From 62b7d12dab5631d8e20dd521660fc60e6f7d1a8f Mon Sep 17 00:00:00 2001 From: Till Englert Date: Thu, 13 Jul 2023 10:06:49 +0200 Subject: [PATCH] Add epytope to methods and references --- assets/methods_description_template.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/assets/methods_description_template.yml b/assets/methods_description_template.yml index 9e789ed3..f77ee2fa 100644 --- a/assets/methods_description_template.yml +++ b/assets/methods_description_template.yml @@ -7,7 +7,7 @@ plot_type: "html" data: |

Methods

Data was processed using nf-core/metapep v${workflow.manifest.version} ${doi_text} of the nf-core collection of workflows (Ewels et al., 2020). - Briefly the pipeline uses prodigal (Hyatt, D., Chen, GL., LoCascio, P.F. et al., 2010) to predict proteins from the genomic input files or taxids. Peptides are generated in discrete lengths from proteins and predicted against chosen alleles using either SYFPEITHI (Rammensee et al., 1999), MHCFlurry ( O'Donnelet al., 2020) or MHCnuggets ( Shaoet al., 2019). + Briefly the pipeline uses prodigal (Hyatt, D., Chen, GL., LoCascio, P.F. et al., 2010) to predict proteins from the genomic input files or taxids. Peptides are generated in discrete lengths from proteins and predicted against chosen alleles using either SYFPEITHI (Rammensee et al., 1999), MHCFlurry (O'Donnel et al., 2020) or MHCnuggets (Shao et al., 2019), which are all embedded in the epytope framework (Schubert et al., 2016). Resulting epitopeprediction scores distributions and entity binding ratios are plotted using R (R Core Team, 2022). The large amounts of data are handled using a python (Python Core Team, 2022) framework. All specific software versions and used libraries can be found in the following section.

The pipeline was executed with Nextflow v${workflow.nextflow.version} (Di Tommaso et al., 2017) with the following command:

${workflow.commandLine}
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  • Python Core Team (2022). Python: A dynamic, open source programming language. Python Software Foundation. https://www.python.org/.
  • 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. https://doi.org/10.1007/s002510050595.
  • R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
    • +
  • Schubert, B., Walzer, M., Brachvogel, H-P., Sozolek, A., Mohr, C., and Kohlbacher, O. (2016). FRED 2 - An Immunoinformatics Framework for Python. Bioinformatics 2016. https://doi.org/10.1093/bioinformatics/btw113.
  • 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. https://doi.org/10.1158/2326-6066.cir-19-0464.