Introduction Usage Parameters Output Results Releases

Define where the pipeline should find input data and save output data.

Starting step

type: string

The pipeline starts from this step and then runs through the possible subsequent steps.

Path to comma-separated file containing information about the samples in the experiment.

type: string
pattern: \.csv$

A design file with information about the samples in your experiment. Use this parameter to specify the location of the input files. It has to be a comma-separated file with a header row. See usage docs.

If no input file is specified, sarek will attempt to locate one in the {outdir} directory.

The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure.

type: string

Most common options used for the pipeline

Specify how many reads each split of a FastQ file contains. Set 0 to turn off splitting at all.

type: integer
default: 50000000

Use the the tool FastP to split FASTQ file by number of reads. This parallelizes across fastq file shards speeding up mapping.

Enable when exome or panel data is provided.

type: boolean

With this parameter flags in various tools are set for targeted sequencing data. It is recommended to enable for whole-exome and panel data analysis.

Path to target bed file in case of whole exome or targeted sequencing or intervals file.

type: string

To speed up preprocessing and variant calling processes, the execution is parallelized across a reference chopped into smaller pieces.

Parts of preprocessing and variant calling are done by these intervals, the different resulting files are then merged. This can parallelize processes, and push down wall clock time significantly.

We are aligning to the whole genome, and then run Base Quality Score Recalibration and Variant Calling on the supplied regions.

Whole Genome Sequencing:

The (provided) intervals are chromosomes cut at their centromeres (so each chromosome arm processed separately) also additional unassigned contigs.

We are ignoring the hs37d5 contig that contains concatenated decoy sequences.

The calling intervals can be defined using a .list or a BED file. A .list file contains one interval per line in the format chromosome:start-end (1-based coordinates). A BED file must be a tab-separated text file with one interval per line. There must be at least three columns: chromosome, start, and end (0-based coordinates). Additionally, the score column of the BED file can be used to provide an estimate of how many seconds it will take to call variants on that interval. The fourth column remains unused.

|chr1|10000|207666|NA|47.3|

This indicates that variant calling on the interval chr1:10001-207666 takes approximately 47.3 seconds.

The runtime estimate is used in two different ways. First, when there are multiple consecutive intervals in the file that take little time to compute, they are processed as a single job, thus reducing the number of processes that needs to be spawned. Second, the jobs with largest processing time are started first, which reduces wall-clock time. If no runtime is given, a time of 1000 nucleotides per second is assumed. See -nucleotides_per_second on how to customize this. Actual figures vary from 2 nucleotides/second to 30000 nucleotides/second. If you prefer, you can specify the full path to your reference genome when you run the pipeline:

NB If none provided, will be generated automatically from the FASTA reference NB Use --no_intervals to disable automatic generation.

Targeted Sequencing:

The recommended flow for targeted sequencing data is to use the workflow as it is, but also provide a BED file containing targets for all steps using the --intervals option. In addition, the parameter --wes should be set. It is advised to pad the variant calling regions (exons or target) to some extent before submitting to the workflow.

The procedure is similar to whole genome sequencing, except that only BED file are accepted. See above for formatting description. Adding every exon as an interval in case of WES can generate >200K processes or jobs, much more forks, and similar number of directories in the Nextflow work directory. These are appropriately grouped together to reduce number of processes run in parallel (see above and --nucleotides_per_second for details). Furthermore, primers and/or baits are not 100% specific, (certainly not for MHC and KIR, etc.), quite likely there going to be reads mapping to multiple locations. If you are certain that the target is unique for your genome (all the reads will certainly map to only one location), and aligning to the whole genome is an overkill, it is actually better to change the reference itself.

Estimate interval size.

type: number
default: 1000

Intervals are parts of the chopped up genome used to speed up preprocessing and variant calling. See --intervals for more info.

Changing this parameter, changes the number of intervals that are grouped and processed together. Bed files from target sequencing can contain thousands or small intervals. Spinning up a new process for each can be quite resource intensive. Instead it can be desired to process small intervals together on larger nodes. In order to make use of this parameter, no runtime estimate can be present in the bed file (column 5).

Disable usage of intervals.

type: boolean

Intervals are parts of the chopped up genome used to speed up preprocessing and variant calling. See --intervals for more info.

If --no_intervals is set no intervals will be taken into account for speed up or data processing.

