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.

To create a screen session:

screen -R eager2

To disconnect, press ctrl+a then d.

To reconnect, type :

screen -r eager2

to end the screen session while in it type exit.

Automatic Resubmission

By default, if a pipeline step fails, EAGER2 will resubmit the job with twice the amount of CPU and memory. This will occur two times before failing.

Help Message

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

Running the pipeline

Before you start you should change into the output directory you wish your results to go in. When you start the nextflow job, it will place all the log files and ‘working’ folders in the current directory and NOT necessarily the directory the output files will be in.

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

where the reads are from libraries of the same pairing.

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

By default, if a pipeline step fails, EAGER2 will resubmit the job with twice the amount of CPU and memory. This will occur two times before failing.

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

See below for more details about EAGER2 versioning.

Mandatory Arguments


Use this parameter to choose a configuration profile. Profiles can give configuration presets for different computing environments (e.g. schedulers, software environments, memory limits etc). Note that multiple profiles can be loaded, for example: -profile standard,docker - the order of arguments is important! The first entry takes precendence over the others, e.g. if a setting is set by both the first and second profile, the first entry will be used and the second entry ignored.

Important: If running EAGER2 on a cluster - ask your system administrator what profile to use.

For more details on how to set up your own private profile, please see installation.

Basic profiles These are basic profiles which primarily define where you derive the pipeline’s software packages from. These are typically the profiles you would use if you are running the pipeline on your own PC (vs. a HPC cluster - see below).

  • awsbatch
    • A generic configuration profile to be used with AWS Batch.
  • conda
    • Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker or Singularity.
    • A generic configuration profile to be used with conda
    • Pulls most software from Bioconda
  • 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
  • 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).

Institution Specific Profiles These are profiles specific to certain HPC clusters, and are centrally maintained at nf-core/configs. Those listed below are regular users of EAGER2, if you don’t see your own institution here check the nf-core/configs repository.

  • uzh
    • A profile for the University of Zurich Research Cloud
    • Loads Singularity and defines appropriate resources for running the pipeline.
  • binac
    • A profile for the BinAC cluster at the University of Tuebingen
    • Loads Singularity and defines appropriate resources for running the pipeline
  • shh
    • A profiler for the S/CDAG cluster at the Department of Archaeogenetics of the Max Planck Institute for the Science of Human History
    • Loads Singularity and defines appropriate resources for running the pipeline

Pipeline Specific Institution Profiles There are also pipeline-specific institution profiles. I.e., we can also offer a profile which sets special resource settings to specific steps of the pipeline, which may not apply to all pipelines. This can be seen at nf-core/configs under conf/pipelines/eager/.

We currently offer a EAGER specific profile for

  • shh
    • A profiler for the S/CDAG cluster at the Department of Archaeogenetics of the Max Planck Institute for the Science of Human History
    • In addition to the nf-core wide profile, this also sets the MALT resources to match our commonly used databases

Further institutions can be added at nf-core/configs. Please ask the eager developers to add your institution to the list above, if you add one!


Use this to specify the location of your input FastQ (optionally gzipped) or BAM file(s). The files maybe either from a single, or multiple samples. For example:

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

for a single sample, or

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

for multiple samples, where each sample’s FASTQs are in it’s own directory (indicated by the first *).

Please note the following requirements:

  1. Valid file extensions: .fastq.gz, .fastq, .fq.gz, .fq, .bam.
  2. The path must be enclosed in quotes
  3. The path must have at least one * wildcard character
  4. 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

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


If you have single-end data or BAM files, you need to specify --single_end 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:

--single_end --reads 'path/to/data/*.fastq.gz'

for a single sample, or

--single_end --reads 'path/to/data/*/*.fastq.gz'

for multiple samples, where each sample’s FASTQs are in it’s own directory (indicated by the first *)

Note: It is currently 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 --paired_end 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:

--paired_end --reads '*_R{1,2}_*.fastq.gz'

Important: You must always supply a read-grouping with the {1,2} system for each sample when using the --paired_end flag.


