HaplotypeCaller

Call germline SNPs and indels via local re-assembly of haplotypes

Category Variant Discovery Tools

Traversal ActiveRegionWalker

PartitionBy LOCUS


Overview

The HaplotypeCaller is capable of calling SNPs and indels simultaneously via local de-novo assembly of haplotypes in an active region. In other words, whenever the program encounters a region showing signs of variation, it discards the existing mapping information and completely reassembles the reads in that region. This allows the HaplotypeCaller to be more accurate when calling regions that are traditionally difficult to call, for example when they contain different types of variants close to each other. It also makes the HaplotypeCaller much better at calling indels than position-based callers like UnifiedGenotyper.

In the so-called GVCF mode used for scalable variant calling in DNA sequence data, HaplotypeCaller runs per-sample to generate an intermediate genomic gVCF (gVCF), which can then be used for joint genotyping of multiple samples in a very efficient way, which enables rapid incremental processing of samples as they roll off the sequencer, as well as scaling to very large cohort sizes (e.g. the 92K exomes of ExAC).

In addition, HaplotypeCaller is able to handle non-diploid organisms as well as pooled experiment data. Note however that the algorithms used to calculate variant likelihoods is not well suited to extreme allele frequencies (relative to ploidy) so its use is not recommended for somatic (cancer) variant discovery. For that purpose, use MuTect2 instead.

Finally, HaplotypeCaller is also able to correctly handle the splice junctions that make RNAseq a challenge for most variant callers.

How HaplotypeCaller works


1. Define active regions

The program determines which regions of the genome it needs to operate on, based on the presence of significant evidence for variation.


2. Determine haplotypes by assembly of the active region

For each ActiveRegion, the program builds a De Bruijn-like graph to reassemble the ActiveRegion, and identifies what are the possible haplotypes present in the data. The program then realigns each haplotype against the reference haplotype using the Smith-Waterman algorithm in order to identify potentially variant sites.


3. Determine likelihoods of the haplotypes given the read data

For each ActiveRegion, the program performs a pairwise alignment of each read against each haplotype using the PairHMM algorithm. This produces a matrix of likelihoods of haplotypes given the read data. These likelihoods are then marginalized to obtain the likelihoods of alleles for each potentially variant site given the read data.


4. Assign sample genotypes

For each potentially variant site, the program applies Bayes' rule, using the likelihoods of alleles given the read data to calculate the likelihoods of each genotype per sample given the read data observed for that sample. The most likely genotype is then assigned to the sample.

Input

Input bam file(s) from which to make calls

Output

Either a VCF or gVCF file with raw, unfiltered SNP and indel calls. Regular VCFs must be filtered either by variant recalibration (best) or hard-filtering before use in downstream analyses. If using the reference-confidence model workflow for cohort analysis, the output is a GVCF file that must first be run through GenotypeGVCFs and then filtering before further analysis.

Usage examples

These are example commands that show how to run HaplotypeCaller for typical use cases. Square brackets ("[ ]") indicate optional arguments. Note that parameter values shown here may not be the latest recommended; see the Best Practices documentation for detailed recommendations.


Single-sample GVCF calling on DNAseq (for `-ERC GVCF` cohort analysis workflow)

   java -jar GenomeAnalysisTK.jar \
     -R reference.fasta \
     -T HaplotypeCaller \
     -I sample1.bam \
     --emitRefConfidence GVCF \
     [--dbsnp dbSNP.vcf] \
     [-L targets.interval_list] \
     -o output.raw.snps.indels.g.vcf
 

Single-sample GVCF calling on DNAseq with allele-specific annotations (for allele-specific cohort analysis workflow)

   java -jar GenomeAnalysisTK.jar \
     -R reference.fasta \
     -T HaplotypeCaller \
     -I sample1.bam \
     --emitRefConfidence GVCF \
     [--dbsnp dbSNP.vcf] \
     [-L targets.interval_list] \
     -G Standard -G AS_Standard \
     -o output.raw.snps.indels.AS.g.vcf
 

Variant-only calling on DNAseq

   java -jar GenomeAnalysisTK.jar \
     -R reference.fasta \
     -T HaplotypeCaller \
     -I sample1.bam [-I sample2.bam ...] \
     [--dbsnp dbSNP.vcf] \
     [-stand_call_conf 30] \
     [-L targets.interval_list] \
     -o output.raw.snps.indels.vcf
 

Variant-only calling on RNAseq

   java -jar GenomeAnalysisTK.jar \
     -R reference.fasta \
     -T HaplotypeCaller \
     -I sample1.bam \
     [--dbsnp dbSNP.vcf] \
     -stand_call_conf 20 \
     -o output.raw.snps.indels.vcf
 

Caveats

  • We have not yet fully tested the interaction between the GVCF-based calling or the multisample calling and the RNAseq-specific functionalities. Use those in combination at your own risk.
  • Many users have reported issues running HaplotypeCaller with the -nct argument, so we recommend using Queue to parallelize HaplotypeCaller instead of multithreading.

