4["Extract samples’ name"];
  0 -->|output| 4;
  5["Compute gene length"];
  1 -->|output| 5;
  6["Extract groups"];
  4 -->|output| 6;
  7["Change Case"];
  5 -->|length| 7;
  8["Tag elements with groups"];
  0 -->|output| 8;
  6 -->|outfile| 8;
  9["Differential Analysis"];
  8 -->|output| 9;
  c1ff3e9a-46d3-4862-adb6-076ea951be26["Output\nDESeq2_plots"];
  9 --> c1ff3e9a-46d3-4862-adb6-076ea951be26;
  style c1ff3e9a-46d3-4862-adb6-076ea951be26 stroke:#2c3143,stroke-width:4px;
  b430e84e-cfa5-48e8-92a7-ef5630ced1e5["Output\nDESeq2_normalized_counts"];
  9 --> b430e84e-cfa5-48e8-92a7-ef5630ced1e5;
  style b430e84e-cfa5-48e8-92a7-ef5630ced1e5 stroke:#2c3143,stroke-width:4px;
  10["Compute"];
  9 -->|deseq_out| 10;
  11["Annotate DESeq2/DEXSeq output tables"];
  1 -->|output| 11;
  9 -->|deseq_out| 11;
  12["Table Compute"];
  9 -->|counts_out| 12;
  13["Cut"];
  10 -->|out_file1| 13;
  14["Concatenate datasets"];
  2 -->|output| 14;
  11 -->|output| 14;
  11f46837-7dbb-453b-962a-3c97cb0faad4["Output\nDESeq2_annotated_results_with_header"];
  14 --> 11f46837-7dbb-453b-962a-3c97cb0faad4;
  style 11f46837-7dbb-453b-962a-3c97cb0faad4 stroke:#2c3143,stroke-width:4px;
  15["Table Compute"];
  9 -->|counts_out| 15;
  12 -->|table| 15;
  32004d23-7c99-4405-a65f-fd861b40c949["Output\nz_score"];
  15 --> 32004d23-7c99-4405-a65f-fd861b40c949;
  style 32004d23-7c99-4405-a65f-fd861b40c949 stroke:#2c3143,stroke-width:4px;
  16["Change Case"];
  13 -->|out_file1| 16;
  17["Filter"];
  14 -->|out_file1| 17;
  18["goseq"];
  16 -->|out_file1| 18;
  7 -->|out_file1| 18;
  a0dd7397-7262-4336-abd6-a915ab6be36a["Output\ngo_genes"];
  18 --> a0dd7397-7262-4336-abd6-a915ab6be36a;
  style a0dd7397-7262-4336-abd6-a915ab6be36a stroke:#2c3143,stroke-width:4px;
  659fec58-4ed5-4aee-9e50-7acce21113cb["Output\ngo_plot"];
  18 --> 659fec58-4ed5-4aee-9e50-7acce21113cb;
  style 659fec58-4ed5-4aee-9e50-7acce21113cb stroke:#2c3143,stroke-width:4px;
  c06c58d4-ab5d-49da-b481-baf668c6fc29["Output\ngo_terms"];
  18 --> c06c58d4-ab5d-49da-b481-baf668c6fc29;
  style c06c58d4-ab5d-49da-b481-baf668c6fc29 stroke:#2c3143,stroke-width:4px;
  19["goseq"];
  16 -->|out_file1| 19;
  7 -->|out_file1| 19;
  9872b017-9b39-4cd3-b232-a305979a782b["Output\nkegg_genes"];
  19 --> 9872b017-9b39-4cd3-b232-a305979a782b;
  style 9872b017-9b39-4cd3-b232-a305979a782b stroke:#2c3143,stroke-width:4px;
  8d0ac5be-ac7d-4f10-85ac-d3461f561a50["Output\nkegg_pathways"];
  19 --> 8d0ac5be-ac7d-4f10-85ac-d3461f561a50;
  style 8d0ac5be-ac7d-4f10-85ac-d3461f561a50 stroke:#2c3143,stroke-width:4px;
  20["Cut"];
  17 -->|out_file1| 20;
  21["Filter"];
  17 -->|out_file1| 21;
  22["Filter"];
  18 -->|wallenius_tab| 22;
  ae762394-0e90-4b9a-a7b4-7cffeccc80a7["Output\ngo_underrepresented"];
  22 --> ae762394-0e90-4b9a-a7b4-7cffeccc80a7;
  style ae762394-0e90-4b9a-a7b4-7cffeccc80a7 stroke:#2c3143,stroke-width:4px;
  23["Filter"];
  18 -->|wallenius_tab| 23;
  b788d5b7-119c-418a-a12b-a97ef7ea5bf2["Output\ngo_overrepresented"];
  23 --> b788d5b7-119c-418a-a12b-a97ef7ea5bf2;
  style b788d5b7-119c-418a-a12b-a97ef7ea5bf2 stroke:#2c3143,stroke-width:4px;
