Workflows

These workflows are associated with Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition

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\nNanopore Sequenced Reads Collection"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nMLST Report Header"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["Build list"];
  0 -->|output| 2;
  d63f1fa1-14ae-4646-b73b-d45b06f59fff["Output\nList of Lists of Processed Samples"];
  2 --> d63f1fa1-14ae-4646-b73b-d45b06f59fff;
  style d63f1fa1-14ae-4646-b73b-d45b06f59fff stroke:#2c3143,stroke-width:4px;
  3["Extract element identifiers"];
  0 -->|output| 3;
  203bf2c8-195a-40d8-9e3a-0615e5c7a34c["Output\nExtracted Lables"];
  3 --> 203bf2c8-195a-40d8-9e3a-0615e5c7a34c;
  style 203bf2c8-195a-40d8-9e3a-0615e5c7a34c stroke:#2c3143,stroke-width:4px;
  4["Flye"];
  2 -->|output| 4;
  05a7b98b-ee61-4c6c-bddd-48552fd6c545["Output\nFlye Assembly GFA"];
  4 --> 05a7b98b-ee61-4c6c-bddd-48552fd6c545;
  style 05a7b98b-ee61-4c6c-bddd-48552fd6c545 stroke:#2c3143,stroke-width:4px;
  b57041a7-6a1d-4dfa-b599-815f4152b7c6["Output\nFlye Assembly Graph"];
  4 --> b57041a7-6a1d-4dfa-b599-815f4152b7c6;
  style b57041a7-6a1d-4dfa-b599-815f4152b7c6 stroke:#2c3143,stroke-width:4px;
  357bbc74-9c8d-47c1-906d-7338d39d4f78["Output\nFlye Assembly Info Tabular"];
  4 --> 357bbc74-9c8d-47c1-906d-7338d39d4f78;
  style 357bbc74-9c8d-47c1-906d-7338d39d4f78 stroke:#2c3143,stroke-width:4px;
  d2a6d4ef-a89c-4eeb-81e7-eb742da32242["Output\nFlye Consensus Fasta"];
  4 --> d2a6d4ef-a89c-4eeb-81e7-eb742da32242;
  style d2a6d4ef-a89c-4eeb-81e7-eb742da32242 stroke:#2c3143,stroke-width:4px;
  5["Split file"];
  3 -->|output| 5;
  2980230b-5248-4a54-b3ae-30fcd055d69d["Output\nSplitted Extracted Lables"];
  5 --> 2980230b-5248-4a54-b3ae-30fcd055d69d;
  style 2980230b-5248-4a54-b3ae-30fcd055d69d stroke:#2c3143,stroke-width:4px;
  6["Bandage Image"];
  4 -->|assembly_gfa| 6;
  07176f8f-6974-4d78-b4b4-73653b320723["Output\nBandage Image on input dataset(s): Assembly Graph Image"];
  6 --> 07176f8f-6974-4d78-b4b4-73653b320723;
  style 07176f8f-6974-4d78-b4b4-73653b320723 stroke:#2c3143,stroke-width:4px;
  7["medaka consensus pipeline"];
  4 -->|consensus| 7;
  0 -->|output| 7;
  511579ff-c4df-432e-a73f-0aa9988f14f6["Output\nMedaka Gaps in draft bed file"];
  7 --> 511579ff-c4df-432e-a73f-0aa9988f14f6;
  style 511579ff-c4df-432e-a73f-0aa9988f14f6 stroke:#2c3143,stroke-width:4px;
  40e198a9-029d-4341-8129-32f3e72867c0["Output\nMedaka propability h5 file"];
  7 --> 40e198a9-029d-4341-8129-32f3e72867c0;
  style 40e198a9-029d-4341-8129-32f3e72867c0 stroke:#2c3143,stroke-width:4px;
  62f819ea-2caa-43c8-a677-565202166e21["Output\nMedaka log file"];
  7 --> 62f819ea-2caa-43c8-a677-565202166e21;
  style 62f819ea-2caa-43c8-a677-565202166e21 stroke:#2c3143,stroke-width:4px;
  fc7a33a1-c2f9-45be-ad65-6383a903f68d["Output\nMedaka calls of draft Bam file"];
  7 --> fc7a33a1-c2f9-45be-ad65-6383a903f68d;
  style fc7a33a1-c2f9-45be-ad65-6383a903f68d stroke:#2c3143,stroke-width:4px;
  46f43f5c-04ac-4e25-8e32-846a273dd309["Output\nMedaka consensus with all contigs Fasta file"];
  7 --> 46f43f5c-04ac-4e25-8e32-846a273dd309;
  style 46f43f5c-04ac-4e25-8e32-846a273dd309 stroke:#2c3143,stroke-width:4px;
  8["Parse parameter value"];
  5 -->|list_output_txt| 8;
  adc77934-abf6-4df2-8cf8-ae81b85227ff["Output\nParsed Extracted Lables to text"];
  8 --> adc77934-abf6-4df2-8cf8-ae81b85227ff;
  style adc77934-abf6-4df2-8cf8-ae81b85227ff stroke:#2c3143,stroke-width:4px;
  9["Build list"];
  7 -->|out_consensus| 9;
  89b6d5ea-b24e-47e1-affa-b2c99dda6f2d["Output\nList of Lists of Assembles samples"];
  9 --> 89b6d5ea-b24e-47e1-affa-b2c99dda6f2d;
  style 89b6d5ea-b24e-47e1-affa-b2c99dda6f2d stroke:#2c3143,stroke-width:4px;
  10["ABRicate"];
  7 -->|out_consensus| 10;
  ad90e04d-2c38-4d9a-a879-c78754fcc0c3["Output\nABRicate with VFDB to Idetify genes with VFs "];
  10 --> ad90e04d-2c38-4d9a-a879-c78754fcc0c3;
  style ad90e04d-2c38-4d9a-a879-c78754fcc0c3 stroke:#2c3143,stroke-width:4px;
  11["FASTA-to-Tabular"];
  7 -->|out_consensus| 11;
  bf358cd9-af19-4319-83d3-63b3745e550d["Output\nPreparing for a Sample Specific Contigs Tabular file"];
  11 --> bf358cd9-af19-4319-83d3-63b3745e550d;
  style bf358cd9-af19-4319-83d3-63b3745e550d stroke:#2c3143,stroke-width:4px;
  12["ABRicate"];
  7 -->|out_consensus| 12;
  dfc81350-2047-48b7-bbfa-f532cbf71145["Output\nABRicate report using NCBI database to Indentify AMR"];
  12 --> dfc81350-2047-48b7-bbfa-f532cbf71145;
  style dfc81350-2047-48b7-bbfa-f532cbf71145 stroke:#2c3143,stroke-width:4px;
  13["Compose text parameter value"];
  8 -->|text_param| 13;
  9f9c9cf3-a311-46a7-b0b5-2af5814915cb["Output\nSampleID Regex Expression2"];
  13 --> 9f9c9cf3-a311-46a7-b0b5-2af5814915cb;
  style 9f9c9cf3-a311-46a7-b0b5-2af5814915cb stroke:#2c3143,stroke-width:4px;
  14["MLST"];
  9 -->|output| 14;
  9f3a7d25-57dc-4658-a4f6-06d418ef058e["Output\nMLST on input dataset(s): report.tsv"];
  14 --> 9f3a7d25-57dc-4658-a4f6-06d418ef058e;
  style 9f3a7d25-57dc-4658-a4f6-06d418ef058e stroke:#2c3143,stroke-width:4px;
  15["Cut"];
  10 -->|report| 15;
  5f491cdd-f47f-4a08-83b6-b3c034205a56["Output\nVFs accessions"];
  15 --> 5f491cdd-f47f-4a08-83b6-b3c034205a56;
  style 5f491cdd-f47f-4a08-83b6-b3c034205a56 stroke:#2c3143,stroke-width:4px;
  16["Cut"];
  12 -->|report| 16;
  a183ffb5-b270-4799-89fc-80802b4a2ee9["Output\nAMR NCBI Accession"];
  16 --> a183ffb5-b270-4799-89fc-80802b4a2ee9;
  style a183ffb5-b270-4799-89fc-80802b4a2ee9 stroke:#2c3143,stroke-width:4px;
  17["Replace"];
  8 -->|text_param| 17;
  13 -->|out1| 17;
  10 -->|report| 17;
  e08927b4-c5b0-4dee-b5b4-0183dfc7b151["Output\nVFs "];
  17 --> e08927b4-c5b0-4dee-b5b4-0183dfc7b151;
  style e08927b4-c5b0-4dee-b5b4-0183dfc7b151 stroke:#2c3143,stroke-width:4px;
  18["Replace"];
  13 -->|out1| 18;
  11 -->|output| 18;
  31a8f807-f01b-46ef-8f56-70b7c0b20d64["Output\nSample Specific Contigs Tabular file"];
  18 --> 31a8f807-f01b-46ef-8f56-70b7c0b20d64;
  style 31a8f807-f01b-46ef-8f56-70b7c0b20d64 stroke:#2c3143,stroke-width:4px;
  19["Replace Text"];
  14 -->|report| 19;
  8 -->|text_param| 19;
  ed91520b-2dc0-4720-ad3b-8c422a109621["Output\nMLST Report Tabular with the sample name"];
  19 --> ed91520b-2dc0-4720-ad3b-8c422a109621;
  style ed91520b-2dc0-4720-ad3b-8c422a109621 stroke:#2c3143,stroke-width:4px;
  20["VFDB Accession Tabular with SampleID as a header"];
  15 -->|out_file1| 20;
  8 -->|text_param| 20;
  0fbbc648-791e-4a6e-8bf5-133b9cf89716["Output\nVFs accessions with SampleID"];
  20 --> 0fbbc648-791e-4a6e-8bf5-133b9cf89716;
  style 0fbbc648-791e-4a6e-8bf5-133b9cf89716 stroke:#2c3143,stroke-width:4px;
  21["AMR NCBI Accession Tabular with SampleID as a header"];
  16 -->|out_file1| 21;
  8 -->|text_param| 21;
  5e929a43-8dd4-4961-85ce-b619937d0357["Output\nAMR NCBI Accession Tabular with SampleID as a header"];
  21 --> 5e929a43-8dd4-4961-85ce-b619937d0357;
  style 5e929a43-8dd4-4961-85ce-b619937d0357 stroke:#2c3143,stroke-width:4px;
  22["Tabular-to-FASTA"];
  18 -->|outfile| 22;
  54b09443-83f3-42c9-be7f-3828d13c4a1a["Output\nContigs"];
  22 --> 54b09443-83f3-42c9-be7f-3828d13c4a1a;
  style 54b09443-83f3-42c9-be7f-3828d13c4a1a stroke:#2c3143,stroke-width:4px;
  23["MLST Report with Header"];
  1 -->|output| 23;
  19 -->|outfile| 23;
  20ca955e-bc82-4269-9104-068c938abc23["Output\nMLST Report with Header"];
  23 --> 20ca955e-bc82-4269-9104-068c938abc23;
  style 20ca955e-bc82-4269-9104-068c938abc23 stroke:#2c3143,stroke-width:4px;
	

flowchart TD
  0["ℹ️ Input Collection\nVFs"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Collection\nContigs"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["ℹ️ Input Collection\nVFs accessions"];
  style 2 stroke:#2c3143,stroke-width:4px;
  3["ℹ️ Input Collection\nVFs accessions with SampleID"];
  style 3 stroke:#2c3143,stroke-width:4px;
  4["Collapse Collection"];
  0 -->|output| 4;
  7a18e075-d238-4570-8fba-2c8f05376d9a["Output\nAll VFs in one Tabular"];
  4 --> 7a18e075-d238-4570-8fba-2c8f05376d9a;
  style 7a18e075-d238-4570-8fba-2c8f05376d9a stroke:#2c3143,stroke-width:4px;
  5["Collapse Collection"];
  1 -->|output| 5;
  81ade1e9-9942-4ac8-9ee0-ab141355864d["Output\nAll Samples Contigs in one Fasta file"];
  5 --> 81ade1e9-9942-4ac8-9ee0-ab141355864d;
  style 81ade1e9-9942-4ac8-9ee0-ab141355864d stroke:#2c3143,stroke-width:4px;
  6["Collapse Collection"];
  2 -->|output| 6;
  f0a6eae7-4791-4300-b231-52c093eccb38["Output\nVFs accessions in one Tabular "];
  6 --> f0a6eae7-4791-4300-b231-52c093eccb38;
  style f0a6eae7-4791-4300-b231-52c093eccb38 stroke:#2c3143,stroke-width:4px;
  7["Split by group"];
  4 -->|output| 7;
  19292bb5-ef34-4451-aea7-204370016c33["Output\nSplit by group collection"];
  7 --> 19292bb5-ef34-4451-aea7-204370016c33;
  style 19292bb5-ef34-4451-aea7-204370016c33 stroke:#2c3143,stroke-width:4px;
  8["Add line to file"];
  6 -->|output| 8;
  c08dfea8-de8f-43e4-b4c9-e68525f9bb13["Output\nVFDB Accession Column without Sample ID in one Tabular with a header"];
  8 --> c08dfea8-de8f-43e4-b4c9-e68525f9bb13;
  style c08dfea8-de8f-43e4-b4c9-e68525f9bb13 stroke:#2c3143,stroke-width:4px;
  9["Cut"];
  7 -->|split_output| 9;
  80240279-b99b-4bef-8767-d445b8ecb0fb["Output\nAdjusted ABRicate VFs tabular part1"];
  9 --> 80240279-b99b-4bef-8767-d445b8ecb0fb;
  style 80240279-b99b-4bef-8767-d445b8ecb0fb stroke:#2c3143,stroke-width:4px;
  10["Filter sequences by ID"];
  7 -->|split_output| 10;
  5 -->|output| 10;
  af8e144f-a116-4aa7-bcc6-c307a2a25c63["Output\nFiltered Sequences with VFs"];
  10 --> af8e144f-a116-4aa7-bcc6-c307a2a25c63;
  style af8e144f-a116-4aa7-bcc6-c307a2a25c63 stroke:#2c3143,stroke-width:4px;
  11["Multi-Join"];
  3 -->|output| 11;
  8 -->|outfile| 11;
  bfa17fb2-6dda-4cce-a92a-e753cb179e50["Output\nbacteria pathogen genes in all samples"];
  11 --> bfa17fb2-6dda-4cce-a92a-e753cb179e50;
  style bfa17fb2-6dda-4cce-a92a-e753cb179e50 stroke:#2c3143,stroke-width:4px;
  12["Remove beginning"];
  9 -->|out_file1| 12;
  41f4240f-ecf3-4085-ad54-3d6916a5b100["Output\nAdjusted ABRicate VFs tabular part2"];
  12 --> 41f4240f-ecf3-4085-ad54-3d6916a5b100;
  style 41f4240f-ecf3-4085-ad54-3d6916a5b100 stroke:#2c3143,stroke-width:4px;
  13["Replace"];
  11 -->|outfile| 13;
  16e61d6c-6d2f-4076-805d-6b142932f021["Output\nbacteria pathogen genes in all samples prep 1 for heatmap"];
  13 --> 16e61d6c-6d2f-4076-805d-6b142932f021;
  style 16e61d6c-6d2f-4076-805d-6b142932f021 stroke:#2c3143,stroke-width:4px;
  14["Advanced Cut"];
  11 -->|outfile| 14;
  f989ff37-3d82-4862-81fa-662fa77bb7f1["Output\nbacteria pathogen genes in all samples first column"];
  14 --> f989ff37-3d82-4862-81fa-662fa77bb7f1;
  style f989ff37-3d82-4862-81fa-662fa77bb7f1 stroke:#2c3143,stroke-width:4px;
  15["bedtools getfasta"];
  10 -->|output_pos| 15;
  12 -->|out_file1| 15;
  30d023b4-0764-4f0a-a5fb-a05f4a5e5e56["Output\nFiltered Sequences with VFs FASTA"];
  15 --> 30d023b4-0764-4f0a-a5fb-a05f4a5e5e56;
  style 30d023b4-0764-4f0a-a5fb-a05f4a5e5e56 stroke:#2c3143,stroke-width:4px;
  16["Replace"];
  13 -->|outfile| 16;
  a92fbb34-eca4-45bf-be18-803b23721566["Output\nbacteria pathogen genes in all samples prep 2 for heatmap"];
  16 --> a92fbb34-eca4-45bf-be18-803b23721566;
  style a92fbb34-eca4-45bf-be18-803b23721566 stroke:#2c3143,stroke-width:4px;
  17["ClustalW"];
  15 -->|output| 17;
  6e8b431a-8873-43ff-a165-d5cd21974b73["Output\nClustalW on input dataset(s): dnd"];
  17 --> 6e8b431a-8873-43ff-a165-d5cd21974b73;
  style 6e8b431a-8873-43ff-a165-d5cd21974b73 stroke:#2c3143,stroke-width:4px;
  5fda5b67-5027-45be-b4c8-6203f726a565["Output\nClustalW on input dataset(s): clustal"];
  17 --> 5fda5b67-5027-45be-b4c8-6203f726a565;
  style 5fda5b67-5027-45be-b4c8-6203f726a565 stroke:#2c3143,stroke-width:4px;
  18["Replace"];
  16 -->|outfile| 18;
  8d56febc-da67-45d2-bf59-d196b436f997["Output\nbacteria pathogen genes in all samples prep 3 for heatmap"];
  18 --> 8d56febc-da67-45d2-bf59-d196b436f997;
  style 8d56febc-da67-45d2-bf59-d196b436f997 stroke:#2c3143,stroke-width:4px;
  19["Filter empty datasets"];
  17 -->|output| 19;
  100bbbfa-0936-4ab1-be4d-405971dfce7b["Output\ninput dataset(s) (filtered empty datasets)"];
  19 --> 100bbbfa-0936-4ab1-be4d-405971dfce7b;
  style 100bbbfa-0936-4ab1-be4d-405971dfce7b stroke:#2c3143,stroke-width:4px;
  20["Advanced Cut"];
  18 -->|outfile| 20;
  b385d66e-e7ae-4c85-a4a6-ef776593799a["Output\nbacteria pathogen genes in all samples prep 4 for heatmap"];
  20 --> b385d66e-e7ae-4c85-a4a6-ef776593799a;
  style b385d66e-e7ae-4c85-a4a6-ef776593799a stroke:#2c3143,stroke-width:4px;
  21["FASTTREE"];
  19 -->|output| 21;
  8c6f2919-bfb7-4214-b4f3-cce9a484574e["Output\nFASTTREE on input dataset(s):tree.nhx"];
  21 --> 8c6f2919-bfb7-4214-b4f3-cce9a484574e;
  style 8c6f2919-bfb7-4214-b4f3-cce9a484574e stroke:#2c3143,stroke-width:4px;
  22["Paste"];
  14 -->|output| 22;
  20 -->|output| 22;
  a069e610-d8b1-428c-8e39-2df6a75e4d70["Output\nbacteria pathogen genes in all samples prep 5 for heatmap"];
  22 --> a069e610-d8b1-428c-8e39-2df6a75e4d70;
  style a069e610-d8b1-428c-8e39-2df6a75e4d70 stroke:#2c3143,stroke-width:4px;
  23["Newick Display"];
  21 -->|output| 23;
  2418f286-5d89-4222-a5ce-d03041d3fc2b["Output\nNewick Genes: Tree Graphs Collection"];
  23 --> 2418f286-5d89-4222-a5ce-d03041d3fc2b;
  style 2418f286-5d89-4222-a5ce-d03041d3fc2b stroke:#2c3143,stroke-width:4px;
  24["Transpose"];
  22 -->|out_file1| 24;
  93400928-3170-4a73-b972-fae67c4b28f7["Output\nTranspose on input dataset(s)"];
  24 --> 93400928-3170-4a73-b972-fae67c4b28f7;
  style 93400928-3170-4a73-b972-fae67c4b28f7 stroke:#2c3143,stroke-width:4px;
  25["Replace"];
  22 -->|out_file1| 25;
  b91ea6d5-624d-4b02-b3dc-c33b59db72a6["Output\nTabular For Hearmap"];
  25 --> b91ea6d5-624d-4b02-b3dc-c33b59db72a6;
  style b91ea6d5-624d-4b02-b3dc-c33b59db72a6 stroke:#2c3143,stroke-width:4px;
  26["Heatmap w ggplot"];
  24 -->|out_file| 26;
  d1766dc9-d2fe-4ca2-bb8c-c018c77db3fa["Output\nHeatmap w ggplot on input dataset(s): png"];
  26 --> d1766dc9-d2fe-4ca2-bb8c-c018c77db3fa;
  style d1766dc9-d2fe-4ca2-bb8c-c018c77db3fa stroke:#2c3143,stroke-width:4px;
	

flowchart TD
  0["ℹ️ Input Collection\nCollection of all samples"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["Porechop"];
  0 -->|output| 1;
  4f51f33f-e2a9-4a3f-81d3-b924e2e6d751["Output\nPorechop output Trimmed Reads"];
  1 --> 4f51f33f-e2a9-4a3f-81d3-b924e2e6d751;
  style 4f51f33f-e2a9-4a3f-81d3-b924e2e6d751 stroke:#2c3143,stroke-width:4px;
  2["NanoPlot"];
  0 -->|output| 2;
  2e38fcb0-9e13-4ef5-9b7e-7423b4794dcc["Output\nNanoplot on Reads Before Preprocessing Log Transformed Histogram Read Length"];
  2 --> 2e38fcb0-9e13-4ef5-9b7e-7423b4794dcc;
  style 2e38fcb0-9e13-4ef5-9b7e-7423b4794dcc stroke:#2c3143,stroke-width:4px;
  c1513a6f-392c-406b-9a9e-5a05f54014a8["Output\nNanoplot QC on Reads Before Preprocessing HTML Report"];
  2 --> c1513a6f-392c-406b-9a9e-5a05f54014a8;
  style c1513a6f-392c-406b-9a9e-5a05f54014a8 stroke:#2c3143,stroke-width:4px;
  059eab67-5735-4a8c-bc9f-408da9f088c0["Output\nNanoplot on Reads Before Preprocessing NanoStats"];
  2 --> 059eab67-5735-4a8c-bc9f-408da9f088c0;
  style 059eab67-5735-4a8c-bc9f-408da9f088c0 stroke:#2c3143,stroke-width:4px;
  dcc872d2-02f4-4fb7-bea5-c7b2e69ba3b1["Output\nNanoplot on Reads Before Preprocessing NanoStats post filtering"];
  2 --> dcc872d2-02f4-4fb7-bea5-c7b2e69ba3b1;
  style dcc872d2-02f4-4fb7-bea5-c7b2e69ba3b1 stroke:#2c3143,stroke-width:4px;
  c727d318-e1a7-4f06-91ea-5edbc6fade01["Output\nNanoplot on Reads Before Preprocessing Histogram Read Length"];
  2 --> c727d318-e1a7-4f06-91ea-5edbc6fade01;
  style c727d318-e1a7-4f06-91ea-5edbc6fade01 stroke:#2c3143,stroke-width:4px;
  3["FastQC"];
  0 -->|output| 3;
  bd2e36d7-a4f8-425c-a732-d88f42f94f8c["Output\nFastQC Quality Check Before Preprocessing Text file"];
  3 --> bd2e36d7-a4f8-425c-a732-d88f42f94f8c;
  style bd2e36d7-a4f8-425c-a732-d88f42f94f8c stroke:#2c3143,stroke-width:4px;
  57e6b0be-4896-4da0-ac1a-31d7bf3a26c7["Output\nFastQC Quality Check Before Preprocessing HTML file"];
  3 --> 57e6b0be-4896-4da0-ac1a-31d7bf3a26c7;
  style 57e6b0be-4896-4da0-ac1a-31d7bf3a26c7 stroke:#2c3143,stroke-width:4px;
  4["fastp"];
  1 -->|outfile| 4;
  cd5d8fc9-e725-4835-98d9-0bca217f4dd8["Output\nNanopore sequenced reads processed with Fastp HTML Report "];
  4 --> cd5d8fc9-e725-4835-98d9-0bca217f4dd8;
  style cd5d8fc9-e725-4835-98d9-0bca217f4dd8 stroke:#2c3143,stroke-width:4px;
  cf423d0c-ab15-4a1e-930c-0c8ed367aeba["Output\nNanopore sequenced reads processed with Fastp"];
  4 --> cf423d0c-ab15-4a1e-930c-0c8ed367aeba;
  style cf423d0c-ab15-4a1e-930c-0c8ed367aeba stroke:#2c3143,stroke-width:4px;
  5["NanoPlot"];
  4 -->|out1| 5;
  f32ba6ef-9bc1-4a81-8146-bb346b6ffa8e["Output\nNanoplot on Reads After Preprocessing Histogram Read Length"];
  5 --> f32ba6ef-9bc1-4a81-8146-bb346b6ffa8e;
  style f32ba6ef-9bc1-4a81-8146-bb346b6ffa8e stroke:#2c3143,stroke-width:4px;
  0cfee358-4f8b-4a9d-8c1a-0a940cdeedae["Output\nNanoplot on Reads After Preprocessing NanoStats"];
  5 --> 0cfee358-4f8b-4a9d-8c1a-0a940cdeedae;
  style 0cfee358-4f8b-4a9d-8c1a-0a940cdeedae stroke:#2c3143,stroke-width:4px;
  8f3cfd7b-6ac5-4917-bd48-1bedc12a0a76["Output\nNanoplot on Reads After Preprocessing NanoStats post filtering"];
  5 --> 8f3cfd7b-6ac5-4917-bd48-1bedc12a0a76;
  style 8f3cfd7b-6ac5-4917-bd48-1bedc12a0a76 stroke:#2c3143,stroke-width:4px;
  3ae0f96b-10bc-4869-a810-817b3467e657["Output\nNanoplot QC on Reads After Preprocessing HTML Report"];
  5 --> 3ae0f96b-10bc-4869-a810-817b3467e657;
  style 3ae0f96b-10bc-4869-a810-817b3467e657 stroke:#2c3143,stroke-width:4px;
  a678588d-233a-47d7-95e8-c7939d7677d5["Output\nNanoplot on Reads After Preprocessing Log Transformed Histogram Read Length"];
  5 --> a678588d-233a-47d7-95e8-c7939d7677d5;
  style a678588d-233a-47d7-95e8-c7939d7677d5 stroke:#2c3143,stroke-width:4px;
  6["Kraken2"];
  4 -->|out1| 6;
  dadf4422-55c7-412b-a735-dbd638a80736["Output\nKraken2 with Kalamri database output"];
  6 --> dadf4422-55c7-412b-a735-dbd638a80736;
  style dadf4422-55c7-412b-a735-dbd638a80736 stroke:#2c3143,stroke-width:4px;
  4cbf81c5-07cc-4466-ad62-7ffce8fc99a4["Output\nKraken2 with Kalamri database Report"];
  6 --> 4cbf81c5-07cc-4466-ad62-7ffce8fc99a4;
  style 4cbf81c5-07cc-4466-ad62-7ffce8fc99a4 stroke:#2c3143,stroke-width:4px;
  7["FastQC"];
  4 -->|out1| 7;
  b03b7ac6-5759-427b-b613-2bd822606338["Output\nFastQC Quality Check After Preprocessing HTML file"];
  7 --> b03b7ac6-5759-427b-b613-2bd822606338;
  style b03b7ac6-5759-427b-b613-2bd822606338 stroke:#2c3143,stroke-width:4px;
  218c340c-d245-42c2-afb0-5b493faad109["Output\nFastQC Quality Check After Preprocessing Text file"];
  7 --> 218c340c-d245-42c2-afb0-5b493faad109;
  style 218c340c-d245-42c2-afb0-5b493faad109 stroke:#2c3143,stroke-width:4px;
  8["Filter failed datasets"];
  6 -->|output| 8;
  3c1cc394-29be-4d4a-a02c-b695ed34fe36["Output\nSuccessful Kraken2 with Kalamari Tabular output"];
  8 --> 3c1cc394-29be-4d4a-a02c-b695ed34fe36;
  style 3c1cc394-29be-4d4a-a02c-b695ed34fe36 stroke:#2c3143,stroke-width:4px;
  9["Filter failed datasets"];
  6 -->|report_output| 9;
  5c034d4b-e26d-4357-bd23-25f766e351bb["Output\nSuccessful Kraken2 with Kalamari Tabular Report output"];
  9 --> 5c034d4b-e26d-4357-bd23-25f766e351bb;
  style 5c034d4b-e26d-4357-bd23-25f766e351bb stroke:#2c3143,stroke-width:4px;
  10["MultiQC"];
  3 -->|text_file| 10;
  7 -->|text_file| 10;
  a9025e09-e05d-4ed0-beaf-39d212996b55["Output\nMultiQC HTML report Before and After Preprocessing"];
  10 --> a9025e09-e05d-4ed0-beaf-39d212996b55;
  style a9025e09-e05d-4ed0-beaf-39d212996b55 stroke:#2c3143,stroke-width:4px;
  1f055aaa-a283-4620-b926-f0b6de57410b["Output\nMultiQC Stats Before and After Preprocessing"];
  10 --> 1f055aaa-a283-4620-b926-f0b6de57410b;
  style 1f055aaa-a283-4620-b926-f0b6de57410b stroke:#2c3143,stroke-width:4px;
  11["Krakentools: Extract Kraken Reads By ID"];
  4 -->|out1| 11;
  9 -->|output| 11;
  8 -->|output| 11;
  0f9bb996-d78f-4dd7-bc2b-334558a04865["Output\nNanopore Processed Sequenced Reads"];
  11 --> 0f9bb996-d78f-4dd7-bc2b-334558a04865;
  style 0f9bb996-d78f-4dd7-bc2b-334558a04865 stroke:#2c3143,stroke-width:4px;
	

flowchart TD
  0["ℹ️ Input Collection\nNanopore Preprocessed reads collection"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nReference Genome of Tested Strain"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["Convert compressed file to uncompressed."];
  1 -->|output| 2;
  184566b5-5604-4c6c-9501-6503a183ca69["Output\ndecompressed RG file"];
  2 --> 184566b5-5604-4c6c-9501-6503a183ca69;
  style 184566b5-5604-4c6c-9501-6503a183ca69 stroke:#2c3143,stroke-width:4px;
  3["Map with minimap2"];
  0 -->|output| 3;
  2 -->|output1| 3;
  cdf75d72-4500-4bf7-914f-ad5e6fec1a80["Output\nMap with minimap2 on input dataset(s) (mapped reads in BAM format)"];
  3 --> cdf75d72-4500-4bf7-914f-ad5e6fec1a80;
  style cdf75d72-4500-4bf7-914f-ad5e6fec1a80 stroke:#2c3143,stroke-width:4px;
  4["Clair3"];
  3 -->|alignment_output| 4;
  2 -->|output1| 4;
  b555896e-93a2-40a2-b936-2dedfa400d51["Output\nClair3: Pileup VCF"];
  4 --> b555896e-93a2-40a2-b936-2dedfa400d51;
