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'


To access the nextflow help message run: nextflow run -help

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/eager --reads '*_R{1,2}.fastq.gz' --fasta 'some.fasta' -profile standard,docker

Note, that you might need to use -profile standard,singularity if you installed Singularity and don’t want to use Docker. Also make sure, that you specify how much memory is available on your machine by using the --max_cpus, --max_memory options.

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.

To see the the EAGER pipeline help message run: nextflow run nf-core/eager --help

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/eager


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/eager releases page and find the latest version number - numeric only (eg. 2.0). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 2.0.

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.

Mandatory Arguments


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.
  • uzh
    • A profile for the University of Zurich Research Cloud
    • Loads Singularity and defines appropriate resources for running the pipeline.
  • docker
    • A generic configuration profile to be used with Docker
    • Pulls software from dockerhub: nfcore/eager
  • singularity
    • A generic configuration profile to be used with Singularity
    • Pulls software from singularity-hub
  • binac
    • A profile for the BinAC cluster at the University of Tuebingen
    • Loads Singularity and defines appropriate resources for running the pipeline
  • 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).


Use this to specify the location of your input FastQ files. For example:

--reads 'path/to/data/sample_*_{1,2}.fastq'

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The path must have at least one * wildcard character
  3. When using the pipeline with paired end data, the path must use {1,2} notation to specify read pairs.

If left unspecified, a default pattern is used: data/*{1,2}.fastq.gz


If you have single-end data, you need to specify --singleEnd on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for --reads. For example:

--singleEnd --reads '*.fastq'

It is not possible to run a mixture of single-end and paired-end files in one run.


If you have paired-end data, you need to specify --pairedEnd on the command line when you launc hthe pipeline.

A normal glob pattern, enclosed in quotation marks, can then be used for --reads. For example:

--pairedEnd --reads '*.fastq'


If you prefer, you can specify the full path to your reference genome when you run the pipeline:

--fasta '[path to Fasta reference]'

If you don’t specify appropriate --bwa_index, --fasta_index parameters, the pipeline will create these indices for you automatically. Note, that saving these for later has to be turned on using --saveReference.

--genome (using iGenomes)

The pipeline config files come bundled with paths to the illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the AWS-iGenomes resource.

There are 31 different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the --genome flag.

You can find the keys to specify the genomes in the iGenomes config file. Common genomes that are supported are:

  • Human
    • --genome GRCh37
    • --genome GRCh38
  • Mouse
    • --genome GRCm38
  • Drosophila
    • --genome BDGP6
  • S. cerevisiae
    • --genome 'R64-1-1'

There are numerous others - check the config file for more.

Note that you can use the same configuration setup to save sets of reference files for your own use, even if they are not part of the iGenomes resource. See the Nextflow documentation for instructions on where to save such a file.

The syntax for this reference configuration is as follows:

params {
  genomes {
    'GRCh37' {
      fasta   = '<path to the genome fasta file>' // Used if no star index given
    // Any number of additional genomes, key is used with --genome

Optional Reference Utility Files


Use this to specify a previously created BWA index. This saves time in pipeline execution and is especially advised when running multiple times on the same cluster system for example. You can even add a resource specific profile that sets paths to pre-computed reference genomes, saving even time when specifying these.

--seq_dict false

Use this to specify the required sequence dictionary file for the selected reference genome.

--fasta_index false

Use this to specify the required FastA index file for the selected reference genome.

--saveReference false

If you turn this on, the generated indices will be stored in the ./results/reference_genomes for you.

Other command line parameters


The output directory where the results will be saved.


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 speicfy this on the command line for every run.


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)


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)


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 defaults. For example, you can specify a config file using -c that contains the following:

process.$multiqc.module = []


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'


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'


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


Set to receive plain-text e-mails instead of HTML formatted.


Specify a path to a custom MultiQC configuration file.

Adjustable parameters for nf-core/eager

This part of the readme contains a list of user-adjustable parameters in nf-core/eager. You can specify any of these parameters on the command line when calling the pipeline by simply prefixing the respective parameter with a double dash --.


nextflow run nf-core/eager -r 2.0 -profile standard,docker --singleEnd [...]

This would run the pipeline in single end mode, thus assuming that all entered FastQ files are sequenced following a single end sequencing protocol.

General Pipeline Parameters

These parameters are required in some cases, e.g. when performing in-solution SNP capture protocols (390K,1240K, …) for population genetics for example. Make sure to specify the required parameters in such cases.

--snpcapture false

This is by default set to false, but can be turned on to calculate on target metrics automatically for you. Note, that this requires setting --bedfile with the target SNPs simultaneously.


Can be used to set a path to a BED file (3/6 column format) to calculate capture target efficiency on the fly. Will not be used without --bedfile set as parameter.

--udg false

Defines whether Uracil-DNA glycosylase (UDG) treatment was used to repair DNA damage on the sequencing libraries. If set, the parameter is used by downstream tools such as PMDTools to estimate damage only on CpG sites that are left after such a treatment.

--udg_type "Half"

If you have UDGhalf treated data (Rohland et al 2016), specify this parameter additionally to --udg to use a different model for DNA damage assessment in PMDTools.

Step skipping parameters

Some of the steps in the pipeline can be executed optionally. If you specify specific steps to be skipped, there won’t be any output related to these modules.


Turns off the computation of library complexity estimation.


