Merge pull request #84 from tillenglert/multiqc

Multiqc

CHANGELOG.md

@@ -19,6 +19,7 @@ Pipeline has been re-implemented in [Nextflow DSL2](https://www.nextflow.io/docs
 - [#70](https://github.com/nf-core/metapep/pull/70) - Added check for supported models and functionality to output all supported models.
 - [#75](https://github.com/nf-core/metapep/pull/75) - Peptide lengths are checked if supported generally and unified if models are not available in `UNIFY_MODEL_LENGTHS`
 - [#80](https://github.com/nf-core/metapep/pull/80) - Add mean comparison to entity binding ratios plots.
+- [#84](https://github.com/nf-core/metapep/pull/84) - Added MultiQC again with updated method description and updated references
 - [#88](https://github.com/nf-core/metapep/pull/88) - Updated Citations.md
 
 ### `Changed`

assets/methods_description_template.yml

@@ -3,11 +3,11 @@ description: "Suggested text and references to use when describing pipeline usag
 section_name: "nf-core/metapep Methods Description"
 section_href: "https://github.com/nf-core/metapep"
 plot_type: "html"
-## TODO nf-core: Update the HTML below to your preferred methods description, e.g. add publication citation for this pipeline
-## You inject any metadata in the Nextflow '${workflow}' object
+
 data: |
   <h4>Methods</h4>
-  <p>Data was processed using nf-core/metapep v${workflow.manifest.version} ${doi_text} of the nf-core collection of workflows (<a href="https://doi.org/10.1038/s41587-020-0439-x">Ewels <em>et al.</em>, 2020</a>), utilising reproducible software environments from the Bioconda (<a href="https://doi.org/10.1038/s41592-018-0046-7">Grüning <em>et al.</em>, 2018</a>) and Biocontainers (<a href="https://doi.org/10.1093/bioinformatics/btx192">da Veiga Leprevost <em>et al.</em>, 2017</a>) projects.</p>
+  <p>Data was processed using nf-core/metapep v${workflow.manifest.version} ${doi_text} of the nf-core collection of workflows (<a href="https://doi.org/10.1038/s41587-020-0439-x">Ewels <em>et al.</em>, 2020</a>), utilising reproducible software environments from the Bioconda (<a href="https://doi.org/10.1038/s41592-018-0046-7">Grüning <em>et al.</em>, 2018</a>) and Biocontainers (<a href="https://doi.org/10.1093/bioinformatics/btx192">da Veiga Leprevost <em>et al.</em>, 2017</a>) projects. Briefly the pipeline uses prodigal (<a href="https://doi.org/10.1186/1471-2105-11-119">Hyatt, D., Chen, GL., LoCascio, P.F. <em>et al.</em>, 2010</a>) to predict proteins from the genomic input files or  downloads the proteins from the taxid input directly using Entrez (<a href=https://doi.org/10.1093/nar/gki031">Maglott <em>et al.</em>, 2005</a>). Peptides are generated in discrete lengths from proteins and predicted against chosen alleles using either SYFPEITHI (<a href="https://doi.org/10.1007/s002510050595">Rammensee <em>et al.</em>, 1999</a>), MHCFlurry (<a href="https://doi.org/10.1016/j.cels.2020.06.010">O'Donnel <em>et al.</em>, 2020</a>) or MHCnuggets (<a href="https://doi.org/10.1158/2326-6066.cir-19-0464">Shao <em>et al.</em>, 2019</a>), which are embedded in the epytope framework (<a href="https://doi.org/10.1093/bioinformatics/btw113">Schubert <em>et al.</em>, 2016</a>).
+  Resulting epitopeprediction scores distributions and entity binding ratios are plotted using R (<a href="https://www.R-project.org/">R Core Team, 2022</a>). The large amounts of data are handled using a python (<a href="https://www.python.org/">Python Core Team, 2022</a>) framework. All specific software versions and used libraries can be found in the following section and the <a href="https://github.com/nf-core/metapep/blob/dev/CITATIONS.md">CITATIONS.md</a> file.</p>
   <p>The pipeline was executed with Nextflow v${workflow.nextflow.version} (<a href="https://doi.org/10.1038/nbt.3820">Di Tommaso <em>et al.</em>, 2017</a>) with the following command:</p>
   <pre><code>${workflow.commandLine}</code></pre>
   <p>${tool_citations}</p>
@@ -16,6 +16,14 @@ data: |
     <li>Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: <a href="https://doi.org/10.1038/nbt.3820">10.1038/nbt.3820</a></li>
     <li>Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: <a href="https://doi.org/10.1038/s41587-020-0439-x">10.1038/s41587-020-0439-x</a></li>
     <li>Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: <a href="https://doi.org/10.1038/s41592-018-0046-7">10.1038/s41592-018-0046-7</a></li>
+    <li>Hyatt, D., Chen, GL., LoCascio, P. F., Land M. L., Larimer F. W., Hauser L. J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119. doi: <a href="https://doi.org/10.1186/1471-2105-11-119">10.1186/1471-2105-11-119</a></li>
+    <li>Maglott D, Ostell J, Pruitt KD, Tatusova T. (2005) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D54-8. Update in: Nucleic Acids Res. 2007 Jan;35(Database issue):D26-31. doi: <a href="https://doi.org/10.1093/nar/gki031">10.1093/nar/gki031</a>.</li>
+    <li>O'Donnell T. J., Rubinsteyn A., Laserson U., (2020). MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. Cell Systems 11, 42-48. doi: <a href="https://doi.org/10.1016/j.cels.2020.06.010">10.1016/j.cels.2020.06.010</a>.</li>
+    <li>Python Core Team (2022). Python: A dynamic, open source programming language. Python Software Foundation. <a href="https://www.python.org/">https://www.python.org/</a>.</li>
+    <li>Rammensee H., Bachmann J., Emmerich N. P., Bachor O. A., Stevanović S. (1999). SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999 Nov;50(3-4):213-9. doi: <a href="https://doi.org/10.1007/s002510050595">10.1007/s002510050595</a>.</li>
+    <li>R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</li>
+    <li>Schubert, B., Walzer, M., Brachvogel, H-P., Sozolek, A., Mohr, C., and Kohlbacher, O. (2016). FRED 2 - An Immunoinformatics Framework for Python. Bioinformatics 2016. doi: <a href="https://doi.org/10.1093/bioinformatics/btw113">10.1093/bioinformatics/btw113</a>.</li>
+    <li>Shao X. M., Bhattacharya R., Huang J., Sivakumar I. K. A., Tokheim C., Zheng L., Hirsch D., Kaminow B., Omdahl A., Bonsack M., Riemer A. B., Velculescu V. E., Anagnostou V., Pagel K. A., Karchin R. (2020). High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets. Cancer Immunol Res. 2020 Mar;8(3):396-408. doi: <a href="https://doi.org/10.1158/2326-6066.cir-19-0464">10.1158/2326-6066.cir-19-0464</a>.</li>
     <li>da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: <a href="https://doi.org/10.1093/bioinformatics/btx192">10.1093/bioinformatics/btx192</a></li>
     ${tool_bibliography}
   </ul>

