Introduction Usage Output Results Releases

Introduction

Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/mhcquant --mzmls '*.mzML' --fasta 'SWISSPROT_12_2018.fasta' --class_1_alleles 'alleles.tsv' --vcf 'variants.vcf' --include_proteins_from_vcf --predict_class_1 -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work            # Directory containing the nextflow working files
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/mhcquant

Reproducibility

It’s a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/mhcquant releases page and find the latest version number - numeric only (eg. 1.3). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future.

Main arguments

--mzmls

Use this to specify the location of your input mzML files. For example:

--mzmls 'path/to/data/*.mzML'

--fasta

If you prefer, you can specify the full path to your fasta input protein database when you run the pipeline:

--fasta '[path to Fasta protein database]'

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Conda) - see below.

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended.

  • docker
  • singularity
  • conda
    • Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker or Singularity.
    • A generic configuration profile to be used with Conda
    • Pulls most software from Bioconda
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

--peptide_min_length

Specify the minimum length of peptides considered after processing

--peptide_max_length

Specify the maximum length of peptides considered after processing

--fragment_mass_tolerance

Specify the fragment mass tolerance used for the comet database search. For High-Resolution instruments a fragment mass tolerance value of 0.02 is recommended. (See the Comet parameter documentation: eg. 0.02)

--precursor_mass_tolerance

Specify the precursor mass tolerance used for the comet database search. For High-Resolution instruments a precursor mass tolerance value of 5ppm is recommended. (eg. 5)

--fragment_bin_offset

Specify the fragment bin offset used for the comet database search. For High-Resolution instruments a fragment bin offset of 0 is recommended. (See the Comet parameter documentation: eg. 0)

--use_a_ions

Include a ions into the peptide spectrum matching

--use_c_ions

Include c ions into the peptide spectrum matching

--use_x_ions

Include x ions into the peptide spectrum matching

--use_z_ions

Include z ions into the peptide spectrum matching

--fdr_threshold

Specify the false discovery rate threshold at which peptide hits should be selected. (eg. 0.01)

--fdr_level

Specify the level at which the false discovery rate should be computed. ‘peptide-level-fdrs’ is recommended. (‘peptide-level-fdrs’, ‘psm-level-fdrs’, ‘protein-level-fdrs’)

--number_mods

Specify the maximum number of modifications that should be contained in a peptide sequence match. (eg. 3)

--num_hits

Specify the number of hits that should be reported for each spectrum. (eg. 1)

--digest_mass_range

Specify the mass range that peptides should fullfill to be considered for peptide spectrum matching. (eg. 800:2500)

--pick_ms_levels

If one ms level in the raw ms data is not centroided, specify the level here. (eg. 2)

--run_centroidisation

Choose whether the specified ms_level in pick_ms_levels is centroided or not. (“True”, “False”)

--prec_charge

Specifiy the precursor charge range that peptides should fullfill to be considered for peptide spectrum matching. (eg. “2:3”)

--activation method

Specify which fragmentation method was used in the MS acquisition (‘ALL’, ‘CID’, ‘ECD’, ‘ETD’, ‘PQD’, ‘HCD’, ‘IRMPD’)

--enzyme

Specify which enzymatic restriction should be applied (‘unspecific cleavage’, ‘Trypsin’, see OpenMS enzymes)

--fixed_mods

Specify which fixed modifications should be applied to the database search (eg. ” or ‘Carbamidomethyl (C)’, see OpenMS modifications)

--variable_mods

Specify which variable modifications should be applied to the database search (eg. ‘Oxidation (M)’, see OpenMS modifications)

Multiple fixed or variable modifications can be specified comma separated (e.g. ‘Carbamidomethyl (C),Oxidation (M)‘)

--max_rt_alignment_shift

Set a maximum retention time shift for the linear rt alignment

--spectrum_batch_size

Size of Spectrum batch for Comet processing (Decrease/Increase depending on Memory Availability)

--skip_decoy_generation

If you want to use your own decoys, you can specify a databaset that includes decoy sequences. However, each database entry should keep the prefix ‘DECOY_’. One should consider though that this option will then prevent to append variants to the database and if not using reversed decoys the subset refinement FDR option will not work.

--quantification_fdr

Set this option to assess and assign quantification of peptides with an FDR measure (Weisser H. and Choudhary J.S. J Proteome Res. 2017 Aug 4)

--quantification_min_prob

Specify a cut off probability value for quantification events as a filter

--predict_RT

Set this option to predict times of all identified peptides and possible neoepitopes based on high scoring ids

Optional binding prediction

--predict_class_1

Set flag depending on whether MHC class 1 binding predictions using the tool mhcflurry should be run. Check whether your alleles are supported by mhcflurry

--predict_class_2

Set flag depending on whether MHC class 2 binding predictions using the tool mhcnugget should be run. Check whether your alleles are supported by mhcnugget

--refine_fdr_on_predicted_subset

Set to ‘True’ or ‘False’ depending on whether binding predictions using the tool mhcflurry should be used to subset all PSMs not passing the q-value threshold. If specified the FDR will be refined using Percolator on the subset of predicted binders among all PSMs resulting in an increased identification rate. (Please be aware that this option is only available for MHC class I data of alleles that are supported by mhcflurry)

--affinity_threshold_subset

Affinity threshold (nM) used to define binders for PSM subset selection in the fdr refinement procedure (eg. 500)

--class_1_alleles

Specify a .tsv file containing the MHC class 1 alleles of your probes. (line separated)

--class_2_alleles

Specify a .tsv file containing the MHC class 2 alleles of your probes. (line separated)

Optional variant translation

--include_proteins_from_vcf

Set to ‘True’ or ‘False’ depending on whether variants should be translated to proteins and included into your fasta for database search.

--vcf

Specify a .vcf file containing the information about genomic variants (vcf < v.4.2).

--variant_annotation_style

Specify style of tool used for variant annotation - currently supported: “SNPEFF”, “VEP”, “ANNOVAR”

--variant_reference

Specify genomic reference used for variant annotation: “GRCH37”, “GRCH38”

--variant_indel_filter

Specify whether insertions and deletions should not be considered for variant translation

--variant_frameshift_filter

Specify whether frameshifts should not be considered for variant translation

--variant_snp_filter

Specify whether snps should not be considered for variant translation

Job resources

Automatic resubmission

Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at nf-core/configs for examples.

If you are likely to be running nf-core pipelines regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter (see definition below). You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

If you have any questions or issues please send us a message on Slack.

AWS Batch specific parameters

Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use -profile awsbatch and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

The AWS region in which to run your job. Default is set to eu-west-1 but can be adjusted to your needs.

--awscli

The AWS CLI path in your custom AMI. Default: /home/ec2-user/miniconda/bin/aws.

Please make sure to also set the -w/--work-dir and --outdir parameters to a S3 storage bucket of your choice - you’ll get an error message notifying you if you didn’t.

Other command line parameters

--outdir

The output directory where the results will be saved.

--email

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.

--email_on_fail

This works exactly as with --email, except emails are only sent if the workflow is not successful.

-name

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

NB: Single hyphen (core Nextflow option)

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

NB: Single hyphen (core Nextflow option)

-c

Specify the path to a specific config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.

--custom_config_version

Provide 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 commid id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96

--custom_config_base

If 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 download command to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.

--max_memory

Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. --max_memory '8.GB'

--max_time

Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. --max_time '2.h'

--max_cpus

Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. --max_cpus 1

--plaintext_email

Set to receive plain-text e-mails instead of HTML formatted.

--monochrome_logs

Set to disable colourful command line output and live life in monochrome.
On this page