WhatsHap is a read-based phasing tool. In the typical case, it expects 1) a VCF file with variants of an individual and 2) a BAM or CRAM file with sequencing reads from that same individual. WhatsHap uses the sequencing reads to reconstruct the haplotypes and then writes out the input VCF augmented with phasing information.
The basic command-line for running WhatsHap is this:
whatshap phase -o phased.vcf --reference=reference.fasta input.vcf input.bam
The reads used for variant calling (to create the input VCF) do not need to be the same as the ones that are used for phasing. We recommend that high-quality short reads are used for variant calling and that the phasing is then done with long reads, see the recommended workflow.
If the input VCF is a multi-sample VCF, WhatsHap will haplotype all samples individually. For this, the input must contain reads from all samples.
Multiple BAM/CRAM files can be provided, even from different technologies.
If you want to phase samples of individuals that are related, you can use pedigree phasing mode to improve results. In this mode, WhatsHap is no longer purely a read-based phasing tool.
You can also phase indels by adding the option
Providing a FASTA reference with
--reference is highly recommended, in
particular for error-prone reads (PacBio, Nanopore), as it is enables the
re-alignment variant detection algorithm. If a reference is not available,
--no-reference can instead be provided, but at the expense of phasing
WhatsHap adds the phasing information to the input VCF file and writes it to the output VCF file. See below to understand how phasing information is represented.
The VCF file can also be gzip-compressed.
Features and limitations¶
WhatsHap can phase SNVs (single-nucleotide variants), insertions, deletions, MNPs (multiple adjacent SNVs) and “complex” variants. Complex variants are those that do not fall in any of the other categories, but are not structural variants. An example is the variant TGCA → AAC. Structural variants are not phased.
If no reference sequence is provided (using
SNVs, insertions and deletions can be phased.
All variants in the input VCF that are marked as being heterozygous (genotype 0/1) and that have appropriate coverage are used as input for the core phasing algorithm. If the algorithm could determine how the variant should be phased, that information will be added to the variant in the output VCF.
Variants can be left unphased for two reasons: Either the variant type is not supported or the phasing algorithm could not make a phasing decision. In both cases, the information from the input VCF is simply copied to the output VCF unchanged.
WhatsHap comes with the following subcommands.
Phase diploid variants
Phase polyploid variants
Print phasing statistics
Compare two or more phasings
Convert hapCUT output format to VCF
Remove phasing information from a VCF file
Tag reads by haplotype
Split reads by haplotype
Not all are fully documented in this manual, yet. To get help for a
whatshap SUBCOMMAND --help
Best phasing results are obtained if you sequence your sample(s) on both PacBio and Illumina: Illumina for high-quality variant calls and PacBio for its long reads.
1. Map your reads to the reference, making sure that you assign each read to a
read group (the
@RG header line in the BAM/CRAM file). WhatsHap supports VCF
files with multiple samples and in order to determine which reads belong to which
sample, it uses the ‘sample name’ (SM) of the read group. If you have a single
sample only and no or incorrect read group headers, you can run WhatsHap with
2. Call variants in your sample(s) using the most accurate reads you have. These will typically be Illumina reads, resulting in a a set of variant calls you can be reasonably confident in. If you do not know which variant caller to use, yet, we recommend FreeBayes, which is fast, Open Source and easy to use. In any case, you will need a standard VCF file as input for WhatsHap in the next step.
3. Run WhatsHap with the VCF file of high-confidence variant calls (obtained in the previous step) and with the longest reads you have. These will typically be PacBio reads. Phasing works best with long reads, but WhatsHap can use any read that covers at least two heterozygous variant calls, so even paired-end or mate-pair reads are somewhat helpful. If you have multiple sets of reads, you can combine them by providing multiple BAM/CRAM files on the command line.
Input data requirements¶
WhatsHap needs correct metadata in the VCF and the BAM/CRAM input files so that it can figure out which read belongs to which sample. As an example, assume you give WhatsHap a VCF file that starts like this:
##fileformat=VCFv4.1 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SampleA SampleB chr1 100 . A T 50.0 . . GT 0/1 0/1 ...
WhatsHap sees that there are two samples in it named “SampleA” and “SampleB”
and expects to find the reads for these samples somewhere in the BAM/CRAM file
(or files) that you provide. For that to happen, all reads belonging to a sample
must have the
RG tag, and at the same time, the read group must occur in the
header of the BAM/CRAM file and have the correct sample name. In this example, a
header might look like this:
@HD VN:1.4 SO:coordinate @SQ SN:... LN:... ... @RG ID:1 SM:SampleA @RG ID:2 SM:SampleB
@RG header line will often contain more fields, such as
the platform and
LB for the library name. WhatsHap only uses the
With the above header, the individual alignments in the file will be tagged with
a read group of
2. For example, an alignment in the BAM/CRAM file
that comes from SampleA would be tagged with
RG:Z:1. This is also described
in the SAM/BAM specification.
It is perfectly fine to have multiple read groups for a single sample:
@RG ID:1a SM:SampleA @RG ID:1b SM:SampleA @RG ID:2 SM:SampleB
What to do when the metadata is not correct¶
If WhatsHap complains that it cannot find the reads for a sample, then chances are that the metadata in the BAM/CRAM and/or VCF file are incorrect. You have the following options:
Edit the sample names in the VCF header.
Set the correct read group info in the BAM/CRAM file, for example with the Picard tool AddOrReplaceReadGroups.
Re-map the reads and pass the correct metadata-setting options to your mapping tool.
--ignore-read-groupsoption of WhatsHap. In this case, WhatsHap ignores all read group metadata in the BAM/CRAM input file(s) and assumes that all reads come from the sample that you want to phase. In this mode, you can only phase a single sample at a time. If the input VCF file contains more than one sample, you need to specify which one to phase by using
Using multiple input BAM/CRAM files¶
WhatsHap supports reading from multiple BAM or CRAM files. Just provide all BAM and CRAM files you want to use on the command-line. All the reads across all those files that to a specific sample are used to phase that sample. This can be used to combine reads from multiple technologies. For example, if you have Nanopore reads in one BAM file and PacBio reads in another CRAM file, you can run the phasing like this:
whatshap phase -o phased.vcf --reference=reference.fasta input.vcf nanopore.bam pacbio.cram
You need to make sure that read group information is accurate in all files.
Using a phased VCF instead of a BAM/CRAM file¶
It is possible to provide a phased VCF file instead of a BAM/CRAM file. WhatsHap will then treat the haplotype blocks (phase sets) it describes as “reads”. For example, if the phased VCF contains only chromosome-sized haplotypes, then each chromosome would give rise to two such “reads”. These reads are then used as any other read in the phasing algorithm, that is, they are combined with the normal sequencing reads and the best solution taking all reads into account is computed.
Read selection and merging¶
Whatshap has multiple ways to reduce the coverage of the input —
allowing faster runtimes — in a way that attempts to minimize the
amount of information lost in this process. The default behaviour is
to ensure a maximum coverage via read selection: a heuristic that
extracts a subset of the reads that is most informative for phasing.
An optional step which can be done before selection is to merge
subsets of reads together to form superreads according to a
probabilistic model of how likely subsets of reads are to appear
together on the same haplotype (p_s) or different haplotypes (p_d).
By default, this feature is not activated, however it can be activated
by specifying the
--merge-reads flag when running
phase. This model is parameterized by the following four parameters
Probability that a nucleotide is wrong
Maximum error any edge of the merging graph can have
Threshold ratio of p_s/p_d to merge two sets
Threshold ratio of p_d/p_s to not merge two sets
which can be specified by the respective flags
--negative-threshold=1000 (note that defaults are shown here for
example) when running
Representation of phasing information in VCFs¶
WhatsHap supports two ways in which it can store phasing information in a VCF
file: The standards-compliant
PS tag and the
HP tag used by GATK’s
ReadBackedPhasing tool. When you run
whatshap phase, you can select which
format is used by setting
We will use a small VCF file as an example in the following. Unphased, it looks like this:
##fileformat=VCFv4.1 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT sample1 sample2 chr1 100 . A T 50.0 . . GT 0/1 0/1 chr1 150 . C G 50.0 . . GT 0/1 1/1 chr1 300 . G T 50.0 . . GT 0/1 0/1 chr1 350 . T A 50.0 . . GT 0/1 0/1 chr1 500 . A G 50.0 . . GT 0/1 1/1
Note that sample 1 is heterozygous at all shown loci (expressed with
0/1 in the
Phasing represented by pipe (
GT fields can be phased by ordering the alleles by haplotype and
separating them with a pipe symbol (
|) instead of a slash (
##fileformat=VCFv4.1 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT sample1 sample2 chr1 100 . A T 50.0 . . GT 0|1 0/1 chr1 150 . C G 50.0 . . GT 1|0 0/1 chr1 300 . G T 50.0 . . GT 1|0 0/1 chr1 350 . T A 50.0 . . GT 0|1 0/1 chr1 500 . A G 50.0 . . GT 0|1 1/1
The alleles on one of the haplotypes of sample1 are: A, G, T, T, A. On the other haplotype, they are: T, C, G, A, G.
Swapping ones and zeros in the
GT fields would result in a VCF file with
the equivalent information.
Phasing represented by PS (“phase set”) tag¶
The pipe notation has problems when not all variants in the VCF file can be
phased. The VCF specification
PS tag to solve some of them. The
PS is a
unique identifier for a “phase set”, which is a set of variants that were
be phased relative to each other. There are usually multiple phase sets in
the file, and variants that belong to the same phase set do not need to
be consecutive in the file:
##fileformat=VCFv4.1 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT sample1 sample2 chr1 100 . A T 50.0 . . GT:PS:PQ 0|1:100:22 0/1:.:. chr1 150 . C G 50.0 . . GT:PS:PQ 1|0:100:18 0/1:.:. chr1 300 . G T 50.0 . . GT:PS:PQ 1|0:300:23 0/1:.:. chr1 350 . T A 50.0 . . GT:PS:PQ 0|1:300:42 0/1:.:. chr1 500 . A G 50.0 . . GT:PS:PQ 0|1:100:12 0/1:.:.
This VCF contains two phase sets named
300. The names are
arbitrary, but WhatsHap will choose the position of the leftmost variant
of the phase set as its name. The variants at 100, 150 and 500 are in the same
phase set, while the variants at 300 and 350 are in a different phase set.
Such a configuration is typically seen when paired-end or mate-pair reads are
used for phasing.
In the case of WhatsHap, the phase sets are identical to the connected components of the variant connectivity graph. Two variants in that graph are connected if a read exists that covers them.
The above example also shows usage of the
PQ tag for “phasing quality”.
WhatsHap currently does not add this tag.
Phasing represented by HP tag¶
GATK’s ReadBackedPhasing tool uses a different way to represent phased variants.
It is in principle the same as the combination of pipe notation with the
tag, but the
GT field is left unchanged and all information is added to a
HP tag (“haplotype identifier”) instead. This file encodes the same
information as the example above:
##fileformat=VCFv4.1 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT sample1 sample2 chr1 100 . A T 50.0 . . GT:HP 0/1:100-1,100-2 0/1:.:. chr1 150 . C G 50.0 . . GT:HP:PQ 0/1:100-2,100-1:18 0/1:.:. chr1 300 . G T 50.0 . . GT:HP:PQ 0/1:300-2,300-1:23 0/1:.:. chr1 350 . T A 50.0 . . GT:HP:PQ 0/1:300-1,300-2:42 0/1:.:. chr1 500 . A G 50.0 . . GT:HP:PQ 0/1:100-1,100-2:12 0/1:.:.
A few notes:
ReadBackedPhasing does not add the
PQto the first variant in a phase set/haplotype group. This probably means that the phasing quality is to be interpreted as relative to the previous or first variant in the set.