Turns off the DamageProfiler module to compute DNA damage profiles.


Turns off QualiMap and thus does not compute coverage and other mapping metrics.


Turns off duplicate removal methods DeDup and MarkDuplicates respectively. No duplicates will be removed on any data in the pipeline.


Performs a poly-G complexity filtering step in the beginning of the pipeline if turne on. This can be useful for especially assembly projects where low-complexity regions might dramatically influence the assembly of contigs.

Complexity Filtering Options


This option can be used to define the minimum value for the poly-G filtering step in low complexity filtering. By default, this is set to a value of 10 unless the user has chosen something specifically using this option.

Adapter Clipping and Merging Options

These options handle various parts of adapter clipping and read merging steps.


Defines the adapter sequence to be used for the forward read. By default, this is set to AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC.


Defines the adapter sequence to be used for the reverse read in paired end sequencing projects. This is set to AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA by default.

--clip_readlength 30

Defines the minimum read length that is required for reads after merging to be considered for downstream analysis after read merging. Default is 30.

--clip_min_read_quality 20

Defines the minimum read quality per base that is required for a base to be kept. Individual bases at the ends of reads falling below this threshold will be clipped off. Default is set to 20.

--clip_min_adap_overlap 1

Sets the minimum overlap between two reads when read merging is performed. Default is set to 1 base overlap.

Read Mapping Parameters

BWA (default)

These parameters configure mapping algorithm parameters.


Configures the bwa aln -n parameter, defining how many mismatches are allowed in a read. By default set to 0.04, if you’re uncertain what to set check out this Shiny App for more information on how to set this parameter efficiently.


Configures the bwa aln -k parameter for the seeding phase in the mapping algorithm. Default is set to 2.


Configures the length of the seed used in bwa aln -l. Default is set to BWA default of 32.



This turns on the CircularMapper application, that enhances the mapping procedure with the BWA algorithm on circular references utilizing a extend-remap procedure (see Peltzer et al 2016, Genome Biology for details).


The number of bases to extend the reference genome with. By default this is set to 500 if not specified otherwise.


The chromosome in your FastA reference that you’d like to be treated as circular. By default this is set to MT but can be configured to match any other chromosome.


If you want to filter out reads that don’t map to a circular chromosome, turn this on. By default this option is turned off.



Turn this on to utilize BWA Mem instead of bwa aln for alignment. Can be quite useful for modern DNA, but is rarely used in projects for ancient DNA.

Read Filtering and Conversion Parameters

Users can configure to keep/discard/extract certain groups of reads efficiently in the nf-core/eager pipeline.


Defines whether unmapped reads should be discarded and stored in FastQ and/or BAM format separately. The behaviour depends on the choice of the --bam_unmapped_type.


Defines how to proceed with unmapped reads: “discard” removes all unmapped reads, “bam” keeps unmapped reads as BAM file, “fastq” keeps unmapped reads as FastQ file, “both” keeps both BAM and FastQ files. Only effective when option --bam_discard_unmapped is turned on.


Specify a mapping quality threshold for mapped reads to be kept for downstream analysis. By default keeps all reads and is therefore set to 0 (basically doesn’t filter anything).

Read DeDuplication Parameters


Sets the duplicate read removal tool. By default uses dedup an ancient DNA specific read deduplication tool. Users can also specify markdup and use Picard MarkDuplicates instead, which is advised when working with paired end data that is not merged beforehand. In all other cases, it is advised to use dedup.


Sets DeDup to treat all reads as merged reads. This is useful if reads are for example not prefixed with M_ in all cases.

Library Complexity Estimation Parameters


Can be used to configure the step size of Preseqs c_curve method. Can be useful when only few and thus shallow sequencing results are used for extrapolation.

DNA Damage Assessment Parameters


Specifies the length filter for DamageProfiler. By default set to 100.


Specifies the length of the read start and end to be considered for profile generation in DamageProfiler. By default set to 15 bases.


Specifies to run PMDTools for damage based read filtering and assessment of DNA damage in sequencing libraries. By default turned off.


Specifies the range in which to consider DNA damage from the ends of reads. By default set to 10.


Specifies the PMDScore threshold to use in the pipeline when filtering BAM files for DNA damage. Only reads which surpass this damage score are considered for downstream DNA analysis. By default set to 3 if not set specifically by the user.


Can be used to set a reference genome mask for PMDTools.


The maximum number of reads used for damage assessment in PMDtools. Can be used to significantly reduce the amount of time required for damage assessment in PMDTools. Note that a too low value can also obtain incorrect results.

BAM Trimming Parameters

For some library preparation protocols, users might want to clip off damaged bases before applying genotyping methods. This can be done in nf-core/eager automatically by turning on the --trim_bam parameter.


Turns on the BAM trimming method. Trims off [n] bases from reads in the deduplicated BAM file. Damage assessment in PMDTools or DamageProfiler remains untouched, as data is routed through this independently.

--bamutils_clip_left / --bamutils_clip_right

Default set to 1 and clipps off one base of the left or right side of reads. Note that reverse reads will automatically be clipped off at the reverse side with this (automatically reverses left and right for the reverse read).


By default, nf-core/eager uses hard clipping and sets clipped bases to N with quality ! in the BAM output. Turn this on to use soft-clipping instead, masking reads at the read ends respectively using the CIGAR string.