The Variant Call Format (VCF) Version 4.2 Specification(Superseded by the VCF v4.3 specification introduced in October 2015)23 Aug 2022
The master version of this document can be found at https://github.com/samtools/hts-specs.
This printing is version 6a6e44a from that repository, last modified on the date shown above.

1

The VCF specification

VCF is a text file format (most likely stored in a compressed manner). It contains meta-information lines, a headerline, and then data lines each containing information about a position in the genome. The format also has the ability
to contain genotype information on samples for each position.

1.1

An example##fileformat=VCFv4.2##fileDate=20090805##source=myImputationProgramV3.1##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens",taxonomy=x>##phasing=partial##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data">##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency">##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele">##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129">##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership">##FILTER=<ID=q10,Description="Quality below 10">##FILTER=<ID=s50,Description="Less than 50% of samples have data">##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth">##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality">#CHROM POS
ID
REF
ALT
QUAL FILTER INFOFORMAT
NA00001NA00002NA0000320
14370
rs6054257 G
A
29
PASS
NS=3;DP=14;AF=0.5;DB;H2
GT:GQ:DP:HQ 0|0:48:1:51,51 1|0:48:8:51,51 1/1:43:5:.,.
20
17330
.
T
A
3
q10
NS=3;DP=11;AF=0.017
GT:GQ:DP:HQ 0|0:49:3:58,50 0|1:3:5:65,3
0/0:41:3
201110696 rs6040355 A
G,T
67
PASS
NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 2|1:2:0:18,2
2/2:35:4
201230237 .
T
.
47
PASS
NS=3;DP=13;AA=T
GT:GQ:DP:HQ 0|0:54:7:56,60 0|0:48:4:51,51 0/0:61:2
20
1234567 microsat1 GTC
G,GTCT 50
PASS
NS=3;DP=9;AA=G
GT:GQ:DP
0/1:35:4
0/2:17:2
1/1:40:3

This example shows (in order): a good simple SNP, a possible SNP that has been filtered out because its quality is
below 10, a site at which two alternate alleles are called, with one of them (T) being ancestral (possibly a reference
sequencing error), a site that is called monomorphic reference (i.e. with no alternate alleles), and a microsatellite with
two alternative alleles, one a deletion of 2 bases (TC), and the other an insertion of one base (T). Genotype data are
given for three samples, two of which are phased and the third unphased, with per sample genotype quality, depth
and haplotype qualities (the latter only for the phased samples) given as well as the genotypes. The microsatellite
calls are unphased.

1.2

Meta-information lines

File meta-information is included after the ## string and must be key=value pairs. It is strongly encouraged that
information lines describing the INFO, FILTER and FORMAT entries used in the body of the VCF file be includedin the meta-information section. Although they are optional, if these lines are present then they must be completely
well-formed.

11.2.1File format

A single ‘fileformat’ field is always required, must be the first line in the file, and details the VCF format version
number. For example, for VCF version 4.2, this line should read:##fileformat=VCFv4.2
1.2.2

Information field format

INFO fields should be described as follows (first four keys are required, source and version are recommended):##INFO=<ID=ID,Number=number,Type=type,Description="description",Source="source",Version="version">
Possible Types for INFO fields are: Integer, Float, Flag, Character, and String. The Number entry is an Integerthat describes the number of values that can be included with the INFO field. For example, if the INFO field contains
a single number, then this value should be 1; if the INFO field describes a pair of numbers, then this value shouldbe 2 and so on. There are also certain special characters used to define special cases:• If the field has one value per alternate allele then this value should be ‘A’.
• If the field has one value for each possible allele (including the reference), then this value should be ‘R’.
• If the field has one value for each possible genotype (more relevant to the FORMAT tags) then this value
should be ‘G’.
• If the number of possible values varies, is unknown, or is unbounded, then this value should be ‘.’.
The ‘Flag’ type indicates that the INFO field does not contain a Value entry, and hence the Number should be
0 in this case. The Description value must be surrounded by double-quotes. Double-quote character can be escapedwith backslash \ and backslash as \\. Source and Version values likewise should be surrounded by double-quotes
and specify the annotation source (case-insensitive, e.g. “dbsnp”) and exact version (e.g. “138”), respectively forcomputational use.
1.2.3

Filter field format

FILTERs that have been applied to the data should be described as follows:##FILTER=<ID=ID,Description="description">
1.2.4

Individual format field format

Likewise, Genotype fields specified in the FORMAT field should be described as follows:##FORMAT=<ID=ID,Number=number,Type=type,Description="description">
Possible Types for FORMAT fields are: Integer, Float, Character, and String (this field is otherwise defined
precisely as the INFO field).
1.2.5

Alternative allele field format

Symbolic alternate alleles for imprecise structural variants:##ALT=<ID=type,Description="description">
The ID field indicates the type of structural variant, and can be a colon-separated list of types and subtypes. ID
values are case sensitive strings and may not contain whitespace or angle brackets. The first level type must be oneof the following:• DEL Deletion relative to the reference• INS Insertion of novel sequence relative to the reference• DUP Region of elevated copy number relative to the reference2• INV Inversion of reference sequence• CNV Copy number variable region (may be both deletion and duplication)
The CNV category should not be used when a more specific category can be applied. Reserved subtypes include:• DUP:TANDEM Tandem duplication• DEL:ME Deletion of mobile element relative to the reference• INS:ME Insertion of a mobile element relative to the reference
In addition, it is highly recommended (but not required) that the header include tags describing the reference
and contigs backing the data contained in the file. These tags are based on the SQ field from the SAM spec; all tags
are optional (see the VCF example above).
For all of the ##INFO, ##FORMAT, ##FILTER, and ##ALT metainformation, extra fields can be included
after the default fields. For example:##INFO=<ID=ID,Number=number,Type=type,Description="description",Source="description",Version="128">
In the above example, the extra fields of “Source” and “Version” are provided. Optional fields should be stored as
strings even for numeric values.
1.2.6

Assembly field format

Breakpoint assemblies for structural variations may use an external file:##assembly=url
The URL field specifies the location of a fasta file containing breakpoint assemblies referenced in the VCF records
for structural variants via the BKPTID INFO key.
1.2.7

Contig field format

As with chromosomal sequences it is highly recommended (but not required) that the header include tags describing
the contigs referred to in the VCF file. This furthermore allows these contigs to come from different files. The format
is identical to that of a reference sequence, but with an additional URL tag to indicate where that sequence can befound. For example:.
##contig=<ID=ctg1,URL=ftp://somewhere.org/assembly.fa,...>
1.2.8

Sample field format

It is possible to define sample to genome mappings as shown below:##SAMPLE=<ID=S_ID,Genomes=G1_ID;G2_ID; ...;GK_ID,Mixture=N1;N2; ...;NK,Description=S1;S2; ...;SK>

1.2.9

Pedigree field format

It is possible to record relationships between genomes using the following syntax:##PEDIGREE=<Name_0=G0-ID,Name_1=G1-ID,...,Name_N=GN-ID>
or a link to a database:##pedigreeDB=URL

3

1.3

Header line syntax

The header line names the 8 fixed, mandatory columns. These columns are as follows:
1. #CHROM
2. POS
3. ID
4. REF
5. ALT
6. QUAL
7. FILTER
8. INFOIf genotype data is present in the file, these are followed by a FORMAT column header, then an arbitrary numberof sample IDs. Duplicate sample IDs are not allowed. The header line is tab-delimited.