Special note on ploidy

This tool is able to handle almost any ploidy (except very high ploidies in large pooled experiments); the ploidy can be specified using the -ploidy argument for non-diploid organisms.

Additional Notes

  • When working with PCR-free data, be sure to set `-pcr_indel_model NONE` (see argument below).
  • When running in `-ERC GVCF` or `-ERC BP_RESOLUTION` modes, the emitting and calling confidence thresholds are automatically set to 0. This cannot be overridden by the command line. The thresholds can be set manually to the desired levels in the next step of the workflow (GenotypeGVCFs)

Additional Information

Read filters

These Read Filters are automatically applied to the data by the Engine before processing by HaplotypeCaller.

Parallelism options

This tool can be run in multi-threaded mode using this option.

Downsampling settings

This tool applies the following downsampling settings by default.

  • Mode: BY_SAMPLE
  • To coverage: 500

ActiveRegion settings

This tool uses ActiveRegions on the reference.

  • Minimum region size: 50 bp
  • Maximum region size: 300 bp
  • Extension increments: 100 bp

Command-line Arguments

Engine arguments

All tools inherit arguments from the GATK Engine' "CommandLineGATK" argument collection, which can be used to modify various aspects of the tool's function. For example, the -L argument directs the GATK engine to restrict processing to specific genomic intervals; or the -rf argument allows you to apply certain read filters to exclude some of the data from the analysis.

HaplotypeCaller specific arguments

This table summarizes the command-line arguments that are specific to this tool. For more details on each argument, see the list further down below the table or click on an argument name to jump directly to that entry in the list.

