nf-core/sarek
Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing
2.7.2). The latest
stable release is
3.4.4
.
Define where the pipeline should find input data and save output data.
Path to input file(s).
stringUse this to specify the location of your input TSV file on mapping, prepare_recalibration, recalibrate, variant_calling and Control-FREEC steps (multiple files can be specified with quotes).
It can also be used to specify the path to a directory on mapping step with a single germline sample only.
Alternatively, it can be used to specify the path to VCF input file on annotate step (multiple files can be specified with quotes).
Starting step.
stringOnly one step.
NB step can be specified with no concern for case, or the presence of
-or_
The output directory where the results will be saved.
string./resultsOption used for most of the pipeline
Tools to use for variant calling and/or for annotation.
stringnullMultiple separated with commas.
Germline variant calling can currently only be performed with the following variant callers:
- FreeBayes, HaplotypeCaller, Manta, mpileup, Strelka, TIDDIT
Somatic variant calling can currently only be performed with the following variant callers:
- ASCAT, Control-FREEC, FreeBayes, Manta, MSIsensor, Mutect2, Strelka
Tumor-only somatic variant calling can currently only be performed with the following variant callers:
- Control-FREEC, Manta, mpileup, Mutect2, TIDDIT
Annotation is done using snpEff, VEP, or even both consecutively.
NB As Sarek will use bgzip and tabix to compress and index VCF files annotated, it expects VCF files to be sorted.
DNAseq, DNAscope and TNscope are only available with --sentieon
NB tools can be specified with no concern for case, or the presence of
-or_
Disable usage of intervals.
booleanIntervals are part of the genome chopped up, used to speed up preprocessing and variant calling
Estimate interval size.
number1000Intervals are part of the genome chopped up, used to speed up preprocessing and variant calling
Enable Sentieon if available.
booleanSentieon is a commercial solution to process genomics data with high computing efficiency, fast turnaround time, exceptional accuracy, and 100% consistency.
NB Adds the following tools for the
--toolsoptions:DNAseq,DNAscopeandTNscope.
Disable specified QC and Reporting tools.
stringnullMultiple tools can be specified, separated by commas.
NB
--skip_qc BaseRecalibratoris actually just not saving the reports.
NB--skip_qc MarkDuplicatesdoes not skipMarkDuplicatesbut prevent the collection of duplicate metrics that slows down performance.
NB tools can be specified with no concern for case, or the presence of-or_
Target BED file for whole exome or targeted sequencing.
stringThis parameter does not imply that the workflow is running alignment or variant calling only for the supplied targets.
Instead, we are aligning for the whole genome, and selecting variants only at the very end by intersecting with the provided target file.
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.
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.
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 --target_bed option.
The workflow will pick up these intervals, and activate any --exome flag in any tools that allow it to process deeper coverage.
It is advised to pad the variant calling regions (exons or target) to some extent before submitting to the workflow.
Run Trim Galore.
booleanUse this to perform adapter trimming with Trim Galore.
cf Trim Galore User Guide
Remove bp from the 5' end of read 1.
integerThis may be useful if the qualities were very poor, or if there is some sort of unwanted bias at the 5' end.
Remove bp from the 5' end of read 2.
integerThis may be useful if the qualities were very poor, or if there is some sort of unwanted bias at the 5' end.
Remove bp from the 3' end of read 1 AFTER adapter/quality trimming has been performed.
integerThis may remove some unwanted bias from the 3' end that is not directly related to adapter sequence or basecall quality.
Remove bp from the 3' end of read 2 AFTER adapter/quality trimming has been performed.
integerThis may remove some unwanted bias from the 3' end that is not directly related to adapter sequence or basecall quality.
Apply the --nextseq=X option, to trim based on quality after removing poly-G tails.
integerThis may remove some unwanted bias from the 3' end that is not directly related to adapter sequence or basecall quality.
Save trimmed FastQ file intermediates
booleanSpecify how many reads should be contained in the split FastQ file
numberUse the Nextflow splitFastq operator to specify how many reads should be contained in the split FASTQ file.
cf splitfastq documentation
Specify aligner to be used to map reads to reference genome.
stringWARNING Current indices for
bwain AWS iGenomes are not compatible withbwa-mem2.
Use--bwa=falseto haveSarekbuild them automatically.
WARNING BWA-mem2 is in active development
Sarek might not be able to require the right amount of resources for it at the moment
We recommend to use pre-built indexes
Establish values for GATK MarkDuplicates memory consumption
string-Xms4000m -Xmx7gEnable usage of GATK Spark implementation
booleanSave Mapped BAMs
booleanSkip GATK MarkDuplicates
booleanThis params will also save the mapped BAMS, to enable restart from step prepare_recalibration
Overwrite ASCAT ploidy
stringnullRequires that --ascat_purity is set
Overwrite ASCAT purity
stringnullRequires that --ascat_ploidy is set
Overwrite Control-FREEC coefficientOfVariation
number0.05Overwrite Control-FREEC contaminationAdjustement
booleanDesign known contamination value for Control-FREEC
stringnullOverwrite Control-FREEC ploidy
number2Overwrite Control-FREEC window size
numberIt is recommended to use a window size of 0 for exome data
Generate g.vcf output from GATK HaplotypeCaller
booleanWill not use Manta candidateSmallIndels for Strelka
booleanNot recommended by Best Practices
Panel-of-normals VCF (bgzipped) for GATK Mutect2 / Sentieon TNscope
stringWithout PON, there will be no calls with PASS in the INFO field, only an unfiltered VCF is written.
It is recommended to make your own PON, as it depends on sequencer and library preparation.
For tests in iGenomes there is a dummy PON file in the Annotation/GermlineResource directory, but it should not be used as a real PON file.
NB PON file should be bgzipped.
Index of PON panel-of-normals VCF
stringIf none provided, will be generated automatically from the PON bgzipped VCF file.
Do not analyze soft clipped bases in the reads for GATK Mutect2
booleanuse the --dont-use-soft-clipped-bases params with GATK.
If provided, UMIs steps will be run to extract and annotate the reads with UMI and create consensus reads
booleanThis part of the pipeline uses fgbio to convert the FASTQ files into a unmapped BAM, where reads are tagged with the UMIs extracted from the FASTQ sequences.
In order to allow the correct tagging, the UMI sequence must be contained in the read sequence itself, and not in the FASTQ filename.
Following this step, the unmapped BAM is aligned and reads are then grouped based on mapping position and UMI tag.
Finally, reads in the same groups are collapsed to create a consensus read.
To create consensus, we have chosen to use the adjacency method
cf fgbio
cf UMIs, the problem, the solution and the proof
NB In order for the correct tagging to be performed, a read structure needs to be specified with
--read_structure1and--readstructure2
When processing UMIs, a read structure should always be provided for each of the fastq files.
stringnullIf the read does not contain any UMI, the structure will be +T (i.e. only template of any length).
The read structure follows a format adopted by different tools and described in the fgbio documentation
When processing UMIs, a read structure should always be provided for each of the fastq files.
stringnullIf the read does not contain any UMI, the structure will be +T (i.e. only template of any length).
The read structure follows a format adopted by different tools and described in the fgbio documentation
Specify from which tools Sarek should look for VCF files to annotate
stringnullOnly for step annotate
Enable the use of cache for annotation
booleanAnd disable usage of Sarek snpeff and vep specific containers for annotation
To be used with --snpeff_cache and/or --vep_cache
Enable CADD cache.
booleanPath to CADD InDels file.
stringnullPath to CADD InDels index.
stringnullPath to CADD SNVs file.
stringnullPath to CADD SNVs index.
stringnullEnable the use of the VEP GeneSplicer plugin.
booleanPath to snpEff cache
stringnullTo be used with --annotation_cache
Path to VEP cache
stringnullTo be used with --annotation_cache
Options for the reference genome files
Name of iGenomes reference.
stringIf 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 ASCAT loci file.
stringPath to ASCAT GC correction file.
stringPath to BWA mem indices.
stringNB If none provided, will be generated automatically from the FASTA reference.
Path to chromosomes folder.
stringPath to chromosomes length file.
stringPath to dbsnp file.
stringPath to dbsnp index.
stringNB If none provided, will be generated automatically from the dbsnp file.
Path to FASTA dictionary file.
stringNB If none provided, will be generated automatically from the FASTA reference.
Path to FASTA genome file.
stringIf you have no genome reference available, the pipeline can build one using a FASTA file. This requires additional time and resources, so it's better to use a pre-build index if possible.
Path to FASTA reference index.
stringNB If none provided, will be generated automatically from the FASTA reference
Path to GATK Mutect2 Germline Resource File
stringThe 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
stringNB If none provided, will be generated automatically from the Germline Resource file, if provided
Path to intervals file
stringTo speed up some preprocessing and variant calling processes, the reference is chopped into smaller pieces.
The 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.
Parts of preprocessing and variant calling are done by these intervals, and the different resulting files are then merged.
This can parallelize processes, and push down wall clock time significantly.
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.
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
Path to known indels file
stringPath to known indels file index
stringNB If none provided, will be generated automatically from the known index file, if provided
Path to Control-FREEC mappability file
stringsnpEff DB version
stringsnpEff species
stringIf you use AWS iGenomes or a local resource with genomes.conf, this has already been set for you appropriately.
VEP cache version
stringSave built references
booleanDirectory / URL base for iGenomes references.
strings3://ngi-igenomes/igenomesDirectory / URL base for genomes references.
stringnullAll files are supposed to be in the same folder
Do not load the iGenomes reference config.
booleanDo 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.
This option will load the genomes.config file instead.
NB You can then specify the genome custom and specify at least a FASTA genome file.
Less common options for the pipeline, typically set in a config file.
Display help text.
booleanYou're reading it.
Method used to save pipeline results to output directory.
stringThe 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.
string^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don't need to specify this on the command line for every run.
Boolean whether to validate parameters against the schema at runtime
booleantrueEmail address for completion summary, only when pipeline fails.
string^([a-zA-Z0-9_\-\.]+)@([a-zA-Z0-9_\-\.]+)\.([a-zA-Z]{2,5})$This works exactly as with --email, except emails are only sent if the workflow is not successful.
Send plain-text email instead of HTML.
booleanSet to receive plain-text e-mails instead of HTML formatted.
File size limit when attaching MultiQC reports to summary emails.
string25.MBIf file generated by pipeline exceeds the threshold, it will not be attached.
Do not use coloured log outputs.
booleanSet to disable colourful command line output and live life in monochrome.
Path to MultiQC custom config file.
stringDirectory to keep pipeline Nextflow logs and reports.
string${params.outdir}/pipeline_infoName of sequencing center to be displayed in BAM file
stringnullIt will be in the CN field
Show all params when using --help
booleanSet the top limit for requested resources for any single job.
integer8Should be an integer e.g. --cpus 7
Use to set memory for a single CPU.
string7 GBShould be a string in the format integer-unit eg. --single_cpu_mem '8.GB'
Maximum number of CPUs that can be requested for any single job.
integer16Use to set an upper-limit for the CPU requirement for each process. Should be an integer e.g. --max_cpus 1
Maximum amount of memory that can be requested for any single job.
string128.GB^\d+(\.\d+)?\.?\s*(K|M|G|T)?B$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.
string240.h^(\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'
Parameters used to describe centralised config profiles. These should not be edited.
Git commit id for Institutional configs.
stringmasterProvide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default: master.
## Download and use config file with following git commit id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96
Base directory for Institutional configs.
stringhttps://raw.githubusercontent.com/nf-core/configs/masterIf 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 the custom_config_base option. For example:
## Download and unzip the config files
cd /path/to/my/configs
wget https://github.com/nf-core/configs/archive/master.zip
unzip master.zip
## Run the pipeline
cd /path/to/my/data
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/
Note that the nf-core/tools helper package has a
downloadcommand to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.
Institutional configs hostname.
stringInstitutional config name.
stringInstitutional config description.
stringInstitutional config contact information.
stringInstitutional config URL link.
string