Specifies the input file type to --reads is in BAM format. This is only valid in combination with --single_end.


You specify the full path to your reference genome here. The FASTA file can have any file suffix, such as .fasta, .fna, .fa, .FastA etc. You may also supply a gzipped reference files, which will be unzipped automatically for you.

For example:

--fasta '/<path>/<to>/my_reference.fasta'

If you don’t specify appropriate --bwa_index, --fasta_index parameters (see below), the pipeline will create these indices for you automatically. Note that you can save the indices created for you for later by giving the --save_reference flag. You must select either a --fasta or --genome

--genome (using iGenomes)

Alternatively, 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'

* Not bundled with nf-core eager by default.

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 iGenomes genome fasta file>'
    // Any number of additional genomes, key is used with --genome

You must select either a --fasta or --genome

Output Directories


The output directory where the results will be saved.

-w / -work-dir

The output directory where intermediate files will be saved. It is highly recommended that this is the same path as --outdir, otherwise you may ‘lose’ your intermediate files if you need to re-run a pipeline. By default, if this flag is not given, the intermediate files will be saved in a work/ and .nextflow/ directory from wherever you have run EAGER from.

Optional Reference Options

We provide various options for indexing of different types of reference genomes. EAGER can index reference genomes for you (with options to save these for other analysis), but you can also supply your pre-made indices.

Supplying pre-made indices 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.


This parameter is required to be set for large reference genomes. If your reference genome is larger than 3.5GB, the samtools index calls in the pipeline need to generate CSI indices instead of BAI indices to accompensate for the size of the reference genome. This parameter is not required for smaller references (including a human hg19 or grch37/grch38 reference), but >4GB genomes have been shown to need CSI indices. Default: off


Use this if you do not have pre-made reference FASTA indices for bwa, samtools and picard. If you turn this on, the indices EAGER2 generates for you will be stored in the <your_output_dir>/results/reference_genomes for you.


If you want to use pre-existing bwa index indices, please supply the path and file to the FASTA you also specified in --fasta (see above). EAGER2 will automagically detect the index files by searching for the FASTA filename with the corresponding bwa index file suffixes.

For example:

nextflow run nf-core/eager \
-profile test,docker \
--paired_end \
--reads '*{R1,R2}*.fq.gz'
--fasta 'results/reference_genome/bwa_index/BWAIndex/Mammoth_MT_Krause.fasta' \
--bwa_index 'results/reference_genome/bwa_index/BWAIndex/Mammoth_MT_Krause.fasta'

bwa index does not give you an option to supply alternative suffixes/names for these indices. Thus, the file names generated by this command must not be changed, otherwise EAGER2 will not be able to find them.


If you want to use a pre-existing picard CreateSequenceDictionary dictionary file, use this to specify the required .dict file for the selected reference genome.

For example:

--seq_dict 'Mammoth_MT_Krause.dict'


If you want to use a pre-existing samtools faidx index, Use this to specify the required FASTA index file for the selected reference genome. This should be generated by samtools faidx and has a file suffix of .fai

For example:

--fasta_index 'Mammoth_MT_Krause.fasta.fai'

Other run specific parameters


By default, EAGER2 will use the latest version of the pipeline that is downloaded on your system. However, 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.

Additionally, EAGER pipeline releases are named after Swabian German Cities. The first release V2.0 is named “Kaufbeuren”. Future releases are named after cities named in the Swabian league of Cities.


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'. If not specified, will be taken from the configuration in the -profile flag.


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'. If not specified, will be taken from the configuration in the -profile flag.


When not using a instutite specific -profile, you can use this parameter to set a top-limit for the default CPU requirement for each process. This is not the maximum number of CPUs that can be used for the whole pipeline, but the maximum number of CPUs each program can use for each program submission (known as a process).

Do not set this higher than what is available on your workstation or computing node can provide. If you’re unsure, ask your local IT administrator for details on compute node capabilities! Should be a string in the format integer-unit. eg. --max_cpus 1. If not specified, will be taken from the configuration in the -profile flag.


