Add epytope to methods and references

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   <h4>Methods</h4>
   <p>Data was processed using nf-core/metapep v${workflow.manifest.version} ${doi_text} of the nf-core collection of workflows (<a href="https://doi.org/10.1038/s41587-020-0439-x">Ewels <em>et al.</em>, 2020</a>).
-  Briefly the pipeline uses prodigal (<a href="https://doi.org/10.1186/1471-2105-11-119">Hyatt, D., Chen, GL., LoCascio, P.F. <em>et al.</em>, 2010</a>) to predict proteins from the genomic input files or taxids. Peptides are generated in discrete lengths from proteins and predicted against chosen alleles using either SYFPEITHI (<a href="https://doi.org/10.1007/s002510050595">Rammensee <em>et al.</em>, 1999</a>), MHCFlurry (<a href="https://doi.org/10.1016/j.cels.2020.06.010"> O'Donnel<em>et al.</em>, 2020</a>) or MHCnuggets (<a href="https://doi.org/10.1158/2326-6066.cir-19-0464"> Shao<em>et al.</em>, 2019</a>).
+  Briefly the pipeline uses prodigal (<a href="https://doi.org/10.1186/1471-2105-11-119">Hyatt, D., Chen, GL., LoCascio, P.F. <em>et al.</em>, 2010</a>) to predict proteins from the genomic input files or taxids. Peptides are generated in discrete lengths from proteins and predicted against chosen alleles using either SYFPEITHI (<a href="https://doi.org/10.1007/s002510050595">Rammensee <em>et al.</em>, 1999</a>), MHCFlurry (<a href="https://doi.org/10.1016/j.cels.2020.06.010">O'Donnel <em>et al.</em>, 2020</a>) or MHCnuggets (<a href="https://doi.org/10.1158/2326-6066.cir-19-0464">Shao <em>et al.</em>, 2019</a>), which are all embedded in the epytope framework (<a href="https://doi.org/10.1093/bioinformatics/btw113">Schubert <em>et al.</em>, 2016</a>).
   Resulting epitopeprediction scores distributions and entity binding ratios are plotted using R (<a href="https://www.R-project.org/">R Core Team, 2022</a>). The large amounts of data are handled using a python (<a href="https://www.python.org/">Python Core Team, 2022</a>) framework. All specific software versions and used libraries can be found in the following section.</p>
   <p>The pipeline was executed with Nextflow v${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>
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     <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. <a href="https://doi.org/10.1007/s002510050595">https://doi.org/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. <a href="https://doi.org/10.1093/bioinformatics/btw113">https://doi.org/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. <a href="https://doi.org/10.1158/2326-6066.cir-19-0464">https://doi.org/10.1158/2326-6066.cir-19-0464</a>.</li>
   </ul>
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