Sarek is a workflow designed to detect germline and somatic variants on whole genome, whole exome, or targeted sequencing data.

Initially designed for human and mouse, it can work on any species if a reference genome is available. Sarek is designed to handle single samples, such as single-normal or single-tumor samples, and tumor-normal pairs including additional relapses.

Running the pipeline


The typical command for running the pipeline is as follows:

nextflow run nf-core/sarek --input samplesheet.csv  --outdir <OUTDIR> -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.

Input: Sample sheet configurations

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 at least 3 columns, and a header row as shown in the examples below.

It is recommended to use the absolute path of the files, but a relative path should also work.

If necessary, a tumor sample can be associated to a normal sample as a pair, if specified with the same patient ID, a different sample, and the respective status. An additional tumor sample (such as a relapse for example), can be added if specified with the same patient ID, a different sample, and the status value 1.

Sarek will output results in a different directory for each sample. If multiple samples IDs are specified in the CSV file, Sarek will consider all files to be from different samples.

Output from Variant Calling and/or Annotation will be in a specific directory for each sample and tool configuration (or normal/tumor pair if applicable).

Multiple CSV files can be specified if the path is enclosed in quotes.

--input '[path to sample sheet file(s)]'

Overview: Samplesheet Columns

patientCustom patient ID; designates the patient/subject; must be unique for each patient, but one patient can have multiple samples (e.g. normal and tumor).
sexSex chromosomes of the patient; i.e. XX, XY…, only used for Copy-Number Variation analysis in a tumor/pair
Optional, Default: NA
statusNormal/tumor status of sample; can be 0 (normal) or 1 (tumor).
Optional, Default: 0
sampleCustom sample ID for each tumor and normal sample; more than one tumor sample for each subject is possible, i.e. a tumor and a relapse; samples can have multiple lanes for which the same ID must be used to merge them later (see also lane). Sample IDs must be unique for unique biological samples
laneLane ID, used when the sample is multiplexed on several lanes. Must be unique for each lane in the same sample (but does not need to be the original lane name), and must contain at least one character
Required for --step mapping
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.
bamFull path to (u)BAM file
baiFull path to BAM index file
cramFull path to CRAM file
craiFull path to CRAM index file
tableFull path to recalibration table file
vcfFull path to vcf file

An example samplesheet has been provided with the pipeline.

Start with mapping (--step mapping [default])

This step can be started either from FastQ files or (u)BAMs. The CSV must contain at least the columns patient, sample, lane, and either fastq_1/fastq_2 or bam.


Minimal config file:


In this example, the sample is multiplexed over three lanes:


Full samplesheet

In this example, all possible columns are used. There are three lanes for the normal sample, two for the tumor sample, and one for the relapse sample, including the sex and status information per patient:


Start with duplicate marking (--step markduplicates)

Duplicate Marking

For starting from duplicate marking, the CSV file must contain at least the columns patient, sample, bam, bai or patient, sample, cram, crai

NB: When using GATK4 MarkduplicatesSpark reads should be name-sorted for efficient execution



The Sarek-generated CSV file is stored under results/csv/mapped.csv if in a previous run --save_bam_mapped was set and will automatically be used as an input when specifying the parameter --step markduplicates. Otherwise this file will need to be manually generated.

Full samplesheet

In this example, all possible columns are used including the sex and status information per patient:


Start with preparing the recalibration tables (--step prepare_recalibration)

For starting directly from preparing the recalibration tables, the CSV file must contain at least the columns patient, sample, bam, bai or patient, sample, cram, crai.



The Sarek-generated CSV file is stored under results/csv/markduplicates_no_table.csv and will automatically be used as an input when specifying the parameter --step prepare_recalibration.

Full samplesheet

In this example, all possible columns are used including the sex and status information per patient:


Start with base quality score recalibration (--step recalibrate)

For starting from base quality score recalibration the CSV file must contain at least the columns patient, sample, bam, bai, table or patient, sample, cram, crai, table containing the paths to non-recalibrated CRAM/BAM files and the associated recalibration table.