Argument name(s) Default value Summary
Optional Inputs
--alleles
none Set of alleles to use in genotyping
--dbsnp
 -D
none dbSNP file
Optional Outputs
--activeRegionOut
 -ARO
NA Output the active region to this IGV formatted file
--activityProfileOut
 -APO
NA Output the raw activity profile results in IGV format
--graphOutput
 -graph
NA Write debug assembly graph information to this file
--out
 -o
stdout File to which variants should be written
Optional Parameters
--contamination_fraction_to_filter
 -contamination
0.0 Fraction of contamination to aggressively remove
--genotyping_mode
 -gt_mode
DISCOVERY Specifies how to determine the alternate alleles to use for genotyping
--group
 -G
[StandardAnnotation, StandardHCAnnotation] One or more classes/groups of annotations to apply to variant calls
--heterozygosity
 -hets
0.001 Heterozygosity value used to compute prior likelihoods for any locus
--heterozygosity_stdev
 -heterozygosityStandardDeviation
0.01 Standard deviation of eterozygosity for SNP and indel calling.
--indel_heterozygosity
 -indelHeterozygosity
1.25E-4 Heterozygosity for indel calling
--maxReadsInRegionPerSample
10000 Maximum reads in an active region
--min_base_quality_score
 -mbq
10 Minimum base quality required to consider a base for calling
--minReadsPerAlignmentStart
 -minReadsPerAlignStart
10 Minimum number of reads sharing the same alignment start for each genomic location in an active region
--sample_name
 -sn
NA Name of single sample to use from a multi-sample bam
--sample_ploidy
 -ploidy
2 Ploidy per sample. For pooled data, set to (Number of samples in each pool * Sample Ploidy).
--standard_min_confidence_threshold_for_calling
 -stand_call_conf
10.0 The minimum phred-scaled confidence threshold at which variants should be called
Optional Flags
--annotateNDA
 -nda
false Annotate number of alleles observed
--useNewAFCalculator
 -newQual
false Use new AF model instead of the so-called exact model
Advanced Inputs
--activeRegionIn
 -AR
NA Use this interval list file as the active regions to process
--comp
[] Comparison VCF file
Advanced Outputs
--bamOutput
 -bamout
NA File to which assembled haplotypes should be written
Advanced Parameters
--activeProbabilityThreshold
 -ActProbThresh
0.002 Threshold for the probability of a profile state being active.
--activeRegionExtension
NA The active region extension; if not provided defaults to Walker annotated default
--activeRegionMaxSize
NA The active region maximum size; if not provided defaults to Walker annotated default
--annotation
 -A
[] One or more specific annotations to apply to variant calls
--bamWriterType
CALLED_HAPLOTYPES Which haplotypes should be written to the BAM
--bandPassSigma
NA The sigma of the band pass filter Gaussian kernel; if not provided defaults to Walker annotated default
--contamination_fraction_per_sample_file
 -contaminationFile
NA Contamination per sample
--emitRefConfidence
 -ERC
false Mode for emitting reference confidence scores
--excludeAnnotation
 -XA
[] One or more specific annotations to exclude
--gcpHMM
10 Flat gap continuation penalty for use in the Pair HMM
--GVCFGQBands
 -GQB
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 70, 80, 90, 99] Exclusive upper bounds for reference confidence GQ bands (must be in [1, 100] and specified in increasing order)
--indelSizeToEliminateInRefModel
 -ERCIS
10 The size of an indel to check for in the reference model
--input_prior
 -inputPrior
[] Input prior for calls
--kmerSize
[10, 25] Kmer size to use in the read threading assembler
--max_alternate_alleles
 -maxAltAlleles
6 Maximum number of alternate alleles to genotype
--max_genotype_count
 -maxGT
1024 Maximum number of genotypes to consider at any site
--max_num_PL_values
 -maxNumPLValues
100 Maximum number of PL values to output
--maxNumHaplotypesInPopulation
128 Maximum number of haplotypes to consider for your population
--maxReadsInMemoryPerSample
30000 Maximum reads per sample given to traversal map() function
--maxTotalReadsInMemory
10000000 Maximum total reads given to traversal map() function
--minDanglingBranchLength
4 Minimum length of a dangling branch to attempt recovery
--minPruning
2 Minimum support to not prune paths in the graph
--numPruningSamples
1 Number of samples that must pass the minPruning threshold
--output_mode
 -out_mode
EMIT_VARIANTS_ONLY Which type of calls we should output
--pcr_indel_model
 -pcrModel
CONSERVATIVE The PCR indel model to use
--phredScaledGlobalReadMismappingRate
 -globalMAPQ
45 The global assumed mismapping rate for reads
Advanced Flags
--allowNonUniqueKmersInRef
false Allow graphs that have non-unique kmers in the reference
--allSitePLs
false Annotate all sites with PLs
--consensus
false 1000G consensus mode
--debug
false Print out very verbose debug information about each triggering active region
--disableOptimizations
false Don't skip calculations in ActiveRegions with no variants
--doNotRunPhysicalPhasing
false Disable physical phasing
--dontIncreaseKmerSizesForCycles
false Disable iterating over kmer sizes when graph cycles are detected
--dontTrimActiveRegions
false If specified, we will not trim down the active region from the full region (active + extension) to just the active interval for genotyping
--dontUseSoftClippedBases
false Do not analyze soft clipped bases in the reads
--emitDroppedReads
 -edr
false Emit reads that are dropped for filtering, trimming, realignment failure
--forceActive
false If provided, all bases will be tagged as active
--useAllelesTrigger
 -allelesTrigger
false Use additional trigger on variants found in an external alleles file
--useFilteredReadsForAnnotations
false Use the contamination-filtered read maps for the purposes of annotating variants

Argument details

Arguments in this list are specific to this tool. Keep in mind that other arguments are available that are shared with other tools (e.g. command-line GATK arguments); see Inherited arguments above.


--activeProbabilityThreshold / -ActProbThresh

Threshold for the probability of a profile state being active.

Double  0.002  [ [ 0  1 ] ]


--activeRegionExtension / -activeRegionExtension

The active region extension; if not provided defaults to Walker annotated default

Integer  NA


--activeRegionIn / -AR

Use this interval list file as the active regions to process

List[IntervalBinding[Feature]]  NA


--activeRegionMaxSize / -activeRegionMaxSize

The active region maximum size; if not provided defaults to Walker annotated default

Integer  NA


--activeRegionOut / -ARO

Output the active region to this IGV formatted file
If provided, this walker will write out its active and inactive regions to this file in the IGV formatted TAB deliminated output: http://www.broadinstitute.org/software/igv/IGV Intended to make debugging the active region calculations easier

PrintStream  NA


--activityProfileOut / -APO

Output the raw activity profile results in IGV format
If provided, this walker will write out its activity profile (per bp probabilities of being active) to this file in the IGV formatted TAB deliminated output: http://www.broadinstitute.org/software/igv/IGV Intended to make debugging the activity profile calculations easier

PrintStream  NA


--alleles / -alleles

Set of alleles to use in genotyping
When --genotyping_mode is set to GENOTYPE_GIVEN_ALLELES mode, the caller will genotype the samples using only the alleles provide in this callset. Note that this is not well tested in HaplotypeCaller, and is definitely not suitable for use with HaplotypeCaller in -ERC GVCF mode. In addition, it does not apply to MuTect2 at all.