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 specify 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 nextflow config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.


Set to disable colourful command line output and live life in monochrome.


Specify a path to a custom MultiQC configuration file.


Provide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default is set to master.

\#\# Download and use config file with following git commid id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96


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

Adjustable parameters for nf-core/eager

This part of the documentation 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 --.

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 FastQC pre- and post-Adapter Removal, to speed up the pipeline. Use of this flag is most common when data has been previously pre-processed and the post-Adapter Removal mapped reads are being re-mapped to a new reference genome.


Turns off adaptor trimming and paired-end read merging. Equivalent to setting both --skip_collapse and --skip_trim.


Allows you to skip mapping step and go straight downstream to BAM processing steps.


Turns off the computation of library complexity estimation.


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


Turns off the DamageProfiler module to compute DNA damage profiles.


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

BAM Conversion Options


Allows you to convert BAM input back to FASTQ for downstream processing. Note this is required if you need to perform AdapterRemoval and/or polyG clipping.

Complexity Filtering Options

More details on can be seen in the fastp documentation


Performs a poly-G tail removal step in the beginning of the pipeline using fastp, if turned on. This can be useful for trimming ploy-G tails from short-fragments sequenced on two-colour Illumina chemistry such as NextSeqs (where no-fluorescence is read as a G on two-colour chemistry), which can inflate reported GC content values.


This option can be used to define the minimum length of a poly-G tail to begin low complexity trimming. 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.

More details can be seen in the AdapterRemoval documentation


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.


Turns off the paired-end read merging.

For example

--paired_end --skip_collapse  --reads '*.fastq'


Turns off adaptor and quality trimming.

For example:

--paired_end --skip_trim  --reads '*.fastq'


Turns off quality based trimming at the 5p end of reads when any of the —trimns, —trimqualities, or —trimwindows options are used. Only 3p end of reads will be removed.

This also entirely disables quality based trimming of collapsed reads, since both ends of these are informative for PCR duplicate filtering. Described here.


This flag means that only merged reads are sent downstream for analysis. Singletons (i.e. reads missing a pair), or unmerged reads (where there wasn’t sufficient overlap) are discarded. You may want to use this if you want ensure only the best quality reads for your analysis, but with the penalty of potentially losing still valid data (even if some reads have slightly lower quality).

Read Mapping Parameters


Specify which mapping tool to use. Options are BWA aln ('bwaaln'), BWA mem ('bwamem'), circularmapper ('circularmapper'). bwa aln is the default and best for short read ancient DNA. bwa mem can be quite useful for modern DNA, but is rarely used in projects for ancient DNA. CircularMapper enhances the mapping procedure to circular references, using the BWA algorithm but utilizing a extend-remap procedure (see Peltzer et al 2016, Genome Biology for details). Default is ‘bwaaln’

More documentation can be seen for each tool under:

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 0.03, 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.



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.

Mapped Reads Stripping

These parameters are used for removing mapped reads from the original input FASTQ files, usually in the context of uploading the original FASTQ files to a public read archive (NCBI SRA/EBI ENA).

These flags will produce FASTQ files almost identical to your input files, except that reads with the same read ID as one found in the mapped bam file, are either removed or ‘masked’ (every base replaced with Ns).

This functionality allows you to provide other researchers who wish to re-use your data to apply their own adapter removal/read merging procedures, while maintaining anonyminity for sample donors - for example with microbiome research.


Create pre-Adapter Removal FASTQ files without reads that mapped to reference (e.g. for public upload of privacy sensitive non-host data)


Read removal mode. Strip mapped reads completely ('strip') or just replace mapped reads sequence by N ('replace')

Read Filtering and Conversion Parameters

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


Turns on the bam filtering module for either mapping quality filtering or unmapped read treatment.


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

More documentation can be seen in the follow links for:


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 what the maximum misincorporation frequency should be displayed as, in the DamageProfiler damage plot. This is set to 0.30 (i.e. 30%) by default as this matches the popular mapDamage2.0 program. However, the default behaviour of DamageProfiler is to ‘autoscale’ the y-axis maximum to zoom in on any possible damage that may occur (e.g. if the damage is about 10%, the highest value on the y-axis would be set to 0.12). This ‘autoscale’ behaviour can be turned on by specifying the number to 0. Default: 0.30.


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

--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.

--pmd_udg_type `half`

If you have UDGhalf treated data (Rohland et al 2016), specify 'half' as option to this parameter to use a different model for DNA damage assessment in PMDTools. Specify the parameter with 'full' and the DNA damage assessment will use CpG context only. If you don’t specify the parameter at all, the library will be treated as non UDG treated.


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 path to 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.

More documentation can be seen in the bamUtil documentation


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.

Captured Library 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.

Feature Annotation Statistics

If you’re interested in looking at coverage stats for certain features on your reference such as genes, SNPs etc., you can use the following bedtools module for this purpose.

More documentation on bedtools can be seen in the bedtools documentation


Specifies to turn on the bedtools module, producing statistics for breadth (or percent coverage), and depth (or X fold) coverages.


Specify the path to a GFF/BED containing the feature coordinates (or any acceptable input for bedtools coverage). Must be in quotes.

Genotyping Parameters

There are options for different genotypers to be used. We suggest you the documentation of each tool to find the ones that suit your needs.

Documentation for each tool:


Turns on genotyping to run on all post-dedup and downstream BAMs. For example if --run_pmdtools and --trim_bam are both supplied, the genotyper will be run on all three BAM files i.e. post-deduplication, post-pmd and post-trimmed BAM files.


Specifies which genotyper to use. Current options are GATK (v3.5) UnifiedGenotyper or GATK (v4.xx). Furthermore, the FreeBayes Caller is available. Specify 'freebayes', 'hc' or 'ug' respectively.

NB that while UnifiedGenotyper is more suitable for low-coverage ancient DNA (HaplotypeCaller does de novo assembly around each variant site), it is officially deperecated by the Broad Institute and is only accessible by an archived version not properly avaliable on conda. Therefore if specifying ‘ug’, will need to supply a GATK 3.5 -jar to the parameter gatk_ug_jar. Note that this means the pipline is not fully reproducible in this configuration, unless you personally supply the .jar file.


Indicates which BAM file to use for genotyping, depending on what BAM processing modules you have turned on. Options are: 'raw' for mapped only, filtered, or DeDup BAMs (with priority right to left); 'trimmed' (for base clipped BAMs); 'pmd' (for pmdtools output). Default is: 'raw'.


Specify a path to a local copy of a GATK 3.5 .jar file, preferably version ‘3.5-0-g36282e4’. The download location of this may be avaliable from the GATK forums of the Broad Institute.

You must manually report your version of GATK 3.5 in publications/MultiQC as it is not included in our container.


If selected a GATK genotyper phred-scaled confidence threshold of a given SNP/INDEL call. Default: 30


If selected a GATK genotyper, what is the ploidy of your reference organism. E.g. do you want to allow heterozygous calls from >= diploid orgaisms. Default: 2


(Optional)Specify VCF file for output VCF SNP annotation e.g. if you want annotate your VCF file with ‘rs’ SNP IDs. Check GATK documentation for more information. Gzip not accepted.


If selected the GATK genotyper UnifiedGenotyper, what type of VCF to create, i.e. produce calls for every site or just confidence sites. Options: 'EMIT_VARIANTS_ONLY', 'EMIT_ALL_CONFIDENT_SITES', 'EMIT_ALL_SITES'. Default: 'EMIT_VARIANTS_ONLY'


If selected the GATK genotyper HaplotypeCaller, what type of VCF to create, i.e. produce calls for every site or just confidence sites. Options: 'EMIT_VARIANTS_ONLY', 'EMIT_ALL_CONFIDENT_SITES', 'EMIT_ALL_ACTIVE_SITES'. Default: 'EMIT_VARIANTS_ONLY'


If selected GATK UnifiedGenotyper, which likelihood model to follow, i.e. whether to call use SNPs or INDELS etc. Options: 'SNP', 'INDEL', 'BOTH', 'GENERALPLOIDYSNP', 'GENERALPLOIDYINDEL’. Default: 'SNP'


If selected GATK HaplotypeCaller, mode for emitting reference confidence calls. Options: 'NONE', 'BP_RESOLUTION', 'GVCF'. Default: 'GVCF'


Maximum depth coverage allowed for genotyping before downsampling is turned on. Any position with a coverage higher than this value will be randomly downsampled to 250 reads. Default: 250


Specify a value to set base quality scores, if reads are missing this information. Maybe useful if you have ‘synthetically’ generated reads (e.g. chopping up a reference genome). Default is set to -1 which is do not set any default quality (turned off). Default: -1


Specify minimum required supporting observations to consider a variant. Default: 1


Specify to skip over regions of high depth by discarding alignments overlapping positions where total read depth is greater than specified C. Not set by default.


Specify ploidy of sample in FreeBayes. Default is diploid. Default: 2

Consensus Sequence Generation


Turn on concensus sequence genome creation via VCF2Genome. Only accepts GATK UnifiedGenotyper VCF files with the --gatk_ug_out_mode 'EMIT_ALL_SITES' and --gatk_ug_genotype_model 'SNP flags. Typically useful for small genomes such as mitochondria.


The name of your requested output FASTA file. Do not include .fasta suffix.


The name of the FASTA entry you would like in your FASTA file.


Minimum depth coverage for a SNP to be called. Else, a SNP will be called as N. Default: 5


Minimum genotyping quality of a call to be called. Else N will be called. Default: 30


In the case of two possible alleles, the frequency of the majority allele required to be called. Else, a SNP will be called as N. Default: 0.8

Mitochondrial to Nuclear Ratio


Turn on the module to estimate the ratio of mitochondrial to nuclear reads.


Specify the FASTA entry in the reference file specified as --fasta, which acts as the mitochondrial ‘chromosome’ to base the ratio calculation from. The tool only accepts the first section of the header before the first space. The default chromosome name is based on hs37d5/GrCH37 human reference genome. Default: ‘MT’

SNP Table Generation

SNP Table Generation here is performed by MultiVCFAnalyzer. The current version of MultiVCFAnalyzer version only accepts GATK UnifiedGenotyper 3.5 VCF files, and when the ploidy was set to 2 (this allows MultiVCFAnalyzer to look for report frequencies of polymorphic positions). A description of how the tool works can be seen in the Supplementary Information of Bos et al. (2014) under “SNP Calling and Phylogenetic Analysis”.

More can be seen in the MultiVCFAnalyzer documentation


Turns on MultiVCFAnalyzer. Will only work when in combination with UnifiedGenotyper genotyping module.


Specify whether to tell MultiVCFAnalyzer to write within the SNP table the frequencies of the allele at that position e.g. A (70%).


The minimal genotyping quality for a SNP to be considered for processing by MultiVCFAnalyzer. The default threshold is 30.


The minimal number of reads covering a base for a SNP at that psitoin to be considered for processing by MultiVCFAnalyzer. The default depth is 5.


The minimal frequency of a nucleotide for a ‘homozygous’ SNP to be called. In other words, e.g. 90% of the reads covering that position must have that SNP to be called. If the threshold is not reached, and the previous two parameters are matched, a reference call is made (displayed as . in the SNP table). If the above two parameters are not met, an ‘N’ is called. The default allele frequency is 0.9.


The minimum frequency of a nucleotide for a ‘hetereozygous’ SNP to be called. If this parameter is set to the same as --min_allele_freq_hom, then only homozygous calls are made. If this value is less than the previous parameter, then a SNP call will be made if it is between this and the previous parameter and displayed as a IUPAC uncertainty call. Default is 0.9.


If you wish to add to the table previously created VCF files, specify here a path with wildcards (in quotes). These VCF files must be created the same way as your settings for GATK UnifiedGenotyping module above.


If you wish to report in the SNP table annotation information for the regions SNPs fall in. This must be in GFF format and the path must be in quotes.


If you wish to exclude SNP regions from consideration by MultiVCFAnalyzer (such as for problematic regions), provide a file in GFF format (the path must be in quotes).


If you wish to include results from SNPEff effect analysis, supply the output from SNPEff in txt format. The path must be in quotes.

Human Sex Determination

An optional process for human DNA. It can be used to calculate the relative coverage of X and Y chromosomes compared to the autosomes (X-/Y-rate). Standard errors for these measurements are also calculated, assuming a binomial distribution of reads across the SNPs.


Specify to run the optional process of sex determination.


Specify an optional bedfile of the list of SNPs to be used for X-/Y-rate calculation. Running without this parameter will considerably increase runtime, and render the resulting error bars unstrustworthy. Theoretically, any set of SNPs that are distant enough that two SNPs are unlikely to be covered by the same read can be used here. The programme was coded with the 1240K panel in mind. The path must be in quotes.

Human Nuclear Contamination


Specify to run the optional processes for nuclear contamination.


The name of the chromosome X in your bam. 'X' for hs37d5, 'chrX' for HG19. Defaults to 'X'.

Metagenomic Screening

An increasingly common line of analysis in high-throughput aDNA analysis today is simultaenously screening off target reads of the host for endogenous microbial signals - particularly of pathogens. Metagenomic screening is currently offered via MALT with aDNA specific verification via MaltExtract, or Kraken2.

Please note the following:

  • MALT database construction functionality is not included within the pipeline - this should be done independently, prior the EAGER run.
    • To use malt-build from the same version as malt-run, load either the docker, singularity or conda environment.
  • MALT can often require very large computing resources depending on your database. We set a absolute minimum of 16 cores and 128GB of memory (which is 1/4 of the recommendation from the developer). Please leave an issue on the nf-core github if you would like to see this changed.

⚠️ Running MALT on a server with less than 128GB of memory should be performed at your own risk.


Turn on the metagenomic screening module.


Specify which taxonomic classifier to use. There are two options avaliable:

⚠️ Important It is very important to run nextflow clean -f on your nextflow run directory once completed. RMA6 files are VERY large and are copied from a work/ directory into the results folder. You should clean the work directory with the command to ensure non-redundency and large HDD footprints!


Specify the minimum number of reads a given taxon is required to have to be retained as a positive ‘hit’.
For malt, this only applies when --malt_min_support_mode is set to ‘reads’. Default: 1 .


Specify the path to the directory containing your taxonomic classifer’s database (malt or kraken).

For Kraken2, it can be either the path to the directory or the path to the .tar.gz compressed directory of the Kraken2 database.


Specify the minimum percent identity (or similarity) a squence must have to the reference for it to be retained. Default is 85

Only used when --metagenomic_tool malt is also supplied


Use this to run the program in ‘BlastN’, ‘BlastP’, ‘BlastX’ modes to align DNA and DNA, protein and protein, or DNA reads against protein references respectively. respectively. Ensure your database matches the mode. Check the MALT manual for more details. Default: ‘BlastN’

Only when --metagenomic_tool malt is also supplied


Specify what alignment algorithm to use. Options are ‘Local’ or ‘SemiGlobal’. Local is a BLAST like alignment, but is much slower. Semi-global alignment aligns reads end-to-end. Default: ‘SemiGlobal’

Only when --metagenomic_tool malt is also supplied


Specify the top percent value of the LCA algorthim. From the MALT manual: “For each read, only those matches are used for taxonomic placement whose bit disjointScore is within 10% of the best disjointScore for that read.”. Default: 1.

Only when --metagenomic_tool malt is also supplied


Specify whether to use a percentage, or raw number of reads as the value used to decide the minimum support a taxon requires to be retained.

Only when --metagenomic_tool malt is also supplied


Specify the minimum number of reads (as a percentage of all assigned reads) a given taxon is required to have to be retained as a positive ‘hit’ in the RMA6 file. This only applies when --malt_min_support_mode is set to ‘percent’. Default 0.01.

