adding workflow reports to the workflows of the training to match the…

… latest version of the IWC PR
galaxyproject · May 26, 2024 · 211b693 · 211b693
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diff --git a/...-detection-from-nanopore-foodborne-data/workflows/Gene_based_pathogenic_Identification.ga b/...-detection-from-nanopore-foodborne-data/workflows/Gene_based_pathogenic_Identification.ga
@@ -1,6 +1,6 @@
 {
     "a_galaxy_workflow": "true",
-    "annotation": "Nanopore datasets analysis - Phylogenetic Identification - ST - antibiotic resistance genes detection and contigs building",
+    "annotation": "Nanopore datasets analysis - Phylogenetic Identification - antibiotic resistance genes detection and contigs building",
     "comments": [
         {
             "child_steps": [
@@ -111,10 +111,10 @@
     ],
     "format-version": "0.1",
     "license": "MIT",
-    "release": "0.1",    
+    "release": "0.1",
     "name": "Gene-based Pathogen Identification",
     "report": {
-        "markdown": "# Nanopore Pathogen Detection - Gene based pathogenic identification Workflow Report\nBelow are the results for the Gene based pathogenic identification Workflow\n\nThis workflow was run on:\n\n```galaxy\ngenerate_time()\n```\n\nWith Galaxy version:\n\n```galaxy\ngenerate_galaxy_version()\n```\n\n## Workflow Inputs\nThe per-processing workflow main output (All samples reads after quality retaining and hosts filtering), and the MLST header report\n\n## Workflow Output\n\r\n```galaxy\ninvocation_outputs()\n```\r\n"
+        "markdown": "# Gene based Pathogen Identification Workflow Report\n\nBelow are the results for the Gene based Pathogen Identification Workflow\n\nThis workflow was run on:\n\n```galaxy\ngenerate_time()\n```\n\nWith Galaxy version:\n\n```galaxy\ngenerate_galaxy_version()\n```\n\n## Workflow Inputs\nThe Perprocessing workflow main output (Collection of all samples reads after quality retaining and hosts filtering)\n\n## Workflow Output\n\n### Identified contigs per sample\n\n```galaxy\nhistory_dataset_display(output=\"contigs\")\n```\n\n### Identified pathogenic genes per sample\n\n```galaxy\nhistory_dataset_display(output=\"vfs\")\n```\n\n### Identified Antimicrobial Resistance genes per sample\n\n```galaxy\nhistory_dataset_display(output=\"amrs\")\n```\n"
     },
     "steps": {
         "0": {
@@ -381,11 +381,6 @@
             "uuid": "a343c482-44b1-4cb3-a46e-2e714f785960",
             "when": null,
             "workflow_outputs": [
-                {
-                    "label": "flye_assembly_info_tabular",
-                    "output_name": "assembly_info",
-                    "uuid": "3f297aed-7cee-4999-bbf4-69d84de6b64f"
-                },
                 {
                     "label": "flye_consensus_fasta",
                     "output_name": "consensus",
@@ -400,6 +395,11 @@
                     "label": "flye_assembly_gfa",
                     "output_name": "assembly_gfa",
                     "uuid": "0e42ce51-c46b-45d8-baa4-45c0e98ac712"
+                },
+                {
+                    "label": "flye_assembly_info_tabular",
+                    "output_name": "assembly_info",
+                    "uuid": "3f297aed-7cee-4999-bbf4-69d84de6b64f"
                 }
             ]
         },
@@ -544,11 +544,6 @@
             "uuid": "9e88fc55-f41e-4e87-852e-044359dcea53",
             "when": null,
             "workflow_outputs": [
-                {
-                    "label": "medaka_log_file",
-                    "output_name": "out_log",
-                    "uuid": "fcbd3e3f-2e93-4798-b696-dad7db9f2efd"
-                },
                 {