This argument supports reference-ordered data (ROD) files in the following formats: BCF2, VCF, VCF3

RodBinding[VariantContext]  none


--allowNonUniqueKmersInRef / -allowNonUniqueKmersInRef

Allow graphs that have non-unique kmers in the reference
By default, the program does not allow processing of reference sections that contain non-unique kmers. Disabling this check may cause problems in the assembly graph.

boolean  false


--allSitePLs / -allSitePLs

Annotate all sites with PLs
Experimental argument FOR USE WITH UnifiedGenotyper ONLY: if SNP likelihood model is specified, and if EMIT_ALL_SITES output mode is set, when we set this argument then we will also emit PLs at all sites. This will give a measure of reference confidence and a measure of which alt alleles are more plausible (if any). WARNINGS: - This feature will inflate VCF file size considerably. - All SNP ALT alleles will be emitted with corresponding 10 PL values. - An error will be emitted if EMIT_ALL_SITES is not set, or if anything other than diploid SNP model is used - THIS WILL NOT WORK WITH HaplotypeCaller, GenotypeGVCFs or MuTect2! Use HaplotypeCaller with -ERC GVCF then GenotypeGVCFs instead. See the Best Practices documentation for more information.

boolean  false


--annotateNDA / -nda

Annotate number of alleles observed
Depending on the value of the --max_alternate_alleles argument, we may genotype only a fraction of the alleles being sent on for genotyping. Using this argument instructs the genotyper to annotate (in the INFO field) the number of alternate alleles that were originally discovered (but not necessarily genotyped) at the site.

boolean  false


--annotation / -A

One or more specific annotations to apply to variant calls
Which annotations to add to the output VCF file. The single value 'none' removes the default annotations. See the VariantAnnotator -list argument to view available annotations.

List[String]  []


--bamOutput / -bamout

File to which assembled haplotypes should be written
The assembled haplotypes and locally realigned reads will be written as BAM to this file if requested. This is intended to be used only for troubleshooting purposes, in specific areas where you want to better understand why the caller is making specific calls. Turning on this mode may result in serious performance cost for the caller, so we do NOT recommend using this argument systematically as it will significantly increase runtime. The candidate haplotypes (called or all, depending on mode) are emitted as single reads covering the entire active region, coming from sample "HC" and a special read group called "ArtificialHaplotype". This will increase the pileup depth compared to what would be expected from the reads only, especially in complex regions. The reads are written out containing an "HC" tag (integer) that encodes which haplotype each read best matches according to the haplotype caller's likelihood calculation. The use of this tag is primarily intended to allow good coloring of reads in IGV. Simply go to "Color Alignments By > Tag" and enter "HC" to more easily see which reads go with these haplotype. You can also tell IGV to group reads by sample, which will separate the potential haplotypes from the reads. These features are illustrated in this screenshot. Note that only reads that are actually informative about the haplotypes are emitted with the HC tag. By informative we mean that there's a meaningful difference in the likelihood of the read coming from one haplotype compared to the next best haplotype. When coloring reads by HC tag in IGV, uninformative reads will remain grey. Note also that not every input read is emitted to the bam in this mode. To include all trimmed, downsampled, filtered and uninformative reads, add the --emitDroppedReads argument. If multiple BAMs are passed as input to the tool (as is common for MuTect2), then they will be combined in the -bamout output and tagged with the appropriate sample names.

GATKSAMFileWriter  NA


--bamWriterType / -bamWriterType

Which haplotypes should be written to the BAM
The type of -bamout output we want to see. This determines whether HC will write out all of the haplotypes it considered (top 128 max) or just the ones that were selected as alleles and assigned to samples.

The --bamWriterType argument is an enumerated type (Type), which can have one of the following values:

ALL_POSSIBLE_HAPLOTYPES
A mode that's for method developers. Writes out all of the possible haplotypes considered, as well as reads aligned to each
CALLED_HAPLOTYPES
A mode for users. Writes out the reads aligned only to the called haplotypes. Useful to understand why the caller is calling what it is

Type  CALLED_HAPLOTYPES


--bandPassSigma / -bandPassSigma

The sigma of the band pass filter Gaussian kernel; if not provided defaults to Walker annotated default

Double  NA


--comp / -comp

Comparison VCF file
If a call overlaps with a record from the provided comp track, the INFO field will be annotated as such in the output with the track name (e.g. -comp:FOO will have 'FOO' in the INFO field). Records that are filtered in the comp track will be ignored. Note that 'dbSNP' has been special-cased (see the --dbsnp argument).

