Genetics-based deconvolution workflow

Genotyped-based deconvolution leverages the unique genetic composition of individual samples to guarantee that the final cell mixture can be deconvolved. This can be conducted with genotype of origin or in a genotype-free mode using a genomic reference from unmatched donors, for example the 1000 genome project genotypes in a genotype-free. The result of this approach is a table of SNP assignment to cells that can be used to computationally infer the donors. One limitation of this approach is the need to produce additional data to genotype the individual donors in order to correctly assign the cell mixtures.

gene_demulti in hadge

Quick start

# use test dataset
nextflow run ${hadge_project_dir}/main.nf -profile test,conda_singularity --mode genetic

Example case

Case 1: Run the entire genotype-based mode without known donor genotype:

nextflow run ${hadge_project_dir}/main.nf -profile conda_singularity --outputdir ${output_dir} --mode genetic --bam ${bam_dir} --bai ${bai_dir} --barcodes ${barcodes_dir}  --nsamples_genetic ${nsamples} --fasta ${fasta_dir} --fasta_index ${fasta_index_dir} --common_variants_scSplit ${common_variant_scsplit} --common_variants_souporcell ${common_variant_souporcell} --common_variants_freemuxlet ${common_variant_freemuxlet}  --common_variants_cellsnp ${common_variant_cellsnp} --demuxlet False

Case 2: Skip cellSNP and run Vireo with available cell genotype file in VCF format:

nextflow run ${hadge_project_dir}/main.nf -profile conda --mode genetic --vireo_variant False --celldata ${cell_data_dir}

Case 3: Run Demuxlet with donor genotype:

nextflow run ${hadge_project_dir}/main.nf -profile conda --mode genetic --outputdir ${output_dir} --bam ${bam_dir} --bai ${bai_dir} --barcodes ${barcodes_dir} --vcf_donor ${donor_genotype_dir}

Case 4: Run scSplit without data pre-processing:

nextflow run ${hadge_project_dir}/main.nf -profile conda --mode genetic --scSplit_preprocess False //additional paramters as in case 1

Case 5: Run the pipeline with different combinations of parameter. This is only available in the single sample mode. The values should be separated by semicolumn and double quoted.

nextflow run ${hadge_project_dir}/main.nf -profile conda_singularity --mode genetic --alpha "0.1;0.3;0.5" //additional paramters as in case 1

Input data preparation

The input data depends heavily on the deconvolution tools. In the following table, you will find the minimal input data required by different tools.

Deconvolution methods	Input data
Demuxlet	- Alignment (BAM) - Barcode (TSV) - Genotype reference per sample (VCF)
Freemuxlet	- Alignment (BAM) - Barcode (TSV)
Vireo	- Genotype per cell (VCF or cellSNP folder)
Souporcell	- Alignment (BAM) - Barcode (TSV) - Reference genome (FASTA)
scSplit	- Alignment (BAM) - Barcode (TSV) - Genotype per pool (VCF)

You may see that some tools share some input data in common, so we set only one parameter for the same input for benchmarking.

Input data	Parameter
Alignment (BAM)	params.bam params.bai
Barcode (TSV)	params.barcodes
Genotype reference per sample (VCF)	params.vcf_donor
Genotype per pool (VCF)	params.vcf_mixed
Reference genome (FASTA)	params.fasta params.fasta_index
Genotype per cell (VCF or cellSNP folder)	params.celldata

Note, this is only the minial input input data set. You may also need the common variants from the population to run genotype-based deconvolution methods without genotype reference. Here we collect different sources of common variants for GRCh38 recommended by different methods.

Method	Paramter	Source
scSplit	common_variants_scSplit	https://melbourne.figshare.com/articles/dataset/Common_SNVS_hg38/17032163
Souporcell	common_variants_souporcell	https://github.com/wheaton5/souporcell
Freemuxlet	common_variants_freemuxlet	https://sourceforge.net/projects/cellsnp/files/SNPlist/
cellSNP-lite	common_variants_cellsnp	https://sourceforge.net/projects/cellsnp/files/SNPlist/

Pre-processing

In case you want to perform genotype-based deconvolution on pre-processed data, we provide a process in concordance with the instruction of scSplit. It only requires the Alignment (BAM) file as input. To specify which method is performed on the pre-processed data : set [method]_preprocess = True.

