nf-core/epitopeprediction
A bioinformatics best-practice analysis pipeline for epitope prediction and annotation
2.0.0
). The latest
stable release is
2.3.1
.
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 uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.
Pipeline summary
- Read variants, proteins, or peptides and HLA alleles
- Generate peptides from variants or proteins or use peptides directly
- Predict HLA-binding peptides for the given set of HLA alleles
Quick Start
-
Install
Nextflow
(>=21.10.3
) -
Install any of
Docker
,Singularity
,Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (please only useConda
as a last resort; see docs) -
Download the pipeline and test it on a minimal dataset with a single command:
Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (
YOURPROFILE
in the example command above). You can chain multiple config profiles in a comma-separated string.- The pipeline comes with config profiles called
docker
,singularity
,podman
,shifter
,charliecloud
andconda
which instruct the pipeline to use the named tool for software management. For example,-profile test,docker
. - 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 eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. - If you are using
singularity
and are persistently observing issues downloading Singularity images directly due to timeout or network issues, then you can use the--singularity_pull_docker_container
parameter to pull and convert the Docker image instead. Alternatively, you can use thenf-core download
command to download images first, before running the pipeline. Setting theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options enables you to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- The pipeline comes with config profiles called
-
Start running your own analysis!
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, parameters, and output.
Credits
nf-core/epitopeprediction was originally written by Christopher Mohr from Medical Data Integration Center 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.
The pipeline was converted to Nextflow DSL2 by Christopher Mohr, Marissa Dubbelaar from Clinical Collaboration Unit Translational Immunology and Quantitative Biology Center, Gisela Gabernet from Quantitative Biology Center, and Jonas Scheid from Quantitative Biology Center
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
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md
file.
You can cite the nf-core
publication as follows:
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.