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PyVCF 0.6.0

Variant Call Format (VCF) parser for Python

Latest Version: 0.6.8

A VCFv4.0 and 4.1 parser for Python.

Online version of PyVCF documentation is available at

The intent of this module is to mimic the ``csv`` module in the Python stdlib,
as opposed to more flexible serialization formats like JSON or YAML. ``vcf``
will attempt to parse the content of each record based on the data types
specified in the meta-information lines -- specifically the ##INFO and
##FORMAT lines. If these lines are missing or incomplete, it will check
against the reserved types mentioned in the spec. Failing that, it will just
return strings.

There main interface is the class: ``Reader``. It takes a file-like
object and acts as a reader::

>>> import vcf
>>> vcf_reader = vcf.Reader(open('vcf/test/example-4.0.vcf', 'r'))
>>> for record in vcf_reader:
... print record
Record(CHROM=20, POS=14370, REF=G, ALT=[A])
Record(CHROM=20, POS=17330, REF=T, ALT=[A])
Record(CHROM=20, POS=1110696, REF=A, ALT=[G, T])
Record(CHROM=20, POS=1230237, REF=T, ALT=[None])
Record(CHROM=20, POS=1234567, REF=GTCT, ALT=[G, GTACT])

This produces a great deal of information, but it is conveniently accessed.
The attributes of a Record are the 8 fixed fields from the VCF spec::

* ``Record.CHROM``
* ``Record.POS``
* ``Record.ID``
* ``Record.REF``
* ``Record.ALT``
* ``Record.QUAL``
* ``Record.FILTER``
* ``Record.INFO``

plus attributes to handle genotype information:

* ``Record.FORMAT``
* ``Record.samples``
* ``Record.genotype``

``samples`` and ``genotype``, not being the title of any column, are left lowercase. The format
of the fixed fields is from the spec. Comma-separated lists in the VCF are
converted to lists. In particular, one-entry VCF lists are converted to
one-entry Python lists (see, e.g., ``Record.ALT``). Semicolon-delimited lists
of key=value pairs are converted to Python dictionaries, with flags being given
a ``True`` value. Integers and floats are handled exactly as you'd expect::

>>> vcf_reader = vcf.Reader(open('vcf/test/example-4.0.vcf', 'r'))
>>> record =
>>> print record.POS
>>> print record.ALT
>>> print record.INFO['AF']

There are a number of convienience methods and properties for each ``Record`` allowing you to
examine properties of interest::

>>> print record.num_called, record.call_rate, record.num_unknown
3 1.0 0
>>> print record.num_hom_ref, record.num_het, record.num_hom_alt
1 1 1
>>> print record.nucl_diversity, record.aaf
0.6 0.5
>>> print record.get_hets()
[Call(sample=NA00002, CallData(GT=1|0, GQ=48, DP=8, HQ=[51, 51]))]
>>> print record.is_snp, record.is_indel, record.is_transition, record.is_deletion
True False True False
>>> print record.var_type, record.var_subtype
snp ts
>>> print record.is_monomorphic

``record.FORMAT`` will be a string specifying the format of the genotype
fields. In case the FORMAT column does not exist, ``record.FORMAT`` is
``None``. Finally, ``record.samples`` is a list of dictionaries containing the
parsed sample column and ``record.genotype`` is a way of looking up genotypes
by sample name::

>>> record =
>>> for sample in record.samples:
... print sample['GT']
>>> print record.genotype('NA00001')['GT']

The genotypes are represented by ``Call`` objects, which have three attributes: the
corresponding Record ``site``, the sample name in ``sample`` and a dictionary of
call data in ``data``::

>>> call = record.genotype('NA00001')
>>> print
Record(CHROM=20, POS=17330, REF=T, ALT=[A])
>>> print call.sample
>>> print
CallData(GT=0|0, GQ=49, DP=3, HQ=[58, 50])

Please note that as of release 0.4.0, attributes known to have single values (such as
``DP`` and ``GQ`` above) are returned as values. Other attributes are returned
as lists (such as ``HQ`` above).

There are also a number of methods::

>>> print call.called, call.gt_type, call.gt_bases, call.phased
True 0 T|T True

Metadata regarding the VCF file itself can be investigated through the
following attributes:

* ``Reader.metadata``
* ``Reader.infos``
* ``Reader.filters``
* ``Reader.formats``
* ``Reader.samples``

For example::

>>> vcf_reader.metadata['fileDate']
>>> vcf_reader.samples
['NA00001', 'NA00002', 'NA00003']
>>> vcf_reader.filters
OrderedDict([('q10', Filter(id='q10', desc='Quality below 10')), ('s50', Filter(id='s50', desc='Less than 50% of samples have data'))])
>>> vcf_reader.infos['AA'].desc
'Ancestral Allele'

ALT records are actually classes, so that you can interrogate them::

>>> reader = vcf.Reader(open('vcf/test/example-4.1-bnd.vcf'))
>>> _ =; row =
>>> print row
Record(CHROM=1, POS=2, REF=T, ALT=[T[2:3[])
>>> bnd = row.ALT[0]
>>> print bnd.withinMainAssembly, bnd.orientation, bnd.remoteOrientation, bnd.connectingSequence
True False True T

Random access is supported for files with tabix indexes. Simply call fetch for the
region you are interested in::

>>> vcf_reader = vcf.Reader(filename='vcf/test/tb.vcf.gz')
>>> for record in vcf_reader.fetch('20', 1110696, 1230237): # doctest: +SKIP
... print record
Record(CHROM=20, POS=1110696, REF=A, ALT=[G, T])
Record(CHROM=20, POS=1230237, REF=T, ALT=[None])

Or extract a single row::

>>> print vcf_reader.fetch('20', 1110696) # doctest: +SKIP
Record(CHROM=20, POS=1110696, REF=A, ALT=[G, T])

The ``Writer`` class provides a way of writing a VCF file. Currently, you must specify a
template ``Reader`` which provides the metadata::

>>> vcf_reader = vcf.Reader(filename='vcf/test/tb.vcf.gz')
>>> vcf_writer = vcf.Writer(open('/dev/null', 'w'), vcf_reader)
>>> for record in vcf_reader:
... vcf_writer.write_record(record)

An extensible script is available to filter vcf files in VCF filters
declared by other packages will be available for use in this script. Please
see :doc:`FILTERS` for full description.  
File Type Py Version Uploaded on Size
PyVCF-0.6.0.tar.gz (md5) Source 2012-07-03 26KB