Variant calling

In case you don’t have any cell genotypes or variants called from mixed samples yet, we provide two processes for variant calling.

Variant calling methods	Input data	Parameter	Output
freebayes	- Alignment (BAM) - Reference genome (FASTA)	params.bam params.bai params.fasta params.fasta_index	Variants from mixed samples
cellsnp-lite	- Alignment (BAM) - Barcode (TSV) - Common SNPs (VCF)	params.bam params.bai params.barcodes params.regionsVCF	Cell genotypes

You can have following options for scsplit_variant:

• True: activate freebayes

• Otherwise: inactivate variant calling, get the input data from params.vcf_mixed

You can have following options for vireo_variant:

• True: activate cellsnp

• Otherwise: inactivate variant calling, get the input data from params.celldata

Output

By default, the pipeline is run on a single sample. In this case, all pipeline output will be saved in the folder $projectDir/$params.outdir/genetic/gene_demulti. When running the pipeline on multiple samples, the pipeline output will be found in the folder "$projectDir/$params.outdir/$sampleId/genetic/gene_demulti. To simplify this, we’ll refer to this folder as $pipeline_output_folder from now on.

Samtools

output directory: $pipeline_output_folder/samtools/samtools_[task_ID/sampleId]

• filtered.bam: processed BAM in a way that reads with any of following patterns be removed: read quality lower than 10, being unmapped segment, being secondary alignment, not passing filters, being PCR or optical duplicate, or being supplementary alignment

• filtered.bam.bai: index of filtered bam

• no_dup.bam: processed BAM after removing duplicated reads based on UMI

• sorted.bam: sorted BAM

• sorted.bam.bai: index of sorted BAM

cellSNP-lite

output directory: $pipeline_output_folder/cellsnp/cellsnp_[task_ID/sampleId]

• cellSNP.base.vcf.gz: a VCF file listing genotyped SNPs and aggregated AD & DP infomation (without GT)

• cellSNP.samples.tsv: a TSV file listing cell barcodes or sample IDs

• cellSNP.tag.AD.mtx: a file in mtx format, containing the allele depths of the alternative (ALT) alleles

• cellSNP.tag.DP.mtx: a file in mtx format, containing the sum of allele depths of the reference and alternative alleles (REF + ALT)

• cellSNP.tag.OTH.mtx: a file in mtx format, containing the sum of allele depths of all the alleles other than REF and ALT.

• cellSNP.cells.vcf.gz: a VCF file listing genotyped SNPs and AD & DP & genotype (GT) information for each cell or sample

• params.csv: specified parameters in the cellsnp-lite task

Freebayes

output directory: $pipeline_output_folder/freebayes/freebayes_[task_ID/sampleId]

• ${region}_${vcf_freebayes}: a VCF file containing variants called from mixed samples in the given chromosome region

Bcftools

output directory: $pipeline_output_folder/bcftools/bcftools_[task_ID/sampleId]

• total_chroms.vcf: a VCF containing variants from all chromosomes

• sorted_total_chroms.vcf: sorted VCF file

• filtered_sorted_total_chroms.vcf: sorted VCF file containing variants with a quality score > 30

Demuxlet

output directory: $pipeline_output_folder/demuxlet/demuxlet_[task_ID/sampleId]

• {demuxlet_out}.best: result of demuxlet containing the best guess of the sample identity, with detailed statistics to reach to the best guess

• params.csv: specified parameters in the Demuxlet task

Optionally:

• {demuxlet_out}.cel: contains the relation between numerated barcode ID and barcode. Also, it contains the number of SNP and number of UMI for each barcoded droplet.

• {demuxlet_out}.plp: contains the overlapping SNP and the corresponding read and base quality for each barcode ID.

• {demuxlet_out}.umi: contains the position covered by each umi

• {demuxlet_out}.var: contains the position, reference allele and allele frequency for each SNP.

Freemuxlet

output directory: $pipeline_output_folder/freemuxlet/freemuxlet_[task_ID/sampleId]

• {freemuxlet_out}.clust1.samples.gz: contains the best guess of the sample identity, with detailed statistics to reach to the best guess.

• {freemuxlet_out}.clust1.vcf.gz: VCF file for each sample inferred and clustered from freemuxlet

• {freemuxlet_out}.lmix: contains basic statistics for each barcode

• params.csv: specified parameters in the Freemuxlet task

Optionally:

• {freemuxlet_out}.cel: contains the relation between numerated barcode ID and barcode. Also, it contains the number of SNP and number of UMI for each barcoded droplet.

• {freemuxlet_out}.plp: contains the overlapping SNP and the corresponding read and base quality for each barcode ID.

• {freemuxlet_out}.umi: contains the position covered by each umi

• {freemuxlet_out}.var: contains the position, reference allele and allele frequency for each SNP.

• {freemuxlet_out}.clust0.samples.gz: contains the best sample identity assuming all droplets are singlets

• {freemuxlet_out}.clust0.vcf.gz}: VCF file for each sample inferred and clustered from freemuxlet assuming all droplets are singlets

• {freemuxlet_out}.ldist.gz: contains the pairwise Bayes factor for each possible pair of droplets

Vireo

output directory: $pipeline_output_folder/vireo/vireo_[task_ID/sampleId]

• donor_ids.tsv: assignment of Vireo with detailed statistics

• summary.tsv: summary of assignment

• prob_singlet.tsv.gz: contains probability of classifing singlets

• prob_doublet.tsv.gz: contains probability of classifing doublets

• GT_donors.vireo.vcf.gz: contains estimated donor genotypes

• filtered_variants.tsv: a minimal set of discriminatory variants

• GT_barcodes.png: a figure for the identified genotype barcodes

• fig_GT_distance_estimated.pdf: a plog showing estimated genotype distance

• _log.txt: vireo log file

• params.csv: specified parameters in the Vireo task

scSplit

output directory: $pipeline_output_folder/scSplit/scsplit_[task_ID/sampleId]

• alt_filtered.csv: count matrix of alternative alleles

• ref_filtered.csv: count matrix of reference alleles

• scSplit_result.csv: barcodes assigned to each of the N+1 cluster (N singlets and 1 doublet cluster), doublet marked as DBL- (n stands for the cluster number), e.g SNG-0 means the cluster 0 is a singlet cluster.

• scSplit_dist_matrix.csv: the ALT allele Presence/Absence (P/A) matrix on distinguishing variants for all samples as a reference in assigning sample to clusters, NOT including the doublet cluster, whose sequence number would be different every run (please pay enough attention to this)

• scSplit_dist_variants.txt: the distinguishing variants that can be used to genotype and assign sample to clusters

• scSplit_PA_matrix.csv: the full ALT allele Presence/Absence (P/A) matrix for all samples, NOT including the doublet cluster, whose sequence number would be different every run (please pay enough attention to this)

• scSplit_P_s_c.csv: the probability of each cell belonging to each sample

• scSplit.log: log file containing information for current run, iterations, and final Maximum Likelihood and doublet sample

• params.csv: specified parameters in the scSplit task

Souporcell

output directory: $pipeline_output_folder/souporcell/souporcell_[task_ID/sampleId]

• alt.mtx: count matrix of alternative alleles

• ref.mtx: count matrix of reference alleles

• clusters.tsv: assignment of Souporcell with the cell barcode, singlet/doublet status, cluster, log_loss_singleton, log_loss_doublet, followed by log loss for each cluster.

• cluster_genotypes.vcf: VCF with genotypes for each cluster for each variant in the input vcf from freebayes