The Sarek-generated CSV file is stored under results/csv/markduplicates.csv and will automatically be used as an input when specifying the parameter --step recalibrate.

Full samplesheet

In this example, all possible columns are used including the sex and status information per patient:


Start with variant calling (--step variant_calling)

For starting from the variant calling step, the CSV file must contain at least the columns patient, sample, bam, bai or patient, sample, cram, crai.



The Sarek-generated CSV file is stored under results/csv/recalibrated.csv and will automatically be used as an input when specifying the parameter --step variant_calling.

Full samplesheet

In this example, all possible columns are used including the sex and status information per patient:


Start with annotation (--step annotate)

For starting from the annotation step, the CSV file must contain at least the columns patient, sample, vcf.

As Sarek will use bgzip and tabix to compress and index the annotated VCF files, it expects the input VCF files to be sorted and compressed.



The Sarek-generated CSV file is stored under results/csv/variantcalled.csv and will automatically be used as an input when specifying the parameter --step annotation.

Full samplesheet

In this example, all possible columns are used including the variantcaller information per sample:


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/sarek


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/sarek releases page and find the latest version number - numeric only (eg. 3.0.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 3.0.1.

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 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.

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running 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).

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 `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1)
Caused by:
    Process `NFCORE_RNASEQ: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 {
        memory = 100.GB

NB: We specify the full process name i.e. NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN in the config file because this takes priority over the short name (STAR_ALIGN) and allows existing configuration using the full process name to be correctly overridden.

If you get a warning suggesting that the process selector isn’t recognised check that the process name has been specified correctly.

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.

Troubleshooting & FAQ

How to test the pipeline

When using default parameters only, sarek runs preprocessing and exits after base quality score recalibration. This is reflected in the default test profile:

nextflow run nf-core/sarek -r 3.0.1 -profile test,<container/institute>

Expected run output:

[91/018ca5] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:BWAMEM1_INDEX (genome.fasta)                       [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:BWAMEM2_INDEX                                      -
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:DRAGMAP_HASHTABLE                                  -
[45/7ad672] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:GATK4_CREATESEQUENCEDICTIONARY (genome.fasta)      [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:MSISENSORPRO_SCAN                                  -
[79/7139ec] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:SAMTOOLS_FAIDX (genome.fasta)                      [100%] 1 of 1 ✔
[44/913bf9] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:TABIX_DBSNP (dbsnp_146.hg38.vcf)                   [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:TABIX_GERMLINE_RESOURCE                            -
[dc/348c16] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:TABIX_KNOWN_INDELS (mills_and_1000G.indels.vcf)    [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_GENOME:TABIX_PON                                          -
[9f/53d6ad] process > NFCORE_SAREK:SAREK:PREPARE_INTERVALS:CREATE_INTERVALS_BED (genome.interval_list)     [100%] 1 of 1 ✔
[57/a9312f] process > NFCORE_SAREK:SAREK:PREPARE_INTERVALS:GATK4_INTERVALLISTTOBED (genome)                [100%] 1 of 1 ✔
[7e/b02b16] process > NFCORE_SAREK:SAREK:PREPARE_INTERVALS:TABIX_BGZIPTABIX_INTERVAL_SPLIT (chr22_1-40001) [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:ALIGNMENT_TO_FASTQ_INPUT:COLLATE_FASTQ_UNMAP                      -
[-        ] process > NFCORE_SAREK:SAREK:ALIGNMENT_TO_FASTQ_INPUT:COLLATE_FASTQ_MAP                        -
[-        ] process > NFCORE_SAREK:SAREK:ALIGNMENT_TO_FASTQ_INPUT:CAT_FASTQ                                -
[37/2d4ea9] process > NFCORE_SAREK:SAREK:RUN_FASTQC:FASTQC (test-test_L1)                                  [100%] 1 of 1 ✔
[a1/a64d09] process > NFCORE_SAREK:SAREK:GATK4_MAPPING:BWAMEM1_MEM (test)                                  [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:GATK4_MAPPING:BWAMEM2_MEM                                         -
[-        ] process > NFCORE_SAREK:SAREK:GATK4_MAPPING:DRAGMAP_ALIGN                                       -
[d3/488df3] process > NFCORE_SAREK:SAREK:MARKDUPLICATES:GATK4_MARKDUPLICATES (test)                        [100%] 1 of 1 ✔
[f1/0b56c6] process > NFCORE_SAREK:SAREK:MARKDUPLICATES:BAM_TO_CRAM:SAMTOOLS_BAMTOCRAM (test)              [100%] 1 of 1 ✔
[ae/e92179] process > NFCORE_SAREK:SAREK:MARKDUPLICATES:BAM_TO_CRAM:SAMTOOLS_STATS_CRAM (test)             [100%] 1 of 1 ✔
[8f/d06f35] process > NFCORE_SAREK:SAREK:MARKDUPLICATES:BAM_TO_CRAM:MOSDEPTH (test)                        [100%] 1 of 1 ✔
[38/af6ec2] process > NFCORE_SAREK:SAREK:PREPARE_RECALIBRATION:BASERECALIBRATOR (test)                     [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:PREPARE_RECALIBRATION:GATHERBQSRREPORTS                           -
[8b/f3ca07] process > NFCORE_SAREK:SAREK:RECALIBRATE:APPLYBQSR (test)                                      [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:RECALIBRATE:MERGE_INDEX_CRAM:MERGE_CRAM                           -
[a7/16bb3f] process > NFCORE_SAREK:SAREK:RECALIBRATE:MERGE_INDEX_CRAM:INDEX_CRAM (test)                    [100%] 1 of 1 ✔
[4d/309cb9] process > NFCORE_SAREK:SAREK:CRAM_QC:SAMTOOLS_STATS (test)                                     [100%] 1 of 1 ✔
[44/06eaf2] process > NFCORE_SAREK:SAREK:CRAM_QC:MOSDEPTH (test)                                           [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:SAMTOOLS_CRAMTOBAM_RECAL                                          [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:CUSTOM_DUMPSOFTWAREVERSIONS                                       [100%] 1 of 1 ✔
[-        ] process > NFCORE_SAREK:SAREK:MULTIQC                                                           [100%] 1 of 1 ✔

The pipeline comes with a number of possible paths and tools that can be used. The easiest and fastest test to see that the preprocessing + variantcalling (in this case Strelka2) works, is to run:

nextflow run nf-core/sarek -r 3.0.1 -profile test,<container/institute> --tools strelka

Due to the small test data size, unfortunately not everything can be tested from top-to-bottom, but often is done by utilizing the pipeline’s --step parameter. Annotation has to tested separatly from the remaining workflow, since we use references for C.elegans, while the remaining tests are run on downsampled human data.

nextflow run nf-core/sarek -r 3.0.1 -profile test,<container/institute> --tools snpeff --step annotation

If you are interested in any of the other tests that are run on every code change or would like to run them yourself, you can take a look at tests/<filename>.yml. For each entry the respective nextflow command run and the expected output is specified.

Some of the currently, available test profiles:

Test profileRun command
annotationnextflow run -profile test,annotation,docker --tools snpeff.vep,merge
no_intervalsnextflow run -profile test,no_intervals,docker
targetednextflow run -profile test,targeted,docker
tools_germlinenextflow run -profile test,tools_germline,docker --tools strelka
tools_tumoronlynextflow run -profile test,tools_tumoronly,docker --tools strelka
tools_somaticnextflow run -profile test,tools_somatic,docker --tools strelka
trimmingnextflow run -profile test,trim_fastq,docker
uminextflow run -profile test,umi,docker
use_gatk_sparknextflow run -profile test,use_gatk_spark,docker

How can the different steps be used

Sarek can be started at different points in the analysis by setting the parameter --step. Once started at a certain point, the pipeline runs through all the following steps without additional intervention. For example when starting from --step mapping (set by default) and --tools strelka,vep, the input reads will be aligned, duplicate marked, recalibrated, variant called with Strelka, and finally VEP will annotate the called variants.

