nf-core/raredisease

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Introduction

nf-core/raredisease is a bioinformatics best-practice analysis pipeline to call, annotate and score variants from WGS/WES of rare disease patients. The pipeline is built using Nextflow.

Prerequisites

1. Install Nextflow (>=24.04.2) using the instructions here.
2. Install one of the following technologies for full pipeline reproducibility: Docker, Singularity, Podman, Shifter or Charliecloud.

   Almost all nf-core pipelines give you the option to use conda as well. However, some tools used in the raredisease pipeline do not have a conda package so we do not support conda at the moment.

Run nf-core/raredisease 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/raredisease \
    -revision dev -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.

Test profile runs the pipeline with a case containing three samples, but if you would like to test the pipeline with one sample, use -profile test_one_sample,<YOURPROFILE>.

Updating the pipeline

The above command downloads the pipeline from GitHub, caches it, and tests it on the test dataset. When you run the command again, it will fetch the pipeline from cache even if a more recent version of the pipeline is available. To make sure that you’re running the latest version of the pipeline, update the cached version of the pipeline by including -latest in the command.

Run nf-core/raredisease with your data

Running the pipeline involves three steps:

1. Prepare a samplesheet
2. Gather all required references
3. Supply samplesheet and references, and run the command

Samplesheet

A samplesheet is used to pass the information about the sample(s), such as the path to the FASTQ files and other meta data (sex, phenotype, etc.,) to the pipeline in csv format.

nf-core/raredisease will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The pedigree information in the samplesheet (sex and phenotype) should be provided as they would be for a ped file (i.e. 1 for male, 2 for female, other for unknown).

It is also possible to include multiple runs of the same sample in a samplesheet. For example, when you have re-sequenced the same sample more than once to increase sequencing depth. In that case, the sample identifiers in the samplesheet have to be the same. The pipeline will align the raw read/read-pairs independently before merging the alignments belonging to the same sample. Below is an example for a trio with the proband sequenced across two lanes:

If you would like to see more examples of what a typical samplesheet looks like for a singleton and a trio, follow these links, singleton and trio.

Reference files and parameters

In nf-core/raredisease, references can be supplied using parameters listed here.

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

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

with:

input: "./samplesheet.csv"
outdir: "./results/"
genome: "GRCh37"

Note that the pipeline is modular in architecture. It offers you the flexibility to choose between different tools. For example, you can align with bwamem2 or bwa or Sentieon BWA mem and call SNVs with either DeepVariant or Sentieon DNAscope. You also have the option to turn off sections of the pipeline if you do not want to run the. For example, snv annotation can be turned off by adding --skip_snv_annotation flag in the command line, or by setting it to true in a parameter file. This flexibility means that in any given analysis run, a combination of tools included in the pipeline will not be executed. So the pipeline is written in a way that can account for these differences while working with reference parameters. If a tool is not going to be executed during the course of a run, parameters used only by that tool need not be provided. For example, for SNV calling if you use DeepVariant as your variant caller, you need not provide the parameter --ml_model, which is only used by Sentieon DNAscope.

nf-core/raredisease consists of several tools used for various purposes. For convenience, we have grouped those tools under the following categories:

1. Alignment (bwamem2/bwa/bwameme/Sentieon BWA mem)
2. QC stats from the alignment files
3. Repeat expansions (ExpansionsHunter & Stranger)
4. Variant calling - SNV (DeepVariant/Sentieon DNAscope)
5. Variant calling - Structural variants (SV) (Tiddit & Manta)
6. Copy number variant calling (GATK’s GermlineCNVCaller)
7. SNV annotation & ranking (rohcall, vcfanno, ensembl VEP, GENMOD)
8. SV annotation & ranking (SVDB query, ensembl VEP, GENMOD)
9. Mitochondrial annotation

We have only listed the groups that require at least one input from the user. For example, the pipeline also runs SMNCopyNumberCaller, but it does not require any input other than the bam files passed by the pipeline. Hence, it is not mentioned in the list above. To know more about the tools used in the pipeline check the README.

The mandatory and optional parameters for each category are tabulated below.

Alignment, QC stats, repeat expansions, SNV & SV variant calling are run by default. Hence, the mandatory parameters used by those features will always have to be provided to the pipeline.

1. Alignment

¹Default value is bwamem2. Other alternatives are bwa, bwameme and sentieon (requires valid Sentieon license ).
²Analysis set reference genome in fasta format, first 25 contigs need to be chromosome 1-22, X, Y and the mitochondria.
³If mito_name is provided, mt_fasta can be generated by the pipeline.
⁴fasta_fai, bwa, bwamem2 and bwameme, if not provided by the user, will be generated by the pipeline when necessary.
⁵Used only by Sentieon.
⁶Default value is 40. Used only by fastp.
⁷Used to limit your analysis to specific contigs. Can be used to remove alignments to unplaced contigs to minimize potential errors. This parameter should be used in conjuction with extract_alignments parameter.

