nfcore/epitopeprediction repository overview

⁠Introduction

nf-core/epitopeprediction is a bioinformatics best-practice analysis pipeline for epitope prediction and annotation. The pipeline performs epitope predictions for a given set of variants or peptides directly using state of the art prediction tools. Additionally, resulting prediction results can be annotated with metadata.

The pipeline is built using Nextflow⁠, 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.

⁠Quick Start

1. Install nextflow⁠

2. Install any of Docker⁠, Singularity⁠, Podman⁠, Shifter⁠ or Charliecloud⁠ for full pipeline reproducibility (please only use Conda⁠ as a last resort; see docs⁠)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/epitopeprediction -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
   

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   Please check nf-core/configs⁠ to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.

4. Start running your own analysis!

   nextflow run nf-core/epitopeprediction -profile <docker/singularity/conda/institute> --input '*.vcf.gz' --genome GRCh37
   

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See usage docs⁠ for all of the available options when running the pipeline.

⁠Documentation

The nf-core/epitopeprediction pipeline comes with documentation about the pipeline: usage⁠ and output⁠.

⁠Credits

nf-core/epitopeprediction was originally written by Christopher Mohr⁠ from Institute for Translational Bioinformatics⁠ and Quantitative Biology Center⁠ and Alexander Peltzer⁠ from Böhringer Ingelheim⁠. Further contributions were made by Sabrina Krakau⁠ from Quantitative Biology Center⁠ and Leon Kuchenbecker⁠ from the Kohlbacher Lab⁠.

⁠Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines⁠.

For further information or help, don't hesitate to get in touch on the Slack #epitopeprediction channel⁠ (you can join with this invite⁠).

⁠Citations

If you use nf-core/epitopeprediction for your analysis, please cite it using the following doi: 10.5281/zenodo.3564666⁠

You can cite the nf-core publication as follows:

In addition, references of tools and data used in this pipeline are as follows:

• MultiQC: summarize analysis results for multiple tools and samples in a single report.
  Philip Ewels, Måns Magnusson, Sverker Lundin, Max Käller
  Bioinformatics 32(19), 3047-3048 (2016). doi: 10.1093/bioinformatics/btw354⁠.

• Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift.
  Pablo Cingolani, Viral M. Patel, Melissa Coon, Tung Nguyen, Susan J. Land, Douglas M. Ruden and Xiangyi Lu1
  Frontiers in Genetics 3, 35 (2012). doi: 10.3389/fgene.2012.00035⁠.

• FRED 2: an immunoinformatics framework for Python.
  Benjamin Schubert, Mathias Walzer, Hans-Philipp Brachvogel, András Szolek, Christopher Mohr, Oliver Kohlbacher
  Bioinformatics 32(13), 2044-2046 (2016). doi: 10.1093/bioinformatics/btw113⁠.

• MHCflurry: open-source class I MHC binding affinity prediction.
  Timothy J. O’Donnell, Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson, Jeff Hammerbacher
  Cell systems 7(1), 129-132 (2018). doi: 10.1016/j.cels.2018.05.014⁠.

• High-throughput prediction of MHC class i and ii neoantigens with MHCnuggets.
  Xiaoshan M. Shao, Rohit Bhattacharya, Justin Huang, I.K. Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Benjamin Kaminow, Ashton Omdahl, Maria Bonsack, Angelika B. Riemer, Victor E. Velculescu, Valsamo Anagnostou, Kymberleigh A. Pagel and Rachel Karchin
  Cancer Immunology Research 8(3), 396-408 (2020). doi: 10.1158/2326-6066.CIR-19-0464⁠.