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
.
Define where the pipeline should find input data and save output data.
Path to comma-separated file containing information about the samples in the experiment.
string^\S+\.csv$You will need to create a design file with information about the samples in your experiment before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row. See usage docs.
Path to the output directory where the results will be saved.
string./resultsEmail address for completion summary.
string^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.
MultiQC report title. Printed as page header, used for filename if not otherwise specified.
stringOptions for the genome and proteome reference.
Specifies the human reference genome version.
stringThis defines against which human reference genome the pipeline performs the analysis including the incorporation of genetic variants e.g..
Specifies the reference proteome.
stringSpecifies the reference proteome files that are used for self-filtering. Should be either a folder of FASTA files or a single FASTA file containing the reference proteome(s).
Reference genome related files and options required for the workflow.
Do not load the iGenomes reference config.
booleanDo not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.
Options for the peptide prediction step.
Filter against human proteome.
booleanSpecifies that peptides should be filtered against the specified human proteome references.
MHC class for prediction.
integerSpecifies whether the predictions should be done for MHC class I or class II.
Specifies the maximum peptide length.
integer11Specifies the maximum peptide length (not applied when --peptides is specified). Default: MHC class I: 11 aa, MHC class II: 16 aa
Specifies the minimum peptide length.
integer8Specifies the minimum peptide length (not applied when --peptides is specified). Default: MCH class I: 8 aa, MHC class II: 15 aa
Specifies the prediction tool(s) to use.
stringsyfpeithiSpecifies the tool(s) to use. Available are: syfpeithi, mhcflurry, mhcnuggets-class-1, mhcnuggets-class-2. Can be combined in a list separated by comma.
Specifies tool-specific binder thresholds in a JSON file. This can be used to override the given default binder threshold values.
stringDefault affinity thresholds to determine whether a peptide is considered as a binder are the following:: syfpeithi > 50, mhcflurry <=500, mhcnuggets-class-1 <= 500, mhcnuggets-class-2 <= 500, netmhc <= 500, netmhcpan <= 500, netmhcii <= 500, netmhciipan <= 500. Thresholds can be customized in a JSON file: tool-name:value
Specifies whether wild-type sequences should be predicted.
booleanSpecifies whether wild-type sequences of mutated peptides should be predicted as well.
Specifies that sequences of proteins, affected by provided variants, will be written to a FASTA file.
booleanSpecifies that sequences of proteins that are affected by the provided genomic variants are written to a FASTA file. The resulting FASTA file will contain the wild-type and mutated protein sequences.
Writes out supported prediction models.
booleanWrites out supported models. Does not run any predictions.
Options for optimising the pipeline run execution.
Specifies the maximum number of peptide chunks.
integer100Used in combination with --peptides or --proteins. Maximum number of peptide chunks that will be created for parallelisation.
Specifies the minimum number of peptides that should be written into one chunk.
integer5000Used in combination with --peptides or --proteins: minimum number of peptides that should be written into one chunk.
External MHC binding prediction software that is not shipped with the pipeline.
To use the 'netmhcpan' tool, specify the path to the original software tarball for NetMHCpan 4.0 (Linux) here.
stringNoneTo use the 'netmhc' tool, specify the path to the original software tarball for NetMHC 4.0 (Linux) here.
stringNoneTo use the 'netmhciipan' tool, specify the path to the original software tarball for NetMHCIIpan 3.1 (Linux) here.
stringNoneTo use the 'netmhcii' tool, specify the path to the original software tarball for NetMHCII 2.2 (Linux) here.
stringNoneParameters used to describe centralised config profiles. These should not be edited.
Git commit id for Institutional configs.
stringmasterProvide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default: master.
## Download and use config file with following git commit id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96
Base directory for Institutional configs.
stringhttps://raw.githubusercontent.com/nf-core/configs/masterIf you're running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the custom_config_base option. For example:
## Download and unzip the config files
cd /path/to/my/configs
wget https://github.com/nf-core/configs/archive/master.zip
unzip master.zip
## Run the pipeline
cd /path/to/my/data
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/
Note that the nf-core/tools helper package has a
downloadcommand to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.
Institutional config name.
stringInstitutional config description.
stringInstitutional config contact information.
stringInstitutional config URL link.
stringSet the top limit for requested resources for any single job.
Maximum number of CPUs that can be requested for any single job.
integer16Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1
Maximum amount of memory that can be requested for any single job.
string128.GB^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$Use to set an upper-limit for the memory requirement for each process. Should be a string in the format integer-unit e.g. --max_memory '8.GB'
Maximum amount of time that can be requested for any single job.
string240.h^(\d+\.?\s*(s|m|h|day)\s*)+$Use to set an upper-limit for the time requirement for each process. Should be a string in the format integer-unit e.g. --max_time '2.h'
Less common options for the pipeline, typically set in a config file.
Display help text.
booleanEmail address for completion summary, only when pipeline fails.
string^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.
Send plain-text email instead of HTML.
booleanFile size limit when attaching MultiQC reports to summary emails.
string25.MB^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$Do not use coloured log outputs.
booleanCustom config file to supply to MultiQC.
stringDirectory to keep pipeline Nextflow logs and reports.
string${params.outdir}/pipeline_infoBoolean whether to validate parameters against the schema at runtime
booleantrueShow all params when using --help
booleanRun this workflow with Conda. You can also use '-profile conda' instead of providing this parameter.
boolean