Which variant calling tool is implemented for which data type?

This list is by no means exhaustive and it will depend on the specific analysis you would like to run. This is a suggestion based on the individual docs of the tools specifically for human genomes and a garden-variety sequencing run as well as what has been added to the pipeline.

ToolWGSWES Panel NormalTumorSomatic
GATK HaplotypeCallerxxxx--
GATK Mutect2xxx-xx

How to run ASCAT with whole-exome sequencing data?

While the ASCAT implementation in sarek is capable of running with whole-exome sequencing data, the needed references are currently not provided with the igenomes.config. According to the developers of ASCAT, loci and allele files (one file per chromosome) can be downloaded directly from the Battenberg repository.

The GC correction file needs to be derived, so one has to concatenate all chromosomes into a single file and modify the header so it fits this example.

The RT correction file is missing for hg38 but can be derived using ASCAT scripts for hg19. For hg38, one needs to lift-over hg38 to hg19, run the script on hg19 positions and set coordinates back to hg38.

Please note that:

Row names (for GC and RT correction files) should be ${chr}_${position} (there is no SNP/probe ID for HTS data). ASCAT developers strongly recommend using a BED file for WES/TS data. This prevents considering SNPs covered by off-targeted reads that would add noise to log/BAF tracks.

What are the bwa/bwa-mem2 parameters?

For mapping, sarek follows the parameter suggestions provided in this paper:

-K 100000000 : for deterministic pipeline results, for more info see here

-Y: force soft-clipping rather than default hard-clipping of supplementary alignments

In addition, currently the mismatch penalty for reads with tumor status in the sample sheet are mapped with a mismatch penalty of -B 3.

How to create a panel-of-normals for Mutect2

For a detailed tutorial on how to create a panel-of-normals, see here.

If you have problems running processes that make use of Spark such as MarkDuplicates. You are probably experiencing issues with the limit of open files in your system. You can check your current limit by typing the following:

ulimit -n

The default limit size is usually 1024 which is quite low to run Spark jobs. In order to increase the size limit permanently you can:

Edit the file /etc/security/limits.conf and add the lines:

*     soft   nofile  65535
*     hard   nofile  65535

Edit the file /etc/sysctl.conf and add the line:

fs.file-max = 65535

Edit the file /etc/sysconfig/docker and add the new limits to OPTIONS like this:

OPTIONS=”—default-ulimit nofile=65535:65535"

Re-start your session.

Note that the way to increase the open file limit in your system may be slightly different or require additional steps.

Cannot delete work folder when using docker + Spark

Currently, when running spark-based tools in combination with docker, it is required to set docker.userEmulation = false. This can unfortunately causes permission issues when work/ is being written with root permissions. In case this happens, you might need to configure docker to run without userEmulation (see here).

How to handle UMIs

Sarek can process UMI-reads, using fgbio tools.

In order to use reads containing UMI tags as your initial input, you need to include --umi_read_structure [structure] in your parameters.

This will enable pre-processing of the reads and UMI consensus reads calling, which will then be used to continue the workflow from the mapping steps. For post-UMI processing depending on the experimental setup, duplicate marking and base quality recalibration can be skipped with [--skip_tools].

UMI Read Structure

This parameter is a string, which follows a convention to describe the structure of the umi.

As an example: if your reads contain a UMI only on the forward read, the string can only represent one structure (i.e. “2M11S+T”); should your reads contain a UMI on both reas, the string will contain two structures separated by a blank space (i.e. “2M11S+T 2M11S+T”); should your reads contain a UMI only on the reverse read, your structure must represent the template only for the forward read and template plus UMI for the reverse read (i.e. +T 12M11S+T). Please do refer to FGBIO documentation for more details, as providing the correct structure is essential and specific to the UMI kit used.

Limitations and future updates

Recent updates to Samtools have been introduced, which can speed-up performance of fgbio tools used in this workflow. The current workflow does not handle duplex UMIs (i.e. where opposite strands of a duplex molecule have been tagged with a different UMI), and best practices have been proposed to process this type of data. Both changes will be implemented in a future release.

