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 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', int)
# or of any other format field:
sb = variant.format('SB', float)
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
Tests can be run with:
python setup.py test
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.