nf-core/sarek

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_only_index, then set --input false

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

Use the the tool FastP to split FASTQ file by number of reads. This parallelizes across fastq file shards speeding up mapping. Note although the minimum value is 250 reads, if you have fewer than 250 reads a single FASTQ shard will still be created.

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).

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 200000 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.

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.

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.

Multiple tools separated with commas.

Variant Calling:

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

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

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

• SNPs/Indels: FreeBayes, mpileup, Mutect2, Lofreq, 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), and bcftools annotate (needs --bcftools_annotation).

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.

This parameter must be a combination of the following values: ascat, bcfann, cnvkit, controlfreec, deepvariant, freebayes, haplotypecaller, lofreq, sentieon_dnascope, sentieon_haplotyper, manta, indexcov, merge, mpileup, msisensorpro, mutect2, ngscheckmate, sentieon_dedup, snpeff, strelka, tiddit, vep

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: baserecalibrator, baserecalibrator_report, bcftools, dnascope_filter, documentation, fastqc, haplotypecaller_filter, haplotyper_filter, markduplicates, markduplicates_report, mosdepth, multiqc, samtools, vcftools, versions

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

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.

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.

This may remove some unwanted bias from the 3'. Corresponds to the FastP flag --trim_tail1.

This may remove some unwanted bias from the 3' end. Corresponds to the FastP flag --trim_tail2.

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

This is the minimum length of reads to keep after trimming. Corresponds to the FastP flag --length_required (default in FastP is 15bp).

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.

Available values: Identity, Edit, Adjacency, Paired

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.

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

Multiple separated with commas.

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

This parameter must be a combination of the following values: baserecalibrator, markduplicates

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.

For more details see here

For more details, see here.

For more details, see here.

ASCAT: optional argument to override ASCAT optimization and supply psi parameter (expert parameter, do not adapt unless you know what you are doing). See here

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

Control-FREEC requires a file containing all chromosome lenghts. By default the fasta.fai is used. If the fasta.fai file contains chromosomes not present in the intervals, it fails (see: https://github.com/BoevaLab/FREEC/issues/106).

In this case, a custom chromosome length can be specified. It must be of the same format as the fai, but only contain the relevant chromosomes.

Details, see ControlFREEC manual.

Details, see ControlFREEC manual.

Details, see ControlFREEC manual.

Details, see ControlFREEC manual.

Details, see ControlFREEC manual.

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.

Details, see ControlFREEC manual.

https://cnvkit.readthedocs.io/en/stable/pipeline.html?highlight=reference.cnn#batch

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

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

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.

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

The option --sentieon_haplotyper_emit_mode can be set to the same string values as the Haplotyper's --emit_mode. To output both a vcf and a gvcf, specify both a vcf-option (currently, all, confident and variant) and gvcf. For example, to obtain a vcf and gvcf one could set --sentieon_haplotyper_emit_mode to variant, gvcf.

This parameter must be a combination of the following values: all, confident, gvcf, variant, gvcf, all, gvcf, confident, gvcf, variant, all, gvcf, confident, gvcf, variant, gvcf

The option --sentieon_dnascope_emit_mode can be set to the same string values as the Dnascope's --emit_mode. To output both a vcf and a gvcf, specify both a vcf-option (currently, all, confident and variant) and gvcf. For example, to obtain a vcf and gvcf one could set --sentieon_dnascope_emit_mode to variant, gvcf.

This parameter must be a combination of the following values: all, confident, gvcf, variant, gvcf, all, gvcf, confident, gvcf, variant, all, gvcf, confident, gvcf, variant, gvcf

PCR indel model used to weed out false positive indels more or less aggressively. The possible MODELs are: NONE (used for PCR free samples), and HOSTILE, AGGRESSIVE and CONSERVATIVE, in order of decreasing aggressiveness. The default value is CONSERVATIVE.

This parameter must be a combination of the following values: NONE, HOSTILE, AGGRESSIVE, CONSERVATIVE

Concatenating the germline vcf-files from each applied variant-caller into one vcf-file using bfctools concat.

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.

For details, see here.

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/

To be used with --vep_dbnsfp.

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'.

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.

For details, see here.

For details, see here.

To be used with --vep_spliceai.

To be used with --vep_spliceai.

To be used with --vep_spliceai.

To be used with --vep_spliceai.

For details, see here and here.

Using this params you can add custom args to VEP.

Used by the loftee plugin that need the full path.

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

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

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

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

Set this parameter, if you wish to download annotation cache.
Using this parameter will download cache even if --snpeff_cache and --vep_cache are provided.

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.

Must be set to run ASCAT, either hg19 or hg38.

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

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

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

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

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.

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

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.

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

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.

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.

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.

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

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

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.

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.

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.

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

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.

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.

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

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

It is recommended to use DNAscope with a machine learning model to perform variant calling with higher accuracy by improving the candidate detection and filtering. Sentieon can provide you with a model trained using a subset of the data from the GiAB truth-set found in https://github.com/genome-in-a-bottle. In addition, Sentieon can assist you in the creation of models using your own data, which will calibrate the specifics of your sequencing and bio-informatics processing.

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

Path to snpEff cache which should contain the relevant genome and build directory in the path ${snpeff_species}.${snpeff_version}

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.

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

Path to VEP cache which should contain the relevant species, genome and build directories at the path ${vep_species}/${vep_genome}_${vep_cache_version}

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

Alternative cache version can be used to specify the correct Ensembl Genomes version number as these differ from the concurrent Ensembl/VEP version numbers.

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

This is used to specify the genome when looking for local cache, or cloud based cache.

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

Alternatively species listed in Ensembl Genomes caches can be used.

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

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.