Tools to use for variant calling and/or for annotation.

type: string
pattern: ^((ascat|cnvkit|controlfreec|deepvariant|freebayes|haplotypecaller|manta|merge|mpileup|msisensorpro|mutect2|snpeff|strelka|tiddit|vep)*,?)*$

Multiple tools separated with commas.

Variant Calling:

Germline variant calling can currently be performed with the following variant callers:

  • SNPs/Indels: DeepVariant, FreeBayes, HaplotypeCaller, mpileup, Strelka
  • Structural Variants: Manta, TIDDIT
  • Copy-number: CNVKit

Tumor-only somatic variant calling can currently be performed with the following variant callers:

  • SNPs/Indels: FreeBayes, mpileup, Mutect2, Strelka
  • Structural Variants: Manta, TIDDIT
  • Copy-number: CNVKit, ControlFREEC

Somatic variant calling can currently only be performed with the following variant callers:

  • SNPs/Indels: FreeBayes, Mutect2, Strelka2
  • Structural variants: Manta, TIDDIT
  • Copy-Number: ASCAT, CNVKit, Control-FREEC
  • Microsatellite Instability: MSIsensorpro

NB Mutect2 for somatic variant calling cannot be combined with --no_intervals

Annotation:

  • snpEff, VEP, merge (both consecutively).

NB As Sarek will use bgzip and tabix to compress and index VCF files annotated, it expects VCF files to be sorted when starting from --step annotate.

Disable specified tools.

type: string
pattern: ^((baserecalibrator|baserecalibrator_report|bcftools|documentation|fastqc|markduplicates|markduplicates_report|mosdepth|multiqc|samtools|vcftools|versions)*(,)*)*$

Multiple tools can be specified, separated by commas.

NB --skip_tools baserecalibrator_report is actually just not saving the reports. NB --skip_tools markduplicates_report does not skip MarkDuplicates but prevent the collection of duplicate metrics that slows down performance.

Trim fastq file or handle UMIs

Run FastP for read trimming

hidden
type: boolean

Use this to perform adapter trimming. Adapter are detected automatically by using the FastP flag --detect_adapter_for_pe. For more info see FastP

Remove bp from the 5' end of read 1

hidden
type: integer

This may be useful if the qualities were very poor, or if there is some sort of unwanted bias at the 5' end. Corresponds to the FastP flag --trim_front1.

Remove bp from the 5' end of read 2

hidden
type: integer

This may be useful if the qualities were very poor, or if there is some sort of unwanted bias at the 5' end. Corresponds to the FastP flag --trim_front2.

Remove bp from the 3' end of read 1

hidden
type: integer

This may remove some unwanted bias from the 3'. Corresponds to the FastP flag --three_prime_clip_r1.

Remove bp from the 3' end of read 2

hidden
type: integer

This may remove some unwanted bias from the 3' end. Corresponds to the FastP flag --three_prime_clip_r2.

Removing poly-G tails.

hidden
type: integer

DetectS polyG in read tails and trim them. Corresponds to the FastP flag --trim_poly_g.

Save trimmed FastQ file intermediates.

hidden
type: boolean

Specify UMI read structure

hidden
type: string

One structure if UMI is present on one end (i.e. '+T 2M11S+T'), or two structures separated by a blank space if UMIs a present on both ends (i.e. '2M11S+T 2M11S+T'); please note, this does not handle duplex-UMIs.

For more info on UMI usage in the pipeline, also check docs here.

Default strategy with UMI

hidden
type: string
default: Adjacency

Available values: Identity, Edit, Adjacency, Paired

If set, publishes split FASTQ files. Intended for testing purposes.

hidden
type: boolean

Configure preprocessing tools

Specify aligner to be used to map reads to reference genome.

hidden
type: string

Sarek will build missing indices automatically if not provided. Set --bwa false if indices should be (re-)built. If DragMap is selected as aligner, it is recommended to skip baserecalibration with --skip_tools baserecalibrator. See here for more info.

Save mapped BAMs.

type: boolean

If the parameter --split-fastq is used, the sharded bam files are merged before saving them.

Saves output from Markduplicates & Baserecalibration as BAM file instead of CRAM

type: boolean

Enable usage of GATK Spark implementation for duplicate marking and/or base quality score recalibration

type: string
pattern: ^((baserecalibrator|markduplicates)*,?)*$

Multiple separated with commas.

The GATK4 Base Quality Score recalibration tools Baserecalibrator and ApplyBQSR are currently available as Beta release. Use with caution!