                     "label": "medaka_propability_h5_file",
                     "output_name": "out_probs",
@@ -568,6 +563,11 @@
                     "label": "medaka_gaps_in_draft_bed_file",
                     "output_name": "out_gaps",
                     "uuid": "9e5ad6ec-b408-4132-ba07-dec9fa626923"
+                },
+                {
+                    "label": "medaka_log_file",
+                    "output_name": "out_log",
+                    "uuid": "fcbd3e3f-2e93-4798-b696-dad7db9f2efd"
                 }
             ]
         },
@@ -1053,6 +1053,6 @@
         "name:IWC",
         "name:microGalaxy"
     ],
-    "uuid": "aab3b78c-d0b3-4ea9-9225-47b395b52e34",
-    "version": 130
+    "uuid": "99635f92-f694-4a3e-bf28-411c0973dc6a",
+    "version": 132
 }
diff --git a/...om-nanopore-foodborne-data/workflows/nanopore_allele_based_pathogenetic_identification.ga b/...om-nanopore-foodborne-data/workflows/nanopore_allele_based_pathogenetic_identification.ga
@@ -102,6 +102,9 @@
     "license": "MIT",
     "release": "0.1",
     "name": "Nanopore Allele-based Pathogen Identification",
+    "report": {
+        "markdown": "# Nanopore - Allele based Pathogen Identification Workflow Report\nBelow are the results for the Allele based Pathogenic Identification Workflow\n\nThis workflow was run on:\n\n```galaxy\ngenerate_time()\n```\n\nWith Galaxy version:\n\n```galaxy\ngenerate_galaxy_version()\n```\n\n## Workflow Inputs\nThe Perprocessing workflow main output (Collection of all samples reads after quality retaining and hosts filtering), and a FASTA file of the reference genome of the main Pathogen identified in the Gene based Pathogen Identification workflow, or per-known to the user.\n\n## Workflow Output: \n\n### All variants found per sample against the reference genome\n\n```galaxy\nhistory_dataset_display(output=\"extracted_fields_from_the_vcf_output\")\n```\n\n### Number of variants per sample\n\n```galaxy\nhistory_dataset_display(output=\"number_of_variants_per_sample\")\n```\n\n### Mapping mean depth per sample\n\n```galaxy\nhistory_dataset_display(output=\"mapping_mean_depth_per_sample\")\n```\n\n### Mapping coverage per sample\n\n```galaxy\nhistory_dataset_display(output=\"mapping_coverage_percentage_per_sample\")\n```\n"
+    },
     "steps": {
         "0": {
             "annotation": "Output collection from the Nanopore Preprocessing workflow",
@@ -355,19 +358,19 @@
             "when": null,
             "workflow_outputs": [
                 {
-                    "label": "clair3_merged_output",
-                    "output_name": "merge_output",
-                    "uuid": "fab82215-f59a-43b1-92d7-37629a1fdb65"
+                    "label": "clair3_pileup_vcf",
+                    "output_name": "pileup",
+                    "uuid": "1cf1ee6c-4cb7-45e3-9c7b-88a1b678dd53"
                 },
                 {
                     "label": "clair3_full_alignment_vcf",
                     "output_name": "full_alignment",
                     "uuid": "987f9145-83ab-44f1-801a-b0d527ecbce8"
                 },
                 {
-                    "label": "clair3_pileup_vcf",
-                    "output_name": "pileup",
-                    "uuid": "1cf1ee6c-4cb7-45e3-9c7b-88a1b678dd53"
+                    "label": "clair3_merged_output",
+                    "output_name": "merge_output",
+                    "uuid": "fab82215-f59a-43b1-92d7-37629a1fdb65"
                 }
             ]
         },
@@ -1257,6 +1260,6 @@
         "name:Nanopore",
         "name:IWC"
     ],
-    "uuid": "fd37c867-e932-47ff-9246-48d181beb7af",
-    "version": 64
+    "uuid": "aab68abd-f5bc-4d2b-bbbb-ec80dab94a02",
+    "version": 65
 }