  24["Filter"];
  19 -->|wallenius_tab| 24;
  69f80c89-fc7d-4bea-836f-34e68615abcd["Output\nkegg_underrepresented"];
  24 --> 69f80c89-fc7d-4bea-836f-34e68615abcd;
  style 69f80c89-fc7d-4bea-836f-34e68615abcd stroke:#2c3143,stroke-width:4px;
  25["Filter"];
  19 -->|wallenius_tab| 25;
  930f1461-0ade-4744-a83d-843108c4b597["Output\nkegg_overrepresented"];
  25 --> 930f1461-0ade-4744-a83d-843108c4b597;
  style 930f1461-0ade-4744-a83d-843108c4b597 stroke:#2c3143,stroke-width:4px;
  26["Pathview"];
  20 -->|out_file1| 26;
  3 -->|output| 26;
  7cdd4475-6de3-4c02-be8d-d76e788980f6["Output\npathview_plot"];
  26 --> 7cdd4475-6de3-4c02-be8d-d76e788980f6;
  style 7cdd4475-6de3-4c02-be8d-d76e788980f6 stroke:#2c3143,stroke-width:4px;
  27["Join two Datasets"];
  9 -->|counts_out| 27;
  21 -->|out_file1| 27;
  28["Group"];
  22 -->|out_file1| 28;
  d1ae921d-2e2b-46d8-95a2-4443f5a1b3b4["Output\ngo_underrepresented_categories"];
  28 --> d1ae921d-2e2b-46d8-95a2-4443f5a1b3b4;
  style d1ae921d-2e2b-46d8-95a2-4443f5a1b3b4 stroke:#2c3143,stroke-width:4px;
  29["Group"];
  23 -->|out_file1| 29;
  4d2d01eb-15d2-4863-a522-9462c15eb51c["Output\ngo_overrepresented_categories"];
  29 --> 4d2d01eb-15d2-4863-a522-9462c15eb51c;
  style 4d2d01eb-15d2-4863-a522-9462c15eb51c stroke:#2c3143,stroke-width:4px;
  30["Cut"];
  27 -->|out_file1| 30;
  31["heatmap2"];
  30 -->|out_file1| 31;
  42cb6b68-4ed6-4b6e-9312-744b9e0c1442["Output\nheatmap_log"];
  31 --> 42cb6b68-4ed6-4b6e-9312-744b9e0c1442;
  style 42cb6b68-4ed6-4b6e-9312-744b9e0c1442 stroke:#2c3143,stroke-width:4px;
  32["heatmap2"];
  30 -->|out_file1| 32;
  0ecef5aa-76ed-4fe7-9244-3b32d231e7f3["Output\nheatmap_zscore"];
  32 --> 0ecef5aa-76ed-4fe7-9244-3b32d231e7f3;
  style 0ecef5aa-76ed-4fe7-9244-3b32d231e7f3 stroke:#2c3143,stroke-width:4px;
	

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;
	

flowchart TD
  0["ℹ️ Input Collection\nPaired list collection with PE fastqs"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nDrosophila_melanogaster.BDGP6.32.109_UCSC.gtf.gz"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["🛠️ Subworkflow\nFastQC"];
  style 2 fill:#edd,stroke:#900,stroke-width:4px;
  0 -->|output| 2;
  5f7652f1-e225-4ad1-8dbd-4d21544edb89["Output\nmultiqc_fastqc_html"];
  2 --> 5f7652f1-e225-4ad1-8dbd-4d21544edb89;
  style 5f7652f1-e225-4ad1-8dbd-4d21544edb89 stroke:#2c3143,stroke-width:4px;
  3["🛠️ Subworkflow\ncutadapt"];
  style 3 fill:#edd,stroke:#900,stroke-width:4px;
  0 -->|output| 3;
  41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee["Output\nmultiqc_cutadapt_html"];
  3 --> 41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee;
  style 41c7fbb5-655b-457c-8ba7-0e2eeab3d7ee stroke:#2c3143,stroke-width:4px;
  4["🛠️ Subworkflow\nSTAR + multiQC"];
  style 4 fill:#edd,stroke:#900,stroke-width:4px;
  1 -->|output| 4;
  3 -->|out_pairs| 4;
  8aa5ef30-3f09-4a93-944d-0d89101c056a["Output\nmultiqc_star_html"];
  4 --> 8aa5ef30-3f09-4a93-944d-0d89101c056a;
  style 8aa5ef30-3f09-4a93-944d-0d89101c056a stroke:#2c3143,stroke-width:4px;
  11af5c57-91b4-496c-9b0c-b02904963f81["Output\nSTAR_BAM"];
  4 --> 11af5c57-91b4-496c-9b0c-b02904963f81;
  style 11af5c57-91b4-496c-9b0c-b02904963f81 stroke:#2c3143,stroke-width:4px;
  5["🛠️ Subworkflow\nmore QC"];
  style 5 fill:#edd,stroke:#900,stroke-width:4px;
  1 -->|output| 5;
  4 -->|STAR_BAM| 5;
  f2eed352-ca21-4d65-8810-f5a1d3c282b4["Output\nmultiqc_read_distrib_html"];
  5 --> f2eed352-ca21-4d65-8810-f5a1d3c282b4;
  style f2eed352-ca21-4d65-8810-f5a1d3c282b4 stroke:#2c3143,stroke-width:4px;
  b306cb12-a275-4c6d-b609-47fdc208864b["Output\nmultiqc_reads_per_chrom_html"];
  5 --> 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"];
  5 --> 3375d63c-cdc3-4fbb-8a55-6f504c934918;
  style 3375d63c-cdc3-4fbb-8a55-6f504c934918 stroke:#2c3143,stroke-width:4px;
  3ea82568-5698-49a7-88fe-91381070aac2["Output\nmultiqc_dup_html"];
  5 --> 3ea82568-5698-49a7-88fe-91381070aac2;
  style 3ea82568-5698-49a7-88fe-91381070aac2 stroke:#2c3143,stroke-width:4px;
  6["🛠️ Subworkflow\nDetermine strandness"];
  style 6 fill:#edd,stroke:#900,stroke-width:4px;
  4 -->|STAR_BAM| 6;
  1 -->|output| 6;
  4 -->|signal_unique_str1| 6;
  4 -->|signal_unique_str2| 6;
  4 -->|reads_per_gene| 6;
  fb810859-f2d0-43f8-ac7c-5c714c5c6805["Output\ninferexperiment"];
  6 --> fb810859-f2d0-43f8-ac7c-5c714c5c6805;
  style fb810859-f2d0-43f8-ac7c-5c714c5c6805 stroke:#2c3143,stroke-width:4px;
  9727824a-3eb2-4430-92d1-b3c40c3041d1["Output\npgt"];
  6 --> 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"];
  6 --> 105313d8-e31a-405d-8fcd-cc5fd93275e2;
  style 105313d8-e31a-405d-8fcd-cc5fd93275e2 stroke:#2c3143,stroke-width:4px;
  7["🛠️ Subworkflow\ncount STAR"];
  style 7 fill:#edd,stroke:#900,stroke-width:4px;
  1 -->|output| 7;
  4 -->|reads_per_gene| 7;
  5fee8aff-4023-43f1-a653-f5af5357d798["Output\ncounts_from_star"];
  7 --> 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"];
  7 --> bd3388e6-5b45-4fdc-9780-3efd1c34ebf8;
  style bd3388e6-5b45-4fdc-9780-3efd1c34ebf8 stroke:#2c3143,stroke-width:4px;
  7b7c698b-4808-4b45-adf1-686f8d273d18["Output\nGene length"];
  7 --> 7b7c698b-4808-4b45-adf1-686f8d273d18;
  style 7b7c698b-4808-4b45-adf1-686f8d273d18 stroke:#2c3143,stroke-width:4px;
  8["🛠️ Subworkflow\ncount featureCount"];
  style 8 fill:#edd,stroke:#900,stroke-width:4px;
  1 -->|output| 8;
  4 -->|STAR_BAM| 8;
  f0de4714-4df8-4506-90d9-384537ad663e["Output\nfeatureCounts_sorted"];
  8 --> f0de4714-4df8-4506-90d9-384537ad663e;
  style f0de4714-4df8-4506-90d9-384537ad663e stroke:#2c3143,stroke-width:4px;
  8b9d6c76-6e82-4691-b8bc-9996d6ae1594["Output\nfeatureCounts_gene_length"];
  8 --> 8b9d6c76-6e82-4691-b8bc-9996d6ae1594;
  style 8b9d6c76-6e82-4691-b8bc-9996d6ae1594 stroke:#2c3143,stroke-width:4px;
  152ba01e-d4f2-4227-8812-87648a1c19ea["Output\nmultiqc_featureCounts_html"];
  8 --> 152ba01e-d4f2-4227-8812-87648a1c19ea;
  style 152ba01e-d4f2-4227-8812-87648a1c19ea stroke:#2c3143,stroke-width:4px;
  46c7a2e8-7819-4715-a028-7ad1de9ed605["Output\nfeatureCounts"];
  8 --> 46c7a2e8-7819-4715-a028-7ad1de9ed605;
  style 46c7a2e8-7819-4715-a028-7ad1de9ed605 stroke:#2c3143,stroke-width:4px;
	

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: