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

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

required
type: string

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. If no input should be supplied, i.e. when --step is supplied or --build_from_index, then set --input false

hidden
type: string
pattern: ^\S+\.csv$
type: integer
default: 50000000

Starting step

type: string

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

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

required
type: string

Save mapped files.

type: boolean

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

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

True if there are RNA samples to be analysed

type: boolean
default: true

Avoids STAR indexing otherwise

True if there are DNA samples to be analysed

type: boolean
default: true

Avoids BWA indexing otherwise

Reference genome related files and options required for the workflow.

Name of iGenomes reference.

type: string
default: 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.

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 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 STAR index folder or compressed file (tar.gz)

type: string

This parameter can be used if there is an pre-defined STAR index available. You can either give the full path to the index directory or a compressed file in tar.gz format.

Splice sites file required for HISAT2.

type: string

Path to STAR index folder or compressed file (tar.gz)

type: string

This parameter can be used if there is an pre-defined STAR index available. You can either give the full path to the index directory or a compressed file in tar.gz format.

Enable STAR 2-pass mapping mode.

type: boolean

This parameter enables STAR to perform 2-pass mapping. Default true.

Do not use GTF file during STAR index buidling step

type: boolean

Do not use parameter --sjdbGTFfile <GTF file> during the STAR genomeGenerate process.

Option to limit RAM when sorting BAM file. Value to be specified in bytes. If 0, will be set to the genome index size.

type: integer

This parameter specifies the maximum available RAM (bytes) for sorting BAM during STAR alignment.

Specifies the number of genome bins for coordinate-sorting

type: integer
default: 50

This parameter specifies the number of bins to be used for coordinate sorting during STAR alignment step.

Specifies the maximum number of collapsed junctions

type: integer
default: 1000000

Read length

type: number
default: 76

Specify the read length for the STAR aligner.

Estimate interval size.

type: number
default: 200000

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).

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.

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 FASTA genome file.

type: string
pattern: ^\S+\.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.

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.

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

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.

Only built references.

type: boolean

Set this parameter, if you wish to compute and save all computed reference files. No alignment or any other downstream steps will be performed.

Download annotation cache.

type: boolean

Set this parameter, if you wish to download annotation cache.

Directory / URL base for iGenomes references.

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

Do not load the iGenomes reference config.

hidden
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.

Minimum memory required to use splice sites and exons in the HiSAT2 index build process.

type: string
default: 200.GB
pattern: ^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$

HiSAT2 requires a huge amount of RAM to build a genome index for larger genomes, if including splice sites and exons e.g. the human genome might typically require 200GB. If you specify less than this threshold for the HISAT2_BUILD process then the splice sites and exons will be ignored, meaning that the process will require a lot less memory. If you are working with a small genome, set this parameter to a lower value to reduce the threshold for skipping this check. If using a larger genome, consider supplying more memory to the HISAT2_BUILD process.

Path to GTF annotation file.

type: string

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

Path to GFF3 annotation file.

type: string

This parameter must be specified if --genome or --gtf are not specified.

Path to BED file containing exon intervals. This will be created from the GTF file if not specified.

type: string

Trim fastq file or handle UMIs

Run FastP for read trimming

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

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

hidden
type: boolean

Define parameters that control the stages in the pipeline

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

type: string

Multiple tools separated with commas.

Variant Calling:

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

  • SNPs/Indels: FreeBayes, Mutect2, Strelka2, SAGE

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

Annotation:

  • VEP (only).

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

This parameter must be a combination of the following values: freebayes, manta, merge, sage, mutect2, strelka, vep, consensus, filtering, normalise, normalize, rna_filtering, vcf_qc, vcf2maf, preprocessing, realignment, rescue

Disable specified tools.

type: string

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.

This parameter must be a combination of the following values: contamination, learnreadorientation, baserecalibrator, baserecalibrator_report, bcftools, documentation, fastqc, markduplicates, markduplicates_report, mosdepth, multiqc, samtools, vcftools, versions, splitncigar, realignment, filtering, variant_calling, rescue

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.

Define parameters related to read alignment

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

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. For more info see here.

Where possible, save unaligned reads from aligner to the results directory.

type: boolean

This may either be in the form of FastQ or BAM files depending on the options available for that particular tool.

Save the intermediate BAM files from the alignment step.

type: boolean

By default, intermediate BAM files will not be saved. The final BAM files created after the appropriate filtering step are always saved to limit storage usage. Set this parameter to also save other intermediate BAM files.

Create a CSI index for BAM files instead of the traditional BAI index. This will be required for genomes with larger chromosome sizes.

type: boolean

type: boolean
type: boolean

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.

type: integer
default: 25

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.

Runs Mutect2 in joint (multi-sample) mode for better concordance among variant calls of tumor samples from the same patient. Mutect2 outputs will be stored in a subfolder named with patient ID under variant_calling/mutect2/ folder. Only a single normal sample per patient is allowed. Tumor-only mode is also supported.

type: boolean

Bed file with known high confidence used as input in Sage variant caller

hidden
type: string

Bed file with ac actionable list of variants used as input in Sage variant caller

hidden
type: string

Known hotspots used as input in Sage variant caller

hidden
type: string

Directory to ensembl cache for SAGE

hidden
type: string

Custom parameters for SAGE

hidden
type: string

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 genesplicer plugin.

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.

Add an extra custom argument to VEP.

hidden
type: string
default: --no_progress --offline --shift_hgvs 1 --check_existing --tsl --domains --total_length --allele_number --no_escape --xref_refseq --failed 1 --flag_pick_allele --pick_order canonical,tsl,biotype,rank,ccds,length --format vcf --biotype --force_overwrite --sift p --polyphen p --variant_class --regulatory --allele_number --af_gnomad --af_gnomadg --gene_phenotype --hgvs --hgvsg --max_af

Using this params you can add custom args to VEP.

Path to VEP cache.

hidden
type: string

Cache directory for VEP

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

hidden
type: string

VEP output-file format.

hidden
type: string

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

Path to BED file with variants to whitelist during filtering

type: string

Path to BED file with positions to blacklist during filtering (e.g. regions difficult to map)

type: string

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

Base path / URL for data used in the test profiles

hidden
type: string
default: https://raw.githubusercontent.com/nf-core/test-datasets/rnadnavar

Warning: The -profile test samplesheet file itself contains remote paths. Setting this parameter does not alter the contents of that file.

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|d|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

Display version and exit.

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

Custom logo file to supply to MultiQC. File name must also be set in the MultiQC config file

hidden
type: string

Custom MultiQC yaml file containing HTML including a methods description.

type: string

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.

Validation of parameters fails when an unrecognised parameter is found.

hidden
type: boolean

By default, when an unrecognised parameter is found, it returns a warinig.

Validation of parameters in lenient more.

hidden
type: boolean

Allows string values that are parseable as numbers or booleans. For further information see JSONSchema docs.

Incoming hook URL for messaging service

hidden
type: string

Incoming hook URL for messaging service. Currently, MS Teams and Slack are supported.