diff --git a/workflows/microbiome/mags-building/CHANGELOG.md b/workflows/microbiome/mags-building/CHANGELOG.md index ec666d4e20..afa2f4b0e1 100644 --- a/workflows/microbiome/mags-building/CHANGELOG.md +++ b/workflows/microbiome/mags-building/CHANGELOG.md @@ -1,5 +1,14 @@ # Changelog +## [0.4] - 2025-10-07 + +### Logic Update + +* **metaSPAdes** now supports running in single-assembly mode when individual samples are submitted. Previously, it always performed co-assembly, which is the default tool setting. +* Grouping for grouped assemblies is now handled in the main workflow through a metadata sample sheet (`Metadata for grouped assembly/binning`). If no sample sheet is provided, individual assemblies are performed by default. +* All tools have been updated to their latest versions. +* Workflow annotations. + ## [0.3] - 2025-06-09 ### Automatic update diff --git a/workflows/microbiome/mags-building/MAGs-generation-tests.yml b/workflows/microbiome/mags-building/MAGs-generation-tests.yml index 4a3d704b46..35dfcd8513 100644 --- a/workflows/microbiome/mags-building/MAGs-generation-tests.yml +++ b/workflows/microbiome/mags-building/MAGs-generation-tests.yml @@ -1,19 +1,5 @@ - doc: Test for Metagenome-Assembled-Genomes-(MAGs)-generation job: - Trimmed reads from grouped samples: - class: Collection - collection_type: list:paired - elements: - - class: Collection - type: paired - identifier: 50contig_reads - elements: - - class: File - identifier: forward - location: https://zenodo.org/records/15089018/files/MAG_reads_forward.fastqsanger.gz - - class: File - identifier: reverse - location: https://zenodo.org/records/15089018/files/MAG_reads_reverse.fastqsanger.gz Trimmed reads: class: Collection collection_type: list:paired @@ -29,6 +15,7 @@ identifier: reverse location: https://zenodo.org/records/15089018/files/MAG_reads_reverse.fastqsanger.gz Choose Assembler: MEGAHIT + Metadata for grouped assembly/binning: null Custom Assemblies: null Minimum length of contigs to output: '200' Read length (CONCOCT): '100' diff --git a/workflows/microbiome/mags-building/MAGs-generation.ga b/workflows/microbiome/mags-building/MAGs-generation.ga index 3c9cced9b4..058963d448 100644 --- a/workflows/microbiome/mags-building/MAGs-generation.ga +++ b/workflows/microbiome/mags-building/MAGs-generation.ga @@ -1,12 +1,189 @@ { "a_galaxy_workflow": "true", "annotation": "This workflow constructs Metagenome-Assembled Genomes (MAGs) using SPAdes or MEGAHIT as assemblers, followed by binning with four different tools and refinement using Binette. The resulting MAGs are dereplicated across the entire input sample set, then annotated and evaluated for quality.\nYou can provide pooled reads (for co-assembly/binning), individual read sets, or a combination of both. The input samples must consist of the original reads, which are used for abundance estimation. In all cases, reads should be trimmed, adapters removed, and cleaned of host or other contaminants before processing.", - "comments": [], + "comments": [ + { + "color": "blue", + "data": { + "title": "Quality" + }, + "id": 10, + "position": [ + 9931, + 1274.2311960629306 + ], + "size": [ + 1663, + 933 + ], + "type": "frame" + }, + { + "color": "turquoise", + "data": { + "title": "Annotation" + }, + "id": 9, + "position": [ + 9646.2, + 155.9311960629306 + ], + "size": [ + 1438, + 836 + ], + "type": "frame" + }, + { + "color": "green", + "data": { + "title": "Depreplication" + }, + "id": 8, + "position": [ + 6702.1, + 274.13119606293054 + ], + "size": [ + 2384, + 1005 + ], + "type": "frame" + }, + { + "color": "yellow", + "data": { + "title": "Binner (CONOCT)" + }, + "id": 7, + "position": [ + 4535.9, + 3496.545569700953 + ], + "size": [ + 1547, + 986 + ], + "type": "frame" + }, + { + "color": "lime", + "data": { + "title": "Bin refinement" + }, + "id": 6, + "position": [ + 5697.9, + 205.23119606293056 + ], + "size": [ + 918, + 1402 + ], + "type": "frame" + }, + { + "color": "yellow", + "data": { + "title": "Binner (MetaBAT2)" + }, + "id": 5, + "position": [ + 4532.700000000001, + 2498.845569700953 + ], + "size": [ + 635.7, + 468.8 + ], + "type": "frame" + }, + { + "color": "yellow", + "data": { + "title": "Binner (MaxBin2)" + }, + "id": 4, + "position": [ + 4751.1, + 1923.645569700953 + ], + "size": [ + 540.4, + 562.4 + ], + "type": "frame" + }, + { + "color": "yellow", + "data": { + "title": "Binning (each binner)" + }, + "id": 3, + "position": [ + 2071.1000000000004, + 2852.2455697009527 + ], + "size": [ + 1673, + 1100 + ], + "type": "frame" + }, + { + "color": "orange", + "data": { + "title": "Assembly" + }, + "id": 2, + "position": [ + 1182.7000000000003, + 1003.5455697009531 + ], + "size": [ + 1323, + 1439 + ], + "type": "frame" + }, + { + "color": "red", + "data": { + "title": "Sample grouping" + }, + "id": 1, + "position": [ + 481.3000000000002, + 998.5455697009531 + ], + "size": [ + 667, + 782 + ], + "type": "frame" + }, + { + "color": "pink", + "data": { + "title": "Input" + }, + "id": 0, + "position": [ + 0, + 991.3455697009531 + ], + "size": [ + 436, + 638 + ], + "type": "frame" + } + ], "creator": [ { "class": "Person", "identifier": "https://orcid.org/0000-0001-9852-1987", - "name": "Bérénice Batut" + "name": "B\u00e9r\u00e9nice Batut" }, { "class": "Person", @@ -21,7 +198,7 @@ { "class": "Person", "identifier": "", - "name": "Patrick Bühler" + "name": "Patrick B\u00fchler" }, { "class": "Person", @@ -30,198 +207,201 @@ } ], "format-version": "0.1", + "help": "The current workflow default parameters are rather conservative, aiming for high-quality MAGs.\nTo report lower quality MAGs as well, change the parameters of completeness and contamination.\nThis workflow is maintained by the microGalaxy community. Feel free to reach out for support: [microGalaxy](https://galaxyproject.org/community/sig/microbial)", "license": "MIT", + "release": "0.4", "name": "Metagenome-Assembled Genomes (MAGs) generation", + "readme": "# Metagenome-Assembled Genomes (MAGs) Generation \n\nThis workflow generates Metagenome-Assembled Genomes (MAGs) from paired short reads. \nDereplicated MAGs for the complete input sample set are reported.\n\n## Workflow Logic \n\nThe workflow supports assembly using **metaSPADES** and **MEGAHIT**. \nFor binning, it utilizes four different tools: **MetaBAT2, MaxBin2, SemiBin, and CONCOCT**. The resulting bins are then refined using **Binette**, the successor of metaWRAP. \n\n## MAGs Annotation and Quality Control \n\nAfter binning, the resulting MAGs are **dereplicated** across all input samples based on **CheckM2 quality metrics** using **dRep**. The following processing steps are then performed: \n\n- **Annotation** with Bakta \n- **Taxonomic Assignment** using GTDB-Tk \n- **Quality Control** via QUAST and CheckM/CheckM2 \n- **Abundance Estimation** per sample with CoverM \n\nAll results are consolidated into a single **MultiQC report** for easy analysis. \n\n## Input Requirements \n\nInput reads must be quality-filtered, with host reads removed. \nSee other MAGs workflows in IWC for quality filtering and host removal.\n\n- **Trimmed reads**: Quality-trimmed reads from individual samples.\n- **Metadata for grouped assembly/binning**: A two-column, tab-separated metadata file. The first column should contain the sample names matching those in the **Trimmed reads** input, and the second column should specify the group indicating how samples are to be grouped for assembly and binning. If no file is provided, individual assemblies are performed. To perform a full co-assembly, assign all samples to the same group.\n\n> **Note**: Merging reads can result in large input files, significantly increasing computational demands\u2014especially during assembly and binning, which may require substantial RAM. Our tests with synthetic samples up to **50 GB** showed feasible performance. For larger datasets, we recommend limiting the approach to **individual or pooled MAGs generation**. \n\n## Optional modifications\n\nSome modifications that can be done via the workflow editor for specific use cases:\n- Compare binners / bin refinement: add checkM/M2 to each binner / refiner and compare completeness, contamination, n. of bins.\n- Optionally use DAS tool instead of Binette (our benchmark showed that Binette produce better MAGs on average).\n- Add annotation for Bakta (currently Bakta only reports rRNAs to save resources), other annotations can be selected in the Bakta options.\n- Downstream: compare differential abundance of MAGs in your samples based on metadata using maaslin2.