assets/multiqc_config.yml

@@ -1,3 +1,7 @@
+custom_logo: "nf-core-metapep_logo_light.png"
+custom_logo_url: https://github.com/nf-core/metapep/
+custom_logo_title: "nf-core/metapep"
+
 report_comment: >
   This report has been generated by the <a href="https://github.com/nf-core/metapep/1.0dev" target="_blank">nf-core/metapep</a>
   analysis pipeline. For information about how to interpret these results, please see the

docs/output.md

@@ -171,7 +171,7 @@ The pipeline generates some basic visualisations comparing the results for the d
 
 </details>
 
-<!-- ### MultiQC
+### MultiQC
 
 <details markdown="1">
 <summary>Output files</summary>
@@ -185,7 +185,7 @@ The pipeline generates some basic visualisations comparing the results for the d
 
 [MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
 
-Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>. -->
+Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.
 
 ## Pipeline information
 

modules.json

@@ -13,7 +13,8 @@
                     "multiqc": {
                         "branch": "master",
                         "git_sha": "911696ea0b62df80e900ef244d7867d177971f73",
-                        "installed_by": ["modules"]
+                        "installed_by": ["modules"],
+                        "patch": "modules/nf-core/multiqc/multiqc.diff"
                     },
                     "prodigal": {
                         "branch": "master",

workflows/metapep.nf

@@ -33,6 +33,7 @@ if (workflow.profile.tokenize(',').intersect(['conda', 'mamba']).size() >= 1) {
 */
 
 ch_multiqc_config          = Channel.fromPath("$projectDir/assets/multiqc_config.yml", checkIfExists: true)
+ch_metapep_logo            = Channel.fromPath("$projectDir/assets/nf-core-metapep_logo_light.png")
 ch_multiqc_custom_config   = params.multiqc_config ? Channel.fromPath( params.multiqc_config, checkIfExists: true ) : Channel.empty()
 ch_multiqc_logo            = params.multiqc_logo   ? Channel.fromPath( params.multiqc_logo, checkIfExists: true ) : Channel.empty()
 ch_multiqc_custom_methods_description = params.multiqc_methods_description ? file(params.multiqc_methods_description, checkIfExists: true) : file("$projectDir/assets/methods_description_template.yml", checkIfExists: true)
@@ -304,6 +305,7 @@ workflow METAPEP {
 
         MULTIQC (
             ch_multiqc_files.collect(),
+            ch_metapep_logo.collect(),
             ch_multiqc_config.toList(),
             ch_multiqc_custom_config.toList(),
             ch_multiqc_logo.toList()