ReadBackedPhasing does not phase indels
- Discussions on the GATK forum on this topic:
Trusting the variant caller¶
WhatsHap will trust the variant caller to have made the right decision of
whether a variant is heterozygous or homozygous. If you use the option
--distrust-genotypes, then this assumption is softened: An optimal solution
could involve switching a variant from being heterozygous to homozygous.
Currently, if that option is enabled and such a switch occurs, the variant
will simply appear as being unphased. No change of the genotype in the VCF is
If you use this option, fewer variants will be phased.
Note that switching homozygous variants to heterozygous is never possible since only heterozygous variants are considered for phasing.
When phasing multiple samples from individuals that are related (such as
parent/child or a trio), then it is possible to provide WhatsHap with
.ped file that describes the pedigree. WhatsHap will use the
pedigree and the reads to infer a combined, much better phasing.
To turn on pedigree mode, run WhatsHap like this:
whatshap phase --ped pedigree.ped --reference=reference.fasta -o phased.vcf input.vcf input.bam
pedigree.ped is a plink-compatible PED file to describe the
relationships between samples and
input.vcf is a multi-sample VCF
with all individuals that should be phased. The reads for all individuals
can be in one or more BAM/CRAM files. WhatsHap will match them based on sample
names provided in the read groups (just like for the default single-individual
In the resulting VCF file (
haplotype alleles of a child are given as paternal|maternal, i.e.
the first allele is the one inherited from the father and the second one
the allele inherited from the mother.
PED file format¶
WhatsHap recognizes PLINK-compatible PED files. A PED file is a white-space (space or tab) delimited file with at least six columns. WhatsHap checks the column count, but uses only
column 2: individual ID
column 3: paternal ID
column 4: maternal ID
The other columns are ignored. Lines starting with
# are considered
comments and are ignored. Empty lines are also ignored.
To define a single trio, it is sufficient to have a single row in the PED file with the child, mother and father. It is not necessary to include “dummy” rows for individuals whose parents are unknown. (You will currently get a warning if you do, but this will be changed.)
Here is an example defining a trio:
# Fields: family, individual_id, paternal_id, maternal_id, sex, phenotype FAMILY01 the_child father mother 0 1
A quartet (note how multiple consecutive spaces are fine):
# Fields: family, individual_id, paternal_id, maternal_id, sex, phenotype FAMILY01 one_child father mother 0 1 FAMILY01 other_child father mother 0 1
Important: The names in the PED file must match the sample names in your VCF and BAM/CRAM files!
Pedigree phasing parameters¶
Phasing in pedigree mode requires costs for recombination events. Per
default, WhatsHap will assume a constant recombination rate across the
chromosome to be phased. The recombination rate (in cM/Mb) can be
changed by providing option
--recombrate. The default value of
1.26 cM/Mb is suitable for human genomes.
In order to use region-specific recombination rates, a genetic map file
can be provided via option
--genmap. WhatsHap expects a three-column
text file like this:
position COMBINED_rate(cM/Mb) Genetic_Map(cM) 55550 0 0 568322 0 0 568527 0 0 721290 2.685807669 0.410292036939447 723819 2.8222713027 0.417429561063975 723891 2.9813105581 0.417644215424158 ...
The first (header) line is ignored and the three columns are expected to
give the pysical position (in bp), the local recombination rate between the
given position and the position given in the previous row (in cM/Mb), and
the cumulative genetic distance from the start of the chromosome (in cM).
The above example was taken from the 1000 Genomes genetic map provided by
Since genetic map files provide information for only one chromosome, the
--genmap option has to be combined with
Creating phased references in FASTA format¶
To reconstruct the two haplotypes that a phased VCF describes, the
bcftools consensus command can be used. It is part of
bcftools. As input, it expects a reference
FASTA file and either an indexed BCF or a compressed and indexed VCF file.
To work with the uncompressed VCF output that WhatsHap produces, proceed
bgzip phased.vcf tabix phased.vcf.gz bcftools consensus -H 1 -f reference.fasta phased.vcf.gz > haplotype1.fasta bcftools consensus -H 2 -f reference.fasta phased.vcf.gz > haplotype2.fasta
reference.fasta is the reference in FASTA format and
is the phased VCF. Afterwards,
will contain the two haplotypes.
whatshap stats: Computing phasing statistics¶
stats subcommand prints phasing statistics for a single VCF file:
whatshap stats input.vcf
The TSV statistics format¶
--tsv=FILENAME, statistics are written in tab-separated value format
to a file. If you use MultiQC, the
file is automatically found and parsed and the key statistics are included in
its generated report.
The following columns are included in the TSV file.
The name of the sample the numbers in this row refer to.
The name of the chromosome the numbers in this row refer to. The special name “ALL” is used for summary statistics about all processed chromosomes.
The VCF file name to which the numbers in this row refer to.
The numbers in these following columns are computed on the variant level.
Number of biallelic variants in the input VCF, but excluding any non-SNV variants if
The number of biallelic, heterozygous variants in the input VCF. This is a subset of variants as defined above.
The number of biallelic, heterozygous SNVs in the input VCF. This is a subset of heterozygous_variants.
The number of biallelic, heterozygous variants that are not marked as phased in the input VCF. This is also a subset of heterozygous_variants.
The number of biallelic, heterozygous variants that are marked as phased in the input VCF, excluding singletons. This is again a subset of heterozygous_variants. Add singletons to get the total number of variants marked as phased in the VCF. Also note that the following is true: phased + unphased + singletons = heterozygous_variants.
The number of biallelic, heterozygous SNVs that are marked as phased in the input VCF. This is a subset of phased.
The fraction of heterozygous variants that are phased. Same as phased / heterozygous_variants.
The fraction of heterozygous SNVs that are phased. Same as phased_snvs / heterozygous_snvs.
Each phased variant is part of exactly one phase set (stored in the PS tag in VCF) or block. The numbers in the following columns describe these blocks.
The total number of phase sets/blocks.
The number of blocks that contain exactly one variant.
These columns describe the distribution of non-singleton block sizes, where the size of a block is the number of variants it contains.
Median number of variants.
Average (mean) number of variants.
Minimum number of variants.
Maximum number of variants.
Sum of the number of variants. Note that this value should be the same as phased.
The following columns describe the distribution of non-singleton block lengths, where the length of a block is the number of basepairs it covers minus 1. That is, a block with two variants at positions 2 and 5 has length 3. Interleaved blocks are cut in order to avoid artificially inflating this value.
Median block length.
Average (mean) block length.
Minimum block length.
Maximum block length.
Total sum of block lengths.
The NG50 value of the distribution of the block lengths.
Note that this is an “NG50” (not “N50”), that is, the threshold of 50% is relative to the true length of the contig as reported in the VCF header. (For an N50, the length would be the sum of the length of all blocks). It is thus possible that the sum of all block lengths does not reach 50% of the length of the contig. In this case, the value in this column is set to 0.
If no contig lengths are available, this is set to
--chr-lengthsto provide an external table with contig lengths in case the VCF header does not contain this information.
Writing haplotype blocks in TSV format¶
--block-list=filename.tsv, a file in tab-separated value
format (TSV) is created with the haplotype blocks, one block per line.
The columns are:
sample, chromosome, phase_set, from, to, variants.
value of the PS tag of this block
1-based starting position of the leftmost variant in this block
1-based starting position of the rightmost variant in this block
Number of variants in this block
This output format does not allow you to see interleaved haplotype blocks. Use –gtf` instead if you need this information.
As an example, assume the input is this VCF:
#CHROM POS ID REF ALT ... FORMAT sample ref 2 . A C ... GT 0|1 ref 5 . G T ... GT 1|0
Then this will be the output:
#sample chromosome phase_set from to variants sample ref 0 2 5 2
Writing haplotype blocks in GTF format¶
--gtf=filename.gtf, a GTF file is created that describes the haplotype blocks,
see GTF with haplotype blocks.
Visualizing phasing results¶
Sometimes it is helpful to visually inspect phasing results by looking at them in a genome browser. The steps here assume that you use the Integrative Genomics Viewer (IGV).
GTF with haplotype blocks¶
WhatsHap can create a GTF file from a phased VCF file that describes the
haplotype blocks. With phasing results in
whatshap stats --gtf=phased.gtf phased.vcf
WhatsHap will print some statistics about the phasing in the VCF, and it
will also create the file
phased.gtf in IGV in order to inspect the
haplotype block structure. In this example, there are four haplotype blocks and
it is clear which variants they connect:
Haplotype blocks can be interleaved or nested if mate-pair or paired-end reads are used for phasing. In the GTF track, you will note this because the blocks appear as “exons” (thick segments) connected by thinner horizontal lines (not shown in the screenshot).
whatshap haplotag: Tagging reads by haplotype for visualization¶
It is often a lot more interesting to also show the reads along with the variants.
For that, run the
whatshap haplotag subcommand on your phased VCF file. It
tags each read in a BAM file with
HP:i:2 depending on which
haplotype it belongs to, and also adds a
PS tag that describes in which
haplotype block the read is. With your aligned reads in
whatshap haplotag -o haplotagged.bam --reference reference.fasta phased.vcf.gz alignments.bam
--output-threads=N with N greater than 1 to use multiple threads for compressing
the BAM file, which will speed up processing significantly.
haplotag command requires a
.bcf input file
for which an index exists (use
tabix to create one).
haplotag commands re-detects the alleles in the reads in the same way
phase command does it. Since availability of a reference influences
how this is done, if you used
--reference with your
phase command, you
should alse use
When using 10X Genomics BAM files,
haplotag reads the BX tags and per default
assigns reads that belong to the same read cloud to the same haplotype.
This feature can be switched off using the
The input VCF may have been phased by any program, not only WhatsHap, as long as
the phasing info is recorded with a
Also, the reads in the input BAM file do not have to be the ones that were used for phasing. That is, you can even phase using one set of reads and then assign haplotypes to an entirely different set of reads (but from the same sample).
The command above creates a BAM file
haplotagged.bam with the tagged reads,
which you can open in IGV.
To visualize the haplotype blocks, right click on the BAM track and choose
Color Alignments by → tag. Then type in
PS and click “Ok”. Here is an
example of how this can look like. From the colors of the reads alone,
it is easy to see that there are four haplotype blocks.
You can also visualize the haplotype assignment. For that, choose
Color Alignments by → tag and type in
HP. Additionally, you may want to
also sort the alignments by the
HP tag using the option Sort Alignments by
in the right-click context menu.
Here is an impression of how this can look like. The reads colored in red belong to one haplotype, while the ones in blue belong to the other. Gray reads are those that could not be tagged, usually because they don’t cover any heterozygous variants.
whatshap split: Splitting reads according to haplotype¶
whatshap split subcommand splits a set of unmapped reads from a FASTQ or BAM input file
according to their haplotype and produces one output file for each haplotype.
The haplotype for each read must be provided through a separate file, typically
whatshap haplotag with the
This file must be in tab-separated values (TSV) format and must have at least two columns with
read name and haplotype. Two additional columns phase set and contig are required
if the command-line option
--only-largest-block was used. A header line is optional.
Input reads are provided as either BAM or FASTQ. The output format is the same as the input format. That is, reading BAM but writing FASTQ (or vice versa) is not possible.
whatshap split --output-h1 h1.fastq.gz --output-h2 h2.fastq.gz reads.fastq.gz haplotypes.tsv whatshap split --output-h1 h1.bam --output-h2 h2.bam reads.bam haplotypes.tsv
whatshap genotype: Genotyping Variants¶
Besides phasing them, WhatsHap can also re-genotype variants. Given a VCF file containing variant positions, it computes genotype likelihoods for all three genotypes (0/0, 0/1, 1/1) and outputs them in a VCF file together with a genotype prediction. Genotyping can be run using the following command:
whatshap genotype -o genotyped.vcf variants.vcf reads.bam
The predicted genotype is stored in the output VCF using the
GT tag and the
provides (log10-scaled) likelihoods computed by the genotyping algorithm.
As for phasing, providing a reference sequence is strongly recommended in order to
enable re-alignment mode:
whatshap genotype --reference ref.fasta -o genotyped.vcf variants.vcf reads.bam
If no input VCF file is available, WhatsHap can produce candidate SNV positions that can be used as an input to the above mentioned genotyping commands. This can be done by running:
whatshap find_snv_candidates ref.fasta input.bam -o variants.vcf
If Nanopore reads are used for calling SNPs, it is recommended to add option –nanopore to the above command.
whatshap polyphase: Polyploid Phasing¶
In addition to diploid phasing, WhatsHap also supports polyploid phasing
through a different algorithm. The
whatshap polyphase command works
almost the same as the
phase command with a few restrictions:
1. An additional integer argument
--ploidy must be specified. This ploidy
must match the ploidy in the provided VCF file(s). The ploidy also greatly
impacts the running time as the phasing becomes more complex. Ploidies
higher than 6 may take very long to process.
2. WhatsHap will use available genotype information from the VCF file(s), but the computed haplotypes are not guaranteed to follow these genotypes, if they deviate too much from the allele distribution among the aligned reads. Therefore the output genotypes can be different than the input genotypes.
Polyploid phasing on pedigrees is not supported yet.
4. The phasing algorithm does not consider copy number variants and always produces the provided number of haplotypes at any location.
There is no strict limitation regarding the coverage of the input reads.
However, the running time grows quadratically with the coverage. For that
reason and we do not recommend to use more than 120X. In principle it is
possible to phase diploid samples via the
polyphase command, but the
results will likely be less accurate than the diploid phasing mode, as the
latter is more specialized for the diploid case.
To achieve reliable phasing, as many haplotypes as possible should be
represented in the input reads. In case of unrepresented haplotypes, phasing
can become impossible and the output haplotypes are broken into phased blocks.
As a result, every phased variant will receive a phased block ID, such that
all variants with the same ID belong to the same haplotype block. By default
WhatsHap is very conservative with these blocks and splits them whenever it
could not resolve ambiguity between consecutive variants. This behavior can be
adjusted via the
--block-cut-sensitivity parameter. Valid values range from
0 to 5 (including) with a default of 4. A lower sensitivity will produce longer
phasing blocks, which might contain more switch errors, though. A sensitivity
of 1 means that haplotypes are only cut at positions where there was no read
connecting two consecutive variants (in any haplotype).
In VCF format, it is common to specifiy the block IDs in the
Phase set identifier field (
PS). Since this ID refers to the variant
itself, it is not possible to report which haplotypes should be cut and which
ones could be phased through. This information can be accessed via the
field in the VCF, if the
--include-haploid-sets flag is set. This is a
custom field, which is only used to provide this information. It is not
supported by other tools and also the
stats modules of
WhatsHap will still use the common
PS field to consider block borders.
whatshap compare: Comparing variant files¶
whatshap compare --names truth,whatshap --tsv-pairwise eval.tsv truth.chr1.vcf phased.chr1.vcf
To improve readibility, option
--names is used to assign the name “truth” to the first
input file and “whatshap” to the second one. Without this option, the input files are given
names “file0”, “file1” etc.
whatshap compare asseses differences mainly in terms of switch errors,
but it also computes flip errors and Hamming distance.
For switch errors, assume there are two variant files A and B and the two phase sets have these phased genotypes:
A B 0|1 0|1 0|1 0|1 0|1 1|0 1|0 0|1 1|0 0|1
The first haplotype of file A can be written as 00011 and the first haplotype of
file B as 00100 (and the second haplotype of A as 11100 and the second of B as
11011). When counting the errors between them,
whatshap compare detects one
switch error between the second and third position because the first haplotype
in A matches the first haplotye in B at positions one and two, but then the
first haplotype matches the second haplotype from position three onwards.
In other words: We can turn 00011 into 00100 by inverting all bits from position three onwards.
The Hamming distance counts the positions at which the haplotypes differ. For example, comparing 00000 to 00011 gives a Hamming distance of 2 because the haplotypes differ (in the last two alleles). On the other hand, comparing these two haplotypes incurs only one switch error.