\n- Downstream: compare your MAGs to a catalogue of MAGs for similar samples. Download desired MAGs e.g. from [MGnify genomes](https://www.ebi.ac.uk/metagenomics/browse/genomes) you can query your MAGs via the [MGnify API](https://www.ebi.ac.uk/metagenomics/browse/genomes?browse-by=mag-search); download the Genomes and compare in Galaxy e.g. using dREP.\n\n\n", "report": { "markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n" }, "steps": { "0": { - "annotation": "The workflow can use MEGAHIT and metaSPAdes as assembler", + "annotation": "A two-column, tab-delimited file with sample_id and group_id.\nThe group_id defines which samples are assembled and binned together. For example, technical replicates are typically assigned the same group.\nTo perform co-assembly, use the same group_id for all samples.\nTo perform individual assemblies, assign a unique group_id to each sample.\nIf no metadata file is provided, each sample will be assembled individually by default.", "content_id": null, "errors": null, "id": 0, "input_connections": {}, "inputs": [ { - "description": "The workflow can use MEGAHIT and metaSPAdes as assembler", - "name": "Choose Assembler" + "description": "A two-column, tab-delimited file with sample_id and group_id.\nThe group_id defines which samples are assembled and binned together. For example, technical replicates are typically assigned the same group.\nTo perform co-assembly, use the same group_id for all samples.\nTo perform individual assemblies, assign a unique group_id to each sample.\nIf no metadata file is provided, each sample will be assembled individually by default.", + "name": "Metadata for grouped assembly/binning" } ], - "label": "Choose Assembler", - "name": "Input parameter", + "label": "Metadata for grouped assembly/binning", + "name": "Input dataset", "outputs": [], "position": { - "left": 0, - "top": 628.549370542723 + "left": 542.6172167714049, + "top": 1051.870980643472 }, "tool_id": null, - "tool_state": "{\"multiple\": false, \"validators\": [], \"restrictions\": [\"MEGAHIT\", \"metaSPAdes\", \"Custom Assembly\"], \"parameter_type\": \"text\", \"optional\": false}", + "tool_state": "{\"optional\": true, \"tag\": null}", "tool_version": null, - "type": "parameter_input", - "uuid": "1dfc26f7-8ba3-45f4-82db-27ae8eaf2715", + "type": "data_input", + "uuid": "155c08ba-db21-486a-ad70-93851613412b", "when": null, "workflow_outputs": [] }, "1": { - "annotation": "This workflow allows using a custom assembly as input. If provided, select `custom assembly` as Assembler.\nProvide one assembly for each group of trimmed input reads.", + "annotation": "These should be the reads from the samples to estimate MAGs abundance in the original samples.", "content_id": null, "errors": null, "id": 1, "input_connections": {}, "inputs": [ { - "description": "This workflow allows using a custom assembly as input. If provided, select `custom assembly` as Assembler.\nProvide one assembly for each group of trimmed input reads.", - "name": "Custom Assemblies" + "description": "These should be the reads from the samples to estimate MAGs abundance in the original samples.", + "name": "Trimmed reads" } ], - "label": "Custom Assemblies", + "label": "Trimmed reads", "name": "Input dataset collection", "outputs": [], "position": { - "left": 200, - "top": 950 + "left": 91.82161527691096, + "top": 1248.722231575075 }, "tool_id": null, - "tool_state": "{\"optional\": true, \"tag\": null, \"collection_type\": \"list\"}", + "tool_state": "{\"optional\": false, \"tag\": null, \"collection_type\": \"list:paired\", \"fields\": null}", "tool_version": null, "type": "data_collection_input", - "uuid": "403b2894-c830-4c80-a2a0-97ad4e8da15e", + "uuid": "a8fd020c-8108-41e3-814e-d2fbf653de2c", "when": null, "workflow_outputs": [] }, "2": { - "annotation": "Minimum length of contigs to output (only for MEGAHIT).", + "annotation": "The workflow can use MEGAHIT and metaSPAdes as assembler", "content_id": null, "errors": null, "id": 2, "input_connections": {}, "inputs": [ { - "description": "Minimum length of contigs to output (only for MEGAHIT).", - "name": "Minimum length of contigs to output" + "description": "The workflow can use MEGAHIT and metaSPAdes as assembler", + "name": "Choose Assembler" } ], - "label": "Minimum length of contigs to output", + "label": "Choose Assembler", "name": "Input parameter", "outputs": [], "position": { - "left": 190, - "top": 1100 + "left": 1218.6987516456284, + "top": 1072.7991986932018 }, "tool_id": null, - "tool_state": "{\"default\": 200, \"validators\": [{\"min\": null, \"max\": null, \"negate\": false, \"type\": \"in_range\"}], \"parameter_type\": \"integer\", \"optional\": false}", + "tool_state": "{\"multiple\": false, \"validators\": [], \"restrictions\": [\"MEGAHIT\", \"metaSPAdes\", \"Custom Assembly\"], \"parameter_type\": \"text\", \"optional\": false}", "tool_version": null, "type": "parameter_input", - "uuid": "e9ef61bf-8e3b-40b7-ae59-dc2cf895bca5", + "uuid": "fa4def93-f089-4514-a4a7-fa531f6cf828", "when": null, "workflow_outputs": [] }, "3": { - "annotation": "CONCOCT requires the read length for coverage. Best use fastQC to estimate the mean value.", + "annotation": "Minimum length of contigs to output (only for MEGAHIT).", "content_id": null, "errors": null, "id": 3, "input_connections": {}, "inputs": [ { - "description": "CONCOCT requires the read length for coverage. Best use fastQC to estimate the mean value.", - "name": "Read length (CONCOCT)" + "description": "Minimum length of contigs to output (only for MEGAHIT).", + "name": "Minimum length of contigs to output" } ], - "label": "Read length (CONCOCT)", + "label": "Minimum length of contigs to output", "name": "Input parameter", "outputs": [], "position": { - "left": 1810, - "top": 330 + "left": 1690.6877659215845, + "top": 1685.252615443602 }, "tool_id": null, - "tool_state": "{\"default\": 100, \"validators\": [{\"min\": null, \"max\": null, \"negate\": false, \"type\": \"in_range\"}], \"parameter_type\": \"integer\", \"optional\": false}", + "tool_state": "{\"default\": 200, \"validators\": [{\"min\": null, \"max\": null, \"negate\": false, \"type\": \"in_range\"}], \"parameter_type\": \"integer\", \"optional\": false}", "tool_version": null, "type": "parameter_input", - "uuid": "d88139d0-6e98-4258-8d7a-56f5c0d65f98", + "uuid": "a846d942-9473-41e1-b981-83cb0f8db6bb", "when": null, "workflow_outputs": [] }, "4": { - "annotation": "Environment for the built-in model (SemiBin), options are: human_gut, dog_gut, ocean, soil, cat_gut, human_oral, mouse_gut, pig_gut, built_environment, wastewater, chicken_caecum, global", + "annotation": "This workflow allows using a custom assembly as input. If provided, select `custom assembly` as Assembler.\nProvide one assembly for each group of trimmed input reads.", "content_id": null, "errors": null, "id": 4, "input_connections": {}, "inputs": [ { - "description": "Environment for the built-in model (SemiBin), options are: human_gut, dog_gut, ocean, soil, cat_gut, human_oral, mouse_gut, pig_gut, built_environment, wastewater, chicken_caecum, global", - "name": "Environment for the built-in model (SemiBin)" + "description": "This workflow allows using a custom assembly as input. If provided, select `custom assembly` as Assembler.\nProvide one assembly for each group of trimmed input reads.", + "name": "Custom Assemblies" } ], - "label": "Environment for the built-in model (SemiBin)", - "name": "Input parameter", + "label": "Custom Assemblies", + "name": "Input dataset collection", "outputs": [], "position": { - "left": 1950, - "top": 170 + "left": 1963.0885585694696, + "top": 1701.8131539167669 }, "tool_id": null, - "tool_state": "{\"default\": \"global\", \"multiple\": false, \"validators\": [], \"restrictions\": [\"global\", \"human_gut\", \"dog_gut\", \"ocean\", \"soil\", \"cat_gut\", \"human_oral\", \"mouse_gut\", \"pig_gut\", \"built_environment\", \"wastewater\", \"chicken_caecum\"], \"parameter_type\": \"text\", \"optional\": false}", + "tool_state": "{\"optional\": true, \"tag\": null, \"collection_type\": \"list\", \"fields\": null}", "tool_version": null, - "type": "parameter_input", - "uuid": "4595f792-8269-4c70-82e8-8889f34bd8b1", + "type": "data_collection_input", + "uuid": "8a8ecd89-805b-42ab-8ec9-85b2bf0cc55a", "when": null, "workflow_outputs": [] }, "5": { - 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"description": "These should be the reads from the samples to estimate MAGs abundance in the original samples.", - "name": "Trimmed reads" + "description": "Minimum MAG completeness percentage for bin refinement (binette) and dereplication (drep)", + "name": "Minimum MAG completeness percentage" } ], - "label": "Trimmed reads", - "name": "Input dataset collection", + "label": "Minimum MAG