• ambient_rna.txt: contains the ambient RNA percentage detected

• params.csv: specified parameters in the Souporcell task

Parameter

Demuxlet and dsc-pileup

demuxlet	Whether to run Demuxlet. Default: False
demuxlet_preprocess	Whether to perform pre-processing on the input params.bam for demuxlet. True: Perform pre-processing. Otherwise pre-processing is not called. Default: False
bam	Input SAM/BAM/CRAM file. Must be sorted by coordinates and indexed.
bai	Index of Input SAM/BAM/CRAM file.
barcodes	List of cell barcodes to consider.
tag_group	Tag representing readgroup or cell barcodes, in the case to partition the BAM file into multiple groups. For 10x genomics, use CB Default: CB
tag_UMI	Tag representing UMIs. For 10x genomiucs, use UB. Default: UB
sm	List of sample IDs to compare to. Default: None (use all)
vcf_donor	Input VCF/BCF file, containing GT, GP or PL for donors. It also requires the AC and AN field if plp_freemuxlet=True.
sm_list	File containing the list of sample IDs to compare. Default: None
sam_verbose	Verbose message frequency for SAM/BAM/CRAM. Default: 1000000
vcf_verbose	Verbose message frequency for VCF/BCF. Default: 10000
skip_umi	Do not generate [prefix].umi.gz file, which stores the regions covered by each barcode/UMI pair. Default: False
cap_BQ	Maximum base quality (higher BQ will be capped). Default: 40
min_BQ	Minimum base quality to consider (lower BQ will be skipped). Default: 13
min_MQ	Minimum mapping quality to consider (lower MQ will be ignored). Default: 20
min_TD	Minimum distance to the tail (lower will be ignored). Default: 0
excl_flag	SAM/BAM FLAGs to be excluded. Default: 3844
min_total	Minimum number of total reads for a droplet/cell to be considered. Default: 0
min_uniq	Minimum number of unique reads (determined by UMI/SNP pair) for a droplet/cell to be considered. Default: 0
min_snp	Minimum number of SNPs with coverage for a droplet/cell to be considered. Default: 0
min_umi	Minimum number of UMIs for a droplet/cell to be considered. Default: 0
plp	Whether to call dsc-pileup. If set True, dsc-pileup will be called. It set False, will use SAM file to call Demuxlet. Default: False
field	FORMAT field to extract the genotype, likelihood, or posterior from. Default: GT
geno_error_offset	Offset of genotype error rate. [error] = [offset] + [1-offset][coeff][1-r2]. Default: 0.1
geno_error_coeff	Slope of genotype error rate. [error] = [offset] + [1-offset][coeff][1-r2]. Default: 0.0
r2_info	INFO field name representing R2 value. Used for representing imputation quality. Default: R2
min_mac	Minimum minor allele frequency. Default: 1
min_callrate	Minimum call rate. Default: 0.5
alpha	Grid of alpha to search for. Default: 0.5
doublet-prior	Prior of doublet. Default: 0.5
demuxlet_out	Prefix out the demuxlet and dsc-pileup output files. Default: demuxlet_res

Freemuxlet and dsc-pileup

freemuxlet	Whether to run Freemuxlet. Default: True
freemuxlet_preprocess	Whether to perform pre-processing on the input params.bam for Freemuxlet. True: Perform pre-processing. Otherwise pre-processing is not called. Default: False
bam	Input SAM/BAM/CRAM file. Must be sorted by coordinates and indexed.
bai	Index of Input SAM/BAM/CRAM file.
barcodes	List of cell barcodes to consider.
nsamples_genetic	Number of samples multiplexed together
tag_group	Tag representing readgroup or cell barcodes, in the case to partition the BAM file into multiple groups. For 10x genomics, use CB Default: CB
tag_UMI	Tag representing UMIs. For 10x genomiucs, use UB. Default: UB
common_variants_freemuxlet	Input VCF/BCF file for dsc-pileup, containing the AC and AN field.
sm	List of sample IDs to compare to. Default: None (use all)
sm_list	File containing the list of sample IDs to compare. Default: None
sam_verbose	Verbose message frequency for SAM/BAM/CRAM. Default: 1000000
vcf_verbose	Verbose message frequency for VCF/BCF. Default: 10000
skip_umi	Do not generate [prefix].umi.gz file, which stores the regions covered by each barcode/UMI pair. Default: False
cap_BQ	Maximum base quality (higher BQ will be capped). Default: 40
min_BQ	Minimum base quality to consider (lower BQ will be skipped). Default: 13
min_MQ	Minimum mapping quality to consider (lower MQ will be ignored). Default: 20
min_TD	Minimum distance to the tail (lower will be ignored). Default: 0
excl_flag	SAM/BAM FLAGs to be excluded. Default: 3844
min_total	Minimum number of total reads for a droplet/cell to be considered. Default: 0
min_uniq	Minimum number of unique reads (determined by UMI/SNP pair) for a droplet/cell to be considered. Default: 0
min_umi	Minimum number of UMIs for a droplet/cell to be considered. Default: 0
min_snp	Minimum number of SNPs with coverage for a droplet/cell to be considered. Default: 0
init_cluster	Input file containing the initial cluster information. Default: None
aux_files	Turn on writing auxiliary output files. Default: False
verbose	Turn on verbose mode with specific verbosity threshold. 0: fully verbose, 100 : no verbose messages. Default: 100
doublet_prior	Prior of doublet. Default: 0.5
bf_thres	Bayes Factor Threshold used in the initial clustering. Default: 5.41
frac_init_clust	Fraction of droplets to be clustered in the very first round of initial clustering procedure. Default: 0.5
iter_init	Iteration for initial cluster assignment (set to zero to skip the iterations). Default: 10
keep_init_missing	Keep missing cluster assignment as missing in the initial iteration. Default: False
freemuxlet_out	Prefix out the freemuxlet and dsc-pileup output files. Default: freemuxlet_out

