Merge pull request #23 from nf-core/diagnostic_summarized_viz

Diagnostic summarized viz

.github/workflows/ci.yml

@@ -55,10 +55,10 @@ jobs:
         # Remember that you can parallelise this by using strategy.matrix
         # We also test basic visualization and reporting options here
         run: |
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --viz
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --smoothxg consensus_spec 10,100,1000
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --vcf_spec "gi|568815561:#,gi|568815567:#"
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --smoothxg_write_maf
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --wfmash_chunks 2
-          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --wfmash_only
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --no_viz --no_layout
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --smoothxg consensus_spec 10,100,1000
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --vcf_spec "gi|568815561:#,gi|568815567:#"
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --smoothxg_write_maf
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --wfmash_chunks 2
+          nextflow run ${GITHUB_WORKSPACE} -profile test,docker --n_mappings 11 --wfmash_only

.github/workflows/linting.yml

@@ -14,7 +14,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-node@v1
         with:
-          node-version: '10'
+          node-version: '14'
       - name: Install markdownlint
         run: npm install -g markdownlint-cli
       - name: Run Markdownlint
@@ -53,7 +53,7 @@ jobs:
       - uses: actions/checkout@v1
       - uses: actions/setup-node@v1
         with:
-          node-version: '10'
+          node-version: '14'
       - name: Install yaml-lint
         run: npm install -g yaml-lint
       - name: Run yaml-lint
@@ -101,7 +101,7 @@ jobs:
 
       - uses: actions/setup-python@v1
         with:
-          python-version: '3.6'
+          python-version: '3.9'
           architecture: 'x64'
 
       - name: Install dependencies
@@ -114,7 +114,7 @@ jobs:
           GITHUB_COMMENTS_URL: ${{ github.event.pull_request.comments_url }}
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           GITHUB_PR_COMMIT: ${{ github.event.pull_request.head.sha }}
-        run: nf-core -l lint_log.txt lint ${GITHUB_WORKSPACE} --markdown lint_results.md
+        run: nf-core -l lint_log.txt lint -d ${GITHUB_WORKSPACE} --markdown lint_results.md
 
       - name: Save PR number
         if: ${{ always() }}

Dockerfile

@@ -1,4 +1,4 @@
-FROM ghcr.io/pangenome/pggb:20211103204137531f85
+FROM ghcr.io/pangenome/pggb:2022092217355879ede8
 LABEL authors="Simon Heumos, Michael Heuer, Lukas Heumos, Erik Garrison, Andrea Guarracino" \
       description="Docker image containing all software requirements for the nf-core/pangenome pipeline"
 

README.md

@@ -10,11 +10,11 @@
 
 ## Introduction
 
-**Warning:** This pipeline is currently UNDER CONSTRUCTION. Some features may not work or not work as intended!
+> **Warning:** This pipeline is currently UNDER CONSTRUCTION. Some features may not work or not work as intended!
 
 <!-- TODO nf-core: Write a 1-2 sentence summary of what data the pipeline is for and what it does -->
 **nf-core/pangenome** is a bioinformatics best-practise analysis pipeline for the rendering of a collection of sequences into a pangenome graph.
-Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for interpretation, visualization, mapping, comparative genomics, and reuse of pangenome graphs**.
+Its goal is to build a graph that is locally directed and acyclic while preserving large-scale variation. Maintaining local linearity is important for interpretation, visualization, mapping, comparative genomics, and reuse of pangenome graphs.
 
 The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.
 
@@ -35,7 +35,7 @@ The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool
 4. Test the workflow on a minimal dataset
 
     ```bash
-    nextflow run nf-core/pangenome -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
+    nextflow run nf-core/pangenome -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute> --n_mappings 11
     ```
 
     [//]: # (```bash nextflow run nf-core/pangenome -profile test,<docker/singularity/conda/institute>```)
@@ -45,10 +45,10 @@ The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool
 5. Start running your own analysis!
 
     ```bash
-    nextflow run nf-core/pangenome -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input "input.fa.gz"
+    nextflow run nf-core/pangenome -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input "input.fa.gz" --n_mappings 11
     ```
 
-See [usage docs](https://nf-co.re/pangenome/usage) for all of the available options when running the pipeline.
+Be careful, the input FASTA must have been compressed with [bgzip](http://www.htslib.org/doc/bgzip.html). See [usage docs](https://nf-co.re/pangenome/usage) for all of the available options when running the pipeline.
 
 ## Pipeline Summary
 
@@ -68,12 +68,12 @@ Many thanks to all who have helped out and contributed along the way, including
 
 | Name                                                     | Affiliation                                                                           |
 |----------------------------------------------------------|---------------------------------------------------------------------------------------|
-| [Philipp Ehmele](https://github.com/imipenem)            | [University of Hamburg, Hamburg, Germany](https://www.uni-hamburg.de/en.html)         |
+| [Philipp Ehmele](https://github.com/imipenem)            | [Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany](https://www.helmholtz-muenchen.de/icb/index.html)         |
 | [Erik Garrison](https://github.com/ekg)                  | [The University of Tennessee Health Science Center, Memphis, Tennessee, TN, USA](https://uthsc.edu/)|
-| [Andrea Guarracino](https://github.com/AndreaGuarracino) | [University of Rome Tor Vergata, Rome, Italy](http://www.scienze.uniroma2.it/)        |
+| [Andrea Guarracino](https://github.com/AndreaGuarracino) | [Genomics Research Centre, Human Technopole, Milan, Italy](https://humantechnopole.it/en/)        |
 | [Michael Heuer](https://github.com/heuermh)              | [UC Berkeley, USA](https://rise.cs.berkeley.edu)                                      |
-| [Lukas Heumos](https://github.com/zethson)               | [Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany](https://www.helmholtz-muenchen.de/icb/index.html) \\ [Institute of Lung Biology and Disease and Comprehensive Pneumology Center, Helmholtz Zentrum München, Munich, Germany](https://www.helmholtz-muenchen.de/ilbd/the-institute/cpc/index.html) |
-| [Simon Heumos](https://github.com/subwaystation)         | [Quantitative Biology Center (QBiC) Tübingen, University of Tübingen, Germany](https://uni-tuebingen.de/en/research/research-infrastructure/quantitative-biology-center-qbic/) |
+| [Lukas Heumos](https://github.com/zethson)               | [Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany](https://www.helmholtz-muenchen.de/icb/index.html) <br /> [Institute of Lung Biology and Disease and Comprehensive Pneumology Center, Helmholtz Zentrum München, Munich, Germany](https://www.helmholtz-muenchen.de/ilbd/the-institute/cpc/index.html) |
+| [Simon Heumos](https://github.com/subwaystation)         | [Quantitative Biology Center (QBiC) Tübingen, University of Tübingen, Germany](https://uni-tuebingen.de/en/research/research-infrastructure/quantitative-biology-center-qbic/) <br /> [Biomedical Data Science, Department of Computer Science, University of Tübingen, Germany](https://uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/department/) |
 
 > \* Listed in alphabetical order
 
@@ -100,8 +100,22 @@ In addition, references of tools and data used in this pipeline are as follows:
 
 > **ODGI: understanding pangenome graphs.**
 >
-> Andrea Guarracino, Simon Heumos, Sven Nahnsen, Pjotr Prins & Erik Garrison.
+> Andrea Guarracino*, Simon Heumos*, Sven Nahnsen, Pjotr Prins & Erik Garrison.
 >
-> _bioRxiv_ 2021 Nov 11 doi: [10.1101/2021.11.10.467921](https://doi.org/10.1101/2021.11.10.467921).
+> _Bioinformatics_ 2022 Jul 01 doi: [10.1093/bioinformatics/btac308](https://doi.org/10.1093/bioinformatics/btac308).
+>
+> *_contributed equally_
+
+> **Unbiased pangenome graphs**
+>
+> Erik Garrison, Andrea Guarracino.
+>
+> _bioRxiv_ 2022 Feb 02 doi: [10.1101/2022.02.14.480413](https://doi.org/10.1101/2022.02.14.480413).
 
 <!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->
+
+## Attention
+
+### MultiQC Report  
+
+In the resulting MultiQC report, in the **Detailed ODGI stats table**, it says `smoothxg`. To be clear, these are the stats of the graph after polishing with `gfaffix`! Some tools were hardcoded in the ODGI MultiQC module, but hopefully this will be fixed in the future.

assets/multiqc_config.yaml

@@ -1,5 +1,8 @@
 # Report section config for nice titles and descriptions
 custom_data:
+  odgi_O:
+    section_name: ODGI Compressed 1D visualization
+    description: This image shows a 1D rendering of the built pangenome graph. The graph nodes are arranged from left to right, forming the pangenome sequence. Summarization of path coverage across all paths. A heatmap color-coding from https://colorbrewer2.org/#type=diverging&scheme=RdBu&n=11 is used. Dark blue means highest coverage. Dark red means lowest coverage. The path names are placed on the left. The black lines under the paths are the links, which represent the graph topology.
   odgi_viz:
     section_name: ODGI 1D visualization
     description: This image shows a 1D rendering of the built pangenome graph. The graph nodes are arranged from left to right, forming the pangenome sequence. The colored bars represent the paths versus the pangenome sequence in a binary matrix. The path names are placed on the left. The black lines under the paths are the links, which represent the graph topology.
@@ -18,6 +21,8 @@ custom_data:
 
 # Custom search patterns to find the image outputs
 sp:
+  odgi_O:
+    fn: "*O_multiqc.png"
   odgi_draw:
     fn: "*draw_multiqc.png"
   odgi_viz:
@@ -40,6 +45,7 @@ module_order:
 # Set the order that the custom content plots should come in
 custom_content:
   order:
+    - odgi_O
     - odgi_viz
     - odgi_viz_pos
     - odgi_viz_inv