completeness percentage", + "name": "Input parameter", "outputs": [], "position": { - "left": 1278.9124368683956, - "top": 1800 + "left": 5730.37010398056, + "top": 614.5939624823972 }, "tool_id": null, - "tool_state": "{\"optional\": false, \"tag\": null, \"collection_type\": \"list:paired\"}", + "tool_state": "{\"default\": 75, \"validators\": [{\"min\": null, \"max\": null, \"negate\": false, \"type\": \"in_range\"}], \"parameter_type\": \"integer\", \"optional\": false}", "tool_version": null, - "type": "data_collection_input", - "uuid": "281767e7-c8ff-4de0-8692-34b2b13ce63c", + "type": "parameter_input", + "uuid": "a1df4fff-e835-45e7-8f39-0a4fb160f6a1", "when": null, "workflow_outputs": [] }, @@ -241,203 +421,203 @@ "name": "Input parameter", "outputs": [], "position": { - 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"uuid": "857a9144-6175-4f1b-80f7-a1a5e5974169", + "uuid": "a6d656e5-4ae0-4ae2-a55e-ce8a3993fdb3", "when": null, "workflow_outputs": [ { "label": "Full MultiQC Report", "output_name": "html_report", - "uuid": "71243244-4ccf-4137-bf1c-9d052d72dfcb" + "uuid": "8e68772e-a5b1-4303-963e-495015f530d1" } ] } @@ -2570,7 +3356,6 @@ "tags": [ "name:FAIRyMAGs" ], - "uuid": "e4e70fc0-b584-4e86-8b8f-c6bcdda2e11a", - "version": 1, - "release": "0.3" + "uuid": "cb3d6f12-21d1-40c0-bfc3-5ece962775c3", + "version": 7 } \ No newline at end of file diff --git a/workflows/microbiome/mags-building/README.md b/workflows/microbiome/mags-building/README.md index cb01556734..09ac3f9bf3 100644 --- a/workflows/microbiome/mags-building/README.md +++ b/workflows/microbiome/mags-building/README.md @@ -22,12 +22,19 @@ All results are consolidated into a single **MultiQC report** for easy analysis. ## Input Requirements Input reads must be quality-filtered, with host reads removed. +See other MAGs workflows in IWC for quality filtering and host removal. -- **Trimmed reads**: Quality-trimmed reads from individual samples, used solely for abundance estimation. -- **Trimmed reads from grouped samples**: These reads need to be grouped based on the desired MAGs generation approach: - - **Individual MAGs Generation**: Use the same input as `Sample-wise Trimmed Paired Reads` to generate MAGs per sample. - - **Pooled MAGs Generation (Co-assembly/Binning)**: Merge all reads input one file for a fully pooled MAGs approach. - - **Grouped MAGs Generation (Co-assembly/Binning)**: Merge samples based on predefined groups. - - **Hybrid MAGs Generation**: Combine individual and grouped reads for a mixed approach. +- **Trimmed reads**: Quality-trimmed reads from individual samples. +- **Metadata for grouped assembly/binning**: A two-column, tab-separated metadata file. The first column should contain the sample names matching those in the **Trimmed reads** input, and the second column should specify the group indicating how samples are to be grouped for assembly and binning. If no file is provided, individual assemblies are performed. To perform a full co-assembly, assign all samples to the same group. > **Note**: Merging reads can result in large input files, significantly increasing computational demands—especially during assembly and binning, which may require substantial RAM. Our tests with synthetic samples up to **50 GB** showed feasible performance. For larger datasets, we recommend limiting the approach to **individual or pooled MAGs generation**. + +## Optional modifications + +Some modifications that can be done via the workflow editor for specific use cases: +- Compare binners / bin refinement: add checkM/M2 to each binner / refiner and compare completeness, contamination, n. of bins. +- Optionally use DAS tool instead of Binette (our benchmark showed that Binette produce better MAGs on average). +- Add annotation for Bakta (currently Bakta only reports rRNAs to save resources), other annotations can be selected in the Bakta options. +- Downstream: compare differential abundance of MAGs in your samples based on metadata using maaslin2. +- Downstream: compare your MAGs to a catalogue of MAGs for similar samples. Download desired MAGs e.g. from [MGnify genomes](https://www.ebi.ac.uk/metagenomics/browse/genomes) you can query your MAGs via the [MGnify API](https://www.ebi.ac.uk/metagenomics/browse/genomes?browse-by=mag-search); download the Genomes and compare in Galaxy e.g. using dREP. +