Vireo

vireo	Whether to run Vireo. Default: True
vireo_preprocess	Whether to perform pre-processing on the input params.bam for cellSNP-lite. True: Perform pre-processing. Otherwise pre-processing is not called. Default: False
vireo_variant	Whether to perform cellSNP-lite before running Vireo. True: Run cellSNP-lite. Otherwise cellSNP-lite is not called and params.celldata is used as input. Default: True
celldata	The cell genotype file in VCF format or cellSNP folder with sparse matrices.
nsamples_genetic	Number of donors to demultiplex; can be larger than provided in vcf_donor
vartrixData	The cell genotype files in vartrix outputs (three/four files, comma separated): alt.mtx,ref.mtx,barcodes.tsv,SNPs.vcf.gz. This will suppress cellData argument. Default: None
vcf_donor	The donor genotype file in VCF format. Default: None
genoTag	The tag for donor genotype: GT, GP, PL. Default: GT
noDoublet	If use, not checking doublets. Default: False
nInit	Number of random initializations, when GT needs to learn. Default: 50
extraDonor	Number of extra donor in pre-cluster, when GT needs to learn. Default: 0
extraDonorMode	Method for searching from extra donors. size: n_cell per donor; distance: GT distance between donor. Default: distance
forceLearnGT	If use, treat donor GT as prior only. Default: False
ASEmode	If use, turn on SNP specific allelic ratio. Default: False
noPlot	If use, turn off plotting GT distance. Default: False
randSeed	Seed for random initialization. Default: None
cellRange	Range of cells to process, eg. 0-10000. Default: all
callAmbientRNAs	If use, detect ambient RNAs in each cell. Default: False
nproc	Number of subprocesses for computing, sacrifices memory for speedups. Default: 4
vireo_out	Dirtectory for output files. Default: vireo_out

scSplit

scSplit	Whether to run scSplit. Default: True
scSplit_preprocess	Whether to perform pre-processing on the input params.bam for Freebayes and scSplit. True: Perform pre-processing. Otherwise pre-processing is not called. Default: True
scSplit_variant	Whether to perform Freebayes before running scSplit. True: run Freebayes. Otherwise freebayes is not called and params.vcf_mixed is used as input. Default: True
vcf_mixed	VCF from mixed BAM. Default: None
bam	Input Mixed sample BAM.
bai	Index of mixed sample BAM.
barcodes	Barcodes whitelist.
tag_group	Tag for barcode. Default: CB
common_variants_scSplit	Common SNVs for scSplit.
nsamples_genetic	Expected number of mixed samples.
refscSplit	Output Ref count matrix. Default: ref_filtered.csv
altscSplit	Output Alt count matrix. Default: alt_filtered.csv
subscSplit	The maximum number of subpopulations in autodetect mode. Default: 10
emsscSplit	Number of EM repeats to avoid local maximum. Default: 30
dblscSplit	Correction for doublets, Setting to 0 means you would expect no doublets. There will be no refinement on the results if this optional parameter is not specified or specified percentage is less than doublet rates detected during the run. Default: None
vcf_donor	Known individual genotypes to limit distinguishing variants to available variants, so that users do not need to redo genotyping on selected variants.
sample_geno	Whether to generate sample genotypes based on the split result. Default: True
scsplit_out	Dirtectory for scSplit output files. Default: scsplit_out