1.4

Data lines

1.4.1

Fixed fields

There are 8 fixed fields per record. All data lines are tab-delimited. In all cases, missing values are specified with adot (‘.’). Fixed fields are:
1. CHROM - chromosome: An identifier from the reference genome or an angle-bracketed ID String (“<ID>”)
pointing to a contig in the assembly file (cf. the ##assembly line in the header). All entries for a specific
CHROM should form a contiguous block within the VCF file. (String, no whitespace permitted, Required).
2. POS - position: The reference position, with the 1st base having position 1. Positions are sorted numerically,in increasing order, within each reference sequence CHROM. It is permitted to have multiple records with thesame POS. Telomeres are indicated by using positions 0 or N+1, where N is the length of the corresponding
chromosome or contig. (Integer, Required)
3. ID - identifier: Semicolon-separated list of unique identifiers where available. If this is a dbSNP variant it isencouraged to use the rs number(s). No identifier should be present in more than one data record. If there is
no identifier available, then the missing value should be used. (String, no whitespace or semicolons permitted)
4. REF - reference base(s): Each base must be one of A,C,G,T,N (case insensitive). Multiple bases are permitted.
The value in the POS field refers to the position of the first base in the String. For simple insertions anddeletions in which either the REF or one of the ALT alleles would otherwise be null/empty, the REF and ALT
Strings must include the base before the event (which must be reflected in the POS field), unless the event
occurs at position 1 on the contig in which case it must include the base after the event; this padding base is
not required (although it is permitted) for e.g. complex substitutions or other events where all alleles have atleast one base represented in their Strings. If any of the ALT alleles is a symbolic allele (an angle-bracketed
ID String “<ID>”) then the padding base is required and POS denotes the coordinate of the base preceding
the polymorphism. Tools processing VCF files are not required to preserve case in the allele Strings. (String,
Required).
5. ALT - alternate base(s): Comma separated list of alternate non-reference alleles. These alleles do not have to
be called in any of the samples. Options are base Strings made up of the bases A,C,G,T,N,*, (case insensitive)
or an angle-bracketed ID String (“<ID>”) or a breakend replacement string as described in the section on
breakends. The ‘*’ allele is reserved to indicate that the allele is missing due to a upstream deletion. If there
are no alternative alleles, then the missing value should be used. Tools processing VCF files are not required
to preserve case in the allele String, except for IDs, which are case sensitive. (String; no whitespace, commas,
or angle-brackets are permitted in the ID String itself)46. QUAL - quality: Phred-scaled quality score for the assertion made in ALT. i.e. −10log10 prob(call in ALT iswrong). If ALT is ‘.’ (no variant) then this is −10log10 prob(variant), and if ALT is not ‘.’ this is −10log10
prob(no variant). If unknown, the missing value should be specified. (Numeric)
7. FILTER - filter status: PASS if this position has passed all filters, i.e., a call is made at this position. Otherwise,
if the site has not passed all filters, a semicolon-separated list of codes for filters that fail. e.g. “q10;s50” might
indicate that at this site the quality is below 10 and the number of samples with data is below 50% of the total
number of samples. ‘0’ is reserved and should not be used as a filter String. If filters have not been applied,then this field should be set to the missing value. (String, no whitespace or semicolons permitted)
8. INFO - additional information: (String, no whitespace, semicolons, or equals-signs permitted; commas are
permitted only as delimiters for lists of values) INFO fields are encoded as a semicolon-separated series of shortkeys with optional values in the format: <key>=<data>[,data]. If no keys are present, the missing value must
be used. Arbitrary keys are permitted, although the following sub-fields are reserved (albeit optional):• AA : ancestral allele• AC : allele count in genotypes, for each ALT allele, in the same order as listed• AF : allele frequency for each ALT allele in the same order as listed: use this when estimated from primarydata, not called genotypes• AN : total number of alleles in called genotypes• BQ : RMS base quality at this position• CIGAR : cigar string describing how to align an alternate allele to the reference allele• DB : dbSNP membership• DP : combined depth across samples, e.g. DP=154• END : end position of the variant described in this record (for use with symbolic alleles)• H2 : membership in hapmap2• H3 : membership in hapmap3• MQ : RMS mapping quality, e.g. MQ=52• MQ0 : Number of MAPQ == 0 reads covering this record• NS : Number of samples with data
• SB : strand bias at this position• SOMATIC : indicates that the record is a somatic mutation, for cancer genomics• VALIDATED : validated by follow-up experiment• 1000G : membership in 1000 Genomes
The exact format of each INFO sub-field should be specified in the meta-information (as described above). Example
for an INFO field: DP=154;MQ=52;H2. Keys without corresponding values are allowed in order to indicate groupmembership (e.g. H2 indicates the SNP is found in HapMap 2). It is not necessary to list all the properties thata site does NOT have, by e.g. H2=0. See below for additional reserved INFO sub-fields used to encode structuralvariants.
1.4.2

Genotype fields

If genotype information is present, then the same types of data must be present for all samples. First a FORMAT
field is given specifying the data types and order (colon-separated alphanumeric String). This is followed by one field
per sample, with the colon-separated data in this field corresponding to the types specified in the format. The firstsub-field must always be the genotype (GT) if it is present. There are no required sub-fields.
As with the INFO field, there are several common, reserved keywords that are standards across the community:• GT : genotype, encoded as allele values separated by either of / or |. The allele values are 0 for the reference
allele (what is in the REF field), 1 for the first allele listed in ALT, 2 for the second allele list in ALT andso on. For diploid calls examples could be 0/1, 1 | 0, or 1/2, etc. For haploid calls, e.g. on Y, male nonpseudoautosomal X, or mitochondrion, only one allele value should be given; a triploid call might look like
0/0/1. If a call cannot be made for a sample at a given locus, ‘.’ should be specified for each missing allele5

in the GT field (for example ‘./.’ for a diploid genotype and ‘.’ for haploid genotype). The meanings of theseparators are as follows (see the PS field below for more details on incorporating phasing information into the
genotypes):◦ / : genotype unphased◦ | : genotype phased• DP : read depth at this position for this sample (Integer)• FT : sample genotype filter indicating if this genotype was “called” (similar in concept to the FILTER field).
Again, use PASS to indicate that all filters have been passed, a semicolon-separated list of codes for filtersthat fail, or ‘.’ to indicate that filters have not been applied. These values should be described in the metainformation in the same way as FILTERs (String, no whitespace or semicolons permitted)• GL : genotype likelihoods comprised of comma separated floating point log10 -scaled likelihoods for all possible
genotypes given the set of alleles defined in the REF and ALT fields. In presence of the GT field the sameploidy is expected and the canonical order is used; without GT field, diploidy is assumed. If A is the allele in
REF and B,C,... are the alleles as ordered in ALT, the ordering of genotypes for the likelihoods is given by:
F(j/k) = (k*(k+1)/2)+j. In other words, for biallelic sites the ordering is: AA,AB,BB; for triallelic sites theordering is: AA,AB,BB,AC,BC,CC, etc. For example: GT:GL 0/1:-323.03,-99.29,-802.53 (Floats)• GLE : genotype likelihoods of heterogeneous ploidy, used in presence of uncertain copy number. For example:
GLE=0:-75.22,1:-223.42,0/0:-323.03,1/0:-99.29,1/1:-802.53 (String)• PL : the phred-scaled genotype likelihoods rounded to the closest integer (and otherwise defined precisely as
the GL field) (Integers)• GP : the phred-scaled genotype posterior probabilities (and otherwise defined precisely as the GL field); intended
to store imputed genotype probabilities (Floats)• GQ : conditional genotype quality, encoded as a phred quality −10log10 p(genotype call is wrong, conditionedon the site’s being variant) (Integer)• HQ : haplotype qualities, two comma separated phred qualities (Integers)• PS : phase set. A phase set is defined as a set of phased genotypes to which this genotype belongs. Phased
genotypes for an individual that are on the same chromosome and have the same PS value are in the same
phased set. A phase set specifies multi-marker haplotypes for the phased genotypes in the set. All phased
genotypes that do not contain a PS subfield are assumed to belong to the same phased set. If the genotype in
the GT field is unphased, the corresponding PS field is ignored. The recommended convention is to use theposition of the first variant in the set as the PS identifier (although this is not required). (Non-negative 32-bit
Integer)• PQ : phasing quality, the phred-scaled probability that alleles are ordered incorrectly in a heterozygote (against
all other members in the phase set). We note that we have not yet included the specific measure for precisely
defining “phasing quality”; our intention for now is simply to reserve the PQ tag for future use as a measureof phasing quality. (Integer)• EC : comma separated list of expected alternate allele counts for each alternate allele in the same order as
listed in the ALT field (typically used in association analyses) (Integers)• MQ : RMS mapping quality, similar to the version in the INFO field. (Integer)
If any of the fields is missing, it is replaced with the missing value. For example if the FORMAT is GT:GQ:DP:HQ
then 0 | 0 : . : 23 : 23, 34 indicates that GQ is missing. Trailing fields can be dropped (with the exception of the GT
field, which should always be present if specified in the FORMAT field).
See below for additional genotype fields used to encode structural variants. Additional Genotype fields can be
defined in the meta-information. However, software support for such fields is not guaranteed.

62

Understanding the VCF format and the haplotype representation

VCF records use a single general system for representing genetic variation data composed of:• Allele: representing single genetic haplotypes (A, T, ATC).
• Genotype: an assignment of alleles for each chromosome of a single named sample at a particular locus.
• VCF record: a record holding all segregating alleles at a locus (as well as genotypes, if appropriate, for multiple
individuals containing alleles at that locus).
VCF records use a simple haplotype representation for REF and ALT alleles to describe variant haplotypes at a
locus. ALT haplotypes are constructed from the REF haplotype by taking the REF allele bases at the POS in thereference genotype and replacing them with the ALT bases. In essence, the VCF record specifies a-REF-t and thealternative haplotypes are a-ALT-t for each alternative allele.

3

INFO keys used for structural variants

When the INFO keys reserved for encoding structural variants are used for imprecise variants, the values should be
best estimates. When a key reflects a property of a single alt allele (e.g. SVLEN), then when there are multiple alt
alleles there will be multiple values for the key corresponding to each allele (e.g. SVLEN=-100,-110 for a deletion
with two distinct alt alleles).
The following INFO keys are reserved for encoding structural variants.
##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">##INFO=<ID=NOVEL,Number=0,Type=Flag,Description="Indicates a novel structural variation">##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">

For precise variants, END is POS + length of REF allele - 1, and the for imprecise variants the corresponding best
estimate.
##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">

Value should be one of DEL, INS, DUP, INV, CNV, BND. This key can be derived from the REF/ALT fields but isuseful for filtering.
##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">One value for each ALT allele. Longer ALT alleles (e.g. insertions) have positive values, shorter ALT alleles (e.g.
deletions) have negative values.
##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants">##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">##INFO=<ID=HOMLEN,Number=.,Type=Integer,Description="Length of base pair identical micro-homology at event breakpoints">##INFO=<ID=HOMSEQ,Number=.,Type=String,Description="Sequence of base pair identical micro-homology at event breakpoints">##INFO=<ID=BKPTID,Number=.,Type=String,Description="ID of the assembled alternate allele in the assembly file">

