picovcf

Single-header C++ library for fast/low-memory VCF (Variant Call Format) parsing. Gzipped VCF (.vcf.gz) is optionally supported.

There are a lot of great tools for processing VCF files out there, but not many C++ libraries that are small (only parsing, no extra functionality) and easy to use. picovcf attempts to fill this niche by providing a header-only library using modern C++ (C++11) that allows clients to be selective about which parts of the VCF file get parsed.

Features:

• Fast and easy to use VCF(.GZ) parsing.
• Convert VCF(.GZ) to Indexable Genotype Data (IGD) format, which is a very simple format that is more than 3x smaller than VCF.GZ at Biobank scale and more than 15x faster to read
• Fast and easy to use IGD parsing.

More details can be found in the supplement of our preprint "Genotype Representation Graph" paper.

Using the library

Either copy the latest header file (picovcf.hpp) into your project directly, or make use of something like git submodules to include https://github.com/aprilweilab/picovcf.

See the vcfpp.cpp for an example of how to use the APIs. Read the docs for an overview of the API.

When building code that uses picovcf.hpp, define VCF_GZ_SUPPORT=1 (-DVCF_GZ_SUPPORT=1 on most compiler command lines) to enable zlib support for compressed VCF files.

Build and run the tests

picovcf does not need to be built to be used, since it is a single header that gets built as part of your project. However, if you want to build and run the unit tests, do the following:

cd picovcf
mkdir build && cd build
cmake ..
make
EXAMPLE_VCFS=../test/example_vcfs/ ./picovcf_test

There is a Dockerfile that encodes all the build steps and dependencies, including documentation build.

Build the documentation

Requires Python packages sphinx, sphinx-rtd-theme, breathe. Requires Doxygen.

From the same build/ directory as above:

DOC_BUILD_DIR=$PWD sphinx-build -c ../doc/ -b html -Dbreathe_projects.picovcf=$PWD/doc/xml ../doc/ $PWD/doc/sphinx/

Indexable Genotype Data (IGD)

picovcf also defines an extremely simple binary file format that can be used for fast access to genotype data. Most other genotype data formats are not indexable directly: that is, you cannot jump directly to the 1 millionth variant without first scanning all the previous (almost million) variants. IGD has the following properties:

• Indexable. You can use math to figure out where the ith variant will be in the file.
• Uncompressed. No need to link in compression libraries.
• Simple format: all variants are expanded into binary variants. So if a Variant has N alternate alleles, then IGD will store that as N rows containing 0 (reference allele) or 1 (alternate allele). Each of these binary variants is stored as either a bitvector (non-sparse) or a list of sample indexes (sparse). A flag in the index indicates which way each variant is stored.
• Very small. Oftentimes smaller than compressed formats like .vcf.gz or .bgen. The more low-frequency mutations (such as for really large sample sizes) the smaller the file, assuming you are using the default implementation of dynamically choosing between sparse/non-sparse representation.

For example, the following are from chromosome 22 of a real dataset:

• .vcf: 11GB
• .vcf.gz: 203MB
• .bgen: 256MB
• .igd: 183MB

Converting the .vcf.gz to .bgen (via qctool) took 23 minutes, but converting to .igd only took 3 minutes. Furthermore, iteratively accessing all the variants (and genotype data) in the .igd file was approximately 15x faster than accessing the same data in the .vcf.gz file (using picovcf). On Biobank-scale real datasets, IGD is on average 3.5x smaller than .vcf.gz.