Configure variant calling tools

If true, skips germline variant calling for matched normal to tumor sample. Normal samples without matched tumor will still be processed through germline variant calling tools.

type: boolean

This can speed up computation for somatic variant calling with matched normal samples. If false, all normal samples are processed as well through the germline variantcalling tools. If true, only somatic variant calling is done.

Turn on the joint germline variant calling for GATK haplotypecaller

type: boolean

Uses all normal germline samples (as designated by status in the input csv) in the joint germline variant calling process.

Overwrite Ascat min base quality required for a read to be counted.

hidden
type: number
default: 20

For more details see here

Overwrite Ascat minimum depth required in the normal for a SNP to be considered.

hidden
type: number
default: 10

For more details, see here.

Overwrite Ascat min mapping quality required for a read to be counted.

hidden
type: number
default: 35

For more details, see here.

Overwrite ASCAT ploidy.

hidden
type: number

ASCAT: optional argument to override ASCAT optimization and supply psi parameter (expert parameter, don’t adapt unless you know what you’re doing). See here

Overwrite ASCAT purity.

hidden
type: number

Overwrites ASCAT's rho_manual parameter. Expert use only, see here for details. Requires that --ascat_ploidy is set.

Overwrite Control-FREEC coefficientOfVariation

hidden
type: number
default: 0.05

Details, see ControlFREEC manual.

Overwrite Control-FREEC contaminationAdjustement

hidden
type: boolean

Details, see ControlFREEC manual.

Design known contamination value for Control-FREEC

hidden
type: number

Details, see ControlFREEC manual.

Minimal sequencing quality for a position to be considered in BAF analysis.

hidden
type: number

Details, see ControlFREEC manual.

Minimal read coverage for a position to be considered in BAF analysis.

hidden
type: number

Details, see ControlFREEC manual.

Genome ploidy used by ControlFREEC

hidden
type: integer
default: 2

In case of doubt, you can set different values and Control-FREEC will select the one that explains most observed CNAs Example: ploidy=2 , ploidy=2,3,4. For more details, see the manual.

Overwrite Control-FREEC window size.

hidden
type: number

Details, see ControlFREEC manual.

Panel-of-normals VCF (bgzipped) for GATK Mutect2

hidden
type: string

Without PON, there will be no calls with PASS in the INFO field, only an unfiltered VCF is written. It is highly recommended to make your own PON, as it depends on sequencer and library preparation.

The pipeline is shipped with a panel-of-normals for --genome GATK.GRCh38 provided by GATK.

See PON documentation

NB PON file should be bgzipped.

Index of PON panel-of-normals VCF.

hidden
type: string

If none provided, will be generated automatically from the PON bgzipped VCF file.

Do not analyze soft clipped bases in the reads for GATK Mutect2.

hidden
type: boolean

use the --dont-use-soft-clipped-bases params with GATK Mutect2.

Allow usage of fasta file for annotation with VEP

hidden
type: boolean

By pointing VEP to a FASTA file, it is possible to retrieve reference sequence locally. This enables VEP to retrieve HGVS notations (--hgvs), check the reference sequence given in input data, and construct transcript models from a GFF or GTF file without accessing a database.

For details, see here.

Enable the use of the VEP dbNSFP plugin.

hidden
type: boolean

For details, see here.

Path to dbNSFP processed file.

hidden
type: string

To be used with --vep_dbnsfp. dbNSFP files and more information are available at https://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html#dbnsfp and https://sites.google.com/site/jpopgen/dbNSFP/

Path to dbNSFP tabix indexed file.

hidden
type: string

To be used with --vep_dbnsfp.

Consequence to annotate with

hidden
type: string

To be used with --vep_dbnsfp. This params is used 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: https://www.ensembl.org/info/genome/variation/prediction/predicted_data.html If one wants to filter using several consequences, then separate those by using '&' (i.e. 'consequence=3_prime_UTR_variant&intron_variant'.

Fields to annotate with

hidden
type: string
default: rs_dbSNP,HGVSc_VEP,HGVSp_VEP,1000Gp3_EAS_AF,1000Gp3_AMR_AF,LRT_score,GERP++_RS,gnomAD_exomes_AF

To be used with --vep_dbnsfp. This params can be used 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.