For precise variants, the consensus sequence of the alternate allele assembly is derivable from the REF and ALT
fields. However, the alternate allele assembly file may contain additional information about the characteristics of thealt allele contigs.
##INFO=<ID=MEINFO,Number=4,Type=String,Description="Mobile element info of the form NAME,START,END,POLARITY">##INFO=<ID=METRANS,Number=4,Type=String,Description="Mobile element transduction info of the form CHR,START,END,POLARITY">##INFO=<ID=DGVID,Number=1,Type=String,Description="ID of this element in Database of Genomic Variation">##INFO=<ID=DBVARID,Number=1,Type=String,Description="ID of this element in DBVAR">##INFO=<ID=DBRIPID,Number=1,Type=String,Description="ID of this element in DBRIP">##INFO=<ID=MATEID,Number=.,Type=String,Description="ID of mate breakends">##INFO=<ID=PARID,Number=1,Type=String,Description="ID of partner breakend">##INFO=<ID=EVENT,Number=1,Type=String,Description="ID of event associated to breakend">##INFO=<ID=CILEN,Number=2,Type=Integer,Description="Confidence interval around the inserted material between breakends">##INFO=<ID=DP,Number=1,Type=Integer,Description="Read Depth of segment containing breakend">##INFO=<ID=DPADJ,Number=.,Type=Integer,Description="Read Depth of adjacency">##INFO=<ID=CN,Number=1,Type=Integer,Description="Copy number of segment containing breakend">##INFO=<ID=CNADJ,Number=.,Type=Integer,Description="Copy number of adjacency">##INFO=<ID=CICN,Number=2,Type=Integer,Description="Confidence interval around copy number for the segment">##INFO=<ID=CICNADJ,Number=.,Type=Integer,Description="Confidence interval around copy number for the adjacency">

7

4

FORMAT keys used for structural variants##FORMAT=<ID=CN,Number=1,Type=Integer,Description="Copy number genotype for imprecise events">##FORMAT=<ID=CNQ,Number=1,Type=Float,Description="Copy number genotype quality for imprecise events">##FORMAT=<ID=CNL,Number=.,Type=Float,Description="Copy number genotype likelihood for imprecise events">##FORMAT=<ID=NQ,Number=1,Type=Integer,Description="Phred style probability score that the variant is novel">##FORMAT=<ID=HAP,Number=1,Type=Integer,Description="Unique haplotype identifier">##FORMAT=<ID=AHAP,Number=1,Type=Integer,Description="Unique identifier of ancestral haplotype">

These keys are analogous to GT/GQ/GL and are provided for genotyping imprecise events by copy number (either
because there is an unknown number of alternate alleles or because the haplotypes cannot be determined). CN
specifies the integer copy number of the variant in this sample. CNQ is encoded as a phred quality −10log10 p(copynumber genotype call is wrong). CNL specifies a list of log10 likelihoods for each potential copy number, startingfrom zero. When possible, GT/GQ/GL should be used instead of (or in addition to) these keys.

5

Representing variation in VCF records

5.1

Creating VCF entries for SNPs and small indels

5.1.1

Example 1

For example, suppose we are looking at a locus in the genome:
ExampleRef
1
2
3

Sequence
atCga
atGga
at - gaa t CAg a

Alteration
C is the reference base
C base is a G in some individuals
C base is deleted w.r.t. the reference sequence
A base is inserted w.r.t. the reference sequence

Representing these as VCF records would be done as follows:
1. A SNP polymorphism of C/G → {C, G} → C is the reference allele
2. A single base deletion of C → {tC, t} → tC is the reference allele
3. A single base insertion of A → {tC, tCA} → tC is the reference allele#CHROM
20
20
20
5.1.2

POS
3
2
2

ID
.
.
.

REF
C
TC
TC

ALT
G
T
TCA

QUAL
.
.
.

FILTER
PASS
PASS
PASS

INFO
DP=100
DP=100
DP=100

Example 2

Suppose I see a the following in a population of individuals and want to represent these three segregating alleles:
ExampleRef
1
2

Sequence
atCga
atGga
at - ga

Alteration
C is the reference base
C base is a G in some individuals
C base is deleted w.r.t. the reference sequence

In this case there are three segregating alleles: {tC, tG, t} with a corresponding VCF record:#CHROM
20
5.1.3

POS
2

ID
.

REF
TC

ALT
TG,T

QUAL
.

FILTERPASS

Example 3

Now suppose I have this more complex example:

8

INFO
DP=100

Example
Ref
1
2
3

Sequence
atCga
at - gaat -- aa t CAg a

Alteration
C is the reference base
C base is is deleted w.r.t. the reference sequence
C and G bases are deleted w.r.t. the reference sequence
A base is inserted w.r.t. the reference sequence

There are actually four segregating alleles: {tCg, tg, t, tCAg} over bases 2-4. This complex set of allele is represented in VCF as:#CHROM
20

POS
2

ID
.

REF
TCG

ALT
TG,T,TCAGQUAL.

FILTERPASS

INFO
DP=100Note that in VCF records, the molecular equivalence explicitly listed above in the per-base alignment is discarded,
so the actual placement of equivalent g isn’t retained. For completeness, VCF records are dynamically typed, so
whether a VCF record is a SNP, Indel, Mixed, or Reference site depends on the properties of the alleles in the record.

5.2

Decoding VCF entries for SNPs and small indels

5.2.1

SNP VCF record

Suppose I receive the following VCF record:#CHROM
20

POS
3

ID
.

REF
C

ALT
T

QUAL
.

FILTERPASS

INFO
DP=100This is a SNP since its only single base substitution and there are only two alleles so I have the two following
segregating haplotypes:
Example
Ref
1
5.2.2

Sequence
a t C g aa t T g a

Alteration
C is the reference base
C base is a T in some individuals

Insertion VCF record

Suppose I receive the following VCF record:#CHROM
20

POS
3

ID
.

REF
C

ALT
CTAG

QUAL
.

FILTERPASS

INFO
DP=100This is a insertion since the reference base C is being replaced by C [the reference base] plus three insertion bases
TAG. Again there are only two alleles so I have the two following segregating haplotypes:
Example
Ref
1
5.2.3

Sequence
a t C - - - g a
a t C T A G g a

Alteration
C is the reference basefollowing the C base is an insertion of 3 bases

Deletion VCF record

Suppose I receive the following VCF record:#CHROM
20

POS
2

ID
.

REF
TCG

ALT
T

QUAL
.

FILTERPASS

INFO
DP=100This is a deletion of two reference bases since the reference allele TCG is being replaced by just the T [thereference base]. Again there are only two alleles so I have the two following segregating haplotypes:
ExampleRef
1

Sequence
a T C G a
a T - - aAlteration
T is the (first) reference basefollowing the T base is a deletion of 2 bases

95.2.4Mixed VCF record for a microsatellite

Suppose I receive the following VCF record:#CHROM
20

POS
4

ID
.

REF
GCG

ALT
G,GCGCGQUAL.

FILTERPASS

INFO
DP=100This is a mixed type record containing a 2 base insertion and a 2 base deletion. There are are three segregating
alleles so I have the three following haplotypes:
ExampleRef
1
2

Sequencea t c G C G - - a
a t c G - - - - a
a t c G C G C G aAlteration
G is the (first) reference basefollowing the G base is a deletion of 2 bases
following the G base is a insertion of 2 bases

Note that in all of these examples dashes have been added to make the haplotypes clearer but of course theequivalence among bases isn’t provided by the VCF. Technically the following is an equivalent alignment:
Example
Ref
1
2

5.3

Sequencea t c G - - C G a
a t c G - - - - a
a t c G C G C G aAlteration
G is the (first) reference basefollowing the G base is a deletion of 2 bases
following the G base is a insertion of 2 bases

Encoding Structural Variants

The following page contains examples of structural variants encoded in VCF:

10

11

##fileformat=VCFv4.2##fileDate=20100501##reference=1000GenomesPilot-NCBI36##assembly=ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/sv/breakpoint_assemblies.fasta##INFO=<ID=BKPTID,Number=.,Type=String,Description="ID of the assembled alternate allele in the assembly file">##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants">##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">##INFO=<ID=HOMLEN,Number=.,Type=Integer,Description="Length of base pair identical micro-homology at event breakpoints">##INFO=<ID=HOMSEQ,Number=.,Type=String,Description="Sequence of base pair identical micro-homology at event breakpoints">##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">##ALT=<ID=DEL,Description="Deletion">##ALT=<ID=DEL:ME:ALU,Description="Deletion of ALU element">##ALT=<ID=DEL:ME:L1,Description="Deletion of L1 element">##ALT=<ID=DUP,Description="Duplication">##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">##ALT=<ID=INS,Description="Insertion of novel sequence">##ALT=<ID=INS:ME:ALU,Description="Insertion of ALU element">##ALT=<ID=INS:ME:L1,Description="Insertion of L1 element">##ALT=<ID=INV,Description="Inversion">##ALT=<ID=CNV,Description="Copy number variable region">##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype quality">##FORMAT=<ID=CN,Number=1,Type=Integer,Description="Copy number genotype for imprecise events">##FORMAT=<ID=CNQ,Number=1,Type=Float,Description="Copy number genotype quality for imprecise events">#CHROM POS
ID
REF
ALT
QUAL FILTER INFO
12827694 rs2376870 CGTGGATGCGGGGAC C.
PASSSVTYPE=DEL;END=2827708;HOMLEN=1;HOMSEQ=G;SVLEN=-142321682 .
T
<DEL>
6
PASS
SVTYPE=DEL;END=321887;SVLEN=-205;CIPOS=-56,20;CIEND=-10,62214477084 .
C<DEL:ME:ALU> 12
PASS
SVTYPE=DEL;END=14477381;SVLEN=-297;CIPOS=-22,18;CIEND=-12,32
39425916 .
C<INS:ME:L1> 23
PASS
SVTYPE=INS;END=9425916;SVLEN=6027;CIPOS=-16,22
312665100 .
A
<DUP>
14
PASS
SVTYPE=DUP;END=12686200;SVLEN=21100;CIPOS=-500,500;CIEND=-500,500
418665128 .
T<DUP:TANDEM> 11
PASS
SVTYPE=DUP;END=18665204;SVLEN=76;CIPOS=-10,10;CIEND=-10,10

VCF STRUCTURAL VARIANT EXAMPLE

FORMAT
NA00001
GT:GQ
1/1:14
GT:GQ
0/1:12
GT:GQ
0/1:12
GT:GQ
1/1:15
GT:GQ:CN:CNQ ./.:0:3:16.2
GT:GQ:CN:CNQ ./.:0:5:8.3

The example shows in order:
1. A precise deletion with known breakpoint, a one base micro-homology, and a sample that is homozygous forthe deletion.
2. An imprecise deletion of approximately 205 bp.
3. An imprecise deletion of an ALU element relative to the reference.
4. An imprecise insertion of an L1 element relative to the reference.
5. An imprecise duplication of approximately 21Kb. The sample genotype is copy number 3 (one extra copy ofthe duplicated sequence).
6. An imprecise tandem duplication of 76bp. The sample genotype is copy number 5 (but the two haplotypes are
not known).