Souporcell

souporcell	Whether to run Souporcell. Default: True
souporcell_preprocess	Whether to perform pre-processing on the input params.bam for Souporcell. True: Perform pre-processing. Otherwise pre-processing is not called. Default: False
bam	Cellranger bam.
bai	Index of cellranger bam.
barcodes	Barcodes.tsv from cellranger
fasta	Reference fasta file.
fasta_index	Index of reference fasta file.
nsamples_genetic	Number of clusters in the BAM file.
threads	Max threads to use. Default: 5
ploidy	Ploidy, must be 1 or 2. Default: 2
min_alt	Min alt to use locus. Default: 10
min_ref	Min ref to use locus. Default: 10
max_loci	Max loci per cell, affects speed. Default: 2048
restarts	Number of restarts in clustering, when there are > 12 clusters we recommend increasing this to avoid local minima. Default: None
common_variants_souporcell	Common variant loci or known variant loci vcf, must be vs same reference fasta.
use_known_genotype	Whether to use known donor genotype. Default: True
vcf_donor	Known variants per clone in population vcf mode, must be VCF file.
known_genotypes_sample_names	Which samples in population vcf from known genotypes option represent the donors in your sample. Default: None
skip_remap	Don’t remap with minimap2, not recommended unless in conjunction with comman variants. Default: True
ignore	Set to True to ignore data error assertions. Default: False
souporcell_out	Dirtectory for Souporcell output files. Default: souporcell_out

cellSNP-lite

bam	An indexed sam/bam file, comma separated multiple samples.
barcodes	A plain file listing all effective cell barcode.
common_variants_cellsnp	A VCF file listing all candidate snps, for fetch each variants.
targetsVCF	Similar as regionsVCF, but the next position is accessed by streaming rather than indexing/jumping. Default: None
sampleList	A list file containing sample IDs, each per line. Default: None
sampleIDs	Comma separated sample ids. Default: None
genotype_cellSNP	If use, do genotyping in addition to counting. Default: True
gzip_cellSNP	If use, the output files will be zipped into BGZF format. Default: True
printSkipSNPs	If use, the SNPs skipped when loading VCF will be printed. Default: False
nproc_cellSNP	min alt to use locus. Default: 10
refseq_cellSNP	Faidx indexed reference sequence file. If set, the real (genomic) ref extracted from this file would be used for Mode 2 or for the missing REFs in the input VCF for Mode 1. Default: None.
chrom	The chromosomes to use, comma separated. Default: None (1-22)
cellTAG	Tag for cell barcodes, turn off with None. Default: CB
UMItag	Tag for UMI: UB, Auto, None. For Auto mode, use UB if barcodes are inputted, otherwise use None. None mode means no UMI but read counts. Default: Auto
minCOUNT	Minimum aggragated count. Default: 20
minMAF	Minimum minor allele frequency. Default: 0.0
doubletGL	If use, keep doublet GT likelihood. Default: False
inclFLAG	Required flags: skip reads with all mask bits unset []. Default: None
exclFLAG	Filter flags: skip reads with any mask bits set [UNMAP,SECONDARY,QCFAIL (when use UMI) or UNMAP,SECONDARY,QCFAIL,DUP (otherwise)]. Default: None
minLEN	Minimum mapped length for read filtering. Default: 30
minMAPQ	Minimum MAPQ for read filtering. Default: 20
maxDEPTH	Maximum depth for one site of one file (excluding those filtered reads), avoids excessive memory usage; 0 means highest possible value. Default: 0
countORPHAN	If use, do not skip anomalous read pairs. Default: False
cellsnp_out	Dirtectory for cellSNP-lite output files. Default: cellSNP_out

Freebayes

bam	Input BAM file to be analyzed.
bai	Index of input BAM file to be analyzed.
fasta	A reference sequence for analysis.
fasta_index	The index of the reference sequence for analysis.
stdin	Read BAM input on stdin. Default: False
targets	Limit analysis to targets listed in the BED-format file. Default: None
region	Limit analysis to the specified chromosome region, 0-base coordinates. If set to None, all chromosomes are considered. Default: None.
samples	Limit analysis to samples listed (one per line) in the file. By default FreeBayes will analyze all samples in its input BAM files. Default: None
populations	Each line of FILE should list a sample and a population which it is part of. The population-based bayesian inference model will then be partitioned on the basis of the populations. Default: None
cnv_map	Read a copy number map from the BED file. Default: None
vcf_freebayes	Name of output VCF file, must be end with .vcf. Default: vcf_freebayes_output.vcf
gvcf	Write gVCF output, which indicates coverage in uncalled regions. Default: False
gvcf_chunk	When writing gVCF output emit a record for every specified number of bases. Default: None
gvcf_dont_use_chunk	When writing gVCF output don’t emit a record for every specified number of bases. Default: None
variant_input	Use variants reported in VCF file as input to the algorithm. Variants in this file will included in the output even if there is not enough support in the data to pass input filters. Default: None
only_use_input_alleles	Only provide variant calls and genotype likelihoods for sites and alleles which are provided in the VCF input, and provide output in the VCF for all input alleles, not just those which have support in the data. Default: False
haplotype_basis_alleles	When specified, only variant alleles provided in this input VCF will be used for the construction of complex or haplotype alleles. Default: None
report_all_haplotype_alleles	At sites where genotypes are made over haplotype alleles, provide information about all alleles in output, not only those which are called. Default: False
report_monomorphic	Report even loci which appear to be monomorphic, and report all considered alleles, even those which are not in called genotypes. Default: False
pvar	Report sites if the probability that there is a polymorphism at the site is greater than N. Default: 0.0
strict_vcf	Generate strict VCF format (FORMAT/GQ will be an int). Default: False
theta	The expected mutation rate or pairwise nucleotide diversity among the population under analysis. This serves as the single parameter to the Ewens Sampling Formula prior model. Default: 0.001
ploidy	Sets the default ploidy for the analysis. Default: 2
pooled_discrete	Assume that samples result from pooled sequencing. Model pooled samples using discrete genotypes across pools. When using this flag, set –ploidy to the number of alleles in each sample or use the –cnv-map to define per-sample ploidy. Default: False
pooled_continuous	Output all alleles which pass input filters, regardless of genotyping outcome or model. Default: False
use_reference_allele	This flag includes the reference allele in the analysis as if it is another sample from the same population. Default: False
reference_quality	Assign mapping quality to the reference allele at each site and base quality. Default: 100,60
no_snps	Ignore SNP alleles. Default: False
no_indels	Ignore insertion and deletion alleles. Default: True
no_mnps	Ignore multi-nuceotide polymorphisms, MNPs. Default: True
no_complex	Ignore complex events (composites of other classes). Default: True
use_best_n_alleles	Evaluate only the best N SNP alleles, ranked by sum of supporting quality scores. Set to 0 to use all. Default: 0
haplotype_length	Allow haplotype calls with contiguous embedded matches of up to this length. Set N=-1 to disable clumping. Default: 3
min_repeat_size	When assembling observations across repeats, require the total repeat length at least this many bp. Default: 5
min_repeat_entropy	To detect interrupted repeats, build across sequence until it has entropy > N bits per bp. Set to 0 to turn off. Default: 1
no_partial_observations	Exclude observations which do not fully span the dynamically-determined detection window. Default: None, to use all observations, dividing partial support across matching haplotypes when generating haplotypes.
dont_left_align_indels	Turn off left-alignment of indels, which is enabled by default. Default: False
use_duplicate_reads	Include duplicate-marked alignments in the analysis. Default: False, to exclude duplicates marked as such in alignments
min_mapping_quality	Exclude alignments from analysis if they have a mapping quality less than Q. Default: 1