Default value are explained below:

rs_dbSNP - rs number from dbSNP HGVSc_VEP - HGVS coding variant presentation from VEP. Multiple entries separated by ';', corresponds to Ensembl_transcriptid HGVSp_VEP - HGVS protein variant presentation from VEP. Multiple entries separated by ';', corresponds to Ensembl_proteinid 1000Gp3_EAS_AF - Alternative allele frequency in the 1000Gp3 East Asian descendent samples 1000Gp3_AMR_AF - Alternative allele counts in the 1000Gp3 American descendent samples LRT_score - Original LRT two-sided p-value (LRTori), ranges from 0 to 1 GERP++_RS - Conservation score. The larger the score, the more conserved the site, ranges from -12.3 to 6.17 gnomAD_exomes_AF - Alternative allele frequency in the whole gnomAD exome samples.

Enable the use of the VEP LOFTEE plugin.

hidden
type: boolean

For details, see here.

Enable the use of the VEP SpliceAI plugin.

hidden
type: boolean

For details, see here.

Path to spliceai raw scores snv file.

hidden
type: string

To be used with --vep_spliceai.

Path to spliceai raw scores snv tabix indexed file.

hidden
type: string

To be used with --vep_spliceai.

Path to spliceai raw scores indel file.

hidden
type: string

To be used with --vep_spliceai.

Path to spliceai raw scores indel tabix indexed file.

hidden
type: string

To be used with --vep_spliceai.

Enable the use of the VEP SpliceRegion plugin.

hidden
type: boolean

For details, see here and here.

Path to snpEff cache.

hidden
type: string

To be used with --annotation_cache.

Path to VEP cache.

hidden
type: string

To be used with --annotation_cache.

VEP output-file format.

hidden
type: string

Sets the format of the output-file from VEP. Available formats: json, tab and vcf.

Reference genome related files and options required for the workflow.

Name of iGenomes reference.

type: string
default: GATK.GRCh38

If using a reference genome configured in the pipeline using iGenomes, use this parameter to give the ID for the reference. This is then used to build the full paths for all required reference genome files e.g. --genome GRCh38.

See the nf-core website docs for more details.

ASCAT genome.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Must be set to run ASCAT, either hg19 or hg38. If you use AWS iGenomes, this has already been set for you appropriately.

Path to ASCAT allele zip file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to ASCAT loci zip file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to ASCAT GC content correction file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to ASCAT RT (replictiming) correction file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to BWA mem indices.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

If you wish to recompute indices available on igenomes, set --bwa false.

NB If none provided, will be generated automatically from the FASTA reference. Combine with --save_reference to save for future runs.

Path to bwa-mem2 mem indices.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

If you wish to recompute indices available on igenomes, set --bwamem2 false.

NB If none provided, will be generated automatically from the FASTA reference, if --aligner bwa-mem2 is specified. Combine with --save_reference to save for future runs.

Path to chromosomes folder used with ControLFREEC.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to dbsnp file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to dbsnp index.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the dbsnp file. Combine with --save_reference to save for future runs.

label string for VariantRecalibration (haplotypecaller joint variant calling)

type: string

Path to FASTA dictionary file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the FASTA reference. Combine with --save_reference to save for future runs.

Path to dragmap indices.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

If you wish to recompute indices available on igenomes, set --dragmap false.

NB If none provided, will be generated automatically from the FASTA reference, if --aligner dragmap is specified. Combine with --save_reference to save for future runs.

Path to FASTA genome file.

type: string
pattern: \.fn?a(sta)?(\.gz)?$

If you use AWS iGenomes, this has already been set for you appropriately.

This parameter is mandatory if --genome is not specified.

Path to FASTA reference index.

type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the FASTA reference. Combine with --save_reference to save for future runs.

Path to GATK Mutect2 Germline Resource File.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

The germline resource VCF file (bgzipped and tabixed) needed by GATK4 Mutect2 is a collection of calls that are likely present in the sample, with allele frequencies. The AF info field must be present. You can find a smaller, stripped gnomAD VCF file (most of the annotation is removed and only calls signed by PASS are stored) in the AWS iGenomes Annotation/GermlineResource folder.

Path to GATK Mutect2 Germline Resource Index.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the Germline Resource file, if provided. Combine with --save_reference to save for future runs.

Path to known indels file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to known indels file index.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the known index file, if provided. Combine with --save_reference to save for future runs.

If you use AWS iGenomes, this has already been set for you appropriately.

1st label string for VariantRecalibration (haplotypecaller joint variant calling)

type: string

If you use AWS iGenomes, this has already been set for you appropriately.

Path to known snps file.

type: string

Path to known snps file snps.

type: string

If you use AWS iGenomes, this has already been set for you appropriately.