5.4

Specifying complex rearrangements with breakends

An arbitrary rearrangement event can be summarized as a set of novel adjacencies. Each adjacency ties together
2 breakends. The two breakends at either end of a novel adjacency are called mates.
There is one line of VCF (i.e. one record) for each of the two breakends in a novel adjacency. A breakend record isidentified with the tag “SVTYPE=BND” in the INFO field. The REF field of a breakend record indicates a base or
sequence s of bases beginning at position POS, as in all VCF records. The ALT field of a breakend record indicatesa replacement for s. This “breakend replacement” has three parts:
1. The string t that replaces places s. The string t may be an extended version of s if some novel bases are inserted
during the formation of the novel adjacency.
2. The position p of the mate breakend, indicated by a string of the form “chr:pos”. This is the location of thefirst mapped base in the piece being joined at this novel adjacency.
3. The direction that the joined sequence continues in, starting from p. This is indicated by the orientation ofsquare brackets surrounding p.
These 3 elements are combined in 4 possible ways to create the ALT. In each of the 4 cases, the assertion is that s
is replaced with t, and then some piece starting at position p is joined to t. The cases are:
REF
s
s
s
s

ALT
t[p[
t]p]
]p]t
[p[t

Meaning
piece extending to the right of p is joined after t
reverse comp piece extending left of p is joined after t
piece extending to the left of p is joined before treverse comp piece extending right of p is joined before t

The example in Figure 1 shows a 3-break operation involving 6 breakends. It exemplifies all possible orientationsof breakends in adjacencies. Notice how the ALT field expresses the orientation of the breakends.
#CHROM
2
2
13
13
17
17
5.4.1

POS
321681321682123456123457
198982
198983

ID
bnd W
bnd V
bnd U
bnd X
bnd Y
bnd Z

REF
G
T
C
A
A
C

ALT
G]17:198982]]13:123456]TC[2:321682[[17:198983[AA]2:321681][13:123457[C

QUAL
6
6
6
6
6
6

FILTER
PASS
PASS
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND

Inserted Sequence

Sometimes, as shown in Figure 2, some bases are inserted between the two breakends, this information is also carriedin the ALT column:#CHROM
2
13

POS321682123456

ID
bnd V
bnd U

REF
T
C

ALT
]13 : 123456]AGTNNNNNCATCAGTNNNNNCA[2 : 321682[

12

QUAL
6
6

FILTER
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd V

Figure 1: All possible orientations of breakends

Figure 2: Inserted sequence between breakends
5.4.2

Large Insertions

If the insertion is too long to be conveniently stored in the ALT column, as in the 329 base insertion shown in Figure
3, it can be represented by a contig from the assembly file:#CHROM
13
13

POS123456123457

ID
bnd U
bnd V

REF
C
A

ALT
C[<ctg1>: 1[] <ctg1 >: 329]A

QUAL
6
6

FILTER
PASS
PASS

INFO
SVTYPE=BND
SVTYPE=BND

Note: In the special case of the complete insertion of a sequence between two base pairs, it is recommended touse the shorthand notation described above:#CHROM
13

POS321682

ID
INS0

REF
T

ALT
C<ctg1 >QUAL
6FILTERPASS

INFO
SVTYPE=INS

If only a portion of <ctg1 >, say from position 7 to position 214, is inserted, the VCF would be:#CHROM
13
13

POS123456123457

ID
bnd U
bnd V

REF
C
A

ALT
C[<ctg1>: 7[] <ctg1 >: 214]A

QUAL
6
6

FILTER
PASS
PASS

INFO
SVTYPE=BND
SVTYPE=BND

If <ctg1 > is circular and a segment from position 229 to position 45 is inserted, i.e., continuing from position
329 on to position 1, this is represented by adding a circular adjacency:

13

Figure 3: Inserted contig#CHROM13
13<ctg1 >
<ctg1 >

5.4.3

POS
123456123457
1
329

ID
bnd U
bnd V
bnd X
bnd Y

REF
C
A
A
T

ALT
C[<ctg1 >: 229[
] <ctg1 >: 45]A
] <ctg1 >: 329]AT[<ctg1 >: 1[

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND
SVTYPE=BND

Multiple mates

If a breakend has multiple mates such as in Figure 4 (either because of breakend reuse or of uncertainty in themeasurement), these alternate adjacencies are treated as alternate alleles:

Figure 4: Breakend with multiple mates#CHROM
2
13
17

5.4.4

POS
321682123456
198983

ID
bnd V
bnd U
bnd Z

REF
T
C
A

ALT
]13 : 123456]TC[2 : 321682[,C[17 : 198983[]13 : 123456]A

QUAL
6
6
6

FILTER
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd V,bnd Z
SVTYPE=BND;MATEID=bnd U

Explicit partners

Two breakends which are connected in the reference genome but disconnected in the variants are called partners.
Each breakend only has one partner, typically one basepair left or right. However, it is not uncommon to observe

14

loss of a few basepairs during the rearrangement. It is then possible to explicitly name a breakend’s partner, such as
in Figure 5.:

Figure 5: Partner breakends#CHROM
2
2
13
13

5.4.5

POS
321681321682123456123460

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
C
A

ALT
G[13 : 123460[]13 : 123456]TC[2 : 321682[]2 : 321681]A

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
PARID=bnd V;MATEID=bnd X
PARID=bnd W;MATEID=bnd U
PARID=bnd X;MATEID=bnd V
PARID=bnd U;MATEID=bnd W

Telomeres

For a rearrangement involving the telomere end of a reference chromosome, we define a virtual telomeric breakend
that serves as a breakend partner for the breakend at the telomere. That way every breakend has a partner. If thechromosome extends from position 1 to N, then the virtual telomeric breakends are at positions 0 and N+1. For
example, to describe the reciprocal translocation of the entire chromosome 1 into chromosome 13, as illustrated in
Figure 6:

Figure 6: Telomeres
the records would look like:#CHROM
1
1
13
13

POS
0
1
123456123457

ID
bnd X
bnd Y
bnd U
bnd V

REF
N
T
C
A

ALT
.[13 : 123457[]13 : 123456]TC[1 : 1[
]1 : 0]A

QUAL
6
6
6
6

15

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd V
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd Y
SVTYPE=BND;MATEID=bnd X

5.4.6

Event modifiers

As mentioned previously, a single rearrangement event can be described as a set of novel adjacencies. For example,
a reciprocal rearrangement such as in Figure 7:

Figure 7: Rearrangements
would be described as:#CHROM
2
2
13
13

5.4.7

POS
321681321682123456123457

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
C
A

ALT
G[13 : 123457[]13 : 123456]TC[2 : 321682[]2 : 321681]A

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd X;EVENT=RR0
SVTYPE=BND;MATEID=bnd U;EVENT=RR0
SVTYPE=BND;MATEID=bnd V;EVENT=RR0
SVTYPE=BND;MATEID=bnd W;EVENT=RR0

Inversions

Similarly an inversion such as in Figure 8:

Figure 8: Inversion
can be described equivalently in two ways. Either one uses the short hand notation described previously (recommended for simple cases):#CHROM
2

POS321682

ID
INV0

REF
T

ALT
<INV>

QUAL
6

FILTERPASS

INFO
SVTYPE=INV;END=421681

or one describes the breakends:#CHROM
2
2
2
2

5.4.8

POS
321681321682421681421682

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
A
C

ALT
G]2 : 421681][2 : 421682[TA]2 : 321681][2 : 321682[C

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U;EVENT=INV0
SVTYPE=BND;MATEID=bnd X;EVENT=INV0
SVTYPE=BND;MATEID=bnd W;EVENT=INV0
SVTYPE=BND;MATEID=bnd V;EVENT=INV0

Uncertainty around breakend location

It sometimes is difficult to determine the exact position of a break, generally because of homologies between thesequences being modified, such as in Figure 9. The breakend is then placed arbitrarily at the left most position,and the uncertainty is represented with the CIPOS tag. The ALT string is then constructed assuming this arbitrary
breakend choice.
The figure above represents a nonreciprocal translocation with microhomology. Even if we know that breakend
U is rearranged with breakend V, actually placing these breaks can be extremely difficult. The red and green dashed16

Figure 9: Homology
lines represent the most extreme possible recombination events which are allowed by the sequence evidence available.
We therefore place both U and V arbitrarily within the interval of possibility:#CHROM
2
13

POS321681123456

ID
bnd V
bnd U

REF
T
A

ALT
T]13 : 123462]
A]2 : 321687]

QUAL
6
6

FILTER
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U;CIPOS=0,6
SVTYPE=BND;MATEID=bnd V;CIPOS=0,6

Note that the coordinate in breakend U’s ALT string does not correspond to the designated position of breakend
V, but to the position that V would take if U’s position were fixed (and vice-versa). The CIPOS tags describe theuncertainty around the positions of U and V.
The fact that breakends U and V are mates is preserved thanks to the MATEID tags. If this were a reciprocaltranslocation, then there would be additional breakends X and Y, say with X the partner of V on Chr 2 and Y
the partner of U on Chr 13, and there would be two more lines of VCF for the XY novel adjacency. Dependingon which positions are chosen for the breakends X and Y, it might not be obvious that X is the partner of V and
Y is the partner of U from their locations alone. This partner relationship can be specified explicitly with the tag
PARID=bnd X in the VCF line for breakend V and PARID=bnd Y in the VCF line for breakend U, and vice versa.
5.4.9

