radseq is a workflow designed to detect variants from restriction site-associated DNA sequences (RAD-seq). If a reference genome is available this workflow can be used on almost any kind of NGS data set.

radseq is designed to call variants from species with or without a reference genome and can deduplicate reads based on unique moleculor identifier (umi) barcodes.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/radseq --input samplesheet.csv --genome GRCh37 -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.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use the parameter --input to specify its location. It has to be a comma-separated file with 4 columns, and a header row as shown in the examples below.

radseq does not handle duplicate samples in the input samplesheet. All samples must have a unique identifier.

Full samplesheet

The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below. Important the pipeline will group together individuals based on shared characters up to the first number. Therefore it is important to start sample ID’s with a shared character and start the unique identifier with a number.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. Files grouped together will have the prefix sample, like for example, the final vcf will be named sample.vcf.gz.

sampleCustom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
fastq_1Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_2Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
umi_barcodesBoolean variable (true/false) describing describing the presence a of unique moleculor identifier (umi) in the sample.
popDesignated population the sample belongs to.

An example samplesheet has been provided with the pipeline.

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/radseq


It is 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/radseq 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.

Core Nextflow arguments

NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).


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, Conda) - see below. When using Biocontainers, most of these software packaging methods pull Docker containers from e.g FastQC except for Singularity which directly downloads Singularity images via https hosted by the Galaxy project and Conda which downloads and installs software locally from Bioconda.

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 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
    • 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
  • 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 or Charliecloud.
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters


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.


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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the STAR_ALIGN process due to an exit code of 137 this would indicate that there is an out of memory issue:

[62/149eb0] NOTE: Process `RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Error executing process > 'RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)'
Caused by:
    Process `RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137)
Command executed:
    STAR \
        --genomeDir star \
        --readFilesIn WT_REP1_trimmed.fq.gz  \
        --runThreadN 2 \
        --outFileNamePrefix WT_REP1. \
Command exit status:
Command output:
Command error: line 9:  30 Killed    STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. <TRUNCATED>
Work dir:
Tip: you can replicate the issue by changing to the process work dir and entering the command `bash`

To bypass this error you would need to find exactly which resources are set by the STAR_ALIGN process. The quickest way is to search for process STAR_ALIGN in the nf-core/rnaseq Github repo. We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the modules/ directory and so based on the search results the file we want is modules/nf-core/software/star/align/ If you click on the link to that file you will notice that there is a label directive at the top of the module that is set to label process_high. The Nextflow label directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. The default values for the process_high label are set in the pipeline’s base.config which in this case is defined as 72GB. Providing you haven’t set any other standard nf-core parameters to cap the maximum resources used by the pipeline then we can try and bypass the STAR_ALIGN process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. The custom config below can then be provided to the pipeline via the -c parameter as highlighted in previous sections.

process {
    withName: STAR_ALIGN {
        memory = 100.GB

NB: We specify just the process name i.e. STAR_ALIGN in the config file and not the full task name string that is printed to screen in the error message or on the terminal whilst the pipeline is running i.e. RNASEQ:ALIGN_STAR:STAR_ALIGN. You may get a warning suggesting that the process selector isn’t recognised but you can ignore that if the process name has been specified correctly. This is something that needs to be fixed upstream in core Nextflow.

Updating containers

The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the process name and override the Nextflow container definition for that process using the withName declaration. For example, in the nf-core/viralrecon pipeline a tool called Pangolin has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn’t make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via -c custom.config.

  1. Check the default version used by the pipeline in the module file for Pangolin

  2. Find the latest version of the Biocontainer available on

  3. Create the custom config accordingly:

    • For Docker:

      process {
          withName: PANGOLIN {
              container = ''
    • For Singularity:

      process {
          withName: PANGOLIN {
              container = ''
    • For Conda:

      process {
          withName: PANGOLIN {
              conda = 'bioconda::pangolin=3.0.5'

NB: If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the work/ directory otherwise the -resume ability of the pipeline will be compromised and it will restart from scratch.


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'

Lost in parameter space?

How to run in reference or denovo modes?

To run the workflow with no reference genome you must specify --method 'denovo' in your parameters or --method 'reference' in case a reference genome is available.

Pre-processing reads

radseq simultaneously trims UMI-barcodes and low quality reads using fastp.


  • --dont-eval-duplication : save processing speed by not evaluated for read duplication
  • --cut_right : enable cutting from left to right
  • --cut_window_size 25 : window size to measure average quality from
  • --cut_mean_quality 20 : minimum mean quality within the window
  • --correction : enable corrections for paired end data
  • --overlap_diff_limit 1 : allow a minimum overlap difference
  • --trim_front1 : number of base paires to remove in the forward sequence
  • --trim_front2 : number of base pairs to remove in the reverse sequence
  • --trim_polyg : enable the trimming off of poly G tails

How to handle UMI barcodes

In order to reposition UMI tags to the header of the fastq file you must provide additional information to --umi_read_structure [structure] in your parameters.

Denovo parameters

For psuedo-reference construction this version of radseq follows dDocent paper, GitHub

--sequence_type : An acronym describing the type of sequencing method used. Avaiable options include SE, PE, RPE, OL, ROL

--need_to_trim_fastq : perform any read trimming with TRIM_FASTP on reads prior to denovo construction.

--minReadDepth_WithinIndividual : minimum number of reads within an individual to include in psuedo-reference construction

--minReadDepth_BetweenIndividual : minimum number of reads across individuals to include in psuedo-reference construction

--denovo_intermediate_files : save intermediate files generated throughout the denovo construction


  • -g 1 : type of cluster algorithm to deploy
  • -d 100 : description length
  • -c 0.9 : sequence similiarty

rainbow div

  • -f 0.5 : similarity fraction
  • -K 10 : max variants for splitting

rainbow merge

  • -r 2 : minimum number of reads
  • -N 10000 : max number of clusters to merge
  • -R 10000 : max number of reads to assemble
  • -l 20 : minimum read overlap
  • -f 0.75 : minimum similarity fraction

Alignment parameters

You can adjust the aligner in the parameters --aligner : ['bwa','bwa2'], radseq currently supports bwa mem and bwa mem2.

Parameters for bwa/bwa-mem2


  • -L 20,5 : clipping penalty
  • -a : output secondary sequences
  • -M : mark short seqeuences as secondary
  • -T 30 : minimum alignment quality
  • -A 1 : matching score
  • -B 4 : mismatch score
  • -O 6 : gap penalty

samtools view

  • -q 1 : quality score

What does the subworkflow do?

Passes multiple files containing region information for multithreading with freebayes.

The threshold --splitByReadCoverage determines the amount of read depth to split an interval into smaller, 1/2 read-length sized intervals with a default of 500000.

Warning For large sample size analysis or large fastq files, it’s recommended to randomly subset bam file input into subworkflow by passing --subset_intervals_channel [integer] into parameters.

Variant Calling Parameters


  • -m 5 : minimum map quality
  • -q 5 : minimum base quality
  • -E 3 : the complexity gap
  • -n 1 : number of alleles considered
  • -F 10 : minimum fraction of readuces supporting the alternate allele
  • --min-repeat-entropy 1 : requires 1 bit per base of entropy in a haplotype window