How to run sarek when no(t all) reference files are in igenomes

For common genomes, such as GRCh38 and GRCh37, the pipeline is shipped with (almost) all necessary reference files. However, sometimes it is necessary to use custom references for some or all files:

No igenomes reference files are used

If none of your required genome files are in igenomes, --igenomes_ignore must be set to ignore any igenomes input and --genome null. The fasta file is the only required input file and must be provided to run the pipeline. All other possible reference file can be provided in addition. For details, see the paramter documentation.

Minimal example for custom genomes:

nextflow run nf-core/sarek --genome null --igenomes_ignore --fasta <custom.fasta>

Overwrite specific reference files

If you don’t want to use some of the provided reference genomes, they can be overwritten by either providing a new file or setting the respective file parameter to false, if it should be ignored:

Example for using a custom known indels file:

nextflow run nf-core/sarek --known_indels <my_known_indels.vcf.gz> --genome GRCh38.GATK

Example for not using known indels, but all other provided reference file:

nextflow run nf-core/sarek --known_indels false --genome GRCh38.GATK

Where do the used reference genomes originate from

For GATK.GRCh38 the links for each reference file and the corresponding processes that use them is listed below. For GATK.GRCh37 the files originate from the same sources:

bwabwa-membwa index -p bwa/fasta.baseName{fasta.baseName} fasta
bwamem2bwa-mem2bwa-mem2 index -p bwamem2/fasta{fasta} fasta
dragmapDragMapdragen-os —build-hash-table true —ht-reference $fasta —output-directory dragmap
dbsnpBaserecalibrator, ControlFREEC, GenotypeGVCF, HaplotypeCallerGATKBundle
dbsnp_tbiBaserecalibrator, ControlFREEC, GenotypeGVCF, HaplotypeCallerGATKBundle
dictBaserecalibrator(Spark), CNNScoreVariant, EstimateLibraryComplexity, FilterMutectCalls, FilterVariantTranches, GatherPileupSummaries,GenotypeGVCF, GetPileupSummaries, HaplotypeCaller, MarkDulpicates(Spark), MergeVCFs, Mutect2, VariantrecalibratorGATKBundle
fastaApplyBQSR(Spark), ApplyVQSR, ASCAT, Baserecalibrator(Spark), BWA, BWAMem2, CNNScoreVariant, CNVKit, ControlFREEC, DragMap, DEEPVariant, EnsemblVEP, EstimateLibraryComplexity, FilterMutectCalls, FilterVariantTranches, FreeBayes, GatherPileupSummaries,GenotypeGVCF, GetPileupSummaries, HaplotypeCaller, interval building, Manta, MarkDuplicates(Spark),MergeVCFs,MSISensorPro, Mutect2, Samtools, snpEff, Strelka, Tiddit, VariantrecalibratorGATKBundle
fasta_faiApplyBQSR(Spark), ApplyVQSR, ASCAT, Baserecalibrator(Spark), BWA, BWAMem2, CNNScoreVariant, CNVKit, ControlFREEC, DragMap, DEEPVariant, EnsemblVEP, EstimateLibraryComplexity, FilterMutectCalls, FilterVariantTranches, FreeBayes, GatherPileupSummaries,GenotypeGVCF, GetPileupSummaries, HaplotypeCaller, interval building, Manta, MarkDuplicates(Spark),MergeVCFs,MSISensorPro, Mutect2, Samtools, snpEff, Strelka, Tiddit, VariantrecalibratorGATKBundle
intervalsApplyBQSR(Spark), ASCAT, Baserecalibrator(Spark), BCFTools, CNNScoreVariants, ControlFREEC, Deepvariant, FilterVariantTranches, FreeBayes, GenotypeGVCF, GetPileupSummaries, HaplotypeCaller, Strelka, mpileup, MSISensorPro, Mutect2, VCFToolsGATKBundle
known_indelsBaseRecalibrator(Spark), FilterVariantTranchesGATKBundle
known_indels_tbiBaseRecalibrator(Spark), FilterVariantTranchesGATKBundle
known_snpsBaseRecalibrator(Spark), FilterVariantTranches, VariantRecalibratorGATKBundle
known_snps_tbiBaseRecalibrator(Spark), FilterVariantTranches, VariantRecalibratorGATKBundle

How to customise SnpEff and VEP annotation

Sarek uses nf-core provided containers for both snpEff and VEP for several reference genomes (‘CanFam3’, ‘GRCh37’, ‘GRCh38’, ‘GRCm38’ and ‘WBcel235’).