Single breakends

We allow for the definition of a breakend that is not part of a novel adjacency, also identified by the tag SVTYPE=BND. We call these single breakends, because they lack a mate. Breakends that are unobserved partners ofbreakends in observed novel adjacencies are one kind of single breakend. For example, if the true situation is known
to be either as depicted back in Figure 1, and we only observe the adjacency (U,V), and no adjacencies for W, X, Y,
or Z, then we cannot be sure whether we have a simple reciprocal translocation or a more complex 3-break operation.
Yet we know the partner X of U and the partner W of V exist and are breakends. In this case we can specify theseas single breakends, with unknown mates. The 4 lines of VCF representing this situation would be:#CHROM
2
2
13
13

POS
321681321682123456123457

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
C
A

ALT
G.
]13 : 123456]TC[2 : 321682[
.A

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd V
SVTYPE=BND

On the other hand, if we know a simple reciprocal translocation has occurred as in Figure 7, then even if we haveno evidence for the (W,X) adjacency, for accounting purposes an adjacency between W and X may also be recorded inthe VCF file. These two breakends W and X can still be crossed-referenced as mates. The 4 VCF records describing
this situation would look exactly as below, but perhaps with a special quality or filter value for the breakends W
and X.
Another possible reason for calling single breakends is an observed but unexplained change in copy number alonga chromosome.

17

#CHROM
3
3
3

POS
12665
12665
13686

ID
bnd X
.
bnd Y

REF
A
A
T

ALT
.A
<DUP>
T.

QUAL
6
14
6

FILTER
PASS
PASS
PASS

INFO
SVTYPE=BND;CIPOS=-50,50
SVTYPE=DUP;END=13686;CIPOS=-50,50;CIEND=-50,50
SVTYPE=BND;CIPOS=-50,50

Finally, if an insertion is detected but only the first few base-pairs provided by overhanging reads could beassembled, then this inserted sequence can be provided on that line, in analogy to paired breakends:#CHROM
3
3
3

5.4.10

POS
12665
12665
13686

ID
bnd X
.
bnd Y

REF
A
A
T

ALT
.TGCA
<DUP>
TCC.

QUAL
6
14
6

FILTER
PASS
PASS
PASS

INFO
SVTYPE=BND;CIPOS=-50,50
SVTYPE=DUP;END=13686;CIPOS=-50,50;CIEND=-50,50
SVTYPE=BND;CIPOS=-50,50

Sample mixtures

It may be extremely difficult to obtain clinically perfect samples, with only one type of cell. Let’s imagine that
two samples are taken from a cancer patient: healthy blood, and some tumor tissue with an estimated 30% stromal
contamination. This would then be expressed in the header as:##SAMPLE=<ID=Blood,Genomes=Germline,Mixture=1.,Description="Patient germline genome">##SAMPLE=<ID=TissueSample,Genomes=Germline;Tumor,Mixture=.3;.7,Description="Patient germline genome;Patient tumor genome">

Because of this distinction between sample and genome, it is possible to express the data along both distinctions.
For example, in a first pass, a structural variant caller would simply report counts per sample. Using the example ofthe inversion just above, the VCF code could become:#CHROM
2
2
13
13

POS
321681321682421681421682

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
A
C

ALT
G]2 : 421681][2 : 421682[TA]2 : 321681][2 : 321682[C

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd X
SVTYPE=BND;MATEID=bnd W
SVTYPE=BND;MATEID=bnd V

FORMAT
GT:DPADJ
GT:DPADJ
GT:DPADJ
GT:DPADJ

Blood
0:32
0:29
0:34
0:31

TissueSample
0|1 : 9, 21
0|1 : 11, 25
0|1 : 10, 23
0|1 : 8, 20

However, a more evolved algorithm could attempt actually deconvolving the two genomes and generating copy
number estimates based on the raw data:#CHROM
2
2
13
13

5.4.11

POS
321681321682421681421682

ID
bnd W
bnd V
bnd U
bnd X

REF
G
T
A
C

ALT
G]2 : 421681][2 : 421682[TA]2 : 321681][2 : 321682[C

QUAL
6
6
6
6

FILTER
PASS
PASS
PASS
PASS

INFO
SVTYPE=BND;MATEID=bnd U
SVTYPE=BND;MATEID=bnd X
SVTYPE=BND;MATEID=bnd W
SVTYPE=BND;MATEID=bnd V

FORMAT
GT:CNADJ
GT:CNADJ
GT:CNADJ
GT:CNADJ

Blood
0:1
0:1
0:1
0:1

TumorSample
1:1
1:1
1:1
1:1

Clonal derivation relationships

In cancer, each VCF file represents several genomes from a patient, but one genome is special in that it represents
the germline genome of the patient. This genome is contrasted to a second genome, the cancer tumor genome. In
the simplest case the VCF file for a single patient contains only these two genomes. This is assumed in most of the
discussion of the sections below.
In general there may be several tumor genomes from the same patient in the VCF file. Some of these may besecondary tumors derived from an original primary tumor. We suggest the derivation relationships between genomes
in a cancer VCF file be represented in the header with PEDIGREE tags.
Analogously, there might also be several normal genomes from the same patient in the VCF (typically double
normal studies with blood and solid tissue samples). These normal genomes are then considered to be derived fromthe original germline genome, which has to be inferred by parsimony.
The general format of a PEDIGREE line describing asexual, clonal derivation is:##PEDIGREE=<Derived=ID2,Original=ID1>
This line asserts that the DNA in genome ID2 is asexually or clonally derived with mutations from the DNA in
genome ID1. This is the asexual analog of the VCF format that has been proposed for family relationships between
genomes, i.e., there is one entry per trio of the form:##PEDIGREE=<Child=CHILD-GENOME-ID,Mother=MOTHER-GENOME-ID,Father=FATHER-GENOME-ID>
Let’s consider a cancer patient VCF file with 4 genomes: germline, primary tumor, secondary tumor1, andsecondary tumor2 as illustrated in Figure 10. The primary tumor is derived from the germline and the secondary
tumors are each derived independently from the primary tumor, in all cases by clonal derivation with mutations.
The PEDIGREE lines would look like:18

Figure 10: Pedigree example##PEDIGREE=<Derived=PRIMARY-TUMOR-GENOME-ID,Original=GERMLINE-GENOME-ID>##PEDIGREE=<Derived=SECONDARY1-TUMOR-GENOME-ID,Original=PRIMARY-TUMOR-GENOME-ID>##PEDIGREE=<Derived=SECONDARY2-TUMOR-GENOME-ID,Original=PRIMARY-TUMOR-GENOME-ID>
Alternately, if data on the genomes is compiled in a database, a simple pointer can be provided:##pedigreeDB=URL
The most general form of a pedigree line is:##PEDIGREE=<Name_0=G0-ID,Name_1=G1-ID,...,Name_N=GN-ID>
This means that the genome Name 0 is derived from the N ≥ 1 genomes Name 1, ..., Name N. Based on thesederivation relationships two new pieces of information can be specified.
Firstly, we wish to express the knowledge that a variant is novel to a genome, with respect to its parent genome.
Ideally, this could be derived by simply comparing the features on either genomes. However, insufficient data or
sample mixtures might prevent us from clearly determining at which stage a given variant appeared. This would be
represented by a mutation quality score.
Secondly, we define a haplotype as a set of variants which are known to be on the same chromosome in the
germline genome. Haplotype identifiers must be unique across the germline genome, and are conserved along clonal
lineages, regardless of mutations, rearrangements, or recombination. In the case of the duplication of a region withina haplotype, one copy retains the original haplotype identifier, and the others are considered to be novel haplotypes
with their own unique identifiers. All these novel haplotypes have in common their haplotype ancestor in theparent genome.
5.4.12

Phasing adjacencies in an aneuploid context

In a cancer genome, due to duplication followed by mutation, there can in principle exist any number of haplotypes
in the sampled genome for a given location in the reference genome. We assume each haplotype that the user
chooses to name is named with a numerical haplotype identifier. Although it is difficult with current technologies toassociate haplotypes with novel adjacencies, it might be partially possible to deconvolve these connections in the near
future. We therefore propose the following notation to allow haplotype-ambiguous as well as haplotype-unambiguousconnections to be described. The general term for these haplotype-specific adjacencies is bundles.
The diagram in Figure 11 will be used to support examples below:
In this example, we know that in the sampled genome:
1. A reference bundle connects breakend U, haplotype 5 on chr13 to its partner, breakend X, haplotype 5 on
chr13,