min_base_quality	Exclude alleles from analysis if their supporting base quality is less than Q. Default: 1
min_supporting_allele_qsum	Consider any allele in which the sum of qualities of supporting observations is at least Q. Default: 0
min_supporting_mapping_qsum	Consider any allele in which and the sum of mapping qualities of supporting reads is at least Q. Default: 0
mismatch_base_quality_threshold	Count mismatches toward –read-mismatch-limit if the base quality of the mismatch is >= Q. Default: 10
read_mismatch_limit	Exclude reads with more than N mismatches where each mismatch has base quality >= mismatch-base-quality-threshold. Default: None, ~unbounded
read_max_mismatch_fraction	Exclude reads with more than N [0,1] fraction of mismatches where each mismatch has base quality >= mismatch-base-quality-threshold. Default: 1.0
read_snp_limit	Exclude reads with more than N base mismatches, ignoring gaps with quality >= mismatch-base-quality-threshold. Default: None, ~unbounded
read_indel_limit	Exclude reads with more than N separate gaps. Default: None, ~unbounded
standard_filters	Use stringent input base and mapping quality filters equivalent to -m 30 -q 20 -R 0 -S 0. Default: False
min_alternate_fraction	Require at least this fraction of observations supporting an alternate allele within a single individual in in order to evaluate the position. Default: 0.05
min_alternate_count	Require at least this count of observations supporting an alternate allele within a single individual in order to evaluate the position. Default: 2
min_alternate_qsum	Require at least this sum of quality of observations supporting an alternate allele within a single individual in order to evaluate the position. Default: 0
min_alternate_total	Require at least this count of observations supporting an alternate allele within the total population in order to use the allele in analysis. Default: 1
min_coverage	Require at least this coverage to process a site. Default: 0
max_coverage	Do not process sites with greater than this coverage. Default: None, no limit
no_population_priors	Equivalent to –pooled-discrete –hwe-priors-off and removal of Ewens Sampling Formula component of priors. Default: False
hwe_priors_off	Disable estimation of the probability of the combination arising under HWE given the allele frequency as estimated by observation frequency. Default: False
binomial_obs_priors_off	Disable incorporation of prior expectations about observations. Uses read placement probability, strand balance probability, and read position (5’-3’) probability. Default: False
allele_balance_priors_off	Disable use of aggregate probability of observation balance between alleles as a component of the priors. Default: False
observation_bias	Read length-dependent allele observation biases from the file. Default: None
base_quality_cap	Limit estimated observation quality by capping base quality at Q. Default: None
prob_contamination	An estimate of contamination to use for all samples. Default: 10e-9
legacy_gls	Use legacy (polybayes equivalent) genotype likelihood calculations. Default: False
contamination_estimates	A file containing per-sample estimates of contamination, such as those generated by VerifyBamID. Default: None
report_genotype_likelihood_max	Report genotypes using the maximum-likelihood estimate provided from genotype likelihoods. Default: False
genotyping_max_iterations	Iterate no more than N times during genotyping step. Default: 1000
genotyping_max_banddepth	Integrate no deeper than the Nth best genotype by likelihood when genotyping. Default: 6
posterior_integration_limits	Integrate all genotype combinations in our posterior space which include no more than N samples with their Mth best data likelihood. Default: 1,3
exclude_unobserved_genotypes	Skip sample genotypings for which the sample has no supporting reads. Default: False
genotype_variant_threshold	Limit posterior integration to samples where the second-best genotype likelihood is no more than log(N) from the highest genotype likelihood for the sample. Default: None, ~unbounded
use_mapping_quality	Use mapping quality of alleles when calculating data likelihoods. Default: False
harmonic_indel_quality	Use a weighted sum of base qualities around an indel, scaled by the distance from the indel. Default: False, use a minimum BQ in flanking sequence.
read_dependence_factor	Incorporate non-independence of reads by scaling successive observations by this factor during data likelihood calculations. Default: 0.9
genotype_qualities	Calculate the marginal probability of genotypes and report as GQ in each sample field in the VCF output Default: False
debug	Print debugging output. Default: False
dd	Print more verbose debugging output (requires “make DEBUG”). Default: False