nf-core/rarevariantburden

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Introduction

nf-core/rarevariantburden is a bioinformatics pipeline that performs consistent summary count based rare variant burden test, which is useful when we only have sequenced cases/patients data, no matched control data, here we provided pre-processed and annotated public summary count data, such as gnomAD data, which can be used for rare variant burden test and can be used to identify disease-predisposition genes present in the case study.

Prerequisites

1. Install Nextflow (>=23.10.0) using the instructions here.
2. Install one of the following technologies for full pipeline reproducibility: Docker, Singularity, Podman, Shifter or Charliecloud.

Run nf-core/rarevariantburden with test data

Before running the pipeline with your data, we recommend running it with the test dataset available here. You do not need to download the data as the pipeline is configured to fetch that data automatically for you when you use the test profile.

Run the following command, where YOURPROFILE is the package manager you installed on your machine. For example, -profile test,docker or -profile test,singularity:

nextflow run nf-core/rarevariantburden \
    -profile test,<YOURPROFILE> \
    --outdir <OUTDIR>

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 test,<institute> in your command. This enables the appropriate package manager and sets the appropriate execution settings for your machine. NB: The order of profiles is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

Running the command creates the following files in your working directory:

work                # Directory containing the Nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other Nextflow hidden files, like history of pipeline logs.

Running the pipeline with your data

Running the pipeline involves three steps:

1. Prepare joint called VCF file:

• First, prepare the joint called and VQSR applied VCF file from your case study. You can use nf-core/sarek pipeline’s GATK joint calling module to prepare a joint called and VQSR applied VCF file from your sample VCF files. You also need to prepare a text file containing sample IDs, one sample ID per line.

2. Download control data:

• For control data, you need to download the control data from our Amazon AWS s3 bucket. We provide 3 different control datasets, For build GRCH37, we have gnomAD v2 exome data, for build GRCh38, we have gnomAD v4.1 exome and gnomAD v4.1 genome data as controls.
• As the control data is a huge dataset, it is better to use Amazon AWS command line tool aws-cli to download the data. After installing this, you can use “aws s3” command to list any s3 bucket folder, or download any folder or files from s3. You will find the s3 commands list in here.
• Here are the s3 bucket paths of the 3 gnomAD control datasets:
  • s3://cocorv-resource-files/gnomADv2exome/
  • s3://cocorv-resource-files/gnomADv4.1exome/
  • s3://cocorv-resource-files/gnomADv4.1genome/
• To download the data, you need to run following command:

cd /local-dir-path-where-you-want-download/
aws s3 cp s3://cocorv-resource-files/gnomADv2exome/ . --recursive

• You can check all resource files for our pipeline using this command:

aws s3 ls s3://cocorv-resource-files/

3. Download the annotation tool resources:

• You also need to download the annovar and VEP resource folders for running Annovar and VEP annotation.
• Here are the s3 bucket paths of the annotation tool datasets:
  • s3://cocorv-resource-files/annovarFolder/
  • s3://cocorv-resource-files/vepFolder/

Now, you can run the pipeline using the following command:

nextflow run nf-core/rarevariantburden \
   -profile <docker/singularity/.../institute> \
   --caseJointVCF <jointVCF.vcf.gz> \
   --caseSample <sampleList.txt> \
   --controlDataFolder <controldataFolder> \
   --annovarFoler <annovarFolder> \
   --vepFolder <vepFolder> \
   --build <GRCh37/GRCh38> \
   --gnomADVersion <v2exome/v4exome/v4genome> \
   --outdir <OUTDIR>

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/rarevariantburden -profile docker -params-file params.yaml

with:

caseJointVCF: 'jointVCF.vcf.gz'
caseSample: 'sampleList.txt'
controlDataFolder: 'controldataFolder'
annovarFolder: 'annovarFolder'
vepFolder: 'vepFolder'
build: 'GRCh37'
gnomADVersion: 'v2exome'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

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/rarevariantburden

Reproducibility

It is a good idea to specify the 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/rarevariantburden releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

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. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-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, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

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 check if your system is supported, 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, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

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.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings 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. 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.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'