Finally, two switch errors in a row are also counted as a flip error.
whatshap compare counts normal switch errors (which count any switches,
even those that can be seen as part of a flip error, but it also shows the
“switch/flip” decomposition, where the switches are broken down into
1) switches that are not part of a flip and 2) flip errors.
Any comparisons whatshap compare makes allow the roles of “first’ and “second” haplotype to be reversed. For example, when the first haplotype of A is 00000 and the first haplotype of B is 01111, you might guess that the Hamming distance would be 4, but that is not the case because whatshap compare notices that it is better to instead compare against the second haplotype of file B (which is 10000), resulting in Hamming distance of just 1.
Switch and flip example:
A B C 0|1 0|1 0|1 0|1 0|1 0|1 0|1 1|0 1|0 1|0 0|1 1|0 1|0 0|1 1|0
The A to B comparison contains one switch, whereas A vs C contains one flip (two switches).
Comparing phasings for sample NA12878 FILENAMES truth = truth.chr1.vcf whatshap = phased.chr1.vcf ---------------- Chromosome chr1 ---------------- VARIANT COUNTS (heterozygous / all): truth: 183135 / 314053 whatshap: 183135 / 314053 UNION: 183135 / 314053 INTERSECTION: 183135 / 314053 PAIRWISE COMPARISON: truth <--> whatshap: common heterozygous variants: 183135 (restricting to these below) non-singleton blocks in truth: 1 --> covered variants: 183135 non-singleton blocks in whatshap: 191 --> covered variants: 28764 non-singleton intersection blocks: 191 --> covered variants: 28764 ALL INTERSECTION BLOCKS: --------- phased pairs of variants assessed: 28573 switch errors: 2504 switch error rate: 8.76% switch/flip decomposition: 284/1110 switch/flip rate: 4.88% Block-wise Hamming distance: 3365 Block-wise Hamming distance [%]: 11.70% Different genotypes: 0 Different genotypes [%]: 0.00% LARGEST INTERSECTION BLOCK: --------- phased pairs of variants assessed: 1740 switch errors: 179 switch error rate: 10.29% switch/flip decomposition: 21/79 switch/flip rate: 5.75% Hamming distance: 505 Hamming distance [%]: 29.01% Different genotypes: 0 Different genotypes [%]: 0.00%
The file written by
--tsv-pairwise is in tab-separated values format and
has the following columns (example values are shown in parentheses).
- sample (NA12878)
Sample name as in the variant file header
- chromosome (chr1)
- dataset_name0 (truth)
The name of the first dataset as specified by
- dataset_name1 (whatshap)
The name of the second dataset as specified by
- file_name0 (truth.chr1.vcf)
The file name of the first variant file
- file_name1 (phased.chr1.vcf)
The file name of the second variant file
- intersection_blocks (191)
The number of intersection blocks. Blocks of the (phase sets) of the first and second variant file are split where necessary to make them cover the same set of variants. This is the number of these smaller blocks.
- all_switches (2504)
The number of switch errors, summed up over all intersection blocks.
- all_switch_rate (0.0876)
Switch error rate of all intersection blocks. Computed as all_switches divided by all_assessed_pairs.
- all_switchflips (284/1110)
Switch/flip decomposition (sum over all intersection blocks) as nonflip_switches/flips. The first number is the number of switches that are not part of a flip; the second is the number of flip errors.
nonflip_switches + 2 * flips = all_switches
(284+2*1110 = 2504 in the example)
- all_switchflip_rate 0.0488
Switches and flips from the switch/flip decomposition added up, then divided by all_switches.
Example: (284 + 1110) / 28573 = 4.88%
- largestblock_switches (179)
Number of switch errors in the largest intersection block.
- largestblock_switch_rate (0.1029)
Switch error rate of the largest intersection block.
- largestblock_switchflips 21/79
Switch/flip decompositon of the largest intersection block.
whatshap compare only looks at identical variants when it compares two files. For example, if there is a variant at a position and it is A→C in one file and it is A→G in the other file (at the same position), then these are considered different variants, and they are excluded from comparisons.