Only when --metagenomic_tool malt is also supplied


Specify the maximum number of alignments a read can have. All further alignments are discarded. Default: 100

Only when --metagenomic_tool malt is also supplied


How to load the database into memory. Options are ‘load’, ‘page’ or ‘map’. ‘load’ directly loads the entire database into memory prior seed look up, this is slow but compatible with all servers/file systems. ‘page’ and ‘map’ perform a sort of ‘chunked’ database loading, allow seed look up prior entire database loading. Note that Page and Map modes do not work properly not with many remote filesystems such as GPFS. Default is ‘load’.

Only when --metagenomic_tool malt is also supplied


Turn on MaltExtract for MALT aDNA characteristics authentication of metagenomic output from MALT.

More can be seen in the MaltExtract documentation

Only when --metagenomic_tool malt is also supplied


Path to a .txt file with taxa of interest you wish to assess for aDNA characteristics. In .txt file should be one taxon per row, and the taxon should be in a valid NCBI taxonomy name format.

Only when --metagenomic_tool malt is also supplied


Path to directory containing containing the NCBI resource tree and taxonomy table files (ncbi.tre and; avaliable at the HOPS repository).

Only when --metagenomic_tool malt is also supplied


Specify which MaltExtract filter to use. This is used to specify what types of characteristics to scan for. The default will output statistics on all alignments, and then a second set with just reads with one C to T mismatch in the first 5 bases. Further details on other parameters can be seen in the HOPS documentation. Options: ‘def_anc’, ‘ancient’, ‘default’, ‘crawl’, ‘scan’, ‘srna’, ‘assignment’. Default: ‘def_anc’.

Only when --metagenomic_tool malt is also supplied


Specify percent of top alignments for each read to be considered for each node. Default: 0.01.

Only when --metagenomic_tool malt is also supplied


Turn off destacking. If left on, a read that overlap with another read will be removed (leaving a depth coverage of 1).

Only when --metagenomic_tool malt is also supplied


Turn off downsampling. By default, downsampling is on and will randomly select 10,000 reads if the number of reads on a node exceeds this number. This is to speed up processing, under the assumption at 10,000 reads the species is a ‘true positive’.

Only when --metagenomic_tool malt is also supplied


Turn off duplicate removal. By default, reads that are an exact copy (i.e. same start, stop coordinate and exact sequence match) will be removed as it is considered a PCR duplicate.

Only when --metagenomic_tool malt is also supplied


Export alignments of hits for each node in BLAST format. By default turned off.

Only when --metagenomic_tool malt is also supplied


Export ‘minimal’ summary files (i.e. without alignments) that can be loaded into MEGAN6. By default turned off.

Only when --metagenomic_tool malt is also supplied


Minimum percent identity alignments are required to have to be reported. Higher values allows fewer mismatches between read and reference sequence, but therefore will provide greater confidence in the hit. Lower values allow more mismatches, which can account for damage and divergence of a related strain/species to the reference. Recommended to set same as MALT parameter or higher. Default: 85.0.

Only when --metagenomic_tool malt is also supplied


Use the best alignment of each read for every statistic, except for those concerning read distribution and coverage. Default: off.

Only when --metagenomic_tool malt is also supplied


Switch damage patterns to single-stranded mode. Default: off.

Only when --metagenomic_tool malt is also supplied

Clean up

Once completed a run has completed, you will have lots of (some very large) intermediate files in your output directory, within the directory named work.

Once you have verified your run completed correctly and everything in the module output directories are present as you expect and need, you can perform a clean up.

Important: Once clean up is completed, you will not be able to re-rerun the pipline from an earlier step and you’ll have to re-run from scratch.

While in your output directory, firstly verify you’re only deleting files stored in work/ with the dry run command:

nextflow clean -n

If you’re ready, you can then remove the files with

nextflow clean -f -k

This will make your system administrator very happy as you will halve the harddrive footprint of the run, so be sure to do this!