NB If none provided, will be generated automatically from the known index file, if provided. Combine with --save_reference to save for future runs.

If you use AWS iGenomes, this has already been set for you appropriately.

label string for VariantRecalibration (haplotypecaller joint variant calling)

type: string

Path to Control-FREEC mappability file.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately.

snpEff DB version.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. This is used to specify the database to be use to annotate with. Alternatively databases' names can be listed with the snpEff databases.

snpEff genome.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. This is used to specify the genome when using the container with pre-downloaded cache.

snpEff version.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. This is used to specify the snpeff version when using the container with pre-downloaded cache.

VEP genome.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. This is used to specify the genome when using the container with pre-downloaded cache.

VEP species.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. Alternatively species listed in Ensembl Genomes caches can be used.

VEP cache version.

hidden
type: number

If you use AWS iGenomes, this has already been set for you appropriately. Alternatively cache version can be use to specify the correct Ensembl Genomes version number as these differ from the concurrent Ensembl/VEP version numbers

VEP version.

hidden
type: string

If you use AWS iGenomes, this has already been set for you appropriately. This is used to specify the VEP version when using the container with pre-downloaded cache.

Save built references.

type: boolean

Set this parameter, if you wish to save all computed reference files. This is useful to avoid re-computation on future runs.

Directory / URL base for iGenomes references.

type: string
default: s3://ngi-igenomes/igenomes/

Do not load the iGenomes reference config.

type: boolean

Do not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.

NB You can then run Sarek by specifying at least a FASTA genome file.

Parameters used to describe centralised config profiles. These should not be edited.

Git commit id for Institutional configs.

hidden
type: string
default: master

Base directory for Institutional configs.

hidden
type: string
default: https://raw.githubusercontent.com/nf-core/configs/master

If you're running offline, Nextflow will not be able to fetch the institutional config files from the internet. If you don't need them, then this is not a problem. If you do need them, you should download the files from the repo and tell Nextflow where to find them with this parameter.

Institutional config name.

hidden
type: string

Institutional config description.

hidden
type: string

Institutional config contact information.

hidden
type: string

Institutional config URL link.

hidden
type: string

Sequencing center information to be added to read group (CN field).

hidden
type: string

Sequencing platform information to be added to read group (PL field).

hidden
type: string
default: ILLUMINA

Default: ILLUMINA. Will be used to create a proper header for further GATK4 downstream analysis.

Set the top limit for requested resources for any single job.

Maximum number of CPUs that can be requested for any single job.

hidden
type: integer
default: 16

Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1.

Maximum amount of memory that can be requested for any single job.

hidden
type: string
default: 128.GB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

Use to set an upper-limit for the memory requirement for each process. Should be a string in the format integer-unit e.g. --max_memory '8.GB'.

Maximum amount of time that can be requested for any single job.

hidden
type: string
default: 240.h
pattern: ^(\d+\.?\s*(s|m|h|day)\s*)+$

Use to set an upper-limit for the time requirement for each process. Should be a string in the format integer-unit e.g. --max_time '2.h'.

Less common options for the pipeline, typically set in a config file.

Display help text.

hidden
type: boolean

Method used to save pipeline results to output directory.

hidden
type: string

The Nextflow publishDir option specifies which intermediate files should be saved to the output directory. This option tells the pipeline what method should be used to move these files. See Nextflow docs for details.

Email address for completion summary.

type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

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.

Email address for completion summary, only when pipeline fails.

hidden
type: string
pattern: ^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$

An email address to send a summary email to when the pipeline is completed - ONLY sent if the pipeline does not exit successfully.

Send plain-text email instead of HTML.

hidden
type: boolean

File size limit when attaching MultiQC reports to summary emails.

hidden
type: string
default: 25.MB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

Do not use coloured log outputs.

hidden
type: boolean

MultiQC report title. Printed as page header, used for filename if not otherwise specified.

type: string

Custom config file to supply to MultiQC.

hidden
type: string

Directory to keep pipeline Nextflow logs and reports.

hidden
type: string
default: ${params.outdir}/pipeline_info

Boolean whether to validate parameters against the schema at runtime

hidden
type: boolean
default: true

Show all params when using --help

hidden
type: boolean

By default, parameters set as hidden in the schema are not shown on the command line when a user runs with --help. Specifying this option will tell the pipeline to show all parameters.

Run this workflow with Conda. You can also use '-profile conda' instead of providing this parameter.

hidden
type: boolean
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