2. QC stats from the alignment files

¹These files are Picard’s style interval list files, comprising the entire genome or only the chromosome Y. A version of these files for GRCh37 and for GRCh38 is supplied in the pipeline assets. These files are not necessary if you are using Sentieon.
² If a target_bed file is provided, bait_intervals and target_intervals can be generated by the pipeline.

3. Repeat expansions

¹ We reccomend using the catalogs found here. These catalogs have been extended from the illumina ones to include information on pathogenicity, which is neccesarry for the workflow.

4. Variant calling - SNV

¹Default variant caller is DeepVariant, but you have the option to use Sentieon as well.
²These parameters are only used by Sentieon.
³Default is WGS, but you have the option to choose WES and mito as well.
⁴This parameter is only used by Deepvariant.

5. Variant calling - Structural variants

6. Copy number variant calling

¹ Output from steps 3 & 4 of GATK’s CNV calling pipeline run in cohort mode as described here.
² Sample file can be found here (Note the header ‘models’ in the sample file).
³ Output from step 1 of GATK’s CNV calling pipeline as described here.
⁴ All these files can be generated using the germlinecnvcaller tool option in nf-core/createpanelrefs.

7. SNV annotation & Ranking

¹Genome version is used by VEP. You have the option to choose between GRCh37 and GRCh38.
²Path to VCF files and their indices used by vcfanno. Sample file here.
³Path to a vcfanno configuration file. Sample file here.
⁴ VEP caches can be downloaded here. VEP plugins may be installed in the cache directory, and the plugin pLI is mandatory to install. To supply files required by VEP plugins, use vep_plugin_files parameter. See example cache here.
⁵ GnomAD VCF files can be downloaded from here. The option gnomad_af expects a tab-delimited file with no header and the following columns: CHROM POS REF_ALLELE ALT_ALLELE AF. Sample file here.
⁶Used by GENMOD for ranking the variants. Sample file here.
⁷File containing list of SO terms listed in the order of severity from most severe to lease severe for annotating genomic and mitochondrial SNVs. Sample file here. You can learn more about these terms here. ⁸A CSV file that describes the files used by VEP’s named and custom plugins. Sample file here.
⁹Used by GENMOD while modeling the variants. Contains a list of loci that show reduced penetrance in people. Sample file here.
¹⁰ This file contains a list of candidate genes (with HGNC IDs) that is used to split the variants into canditate variants and research variants. Research variants contain all the variants, while candidate variants are a subset of research variants and are associated with candidate genes. Sample file here. Not required if —skip_vep_filter is set to true.
¹¹Path to a folder containing cadd annotations. Equivalent of the data/annotations/ folder described here, and it is used to calculate CADD scores for small indels.

8. SV annotation & Ranking

¹ A CSV file that describes the databases (VCFs or BEDPEs) used by SVDB for annotating structural variants. Sample file here. Information about the column headers can be found here. ² File containing list of SO terms listed in the order of severity from most severe to lease severe for annotating genomic SVs. Sample file here. You can learn more about these terms here.

9. Mitochondrial annotation

10. Mobile element calling

¹ A TSV file that describes the references (BEDs) used by RetroSeq for calling mobile elements. Sample file here

11. Mobile element annotation

¹ A CSV file that describes the databases (VCFs) used by SVDB for annotating mobile elements with allele frequencies. Sample file here.

12. Variant evaluation

¹ This parameter is set to false by default, set it to true if if you’d like to run the evaluation subworkflow ² A CSV file that describes the truth VCF files used by RTG Tools’ vcfeval for evaluating SNVs. Sample file here. The file contains four columns samplename,vcf,bedregions,evaluationregions where samplename is the user assigned samplename in the input samplesheet, vcf is the path to the truth vcf file, bedregions and evaluationregions are the path to the bed files that are supposed to be passed through —bed_regions and —evaluation_regions options of vcfeval.

13. Prepare data for CNV visualisation in Gens

Optionally the read data can be prepared for CNV visualization in Gens. This subworkflow is turned off by default. You can activate it by supplying the option --skip_gens false.

¹ Instructions on how to generate the panel of normals can be found here
² Interval list for CollectReadCounts. Instructions on how to generate the interval list file can be found here
³ File containing SNVs to be used for the B-allele frequency calculations. The developers of gens uses SNVs in gnomad with an allele frecuency above 5%.

Run the pipeline

You can directly supply the parameters in the command line (CLI) or use a config file from which the pipeline can import the parameters.