This argument supports reference-ordered data (ROD) files in the following formats: BCF2, VCF, VCF3

List[RodBinding[VariantContext]]  []


--consensus / -consensus

1000G consensus mode
This argument is specifically intended for 1000G consensus analysis mode. Setting this flag will inject all provided alleles to the assembly graph but will not forcibly genotype all of them.

boolean  false


--contamination_fraction_per_sample_file / -contaminationFile

Contamination per sample
This argument specifies a file with two columns "sample" and "contamination" (separated by a tab) specifying the contamination level for those samples (where contamination is given as a decimal number, not an integer) per line. There should be no header. Samples that do not appear in this file will be processed with CONTAMINATION_FRACTION.

File  NA


--contamination_fraction_to_filter / -contamination

Fraction of contamination to aggressively remove
If this fraction is greater is than zero, the caller will aggressively attempt to remove contamination through biased down-sampling of reads (for all samples). Basically, it will ignore the contamination fraction of reads for each alternate allele. So if the pileup contains N total bases, then we will try to remove (N * contamination fraction) bases for each alternate allele.

double  0.0  [ [ -∞  ∞ ] ]


--dbsnp / -D

dbSNP file
rsIDs from this file are used to populate the ID column of the output. Also, the DB INFO flag will be set when appropriate. dbSNP is not used in any way for the calculations themselves.

This argument supports reference-ordered data (ROD) files in the following formats: BCF2, VCF, VCF3

RodBinding[VariantContext]  none


--debug / -debug

Print out very verbose debug information about each triggering active region

boolean  false


--disableOptimizations / -disableOptimizations

Don't skip calculations in ActiveRegions with no variants
If set, certain "early exit" optimizations in HaplotypeCaller, which aim to save compute and time by skipping calculations if an ActiveRegion is determined to contain no variants, will be disabled. This is most likely to be useful if you're using the -bamout argument to examine the placement of reads following reassembly and are interested in seeing the mapping of reads in regions with no variations. Setting the -forceActive and -dontTrimActiveRegions flags may also be helpful.

boolean  false


--doNotRunPhysicalPhasing / -doNotRunPhysicalPhasing

Disable physical phasing
As of GATK 3.3, HaplotypeCaller outputs physical (read-based) information (see version 3.3 release notes and documentation for details). This argument disables that behavior.

boolean  false


--dontIncreaseKmerSizesForCycles / -dontIncreaseKmerSizesForCycles

Disable iterating over kmer sizes when graph cycles are detected
When graph cycles are detected, the normal behavior is to increase kmer sizes iteratively until the cycles are resolved. Disabling this behavior may cause the program to give up on assembling the ActiveRegion.

boolean  false


--dontTrimActiveRegions / -dontTrimActiveRegions

If specified, we will not trim down the active region from the full region (active + extension) to just the active interval for genotyping

boolean  false


--dontUseSoftClippedBases / -dontUseSoftClippedBases

Do not analyze soft clipped bases in the reads

boolean  false


--emitDroppedReads / -edr

Emit reads that are dropped for filtering, trimming, realignment failure
Determines whether dropped reads will be tracked and emitted when -bamout is specified. Use this in combination with a specific interval of interest to avoid accumulating a large number of reads in the -bamout file.

boolean  false


--emitRefConfidence / -ERC

Mode for emitting reference confidence scores
Records whether the trimming intervals are going to be used to emit reference confidence, {@code true}, or regular HC output {@code false}.

The --emitRefConfidence argument is an enumerated type (ReferenceConfidenceMode), which can have one of the following values:

NONE
Regular calling without emitting reference confidence calls.
BP_RESOLUTION
Reference model emitted site by site.
GVCF
Reference model emitted with condensed non-variant blocks, i.e. the GVCF format.

ReferenceConfidenceMode  false


--excludeAnnotation / -XA

One or more specific annotations to exclude
Which annotations to exclude from output in the VCF file. Note that this argument has higher priority than the -A or -G arguments, so these annotations will be excluded even if they are explicitly included with the other options. When HaplotypeCaller is run with -ERC GVCF or -ERC BP_RESOLUTION, some annotations are excluded from the output by default because they will only be meaningful once they have been recalculated by GenotypeGVCFs. As of version 3.3 this concerns ChromosomeCounts, FisherStrand, StrandOddsRatio and QualByDepth.

List[String]  []


--forceActive / -forceActive

If provided, all bases will be tagged as active
For the active region walker to treat all bases as active. Useful for debugging when you want to force something like the HaplotypeCaller to process a specific interval you provide the GATK

boolean  false


--gcpHMM / -gcpHMM

Flat gap continuation penalty for use in the Pair HMM

int  10  [ [ -∞  ∞ ] ]


--genotyping_mode / -gt_mode

Specifies how to determine the alternate alleles to use for genotyping

The --genotyping_mode argument is an enumerated type (GenotypingOutputMode), which can have one of the following values:

DISCOVERY
The genotyper will choose the most likely alternate allele
GENOTYPE_GIVEN_ALLELES
Only the alleles passed by the user should be considered.