Using downloaded cache

Both snpEff and VEP enable usage of cache, if no pre-build container is available. The cache needs to be made available on the machine where Sarek is run. You need to specify the cache directory using --snpeff_cache and --vep_cache in the command lines or within configuration files.


nextflow run nf-core/sarek --tools snpEff --step annotate --sample <file.vcf.gz> --snpeff_cache </path/to/snpEff/cache>
nextflow run nf-core/sarek --tools VEP --step annotate --sample <file.vcf.gz> --vep_cache </path/to/VEP/cache>

Similarly, when wanting to use a different cache than the one specified in the iGenomes config file, one can use --snpeff_db, --snpeff_genome, --snpeff_version, --vep_cache_version, --vep_genome, --vep_species and --vep_version to overwrite these default value related to the databases, genomes, versions and caches’ versions used by these tools.

Using VEP plugins


Enable with --vep_dbnsfp. The following parameters are mandatory:

  • --dbnsfp, to specify the path to the dbNSFP processed file.
  • --dbnsfp_tbi, to specify the path to the dbNSFP tabix indexed file.

The following parameters are optionnal:

  • --dbnsfp_consequence, to filter/limit outputs to a specific effect of the variant.
    • The set of consequence terms is defined by the Sequence Ontology and an overview of those used in VEP can be found here.
    • If one wants to filter using several consequences, then separate those by using ’&’ (i.e. --dbnsfp_consequence '3_prime_UTR_variant&intron_variant'.”,
  • --dbnsfp_fields, to retrieve individual values from the dbNSFP file.
    • The values correspond to the name of the columns in the dbNSFP file and are separated by comma.
    • The column names might differ between the different dbNSFP versions. Please check the Readme.txt file, which is provided with the dbNSFP file, to obtain the correct column names. The Readme file contains also a short description of the provided values and the version of the tools used to generate them.

For more details, see here.


Enable with --vep_loftee.

For more details, see here.


Enable with --vep_spliceai. The following parameters are mandatory:

  • --spliceai_snv, to specify the path to SpliceAI raw scores snv file.
  • --spliceai_snv_tbi, to specify the path to SpliceAI raw scores snv tabix indexed file.
  • --spliceai_indel, to specify the path to SpliceAI raw scores indel file.
  • --spliceai_indel_tbi, to specify the path to SpliceAI raw scores indel tabix indexed file.

For more details, see here.


Enable with --vep_spliceregion.

For more details, see here and here.”

Requested resources for the tools

Resource requests are difficult to generalize and are often dependent on input data size. Currently, the number of cpus and memory requested by default were adapted from tests on 5 ICGC paired whole-genome sequencing samples with approximately 40X and 80X depth. For targeted data analysis, this is overshooting by a lot. In this case resources for each process can be limited by either setting --max_memory and -max_cpus or tailoring the request by process name as described here. If you are using sarek for a certain data type regulary, and would like to make these requests available to others on your system, an institution-specific, pipeline-specific config file can be added here.

Plots for SnpEff are missing

When plots are missing, it is possible that the fasta and the custom SnpEff database are not matching The SnpEff completes without throwing an error causing nextflow to complete successfully. An indication for the error are these lines in the .command files:

ERRORS: Some errors were detected
Error type      Number of errors

How to set up sarek to use sentieon

Sarek 3.0.1 is currently not supporting sentieon. It is planned for the upcoming release 3.1. In the meantime, please revert to the last release 2.7.2.