Note: cyvcf2 versions < 0.20.0 require htslib < 1.10. cyvcf2 versions >= 0.20.0 require htslib >= 1.10
The latest documentation for cyvcf2 can be found here:
If you use cyvcf2, please cite the paper
Fast python (2 and 3) parsing of VCF and BCF including region-queries.
cyvcf2 is a cython wrapper around htslib built for fast parsing of Variant Call Format (VCF) files.
Attributes like variant.gt_ref_depths work for diploid samples and return a numpy array directly so they are immediately ready for downstream use.
note that the array is backed by the underlying C data, so, once variant goes out of scope. The array will contain nonsense.
To persist a copy, use: cpy = np.array(variant.gt_ref_depths) instead of just arr = variant.gt_ref_depths.
The example below shows much of the use of cyvcf2.
from cyvcf2 import VCF
for variant in VCF('some.vcf.gz'): # or VCF('some.bcf')
variant.REF, variant.ALT # e.g. REF='A', ALT=['C', 'T']
variant.CHROM, variant.start, variant.end, variant.ID, \
variant.FILTER, variant.QUAL
# numpy arrays of specific things we pull from the sample fields.
# gt_types is array of 0,1,2,3==HOM_REF, HET, UNKNOWN, HOM_ALT
variant.gt_types, variant.gt_ref_depths, variant.gt_alt_depths # numpy arrays
variant.gt_phases, variant.gt_quals, variant.gt_bases # numpy array
## INFO Field.
## extract from the info field by it's name:
variant.INFO.get('DP') # int
variant.INFO.get('FS') # float
variant.INFO.get('AC') # float
# convert back to a string.
str(variant)
## sample info...
# Get a numpy array of the depth per sample:
dp = variant.format('DP')
# or of any other format field:
sb = variant.format('SB')
assert sb.shape == (n_samples, 4) # 4-values per
# to do a region-query:
vcf = VCF('some.vcf.gz')
for v in vcf('11:435345-556565'):
if v.INFO["AF"] > 0.1: continue
print(str(v))pip install cyvcf2
Assuming you have already built and installed htslib version 1.12 or higher.
CYVCF2_HTSLIB_MODE=EXTERNAL pip install --no-binary cyvcf2 cyvcf2
Assuming you have already built and installed htslib.
SETUPTOOLS_USE_DISTUTILS=stdlib pip install cyvcf2
git clone --recursive https://github.com/brentp/cyvcf2
pip install -r requirements.txt
# sometimes it can be required to remove old files:
# python setup.py clean_ext
CYVCF2_HTSLIB_MODE=BUILTIN CYTHONIZE=1 python setup.py install
# or to use a system htslib.so
CYVCF2_HTSLIB_MODE=EXTERNAL python setup.py install
On OSX, using brew, you may have to set the following as indicated by the brew install:
For compilers to find openssl you may need to set:
export LDFLAGS="-L/usr/local/opt/openssl/lib"
export CPPFLAGS="-I/usr/local/opt/openssl/include"
For pkg-config to find openssl you may need to set:
export PKG_CONFIG_PATH="/usr/local/opt/openssl/lib/pkgconfig"
Install pytest, then tests can be run with:
pytest
Run with cyvcf2 path_to_vcf
$ cyvcf2 --help
Usage: cyvcf2 [OPTIONS] <vcf_file> or -
fast vcf parsing with cython + htslib
Options:
-c, --chrom TEXT Specify what chromosome to include.
-s, --start INTEGER Specify the start of region.
-e, --end INTEGER Specify the end of the region.
--include TEXT Specify what info field to include.
--exclude TEXT Specify what info field to exclude.
--loglevel [DEBUG|INFO|WARNING|ERROR|CRITICAL]
Set the level of log output. [default:
INFO]
--silent Skip printing of vcf.
--help Show this message and exit.
Pysam also has a cython wrapper to htslib and one block of code here is taken directly from that library. But, the optimizations that we want for gemini are very specific so we have chosen to create a separate project.
For the performance comparison in the paper, we used thousand genomes chromosome 22 With the full comparison runner here.