GenotypingOutputMode  DISCOVERY


--graphOutput / -graph

Write debug assembly graph information to this file
This argument is meant for debugging and is not immediately useful for normal analysis use.

PrintStream  NA


--group / -G

One or more classes/groups of annotations to apply to variant calls
Which groups of annotations to add to the output VCF file. The single value 'none' removes the default group. See the VariantAnnotator -list argument to view available groups. Note that this usage is not recommended because it obscures the specific requirements of individual annotations. Any requirements that are not met (e.g. failing to provide a pedigree file for a pedigree-based annotation) may cause the run to fail.

List[String]  [StandardAnnotation, StandardHCAnnotation]


--GVCFGQBands / -GQB

Exclusive upper bounds for reference confidence GQ bands (must be in [1, 100] and specified in increasing order)
When HC is run in reference confidence mode with banding compression enabled (-ERC GVCF), homozygous-reference sites are compressed into bands of similar genotype quality (GQ) that are emitted as a single VCF record. See the FAQ documentation for more details about the GVCF format. This argument allows you to set the GQ bands. HC expects a list of strictly increasing GQ values that will act as exclusive upper bounds for the GQ bands. To pass multiple values, you provide them one by one with the argument, as in `-GQB 10 -GQB 20 -GQB 30` and so on (this would set the GQ bands to be `[0, 10), [10, 20), [20, 30)` and so on, for example). Note that GQ values are capped at 99 in the GATK, so values must be integers in [1, 100]. If the last value is strictly less than 100, the last GQ band will start at that value (inclusive) and end at 100 (exclusive).

List[Integer]  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 70, 80, 90, 99]


--heterozygosity / -hets

Heterozygosity value used to compute prior likelihoods for any locus
The expected heterozygosity value used to compute prior probability that a locus is non-reference. See https://software.broadinstitute.org/gatk/documentation/article?id=8603 for more details.

Double  0.001  [ [ -∞  ∞ ] ]


--heterozygosity_stdev / -heterozygosityStandardDeviation

Standard deviation of eterozygosity for SNP and indel calling.
The standard deviation of the distribution of alt allele fractions. The above heterozygosity parameters give the *mean* of this distribution; this parameter gives its spread.

double  0.01  [ [ -∞  ∞ ] ]


--indel_heterozygosity / -indelHeterozygosity

Heterozygosity for indel calling
This argument informs the prior probability of having an indel at a site.

double  1.25E-4  [ [ -∞  ∞ ] ]


--indelSizeToEliminateInRefModel / -ERCIS

The size of an indel to check for in the reference model
This parameter determines the maximum size of an indel considered as potentially segregating in the reference model. It is used to eliminate reads from being indel informative at a site, and determines by that mechanism the certainty in the reference base. Conceptually, setting this parameter to X means that each informative read is consistent with any indel of size < X being present at a specific position in the genome, given its alignment to the reference.

int  10  [ [ -∞  ∞ ] ]


--input_prior / -inputPrior

Input prior for calls
By default, the prior specified with the argument --heterozygosity/-hets is used for variant discovery at a particular locus, using an infinite sites model (see e.g. Waterson, 1975 or Tajima, 1996). This model asserts that the probability of having a population of k variant sites in N chromosomes is proportional to theta/k, for 1=1:N. However, there are instances where using this prior might not be desirable, e.g. for population studies where prior might not be appropriate, as for example when the ancestral status of the reference allele is not known. This argument allows you to manually specify a list of probabilities for each AC>1 to be used as priors for genotyping, with the following restrictions: only diploid calls are supported; you must specify 2 * N values where N is the number of samples; probability values must be positive and specified in Double format, in linear space (not log10 space nor Phred-scale); and all values must sume to 1. For completely flat priors, specify the same value (=1/(2*N+1)) 2*N times, e.g. -inputPrior 0.33 -inputPrior 0.33 for the single-sample diploid case.

List[Double]  []


--kmerSize / -kmerSize

Kmer size to use in the read threading assembler
Multiple kmer sizes can be specified, using e.g. `-kmerSize 10 -kmerSize 25`.

List[Integer]  [10, 25]


--max_alternate_alleles / -maxAltAlleles

Maximum number of alternate alleles to genotype
If there are more than this number of alternate alleles presented to the genotyper (either through discovery or GENOTYPE_GIVEN_ALLELES), then only this many alleles will be used. Note that genotyping sites with many alternate alleles is both CPU and memory intensive and it scales exponentially based on the number of alternate alleles. Unless there is a good reason to change the default value, we highly recommend that you not play around with this parameter. See also {@link #MAX_GENOTYPE_COUNT}.

int  6  [ [ -∞  ∞ ] ]


--max_genotype_count / -maxGT

Maximum number of genotypes to consider at any site
If there are more than this number of genotypes at a locus presented to the genotyper, then only this many genotypes will be used. This is intended to deal with sites where the combination of high ploidy and high alt allele count can lead to an explosion in the number of possible genotypes, with extreme adverse effects on runtime performance. How does it work? The possible genotypes are simply different ways of partitioning alleles given a specific ploidy assumption. Therefore, we remove genotypes from consideration by removing alternate alleles that are the least well supported. The estimate of allele support is based on the ranking of the candidate haplotypes coming out of the graph building step. Note however that the reference allele is always kept. The maximum number of alternative alleles used in the genotyping step will be the lesser of the two: 1. the largest number of alt alleles, given ploidy, that yields a genotype count no higher than {@link #MAX_GENOTYPE_COUNT} 2. the value of {@link #MAX_ALTERNATE_ALLELES} As noted above, genotyping sites with large genotype counts is both CPU and memory intensive. Unless you have a good reason to change the default value, we highly recommend that you not play around with this parameter. See also {@link #MAX_ALTERNATE_ALLELES}.

int  1024  [ [ -∞  ∞ ] ]


--max_num_PL_values / -maxNumPLValues

Maximum number of PL values to output
Determines the maximum number of PL values that will be logged in the output. If the number of genotypes (which is determined by the ploidy and the number of alleles) exceeds the value provided by this argument, then output of all of the PL values will be suppressed.

int  100  [ [ -∞  ∞ ] ]


--maxNumHaplotypesInPopulation / -maxNumHaplotypesInPopulation

Maximum number of haplotypes to consider for your population
The assembly graph can be quite complex, and could imply a very large number of possible haplotypes. Each haplotype considered requires N PairHMM evaluations if there are N reads across all samples. In order to control the run of the haplotype caller we only take maxNumHaplotypesInPopulation paths from the graph, in order of their weights, no matter how many paths are possible to generate from the graph. Putting this number too low will result in dropping true variation because paths that include the real variant are not even considered. You can consider increasing this number when calling organisms with high heterozygosity.

int  128  [ [ -∞  ∞ ] ]


--maxReadsInMemoryPerSample / -maxReadsInMemoryPerSample

Maximum reads per sample given to traversal map() function
What is the maximum number of reads we're willing to hold in memory per sample during the traversal? This limits our exposure to unusually large amounts of coverage in the engine.

int  30000  [ [ -∞  ∞ ] ]


--maxReadsInRegionPerSample / -maxReadsInRegionPerSample

Maximum reads in an active region
When downsampling, level the coverage of the reads in each sample to no more than maxReadsInRegionPerSample reads, not reducing coverage at any read start to less than minReadsPerAlignmentStart

int  10000  [ [ -∞  ∞ ] ]


--maxTotalReadsInMemory / -maxTotalReadsInMemory

Maximum total reads given to traversal map() function
What is the maximum number of reads we're willing to hold in memory per sample during the traversal? This limits our exposure to unusually large amounts of coverage in the engine.

int  10000000  [ [ -∞  ∞ ] ]


--min_base_quality_score / -mbq

Minimum base quality required to consider a base for calling
Bases with a quality below this threshold will not be used for calling.

byte  10  [ [ -∞  ∞ ] ]


--minDanglingBranchLength / -minDanglingBranchLength

Minimum length of a dangling branch to attempt recovery
When constructing the assembly graph we are often left with "dangling" branches. The assembly engine attempts to rescue these branches by merging them back into the main graph. This argument describes the minimum length of a dangling branch needed for the engine to try to rescue it. A smaller number here will lead to higher sensitivity to real variation but also to a higher number of false positives.

int  4  [ [ -∞  ∞ ] ]


--minPruning / -minPruning

Minimum support to not prune paths in the graph
Paths with fewer supporting kmers than the specified threshold will be pruned from the graph. Be aware that this argument can dramatically affect the results of variant calling and should only be used with great caution. Using a prune factor of 1 (or below) will prevent any pruning from the graph, which is generally not ideal; it can make the calling much slower and even less accurate (because it can prevent effective merging of "tails" in the graph). Higher values tend to make the calling much faster, but also lowers the sensitivity of the results (because it ultimately requires higher depth to produce calls).

int  2  [ [ -∞  ∞ ] ]


--minReadsPerAlignmentStart / -minReadsPerAlignStart

Minimum number of reads sharing the same alignment start for each genomic location in an active region

int  10  [ [ -∞  ∞ ] ]


--numPruningSamples / -numPruningSamples

Number of samples that must pass the minPruning threshold
If fewer samples than the specified number pass the minPruning threshold for a given path, that path will be eliminated from the graph.

int  1  [ [ -∞  ∞ ] ]


--out / -o

File to which variants should be written
A raw, unfiltered, highly sensitive callset in VCF format.

VariantContextWriter  stdout


--output_mode / -out_mode

Which type of calls we should output
Experimental argument FOR USE WITH UnifiedGenotyper ONLY. When using HaplotypeCaller, use -ERC instead. When using GenotypeGVCFs, see -allSites.

The --output_mode argument is an enumerated type (OutputMode), which can have one of the following values:

EMIT_VARIANTS_ONLY
produces calls only at variant sites
EMIT_ALL_CONFIDENT_SITES
produces calls at variant sites and confident reference sites
EMIT_ALL_SITES
produces calls at any callable site regardless of confidence; this argument is intended only for point mutations (SNPs) in DISCOVERY mode or generally when running in GENOTYPE_GIVEN_ALLELES mode; it will by no means produce a comprehensive set of indels in DISCOVERY mode

OutputMode  EMIT_VARIANTS_ONLY


--pcr_indel_model / -pcrModel

The PCR indel model to use
When calculating the likelihood of variants, we can try to correct for PCR errors that cause indel artifacts. The correction is based on the reference context, and acts specifically around repetitive sequences that tend to cause PCR errors). The variant likelihoods are penalized in increasing scale as the context around a putative indel is more repetitive (e.g. long homopolymer). The correction can be disabling by specifying '-pcrModel NONE'; in that case the default base insertion/deletion qualities will be used (or taken from the read if generated through the BaseRecalibrator). VERY IMPORTANT: when using PCR-free sequencing data we definitely recommend setting this argument to NONE.

The --pcr_indel_model argument is an enumerated type (PCR_ERROR_MODEL), which can have one of the following values:

NONE
no specialized PCR error model will be applied; if base insertion/deletion qualities are present they will be used
HOSTILE
a most aggressive model will be applied that sacrifices true positives in order to remove more false positives
AGGRESSIVE
a more aggressive model will be applied that sacrifices true positives in order to remove more false positives
CONSERVATIVE
a less aggressive model will be applied that tries to maintain a high true positive rate at the expense of allowing more false positives

PCR_ERROR_MODEL  CONSERVATIVE


--phredScaledGlobalReadMismappingRate / -globalMAPQ

The global assumed mismapping rate for reads
The phredScaledGlobalReadMismappingRate reflects the average global mismapping rate of all reads, regardless of their mapping quality. This term effects the probability that a read originated from the reference haplotype, regardless of its edit distance from the reference, in that the read could have originated from the reference haplotype but from another location in the genome. Suppose a read has many mismatches from the reference, say like 5, but has a very high mapping quality of 60. Without this parameter, the read would contribute 5 * Q30 evidence in favor of its 5 mismatch haplotype compared to reference, potentially enough to make a call off that single read for all of these events. With this parameter set to Q30, though, the maximum evidence against any haplotype that this (and any) read could contribute is Q30. Set this term to any negative number to turn off the global mapping rate.

int  45  [ [ -∞  ∞ ] ]


--sample_name / -sn

Name of single sample to use from a multi-sample bam
You can use this argument to specify that HC should process a single sample out of a multisample BAM file. This is especially useful if your samples are all in the same file but you need to run them individually through HC in -ERC GVC mode (which is the recommended usage). Note that the name is case-sensitive.

String  NA


--sample_ploidy / -ploidy

Ploidy per sample. For pooled data, set to (Number of samples in each pool * Sample Ploidy).
Sample ploidy - equivalent to number of chromosome copies per pool. For pooled experiments this should be set to the number of samples in pool multiplied by individual sample ploidy.

int  2  [ [ -∞  ∞ ] ]


--standard_min_confidence_threshold_for_calling / -stand_call_conf

The minimum phred-scaled confidence threshold at which variants should be called
The minimum phred-scaled Qscore threshold to separate high confidence from low confidence calls. Only genotypes with confidence >= this threshold are emitted as called sites. A reasonable threshold is 30 for high-pass calling (this is the default).

double  10.0  [ [ -∞  ∞ ] ]


--useAllelesTrigger / -allelesTrigger

Use additional trigger on variants found in an external alleles file

boolean  false


--useFilteredReadsForAnnotations / -useFilteredReadsForAnnotations

Use the contamination-filtered read maps for the purposes of annotating variants

boolean  false


--useNewAFCalculator / -newQual

Use new AF model instead of the so-called exact model
This activates a model for calculating QUAL that was introduced in version 3.7 (November 2016). We expect this model will become the default in future versions.

boolean  false


Return to top


See also GATK Documentation Index | Tool Docs Index | Support Forum

GATK version 3.7-0-gcfedb67 built at 2017/02/09 12:35:06.