19

Figure 11: Phasing
2. A novel bundle connects breakend U, haplotype 1 on chr13 to its mate breakend V, haplotype 11 on chr2, andfinally,
3. A novel bundle connects breakend U, haplotypes 2, 3 and 4 on chr13 to breakend V, haplotypes 12, 13 or 14
on chr2 without any explicit pairing.
These three are the bundles for breakend U. Each such bundle is referred to as a haplotype of the breakend U.
Each allele of a breakend corresponds to one or more haplotypes. In the above case there are two alleles: the 0 allele,corresponding to the adjacency to the partner X, which has haplotype (1), and the 1 allele, corresponding to the two
haplotypes (2) and (3) with adjacency to the mate V.
For each haplotype of a breakend, say the haplotype (2) of breakend U above, connecting the end of haplotype 1
on a segment of Chr 13 to a mate on Chr 2 with haplotype 11, in addition to the list of haplotype-specific adjacencies
that define it, we can also specify in VCF several other quantities. These include:
1. The depth of reads on the segment where the breakend occurs that support the haplotype, e.g., the depth ofreads supporting haplotype 1 in the segment containing breakend U
2. The estimated copy number of the haplotype on the segment where the breakend occurs
3. The depth of paired-end or split reads that support the haplotype-specific adjacencies, e.g., that support theadjacency between haplotype 1 on Chr 13 to haplotype 11 on Chr 2
4. The estimated copy number of the haplotype-specific adjacencies
5. An overall quality score indicating how confident we are in this asserted haplotype
These are specified using the using the DP, CN, BDP, BCN, and HQ subfields, respectively. The total information
available about the three haplotypes of breakend U in the figure above may be visualized in a table as follows.
AlleleHaplotype
Segment Depth
Segment Copy Number
Bundle Depth
Bundle Copy Number
Haplotype quality

1
1>11
5
1
4
1
30

1
2,3,4>12,13,14
17
3
0
3
40

0
5>5
4
1
3
1
40

20

6

BCF specification

VCF is very expressive, accommodates multiple samples, and is widely used in the community. Its biggest drawback
is that it is big and slow. Files are text and therefore require a lot of space on disk. A normal batch of 1̃00 exomes
is a few GB, but large-scale VCFs with thousands of exome samples quickly become hundreds of GBs. Because thefile is text, it is extremely slow to parse.
Overall, the idea behind is BCF2 is simple. BCF2 is a binary, compressed equivalent of VCF that can be indexedwith tabix and can be efficiently decoded from disk or streams. For efficiency reasons BCF2 only supports a subset
of VCF, in that all info and genotype fields must have their full types specified. That is, BCF2 requires that if e.g.
an info field AC is present then it must contain an equivalent VCF header line noting that AC is an allele indexedarray of type integer.

6.1

Overall file organization

A BCF2 file is composed of a mandatory header, followed by a series of BGZF compressed blocks of binary BCF2
records. The BGZF blocks allow BCF2 files to be indexed with tabix.
BGZF blocks are composed of a VCF header with a few additional records and a block of records. Following thelast BGZF BCF2 record block is an empty BGZF block (a block containing zero type of data), indicating that therecords are done.
A BCF2 header follows exactly the specification as VCF, with a few extensions/restrictions:• All BCF2 files must have fully specified contigs definitions. No record may refer to a contig not present in theheader itself.
• All INFO and GENOTYPE fields must be fully typed in the BCF2 header to enable type-specific encoding of
the fields in records. An error should be thrown when converting a VCF to BCF2 when an unknown or not
fully specified field is encountered in the records.

6.2

HeaderThe BCF2 header contains the following items:
Field
magic
major version
minor version
l texttext

Typechar[3]
uint8 tuint8 tuint32 tchar[l text]

Notes
The characters “BCF”
2
1
Length of the “text” field, including the terminating NUL character
VCF format header text, NUL-terminated

The “magic” field and version numbers can be used to quickly examine the file to determine that it’s a BCF2.1 file.
The “text” field contains the standard VCF header lines in text format, from ##fileformat=VCFv4.2 to #CHROM ...
inclusive, terminated by a NUL character.
Because the type is encoded directly in the header, the recommended extension for BCF2 formatted files is.bcf. BCF2 supports encoding values in a dictionary of strings. The string map is provided by the keyword##dictionary=S0,S1,...,SN as a comma-separate ordered list of strings. See the “Dictionary of strings” section
for more details.
6.2.1

Dictionary of strings

Throughout the BCF file most string values are be specified by integer reference to their dictionary values. For
example, the following VCF record:##INFO=<ID=ASP,Number=0,Type=Flag,Description="X">##INFO=<ID=RSPOS,Number=1,Type=Integer,Description="Y">##INFO=<ID=dbSNPBuildID,Number=1,Type=Integer,Description="Z">##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens">#CHROM POS ID REF ALT QUAL FILTER INFO
20 10144 rs144773400 TA T . PASS ASP;RSPOS=10145,dbSNPBuildID=134
20 10228 rs143255646 TA T . PASS ASP;RSPOS=10229;dbSNPBuildID=134

21

would be encoded inline in BCF2 by reference to the relative position of the header line in the header (ASP=1,
RSPOS=2, dbSNPBuildID=3, and PASS implicitly encoded in the last offset PASS=4)##INFO=<ID=ASP,Number=0,Type=Flag,Description="X">##INFO=<ID=RSPOS,Number=1,Type=Integer,Description="Y">##INFO=<ID=dbSNPBuildID,Number=1,Type=Integer,Description="Z">##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens">#CHROM POS ID REF ALT QUAL FILTER INFO
0 10144 rs144773400 TA T . s0 s1;s2=10145;s3=134
0 10228 rs143255646 TA T . s0 s1;s2=10229;s3=134

Note that the dictionary encoding has the magic prefix ‘s’ here to indicate that the field’s value is actually in the
dictionary entry giving by the subsequent offset. This representation isn’t actually the one used in BCF2 records
but it provides a clean visual guide for the above example. Note also how the contig has been recoded as a offset
into the list of contig declarations.
Note that “PASS” is always implicitly encoded as the first entry in the header dictionary. This is because VCF
allows FILTER fields to be PASS without explicitly listing this in the FILTER field itself.
6.2.2

Dictionary of contigs

The CHROM field in BCF2 is encoded as an integer offset into the list of ##contig field headers in the VCF header.
The offsets begin, like the dictionary of strings, at 0. So for example if in BCF2 the contig value is 10, this indicatesthat the actual chromosome is the 11th element in the ordered list of ##contig elements. Here’s a more concrete
example:##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens">##contig=<ID=21,length=46944323,assembly=B36,md5=f1b74b7f9f4cdbaeb6832ee86cb426c6,species="Homo sapiens">##contig=<ID=22,length=49691432,assembly=B36,md5=2041e6a0c914b48dd537922cca63acb8,species="Homo sapiens">#CHROM POS ID REF ALT QUAL FILTER INFO
20 1 . T A . PASS .
21 2 . T A . PASS .
22 3 . T A . PASS .

the actual CHROM field values in the encoded BCF2 records would be 0, 1, and 2 corresponding to the first(offset 0) ##contig element, etc.

6.3

BCF2 records

In BCF2, the original VCF records are converted to binary and encoded as BGZF blocks. Each record is conceptually
two parts. First is the site information (chr, pos, INFO field). Immediately after the sites data is the genotype data
for every sample in the BCF2 file. The genotype data may be omitted entirely from the record if there is no genotype
data in the VCF file. Note that it’s acceptable to not BGZF compress a BCF2 file, but not all readers may handle
this uncompressed encoding.

22

6.3.1Site encoding

Field
l sharedl indiv
CHROM
POS
rlen

Type
uint32 t
uint32 t
int32 tint32 tint32 tQUAL
n infon allelen samplefloat
uint16 t
uint16 t
uint24 tn fmt
ID
REF+ALTuint8 ttyped string
list of n allele typed strings

FILTER

Typed vector of integers

INFO

field key/value pairs

Genotype values

see below

6.3.2

Notes
Data length from CHROM to the end of INFO
Data length of FORMAT and individual genotype fields
Given as an offset into the mandatory contig dictionary
0-based leftmost coordinate
Length of the record as projected onto the reference sequence. Must
be the length of the REF allele or the declared length of a symbolic
allele respecting the END attribute
Variant quality; 0x7F800001 for a missing value
The number of INFO fields in this record
The number of REF+ALT alleles in this record
The number of samples in this record, stored as a three byte littleendian value. Note that n sample must be equal to the number ofsamples in the header
The number of FORMAT keys. See 6.3.2
Variant identifier; 0x07 for a missing value
the first allele is REF (mandatory) followed by n alleles - 1 ALT
alleles, all encoded as typed strings
a vector of integer offsets into dictionary, one for each FILTER fieldvalue. “.” is encoded as MISSING
n info pairs of typed vectors. The first value must be a typed atomic
integer giving the offset of the INFO field key into the dictionary. The
second value is a typed vector giving the value of the fieldsee below

Genotype encoding

Genotype fields are encoded not by sample as in VCF but rather by field, with a vector of values for each sample
following each field. In BCF2, the following VCF line:
FORMAT
GT:GQ:DP

NA00001
0/0:48:1

NA00002
0/1:9:8NA00003
1/1:43:5

would encoded as the equivalent of:
GT=0/0,0/1,1/1

GQ=48,9,43

DP=1,8,5

Suppose there are i genotype fields in a specific record. Each i is encoded by a triplet:
BCF2 site information encoding
Field
fmt key
fmt typeTypetyped int
uint8 t+fmt values(by fmt type)

Array of values

Notes
Format key as an offset into the dictionary
Typing byte of each individual value, possibly followed by a typed
int for the vector length. In effect this is the same as the typing
value for a single vector, but for genotype values it appears only
once before the array of genotype field values
The information of each individual is concatenated in the vector.
Every value is of the same fmt type. Variable-length vectors arepadded with missing values; a string is stored as a vector of char

The value is always implicitly a vector of N values, where N is the number of samples. The type byte of the value
field indicates the type of each value of the N length vector. For atomic values this is straightforward (size = 1).
But if the type field indicates that the values are themselves vectors (as often occurs, such as with the PL field) theneach of the N values in the outer vector is itself a vector of values. This encoding is efficient when every value in the
genotype field vector has the same length and type.
Note that the specific order of fields isn’t defined, but it’s probably a good idea to respect the ordering as specified
in the input VCF/BCF2 file.
If there are no sample records (genotype data) in this VCF/BCF2 file, the size of the genotypes block will be 0.

