Inferensys

Glossary

Variant Call Format (VCF)

A standardized text file format for storing gene sequence variations, including single nucleotide polymorphisms, insertions, deletions, and structural variants, along with genotype information and quality metrics.
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FILE FORMAT

What is Variant Call Format (VCF)?

The Variant Call Format (VCF) is the universal standard for storing genetic variation data, encoding everything from single nucleotide polymorphisms (SNPs) to large structural variants alongside rich genotype and quality metadata.

Variant Call Format (VCF) is a tab-delimited text file format that stores gene sequence variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants, against a reference genome. It uses a structured header for metadata and a data section where each row represents a variant locus with columns for chromosome, position, reference allele, alternate allele, quality score, and filter status.

The format supports multi-sample genotype information through per-sample columns containing allelic depth, genotype likelihoods, and phased haplotypes. VCF files are typically compressed to .vcf.gz and indexed with .tbi for fast random access, enabling efficient querying of specific genomic regions in large population-scale studies.

VARIANT CALL FORMAT

Key Features of VCF

The Variant Call Format (VCF) is the universal standard for storing DNA sequence variations. It encodes SNPs, insertions, deletions, and structural variants alongside rich genotype information and quality metrics, serving as the primary interchange format between variant callers and downstream analysis tools.

01

Header Metadata Structure

Every VCF file begins with a structured header section prefixed by ##. This metadata defines the file's provenance, the reference genome used, and the semantics of all downstream fields.

  • INFO fields: Define per-variant annotations like allele frequency (AF) and quality depth (QD)
  • FORMAT fields: Specify per-sample data fields such as genotype (GT) and read depth (DP)
  • FILTER entries: Declare the meaning of filter flags like PASS or LowQual
  • Contig lines: Explicitly list reference sequence names and lengths for validation
02

Genotype Encoding with GT Field

The GT FORMAT field encodes diploid or polyploid genotypes using a compact numeric representation. Alleles are indexed from 0 (reference) upward, separated by / (unphased) or | (phased).

  • 0/0: Homozygous reference — both alleles match the reference
  • 0/1: Heterozygous — one reference and one alternate allele
  • 1/1: Homozygous alternate — both alleles are the variant
  • 0|1: Phased heterozygous — the first haplotype carries the reference, the second carries the variant
  • ./.: Missing genotype — no call could be made at this position
03

Multi-Allelic Site Representation

A single VCF record can represent multiple alternate alleles at the same genomic position, avoiding redundant entries. The ALT column uses comma-separated values, and genotype indices reference these alleles numerically.

  • A site with two alternate alleles lists them as A,T in the ALT column
  • Genotype 1/2 indicates the sample carries one copy of the first alternate allele and one copy of the second
  • Allele-specific annotations use the AD (allele depth) FORMAT field to report read counts per allele
  • Tools like bcftools norm can decompose multi-allelic records into biallelic representations for compatibility
04

Quality Metrics and Filtering

The QUAL column stores a Phred-scaled probability that a variant exists at this site, calculated as -10 * log10(prob(call is wrong)). A QUAL of 30 indicates a 1 in 1000 chance of error.

  • FILTER column: Records whether a variant passed all quality filters (PASS) or failed specific criteria
  • VQSR integration: Variant Quality Score Recalibration uses Gaussian mixture models to assign well-calibrated quality scores based on known truth sets
  • INFO/DP: Total read depth at the site, essential for distinguishing true variants from sequencing noise
  • FORMAT/GQ: Genotype Quality, a Phred-scaled confidence score for the assigned genotype call
05

Structural Variant Representation

VCF v4.3 and later support encoding large-scale genomic rearrangements using symbolic alleles in the ALT column, marked with angle brackets.

  • <DEL>: Deletion — a segment of DNA is missing relative to the reference
  • <DUP>: Duplication — a segment is tandemly duplicated
  • <INV>: Inversion — a segment is reversed in orientation
  • <INS>: Insertion — novel sequence inserted at the breakpoint
  • INFO/SVLEN: The length of the structural variant in base pairs
  • INFO/END: The end coordinate, stored in the INFO column for symbolic alleles
  • BND format: Breakend notation uses ] and [ brackets to specify translocation partner coordinates
06

VCF Compression and Indexing

Large VCF files are routinely compressed with bgzip (block gzip) and indexed with tabix to enable fast random access without full decompression.

  • .vcf.gz: The standard compressed VCF extension using block gzip compression
  • .tbi: The tabix index file enabling region-based queries via genomic coordinates
  • BCF format: A binary variant call format that stores the same information as VCF but in a more compact, machine-readable binary representation
  • Streaming queries: Tools like bcftools view -r chr1:1000000-2000000 use the index to extract specific genomic regions in milliseconds
VCF ESSENTIALS

Frequently Asked Questions

Clear, technically precise answers to the most common questions about the Variant Call Format, its structure, and its role in modern genomic analysis pipelines.

A Variant Call Format (VCF) file is a standardized, tab-delimited text file format for storing gene sequence variations, including single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants, along with rich genotype information and quality metrics. It is the universal output of variant calling pipelines. The structure consists of a meta-information header (lines beginning with ##) that defines the file's provenance, reference genome, and the meaning of INFO and FORMAT fields, followed by a header line (beginning with #CHROM) that names the eight mandatory fixed columns and subsequent sample columns. The data lines then contain tab-separated records for each variant locus. The mandatory fixed columns are:

  • #CHROM: Reference sequence chromosome or contig
  • POS: 1-based position of the variant
  • ID: Semi-colon separated list of unique identifiers (e.g., rsID)
  • REF: Reference base(s)
  • ALT: Alternate allele(s)
  • QUAL: Phred-scaled quality score for the assertion of the ALT allele
  • FILTER: Pass/fail status from applied filters
  • INFO: Semi-colon separated list of additional annotation key-value pairs
FORMAT COMPARISON

VCF vs. Other Genomic Data Formats

A comparison of the Variant Call Format against other common file formats used in genomic sequence analysis pipelines, highlighting their primary purposes, data structures, and interoperability.

FeatureVCFSAMBAMFASTQ

Primary Purpose

Storing variant calls and genotypes

Storing aligned sequence reads

Compressed binary alignment storage

Storing raw sequencing reads with quality

Data Level

Processed variant sites

Read-level alignments

Read-level alignments

Raw nucleotide sequences

Human Readable

Stores Reference Genome

Stores Genotype Likelihoods

Binary Indexing Support

Typical File Size (30x WGS)

< 1 GB

100 GB

~90 GB

200 GB

Compression Method

Bgzip + Tabix

Plain text

Block-level BGZF

Gzip

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.