Pipeline parameters

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:

CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,5M2S+T 5M2S+T
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,5M2S+T 5M2S+T
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,5M2S+T 5M2S+T

The read_structure must be the same for all FASTQs from the same sample. Please see the fgbio documentation for detailed information on read structure syntax and formatting.

The number of FASTQs must match the number of read segments in the read structure (a read structure is a space delimited string where each value is a read segment; see: https://github.com/fulcrumgenomics/fgbio/wiki/Read-Structures). E.g. for paired end reads, there must be two FASTQs (R1 and R2) and two segments in the read structure (e.g. a read structure “12M+T +T” specifies a read segment “12M+T” for R1 and read segment “+T” for R2) Additional FASTQs may be provided, for example for index reads (see One to Four FASTQs below).

One to Four FASTQs

The pipeline supports samples that can have between one and four FASTQs (per sample).

It is common for the index reads (the reads that contain the sample barcodes for sample demultiplexing) to be omitted, when they do not contain any UMI or other important sequence (beyond the sample barcode). In this case, only read one (for single-end), or both read one and read two (for paired-end), are usually provided. Additional FASTQs can be provided in the cases where the UMI is present in the index read(s) themselves.

The sample sheet below shows four samples, each with a different number of FASTQs:

  1. CONTROL1 is a single-end run, with one FASTQ (R1), and the UMI inline at the start of the read
  2. CONTROL2 is a paired-end run, with two FASTQs (R1 and R2), and UMIs inline at the start of read one (R1) and read two (R2).
  3. CONTROL3 is a single-indexed paired-end run, with three FASTQs, UMIs inline at the start of read one (R1) and read two, and a sample barcode in I1 (typically index1/i7)
  4. CONTROL3 is a dual-indexed paired-end run, with four FASTQs, read one (R1) and (R2) containing template bases, with a sample barcode in I1 (typically index1/i7), and the UMI in I2 ((typically index2/i5)
CONTROL2,SAMPLE_S1_L001_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,5M2S+T 5M2S+T
CONTROL3,SAMPLE_S1_L001_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,AEG588A1_S1_L002_I1_001.fastq.gz,5M2S+T 5M2S+T 8B
CONTROL4,SAMPLE_S1_L001_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,AEG588A1_S1_L002_I1_001.fastq.gz,AEG588A1_S1_L002_I22_001.fastq.gz,+T +T +B +M

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 four columns to match those defined in the table below.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3 has been sequenced twice.

CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,5M2S+T 5M2S+T
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz,5M2S+T 5M2S+T
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz,5M2S+T 5M2S+T
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,12M+T +T
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,12M+T +T
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,12M+T +T
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,12M+T +T
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”.
fastq_3Full path to FastQ file for Illumina short reads 3 (e.g. index1/i7). File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
fastq_4Full path to FastQ file for Illumina short reads 4 (e.g. index2/i5). File has to be gzipped and have the extension “.fastq.gz” or “.fq.gz”.
read_structurethe read_structure describes how the bases in a sequencing run should be allocated into logical reads, including the unique molecular index(es)

An example samplesheet has been provided with the pipeline.

Main Options

Two modes of running this pipeline are supported:

  1. Research and Development (R&D): use --mode rd or params.mode=rd. This mode is desirable to be able to branch off from the pipeline and test e.g. multiple consensus calling or filtering parameters.
  2. High Throughput (HT): use --mode ht or params.mode=ht. This mode is intended for high throughput production environments where performance and throughput take precedence over flexibility.

For Duplex-Sequencing, use --duplex_seq true or params.duplex_seq=true, indicating that reads from the same source molecule may observe either strand. Otherwise, the pipeline will assume that reads from the same source molecule are from the same strand. Practically speaking, the former will utilize the fgbio CallDuplexConsensusReads tool, while the latter will utilize the fgbio CallMolecularConsensusReads tool.

Grouping Options

These options pertain to the fgbio GroupReadsByUmi tool and are prefixed by groupreadsbyumi_.

The --groupreadsbyumi_strategy option overrides the tool’s --strategy option. By default, the --strategy paired is used when --duplex_seq true, otherwise --strategy adjacency.

:::warning The strategy used must match the library preparation (i.e. Paired for duplex-sequencing, otherwise one of Identity, Edit, or Adjacency).

The groupreadsbyumi_edits option overrides the tool’s --edits option. This provides the maximum number of allowable edits.

Consensus Calling Options

These options pertain to the fgbio CallMolecularConsensusReads and CallDuplexConsensusReads tools and are prefixed by call_. The former tool processes reads from the same strand of the original source molecule, whereas the latter processes reads that originate from either strand of the original source molecule.

The --call_min_reads option provides the minimum read count to call a consensus, while the --call_min_baseq option provides the minimum input base quality to use when calling a consensus. These two options are typically used for the High Throughput mode, matching the same value used in Consensus Filtering.

Consensus Filtering Options

These options pertain to the fgbio FilterConsensusReads tool and are prefixed by filter_.

The --filter_min_reads option provides the minimum read count to call a consensus, while the option --filter_min_baseq provides the minimum input base quality to use when calling a consensus. These two options are typically used for the High Throughput mode, matching the same value used in Consensus Calling. The --filter_min_reads option can accept up to three values for duplex consensus reads. See the tools documentation for how to use this option.

The --filter_max_base_error_rate option sets the maximum error rate for a single consensus base when filtering a consensus.

Reference Genome Options

Please refer to the nf-core website for general usage docs and guidelines regarding reference genomes.

The minimum reference genome requirement for this pipeline is a FASTA. All other files required to run the pipeline can be generated from the input FASTA. For example, the latest reference FASTA for human can be derived from Ensembl like:

latest_release=$(curl -s 'http://rest.ensembl.org/info/software?content-type=application/json' | grep -o '"release":[0-9]*' | cut -d: -f2)
wget -L ftp://ftp.ensembl.org/pub/release-${latest_release}/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz
gunzip Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz

This FASTA can then be specified to the workflow with the --fasta parameter.


By default, BWA indices are generated dynamically by the workflow. Since indexing is an expensive process in time and resources you should ensure that it is only done once, by retaining the indices generated from each batch of reference files:

  • the --save_reference parameter will save your indices in your results directory

Once you have the indices from a workflow run you should save them somewhere central and reuse them in subsequent runs using custom config files or command line parameters:

  • the --fasta parameter specifies the path to the genome FASTA
  • the --dict parameter specifies the path to the genome sequence dictionary (see samtools dict)
  • the --fasta_fai parameter specifies the path to the genome FASTA index (see samtools faidx)
  • the --bwa parameter specifies the path to the directory containing the BWA index

If the --genome parameter is provided (e.g. --genome GRCh38) then the FASTA file will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by --igenomes_base.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/fastquorum --input ./samplesheet.csv --outdir ./results --genome GRCh38 -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
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, E.g. history of pipeline runs and old logs.

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/fastquorum -profile docker -params-file params.yaml

with params.yaml containing:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh38'

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/fastquorum


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


If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments


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, Apptainer, 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, 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
  • 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.


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.


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.

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.


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.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

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'