Direct input in CLI

All of the pipeline parameters listed here can be passed using the CLI. For example, you can provide the samplesheet, reference FASTA, and turn off annotations for SNVs and SVs by running,

nextflow run nf-core/raredisease \
    -revision dev \
    -profile test,<YOURPROFILE> \
    --input samplesheet.csv \
    --fasta reference.fasta \
    --skip_snv_annotation \
    --skip_sv_annotation \
    --outdir <OUTDIR>

Import from a config file (recommended)

To input or change the default parameters, we recommend using a config file instead. Create a config file that contains all the parameters and supply that file as shown below.

nextflow run nf-core/raredisease \
    -revision dev \
    -profile test,<YOURPROFILE> \
    -c <YOURCONFIG> \
    --outdir <OUTDIR>

A sample config file can be found here.

Best practices

• Singularity cache: If you are using singularity, use the nf-core download command to download images first, before running the pipeline. Define NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to store and re-use the images from a central location for future pipeline runs.

• Save references: While the pipeline can generate indices for the FASTA and some VCF files when they are not provided, it can be benficial to supply them to the pipeline as it saves computing resources. You can use the --save_reference parameter to publish those files, and then update your config file with the paths to these files for your subsequent runs.

• Update pipeline: If you would like to update the pipeline code stored in cache, in addition to running the command with -latest option, you can also run the command below:

nextflow pull nf-core/raredisease

• Reproducibility: 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/raredisease releases page and find the latest pipeline version - numeric only (e.g. 1.3.1). Then specify this when running the pipeline with -r, for example, -r 1.3.1. You can switch to another version by changing the number after the -r flag. The version number will be logged in reports when you run the pipeline. For example, you can view the version number at the bottom of the MultiQC reports.

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

• Restart a previous run: Add -resume to your command when restarting a pipeline. Nextflow will use cached results from any pipeline steps where inputs are the same, and resume the run from where it terminated previously. For input to be considered the same, names and the files’ contents must be identical. For more info about -resume, 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.

• Reusing parameters: 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>.

⚠️ Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args). The above pipeline run specified with a params file in yaml format:

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

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
input: 'data'
<...>

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

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 see if your system is available in these configs please see the nf-core/configs documentation. {% else %} {% endif %} 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 enviroment.

• 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

Changing resources

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 which container or conda environment a step of the pipeline uses 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 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.

Run Sentieon

To use Sentieon you have to:

1. Ensure that Sentieon executable is in path.
2. If necessary, store license information as a secret in Nextflow’s local store.
3. Set environmental variable NXF_ENABLE_SECRETS to an appropriate value.

To elaborate more on item #2 in the list above, if you are running nf-core/raredisease with Sentieon on AWS or on a local machine and you do not want other users to know your license details, we recommend that you use Nextflow’s secrets feature to pass the that information. To do that run the command below after replacing LICENSE with the value corresponding to your license server (for example, 1.1.1.1:4000)

nextflow secrets set SENTIEON_LICENSE_BASE64 <LICENSE>

If you are using Nextflow secrets, you have to set the environment variable NXF_ENABLE_SECRETS to true. This will see to it that the pipeline can retrieve the secret from Nextflow’s secrets store during the pipeline execution. Keep in mind that versions of Nextflow Version 22.09.2-edge and onwards have NXF_ENABLE_SECRETS to true by default. If you are not using secrets set NXF_ENABLE_SECRETS to false, but make sure that the environment variable SENTIEON_LICENSE is set to reflect the value of your license server on your machine.

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 to 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'

Running the pipeline without Internet access

The pipeline and container images can be downloaded using nf-core tools. For running offline, you of course have to make all the reference data available locally, and specify --fasta, etc., see above.

Contrary to the paragraph about Nextflow on the page linked above, it is not possible to use the “-all” packaged version of Nextflow for this pipeline. The online version of Nextflow is necessary to support the necessary nextflow plugins. Download instead the file called just nextflow. Nextflow will download its dependencies when it is run. Additionally, you need to download the nf-schema plugin explicitly:

./nextflow plugin install nf-schema

Now you can transfer the nextflow binary as well as its directory $HOME/.nextflow to the system without Internet access, and use it there. It is necessary to use an explicit version of nf-schema offline, or Nextflow will check for the most recent version online. Find the version of nf-schema you downloaded in $HOME/.nextflow/plugins, then specify this version for nf-schema in your configuration file:

plugins {
        // Set the plugin version explicitly, otherwise nextflow will look for the newest version online.
        id 'nf-schema@2.1.1'
}

This should go in your Nextflow configuration file, specified with -c <YOURCONFIG> when running the pipeline.