Introduction Usage Output Results Releases

nf-core/bcellmagic: Usage

Table of contents

General Nextflow info

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/bcellmagic -profile standard,docker --metadata metasheet_test.tsv --cprimers CPrimers.fasta --vprimers VPrimers.fasta --max_memory 8.GB --max_cpus 8

For more information about the parameters, please refer the corresponding sections. 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/bcellmagic

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/bcellmagic releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1.

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

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: -profile standard,docker - the order of arguments is important!

  • standard
    • The default profile, used if -profile is not specified at all.
    • Runs locally and expects all software to be installed and available on the PATH.
  • docker
  • singularity
    • A generic configuration profile to be used with Singularity
    • Pulls software from singularity-hub
  • conda
    • A generic configuration profile to be used with conda
    • Pulls most software from Bioconda
  • awsbatch
    • A generic configuration profile to be used with AWS Batch.
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters
  • none
    • No configuration at all. Useful if you want to build your own config from scratch and want to avoid loading in the default base config profile (not recommended).

Input files

Use this to specify the location of your input files. Three input files are required for running the pipeline: a metadata sheet, the a fasta file containing the primer sequences for the C-region genes (cprimers) and a fasta file containing the primer sequences for the V-region genes (vprimers). This pipeline was originally designed for a special MiSEQ sequencing read setup requiring 3 fastq files: R1, R2, and I1.

  • R1: C-Primer + V(D)J
  • R2: V-Primer + V(D)J
  • I1: Illumina Index + UMI

The pipeline has been expanded to be able to process data where the UMI and index files are incorporated into the R1 read fastq files (see section UMI handling)

--metadata

The metadata file is a TSV file with the following columns, including the exact same headers:

ID Source Treatment Extraction_time Population R1 R2 I1
QMKMK072AD Patient_2 Drug_treatment baseline p sample_S8_L001_R1_001.fastq.gz sample_S8_L001_R2_001.fastq.gz sample_S8_L001_I1_001.fastq.gz

This metadata will then be automatically annotated in a column with the same header in the tables outputed by the pipeline. Where:

  • ID: sample ID.
  • Source: patient or organism code.
  • Treatment: treatment condition applied to the sample.
  • Extraction_time: time of cell extraction for the sample.
  • Population: B-cell population (e.g. naive, double-negative, memory, plasmablast).
  • R1: path to fastq file with first mates of paired-end sequencing.
  • R2: path to fastq file with second mates of paired-end sequencing.
  • I1: path to fastq with illumina index and UMI (unique molecular identifier) barcode.

Specify the location of your metadata file like this:

--metadata 'path/to/metadata/metadata_sheet.tsv'

--vprimers

Path to fasta file containing your V-primer sequences. Specify like this:

--vprimers 'path/to/vprimers.fasta'

--cprimers

Path to fasta file containing your C-primer sequences. Specify like this:

--cprimers 'path/to/cprimers.fasta'

UMI handling

The pipeline requires UMI barcodes for identifying unique transcripts. These barcodes are typically read from an index file but sometimes can be provided merged with the start of the R1 or R2 reads. If provided in an additional index file, set the --index_file parameter, if provided merged with the R1 or R2 reads, set the --umi_position parameter. Specify the UMI barcode length with the --umi_length parameter.

--index_file

Indicate if Illumina indices and UMI barcodes are provided in a separate fastq file (index_file true). If Illumina indices and UMI barcodes are integrated into R1 reads, leave the default --index_file false.

--index_file true

--umi_position

If provided at the start of the R1 or R2 reads, set the --umi_position to R1 (default) or R2:

--umi_position R1

--umi_length

Specify the length of the UMI barcodes:

--umi_length 8

Reference databases

By default, the pipeline will download the needed igblast and IMGT human databases unless the path to already downloaded databases is specified. To specify the paths set the --igblast_base and --imgtdb_base parameters.

--igblast_base

Path to igblast downloaded database. Set as follows:

--igblast_base 'path/to/igblast_base'

--imgtdb_base

Path to imgt downloaded database. Set as follows:

--imgtdb_base 'path/to/imgtdb_base'

Define clones

By default the pipeline will define clones for each of the samples, as two sequences having the same V gene assignment, C gene assignment, J-gene assignment and junction length. Additionally, the similarity of the junction region sequences will be assessed by hamming distances. A distance threshold for determining if two sequences come from the same clone or not is automatically determined by the process shazam. Alternatively, a hamming distance threshold can be manually set by setting the --set_cluster_threshold and --cluster_threshold parameters as follows:

--set_cluster_threshold

Set the --set_cluster_threshold parameter to allow manual cluster hamming distance threshold definition. Then specify the value in the --cluster_threshold parameter as follows:

--set_cluster_threshold --cluster_threshold 0.14

--cluster_threshold

Set the --set_cluster_threshold parameter to allow manual cluster hamming distance threshold definition. Then specify the value in the --cluster_threshold parameter as follows:

--set_cluster_threshold --cluster_threshold 0.14

Downstream analysis

--downstream_only

In some occasions you might just want to run the pipeline for the clonal analysis and repertoire analysis steps. In this case, run the pipeline setting the --downstream_only parameter, and specify the path to your input Change-O tsv table with the parameter --changeo_tables as follows:

--downstream_only --changeo_tables "path/to/changeo/tables/*.tab"

--changeo_tables

In some occasions you might just want to run the pipeline for the clonal analysis and repertoire analysis steps. In this case, run the pipeline setting the --downstream_only parameter, and specify the path to your input Change-O tsv table with the parameter --changeo_tables as follows:

--downstream_only --changeo_tables "path/to/changeo/tables/*.tab"

--skipDownstream

Skip downstream analysis (clonal analysis and repertoire analysis) by setting this flag.

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 the -awsbatch profile and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

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

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.

-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 is set to 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.

--multiqc_config

Specify a path to a custom MultiQC configuration file.
On this page