23

6.3.3

Type encoding

In BCF2 values are all strongly typed in the file. The type information is encoded in a prefix byte before the value,
which contains information about the low-level type of the value(s) such as int32 or float, as well as the number of
elements in the value. The encoding is as follows:
BCF2 type descriptor byte
Bit
5,6,7,8 bits

1,2,3,4 bits

Meaning
The number of elements of the upcoming type. For atomic values,
the size must be 1. If the size is set to 15, this indicates that thevector has 15 or more elements, and that the subsequent BCF2
byte stream contains a typed Integer indicating the true size ofthe vector. If the size is between 2-14, then this Integer is omitted
from the stream and the upcoming stream begins immediately
with the first value of the vector. A size of 0 indicates that thevalue is MISSING.
Type

The final four bits encodes an unsigned integer that indicates the type of the upcoming value in the data stream.
BCF2 types
Lowest 4 bits
0
1
2
3
5
7

Hexadecimal encoding
0x00
0x?1
0x?2
0x?3
0x?5
0x?7

Corresponding atomic type
A MISSING value without an explicit type provided
Integer [8 bit]
Integer [16 bit]
Integer [32 bit]
Float [32 bit]
Character, ASCII encoded in 8 bits

In BCF2 strings are type=7 with an appropriate length. Single characters are represented as strings withlength=1. Type codes 4,6,8–15 are reserved for future use.
Integers may be encoded as 8, 16, or 32 bit values, in little-endian order. It is up to the encoder to determine
the appropriate ranged value to use when writing the BCF2 file. For each integer size, the values 0x80, 0x8000,
0x80000000 are interpreted as missing values.
Floats are encoded as single-precision (32 bit) in the basic format defined by the IEEE-754-1985 standard. Thisis the standard representation for floating point numbers on modern computers, with direct support in programming
languages like C and Java (see Java’s Double class for example). BCF2 supports the full range of values from
-Infinity to +Infinity, including NaN. BCF2 needs to represent missing values for single precision floating point
numbers. This is accomplished by writing the NaN value as the quiet NaN (qNaN), while the MISSING value is
encoded as a signaling NaN. From the NaN wikipedia entry, we have:
For example, a bit-wise example of a IEEE floating-point standard single precision (32-bit) NaN would be: s111
1111 1axx xxxx xxxx xxxx xxxx xxxx where s is the sign (most often ignored in applications), a determines the typeof NaN, and x is an extra payload (most often ignored in applications). If a = 1, it is a quiet NaN; if a is zero andthe payload is nonzero, then it is a signaling NaN.
A good way to understand these values is to play around with the IEEE encoder website.
BCF2 bit representation for floating point NaN and MISSING
Value
32-bit precision
Hexadecimal representation
NaN
0b0111 1111 1100 0000 0000 0000 0000 0000 0x7FC00000
MISSING 0b0111 1111 1000 0000 0000 0000 0000 0001 0x7F800001
Character values are not explicitly typed in BCF2. Instead, VCF Character values should be encoded by asingle character string. As with Strings, UNICODE characters are not supported.
Flags values – which can only appear in INFO fields – in BCF2 should be encoded by any non-MISSING value.
The recommended best practice is to encode the value as an 1-element INT8 (type 0x11) with value of 1 to indicate

24present. Because FLAG values can only be encoded in INFO fields, BCF2 provides no mechanism to encode FLAG
values in genotypes, but could be easily extended to do so if allowed in a future VCF version.
String values have two basic encodings. For INFO, FORMAT, and FILTER keys these are encoded by integer
offsets into the header dictionary. For string values, such as found in the ID, REF, ALT, INFO, and FORMAT fields,
strings are encoded as typed array of ASCII encoded bytes. The array isn’t terminated by a NUL byte. The lengthof the string is given by the length of the type descriptor.
Suppose you want to encode the string “ACAC”. First, we need the type descriptor byte, which is the string type0x07 or’d with inline size (4) yielding the type byte of 0x40 | 0x07 = 0x47. Immediately following the type byte is
the four byte ASCII encoding of “ACAC”: 0x41 0x43 0x41 0x43. So the final encoding is:
0x47 0x41 0x43 0x41 0x43 String type with inline size of 4 followed by ACAC in ASCII
Suppose you want to encode the string “VariantCallFormatSampleText”, a string of size 27. First, we need thetype descriptor byte, which is the string type 0x07. Because the size exceeds the inline size limit (27 ≥ 15) we set
the size to overflow, yielding the type byte of 0xF0 | 0x07 = 0xF7. Immediately following the type byte is the typed
size of 27, which we encode by the atomic INT8 value: 0x11 followed by the actual size 0x1B. Finally comes theactual bytes of the string: 0x56 0x61 0x72 0x69 0x61 0x6E 0x74 0x43 0x61 0x6C 0x6C 0x46 0x6F 0x72 0x6D 0x61
0x74 0x53 0x61 0x6D 0x70 0x6C 0x65 0x54 0x65 0x78 0x74. So the final encoding is:
0xF7
0x11 0x1B
0x56 0x61 0x72 0x69 0x61 0x6E 0x74 0x43 0x61 0x6C 0x6C
0x46 0x6F 0x72 0x6D 0x61 0x74 0x53 0x61 0x6D 0x70 0x6C
0x65 0x54 0x65 0x78 0x74

string with overflow size
overflow size encoded as INT8 withvalue 27
message in ASCII

Suppose you want to encode the missing value ‘.’. This is simply a string of size 0 = 0x07.
In VCF there are sometimes fields of type list of strings, such as a number field of unbounded size encoding theamino acid changes due to a mutation. Since BCF2 doesn’t directly support vectors of strings (a vector of characteris already a string) we collapse the list of strings into a single comma-separated string, encode it as a regular BCF2
vector of characters, and on reading explode it back into the list of strings. This works because strings in VCF cannot
contain ‘ ,’ (it’s a field separator) and so we can safely use ‘,’ to separate the individual strings. For efficiency reasons
we put a comma at the start of the collapsed string, so that just the first character can be examined to determine if
the string is collapsed.
To be concrete, suppose we have a info field around X=[A,B,C,D]. This is encoded in BCF2 as a single string“,A,B,C,D” of size 8, so it would have type byte 0x87 followed by the ASCII encoding 0x2C 0x41 0x2C 0x42 0x2C
0x43 0x2C 0x44.
Vectors — The BCF2 type byte may indicate that the upcoming data stream contains not a single value but afixed length vector of values. The vector values occur in order (1st, 2nd, 3rd, etc) encoded as expected for the typedeclared in the vector’s type byte. For example, a vector of 3 16-bit integers would be laid out as first the vector
type byte, followed immediately by 3 2-byte values for each integer, including a total of 7 bytes.
Missing values in vectors are handled slightly differently from atomic values. There are two possibilities formissing values:
One (or more) of the values in the vector may be missing, but others in the vector are not. Here each value should
be represented in the vector, and each corresponding BCF2 vector value either set to its present value or the typeequivalent MISSING value. Alternatively the entire vector of values may be missing. In this case the correct encoding
is as a type byte with size 0 and the appropriate type MISSING. Suppose we are encoding the record “AC=[1,2,3]”
from the INFO field. The AC key is encoded in the standard way. This would be immediately followed by a typed
8-bit integer vector of size 3, which is encoded by the type descriptor 0x31. The type descriptor is immediately
followed by the three 8-bit integer values: 0x01 0x02 0x03, for a grant total of 4 bytes: 0x31010203.
Suppose we are at a site with many alternative alleles so AC=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. Since there
are 16 values, we have to use the long vector encoding. The type of this field is 8 bit integer with the size set to 15
to indicate that the size is the next stream value, so this has type of 0xF1. The next value in the stream is the size,
as a typed 8-bit atomic integer: 0x11 with value 16 0x10. Each integer AC value is represented by it’s value as a 8
bit integer. The grand total representation here is:25

0xF1 0x01 0x10
0x01 0x02 0x03 0x04 0x05 0x06 0x07 0x08 0x09 0x0A 0x0B
0x0C 0x0D 0x0E 0x0F 0x10

8 bit integer vector with overflow size
1-16 as hexadecimal 8 bit integers

Suppose this INFO field contains the “AC=.”, indicating that the AC field is missing from a record with two alt
alleles. The correct representation is as the typed pair of AC followed by a MISSING vector of type 8-bit integer:
0x01.
Vectors of mixed length — In some cases genotype fields may be vectors whose length differs among samples.
For example, some CNV call sets encode different numbers of genotype likelihoods for each sample, given the large
number of potential copy number states, rather padding all samples to have the same number of fields. For example,
one sample could have CN0:0,CN1:10 and another CN0:0,CN1:10,CN2:10. In the situation when a genotype fieldcontain vector values of different lengths, these are represented in BCF2 by a vector of the maximum length per
sample, with all values in the each vector aligned to the left, and MISSING values assigned to all values not present
in the original vector. The BCF2 encoder / decoder must automatically add and remove these MISSING values fromthe vectors.
For example, suppose I have two samples, each with a FORMAT field X. Sample A has values [1], while sample
B has [2,3]. In BCF2 this would be encoded as [1, MISSING] and [2, 3]. Diving into the complete details, suppose X
is at offset 3 in the dictionary, which is encoded by the typed INT8 descriptor 0x11 followed by the value 0x03. Next
we have the type of the each format field, which here is a 2 element INT8 vector: 0x21. Next we have the encoding
for each sample, A = 0x01 0x80 followed by B = 0x02 0x03. All together we have:
0x11 0x03
0x21
0x01 0x80
0x02 0x03

X dictionary offset
each value is a 2 element INT8 value
A is [1, MISSING]
B is [2, 3]Note that this means that it’s illegal to encode a vector VCF field with missing values; the BCF2 codec should
signal an error in this case.
A Genotype (GT) field is encoded in a typed integer vector (can be 8, 16, or even 32 bit if necessary) with
the number of elements equal to the maximum ploidy among all samples at a site. For one individual, each integer
in the vector is organized as (allele + 1) << 1 | phased where allele is set to -1 if the allele in GT is a dot ‘.’ (thusthe higher bits are all 0). The vector is padded with missing values if the GT having fewer ploidy.
Examples:
0/1
0/1, 1/1, and 0/0
0|1
./.
0
1
0/1/2
0/1 | 20 and 0/1

in standard format (0 + 1) << 1 | 0 followed by (1 + 1) << 1 | 0
three samples encoded consecutively(1 + 1) << 1 | 1 = 0x05 preceded by the standard first byte value 0x02
where both alleles are missing
as a haploid it is represented by a single byte
as a haploid it is represented by a single byte
is tetraploid, with alleles
is tetraploid with a single phased allele
pad out the final allele for the haploid individual

0x02 04
0x02 04 04 04 02 02
0x02 05
0x00 00
0x02
0x04
0x02 04 06
0x02 04 07
0x02 80 02 04

The final example is something seen on chrX when we have a haploid male and a diploid female. The spare maleallele is just assigned the missing value.

6.4

Encoding a VCF record example

Let’s encode a realistic (but made-up) VCF record. This is a A/C SNP in HM3 (not really) called in 3 samples. In
this section we’ll build up the BCF2 encoding for this record.
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA00003
chr1 101 rs123 A C 30.1 PASS HM3;AC=3;AN=6;AA=C GT:GQ:DP:AD:PL 0/0:10:32:32,0:0,10,100 0/1:10:48:32,16:10,0,100 1/1:10:64:0,64:100,10,0

6.4.1

Encoding CHROM and POS

First, let’s assume that chr1 is the second chromosome to appear in the contig list—right after chrM (MT). So its
offset is 1. The POS BCF2 field value is 101 (obviously). Because these are both typed values in the BCF2 record,26

we encode both in their most compact 8-bit value form. The type byte for an atomic 8-bit integer is 0x11. The value
for the contig offset is 1 = 0x01. The value 101 is encoded as the single byte 0x65. So in total these are represented
as:0x01000000
0x64000000
0x01000000
6.4.2

CHROM offset is at 1 in 32 bit little endian
POS in 0 base 32 bit little endianrlen = 1 (it’s just a SNP)

Encoding QUALThe QUAL field value is 30.1, which we encode as an untyped single precision 32-bit float:
0x41 0xF0 0xCC 0xCD
6.4.3

QUAL = 30.1 as 32-bit float

Encoding ID

For ID type byte would is a 5-element string (type descriptor 0x57), which would then be followed by the five bytes
for the string of 0x72 0x73 0x31 0x32 0x33. The full encoding is:
0x57 0x72 0x73 0x31 0x32 0x33
6.4.4

ID

Encoding REF/ALT fields

We encode each of REF and ALT as typed strings, first REF followed immediately by ALT. Each is a 1 element
string (0x17), which would then be followed by the single bytes for the bases of 0x43 and 0x41:
0x17 0x41
0x17 0x43

REF A
ALT C

Just for discussion, suppose instead that ALT was ALT=C,T. The only thing that could change is that there
would be another typed string following immediately after C encoding 0x17 (1 element string) with the value of 0x54.
6.4.5

Encoding FILTER“PASS” is implicitly encoded as the last entry in the header dictionary (see dictionary of strings). Here we encode
the PASS FILTER field as a vector of size 1 of type 8-bit, which has type byte is 0x11. The value is the offset 0:0x11 0x00
6.4.6

FILTER field PASS

Encoding the INFO fields

HM3;AC=3;AN=6;AA=C Let’s assume that the header dictionary elements for HM3, AC, AN, and AA are at 80,
81, 82, and 83 respectively. All of these can be encoded by 1-element INT8 values (0x11), with associated hex valuesof 0x50, 0x51, 0x52, and 0x53 respectively.
First is HM3. The entry begins with the key: 0x11 0x50. The HM3 field is a flag with no associated value andtherefore the rest of the entry is the special case of a missing value without an explicit type provided. Altogether wehave:
0x11 0x50 0x00

HM3 flag is present

Now let’s encode the two atomic 8-bit integer fields AC and AN:
0x11 0x51
0x11 0x03
0x11 0x52
0x11 0x06

AC keywith value of 3
AN keywith value of 6

The ancestral allele (AA) tell us that among other primates the original allele is C, a Character here. Because
we represent Characters as single element strings in BCF2 (0x17) with value 0x43 (C). So the entire key/value pairis:27

0x11 0x53
0x17 0x43
6.4.7

AA keywith value of C

Encoding Genotypes

Continuing with our example:
FORMAT
GT:GQ:DP:AD:PL

NA00001
0/0:10:32:32,0:0,10,100

NA00002
0/1:10:48:32,16:10,0,100

NA00003
1/1:10:64:0,64:100,10,0Here we have the specially encoded GT field. We have two integer fields GQ and DP. We have the AD field,
which is a vector of 2 values per sample. And finally we have the PL field which is 3 values per sample. Let’s saythat the FORMAT keys for GT, GQ, DP, AD, and PL are at offsets 1, 2, 3, and 4, 5, respectively. Now let’s encode
each of the genotype fields in order of the VCF record (GT, GQ, DP, AD, and then PL):
GT triplet begins with the key: 0x1101. Next is the type of the field, which will be a 2-element (diploid) INT8
type: 0x21. This is followed by 3 2-byte arrays of values 0x0202 0x0204 0x0404 (see genotype encoding example fordetails). The final encoding is 0x1101 0x21 0x020202040404
GQ triplet begins with the key 0x1102. Because these values are small, we encode them as 8 bit atomic integers
with type code 0x11. As each value is the same (10 = 0x0A) the GQ field is encoded as 0x1102 0x11 0x0A0A0A
DP almost identical to GQ. First is the 0x1103 key, followed by 3 8-bit atomic integers encoded as 0x11 (thetype) 0x20 (DP=32), 0x30 (DP=48) and 0x40 (DP=64). So we have: 0x1103 0x11203040
AD is more complex. The key is simple, just like the others, with 0x1104. Because the AD field is a vector of
2 values for each genotype, the value of key/value pair a vector type. Because the integer values in each AD fieldof each sample are small they are encoded by 8 bit values. So the value type is = 0x21. For sample one there are
two values: 32,0 which are 0x30 and 0x00. Samples two and three are 0x30 0x20 and 0x00 0x40 respectively. So
ultimately this field is encoded as 0x1104 0x21 0x300030200040
PL is just like AD but with three values per sample. The key is 0x1105. Because the PL field is a vector of 3
values for each genotype, the value of key/value pair a vector type, and because the size is 3 it’s encoded in the size
field of the type. Again, because the integer values in each PL field of each sample are small they are encoded by
8 bit values. So the value type 0x31. For sample one there are three values: 0, 10, and 100 which are 0x00, 0x0A,
and 0x64. Samples two and three have the same values but in a slightly different order. So ultimately the PL field
is encoded as 0x1105 0x31 0x000A64 0x0A0064 0x640A00
So the genotype block contains:
0x1101 0x21 0x020202040404
0x1102 0x11 0x0A0A0A
0x1103 0x11 0x203040
0x1104 0x21 0x300030200040
0x1105 0x31 0x000A640A0064640A00

GT
GQ
DP
AD
PL

Putting it all together
We need to determine a few values before writing out the final block:l shared = 51 (Data length from CHROM to the end of INFO)
l indiv = 42 (Data length of FORMAT and individual genotype fields)
n info = 4 (Number of INFO fields)n allele = 2 (Number of alleles, including REF)
n sample = 3 (Number of samples)n fmt = 5 (Number of FORMAT keys)

28

0x33000000
0x2A000000
0x01000000
0x64000000
0x01000000
0x41 0xF0 0xCC 0xCD
0x04000x02000x030000
0x05
0x57 0x72 0x73 0x31 0x32 0x33
0x17 0x41
0x17 0x43
0x11 0x00
0x11 0x50 0x00
0x11 0x51
0x11 0x03
0x11 0x52
0x11 0x06
0x11 0x53
0x17 0x43
0x1101 0x21 0x020202040404
0x1102 0x11 0x0A0A0A
0x1103 0x11 0x203040
0x1104 0x21 0x300030200040
0x1105 0x31 0x000A640A0064640A00

l shared as 32-bit little endian hex
l indiv as 32-bit little endian hex
CHROM offset is at 1 in 32 bit little endian
POS in 0-based 32-bit little endianrlen = 1 (it’s just a SNP)QUAL = 30.1 as 32-bit float
n info as 16-bit little-endiann allele as 16-bit little-endiann sample as 24-bit little-endian
n fmt
ID
REF A
ALT C
FILTER field PASS
HM3 flag is present
AC keywith value of 3
AN keywith value of 6
AA keywith value of C
GT
GQ
DP
AD
PL

That’s quite a lot of information encoded in only 96 bytes!

6.5

BCF2 block gzip and indexing

These raw binary records may be subsequently encoded into BGZF blocks following the BGZF compression format,
section 3 of the SAM format specification. BCF2 records can be raw, though, in cases where the decoding/encoding
costs of bgzipping the data make it reasonable to process the data uncompressed, such as streaming BCF2s throughpipes with samtools and bcftools. Here the files should be still compressed with BGZF but with compression 0. Notethat currently the GATK generates raw BCF2 files (not BGZF compression at all) but this will change in the near
future.
BCF2 files are expected to be indexed through the same index scheme, section 4 as BAM files and other blockcompressed files with BGZF.

29