  style b555896e-93a2-40a2-b936-2dedfa400d51 stroke:#2c3143,stroke-width:4px;
  ab134770-8a1d-4f8f-a8f1-2e72b8afe1b2["Output\nClair3: Full_alignment VCF"];
  4 --> ab134770-8a1d-4f8f-a8f1-2e72b8afe1b2;
  style ab134770-8a1d-4f8f-a8f1-2e72b8afe1b2 stroke:#2c3143,stroke-width:4px;
  75af6a5d-c2a3-4b56-b406-1365186eadb4["Output\nClair3: merged output"];
  4 --> 75af6a5d-c2a3-4b56-b406-1365186eadb4;
  style 75af6a5d-c2a3-4b56-b406-1365186eadb4 stroke:#2c3143,stroke-width:4px;
  5["bcftools norm"];
  4 -->|merge_output| 5;
  2 -->|output1| 5;
  7c89cb1b-6054-4725-9048-ee1739fc7ce2["Output\nNormalized VCF output"];
  5 --> 7c89cb1b-6054-4725-9048-ee1739fc7ce2;
  style 7c89cb1b-6054-4725-9048-ee1739fc7ce2 stroke:#2c3143,stroke-width:4px;
  6["SnpSift Filter"];
  5 -->|output_file| 6;
  54f8b736-ec20-4049-94bd-46ddc1a4a2bc["Output\nQuality Filtered VCF output"];
  6 --> 54f8b736-ec20-4049-94bd-46ddc1a4a2bc;
  style 54f8b736-ec20-4049-94bd-46ddc1a4a2bc stroke:#2c3143,stroke-width:4px;
  7["SnpSift Extract Fields"];
  6 -->|output| 7;
  ee8c9713-7644-4e12-b00b-5f0f1c72bf83["Output\nExtracted Fields from the VCF output"];
  7 --> ee8c9713-7644-4e12-b00b-5f0f1c72bf83;
  style ee8c9713-7644-4e12-b00b-5f0f1c72bf83 stroke:#2c3143,stroke-width:4px;
  8["bcftools consensus"];
  6 -->|output| 8;
  2 -->|output1| 8;
  d74f4728-75ed-4b45-9ad6-b7709ac7eb6d["Output\nbcftools consensus on input dataset(s): consensus fasta"];
  8 --> d74f4728-75ed-4b45-9ad6-b7709ac7eb6d;
  style d74f4728-75ed-4b45-9ad6-b7709ac7eb6d stroke:#2c3143,stroke-width:4px;
	

flowchart TD
  0["ℹ️ Input Collection\nNanopore Preprocessed reads collection"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["ℹ️ Input Dataset\nSample Metadata"];
  style 1 stroke:#2c3143,stroke-width:4px;
  2["Kraken2"];
  0 -->|output| 2;
  556fef83-c3d7-41eb-9843-674ed36d75be["Output\nKraken2 with PlusPF database output report"];
  2 --> 556fef83-c3d7-41eb-9843-674ed36d75be;
  style 556fef83-c3d7-41eb-9843-674ed36d75be stroke:#2c3143,stroke-width:4px;
  c11d1cab-2058-4e3f-a7e6-06ceb99dc67f["Output\nKraken2 with PlusPF database output"];
  2 --> c11d1cab-2058-4e3f-a7e6-06ceb99dc67f;
  style c11d1cab-2058-4e3f-a7e6-06ceb99dc67f stroke:#2c3143,stroke-width:4px;
  3["Kraken-biom"];
  2 -->|report_output| 3;
  1 -->|output| 3;
  d0e1ffbd-ad44-49c3-ae37-2113f7634012["Output\nOTU mother.map output"];
  3 --> d0e1ffbd-ad44-49c3-ae37-2113f7634012;
  style d0e1ffbd-ad44-49c3-ae37-2113f7634012 stroke:#2c3143,stroke-width:4px;
  355bdba9-97d2-4c09-b902-79f19219b7e8["Output\nKraken-biom output JSON file"];
  3 --> 355bdba9-97d2-4c09-b902-79f19219b7e8;
  style 355bdba9-97d2-4c09-b902-79f19219b7e8 stroke:#2c3143,stroke-width:4px;
  4["Phinch Visualisation"];
  3 -->|biomOutput| 4;
  1f7547ba-6fde-4f7f-b44e-2d4f75272fc2["Output\nTaxonomy Visualization with Metadata, use Active interactive tools under User"];
  4 --> 1f7547ba-6fde-4f7f-b44e-2d4f75272fc2;
  style 1f7547ba-6fde-4f7f-b44e-2d4f75272fc2 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: