def update(self, data, make_backup=True, **kwargs):
"""
Update database with features in `data`.
data : str, iterable, FeatureDB instance
If FeatureDB, all data will be used. If string, assume it's
a filename of a GFF or GTF file. Otherwise, assume it's an
iterable of Feature objects. The classes in gffutils.iterators may
be helpful in this case.
make_backup : bool
If True, and the database you're about to update is a file on disk,
makes a copy of the existing database and saves it with a .bak
extension.
Notes
-----
Other kwargs are used in the same way as in gffutils.create_db; see the
help for that function for details.
Returns
-------
FeatureDB with updated features.
"""
from gffutils import create
from gffutils import iterators
if make_backup:
if isinstance(self.dbfn, six.string_types):
shutil.copy2(self.dbfn, self.dbfn + '.bak')
# get iterator-specific kwargs
_iterator_kwargs = {}
for k, v in kwargs.items():
if k in constants._iterator_kwargs:
_iterator_kwargs[k] = v
# Handle all sorts of input
data = iterators.DataIterator(data, **_iterator_kwargs)
if self.dialect['fmt'] == 'gtf':
if 'id_spec' not in kwargs:
kwargs['id_spec'] = {
'gene': 'gene_id', 'transcript': 'transcript_id'}
db = create._GTFDBCreator(
data=data, dbfn=self.dbfn, dialect=self.dialect, **kwargs)
elif self.dialect['fmt'] == 'gff3':
if 'id_spec' not in kwargs:
kwargs['id_spec'] = 'ID'
db = create._GFFDBCreator(
data=data, dbfn=self.dbfn, dialect=self.dialect, **kwargs)
else:
raise ValueError
db._populate_from_lines(data)
db._update_relations()
db._finalize()
return db
def __init__(self, data, dbfn, force=False, verbose=False, id_spec=None,
merge_strategy='merge', checklines=10, transform=None,
force_dialect_check=False, from_string=False, dialect=None,
default_encoding='utf-8',
infer_gene_extent=True,
force_merge_fields=None,
text_factory=sqlite3.OptimizedUnicode,
pragmas=constants.default_pragmas):
"""
Base class for _GFFDBCreator and _GTFDBCreator; see create_db()
function for docs
"""
if force_merge_fields is None:
force_merge_fields = []
if merge_strategy == 'merge':
if set(['start', 'end']).intersection(force_merge_fields):
raise ValueError("Can't merge start/end fields since "
"they must be integers")
warn = set(force_merge_fields)\
.intersection(['frame', 'strand'])
for w in warn:
warnings.warn(
"%s field will be merged for features with the same ID; "
"this may result in unusable features." % w)
self.force_merge_fields = force_merge_fields
self.pragmas = pragmas
self.merge_strategy = merge_strategy
self.default_encoding = default_encoding
self.infer_gene_extent = infer_gene_extent
self._autoincrements = collections.defaultdict(int)
if force:
if os.path.exists(dbfn):
os.unlink(dbfn)
self.dbfn = dbfn
self.id_spec = id_spec
if isinstance(dbfn, six.string_types):
conn = sqlite3.connect(dbfn)
else:
conn = dbfn
self.conn = conn
self.conn.row_factory = sqlite3.Row
self.set_verbose(verbose)
if text_factory is not None:
if self.verbose == 'debug':
logger.debug('setting text factory to %s' % text_factory)
self.conn.text_factory = text_factory
self._data = data
self._orig_logger_level = logger.level
self.iterator = iterators.DataIterator(
data=data, checklines=checklines, transform=transform,
force_dialect_check=force_dialect_check, from_string=from_string,
dialect=dialect
)
def create_db(data, dbfn, id_spec=None, force=False, verbose=False,
checklines=10, merge_strategy='error', transform=None,
gtf_transcript_key='transcript_id', gtf_gene_key='gene_id',
gtf_subfeature='exon', force_gff=False,
force_dialect_check=False, from_string=False, keep_order=False,
text_factory=sqlite3.OptimizedUnicode, infer_gene_extent=True,
force_merge_fields=None, pragmas=constants.default_pragmas,
sort_attribute_values=False):
"""
Create a database from a GFF or GTF file.
Parameters
----------
data : string or iterable
If a string (and `from_string` is False), then `data` is the path to
the original GFF or GTF file.
If a string and `from_string` is True, then assume `data` is the actual
data to use.
Otherwise, it's an iterable of Feature objects.
dbfn : string
Path to the database that will be created. Can be the special string
":memory:" to create an in-memory database.
id_spec : string, list, dict, callable, or None
This parameter guides what will be used as the primary key for the
database, which in turn determines how you will access individual
features by name from the database.
If `id_spec=None`, then auto-increment primary keys based on the
feature type (e.g., "gene_1", "gene_2"). This is also the fallback
behavior for the other values below.
If `id_spec` is a string, then look for this key in the attributes. If
it exists, then use its value as the primary key, otherwise
autoincrement based on the feature type. For many GFF3 files, "ID"
usually works well.
If `id_spec` is a list or tuple of keys, then check for each one in
order, using the first one found. For GFF3, this might be ["ID",
"Name"], which would use the ID if it exists, otherwise the Name,
otherwise autoincrement based on the feature type.
If `id_spec` is a dictionary, then it is a mapping of feature types to
what should be used as the ID. For example, for GTF files, `{'gene':
'gene_id', 'transcript': 'transcript_id'}` may be useful. The values
of this dictionary can also be a list, e.g., `{'gene': ['gene_id',
'geneID']}`
If `id_spec` is a callable object, then it accepts a dictionary from
the iterator and returns one of the following:
* None (in which case the feature type will be auto-incremented)
* string (which will be used as the primary key)
* special string starting with "autoincrement:X", where "X" is
a string that will be used for auto-incrementing. For example,
if "autoincrement:chr10", then the first feature will be
"chr10_1", the second "chr10_2", and so on.
force : bool
If `False` (default), then raise an exception if `dbfn` already exists.
Use `force=True` to overwrite any existing databases.
verbose : bool
Report percent complete and other feedback on how the db creation is
progressing.
In order to report percent complete, the entire file needs to be read
once to see how many items there are; for large files you may want to
use `verbose=False` to avoid this.
checklines : int
Number of lines to check the dialect.
merge_strategy : { "merge", "create_unique", "error", "warning" }
This parameter specifies the behavior when two items have an identical
primary key.
Using `merge_strategy="merge"`, then there will be a single entry in
the database, but the attributes of all features with the same primary
key will be merged.
Using `merge_strategy="create_unique"`, then the first entry will use
the original primary key, but the second entry will have a unique,
autoincremented primary key assigned to it
Using `merge_strategy="error"`, a :class:`gffutils.DuplicateID`
exception will be raised. This means you will have to edit the file
yourself to fix the duplicated IDs.
Using `merge_strategy="warning"`, a warning will be printed to the
logger, and the duplicate feature will be skipped.
transform : callable
Function (or other callable object) that accepts a dictionary and
returns a dictionary.
gtf_transcript_key, gtf_gene_key : string
Which attribute to use as the transcript ID and gene ID respectively
for GTF files. Default is `transcript_id` and `gene_id` according to
the GTF spec.
gtf_subfeature : string
Feature type to use as a "gene component" when inferring gene and
transcript extents for GTF files. Default is `exon` according to the
GTF spec.
force_gff : bool
If True, do not do automatic format detection -- only use GFF.
force_dialect_check : bool
If True, the dialect will be checkef for every feature (instead of just
`checklines` features). This can be slow, but may be necessary for
inconsistently-formatted input files.
from_string : bool
If True, then treat `data` as actual data (rather than the path to
a file).
keep_order : bool
If True, all features returned from this instance will have the
order of their attributes maintained. This can be turned on or off
database-wide by setting the `keep_order` attribute or with this
kwarg, or on a feature-by-feature basis by setting the `keep_order`
attribute of an individual feature.
Default is False, since this includes a sorting step that can get
time-consuming for many features.
infer_gene_extent : bool
Only used for GTF files, set this to False in order to disable the
inference of gene and transcript extents. Use this if you don't care
about having gene and transcript features in the database, or if the
input GTF file already has "gene" and "transcript" featuretypes.
force_merge_fields : list
If merge_strategy="merge", then features will only be merged if their
non-attribute values are identical (same chrom, source, start, stop,
score, strand, phase). Using `force_merge_fields`, you can override
this behavior to allow merges even when fields are different. This
list can contain one or more of ['seqid', 'source', 'featuretype',
'score', 'strand', 'frame']. The resulting merged fields will be
strings of comma-separated values. Note that 'start' and 'end' are not
available, since these fields need to be integers.
text_factory : callable
Text factory to use for the sqlite3 database. See
https://docs.python.org/2/library/\
sqlite3.html#sqlite3.Connection.text_factory
for details. The default sqlite3.OptimizedUnicode will return Unicode
objects only for non-ASCII data, and bytestrings otherwise.
pragmas : dict
Dictionary of pragmas used when creating the sqlite3 database. See
http://www.sqlite.org/pragma.html for a list of available pragmas. The
defaults are stored in constants.default_pragmas, which can be used as
a template for supplying a custom dictionary.
sort_attribute_values : bool
All features returned from the database will have their attribute
values sorted. Typically this is only useful for testing, since this
can get time-consuming for large numbers of features.
"""
kwargs = dict(
data=data, checklines=checklines, transform=transform,
force_dialect_check=force_dialect_check, from_string=from_string)
# First construct an iterator so that we can identify the file format.
# DataIterator figures out what kind of data was provided (string of lines,
# filename, or iterable of Features) and checks `checklines` lines to
# identify the dialect.
iterator = iterators.DataIterator(**kwargs)
dialect = iterator.dialect
if isinstance(iterator, iterators.FeatureIterator):
# However, a side-effect of this is that if `data` was a generator,
# then we've just consumed `checklines` items (see
# iterators.BaseIterator.__init__, which calls iterators.peek).
#
# But it also chains those consumed items back onto the beginning, and
# the result is available as as iterator._iter.
#
# That's what we should be using now for `data:
kwargs['data'] = iterator._iter
# Since we've already checked lines, we don't want to do it again
kwargs['checklines'] = 0
if force_gff or (dialect['fmt'] == 'gff3'):
cls = _GFFDBCreator
id_spec = id_spec or 'ID'
add_kwargs = {}
elif dialect['fmt'] == 'gtf':
cls = _GTFDBCreator
id_spec = id_spec or {'gene': 'gene_id', 'transcript': 'transcript_id'}
add_kwargs = dict(
transcript_key=gtf_transcript_key,
gene_key=gtf_gene_key,
subfeature=gtf_subfeature)
kwargs.update(**add_kwargs)
kwargs['dialect'] = dialect
c = cls(dbfn=dbfn, id_spec=id_spec, force=force, verbose=verbose,
merge_strategy=merge_strategy, text_factory=text_factory,
infer_gene_extent=infer_gene_extent,
force_merge_fields=force_merge_fields, pragmas=pragmas, **kwargs)
c.create()
if dbfn == ':memory:':
db = interface.FeatureDB(c.conn, keep_order=keep_order,
pragmas=pragmas,
sort_attribute_values=sort_attribute_values,
text_factory=text_factory)
else:
db = interface.FeatureDB(c, keep_order=keep_order, pragmas=pragmas,
sort_attribute_values=sort_attribute_values,
text_factory=text_factory)
return db
def inspect(data, look_for=['featuretype', 'chrom', 'attribute_keys',
'feature_count'], limit=None, verbose=True):
"""
Inspect a GFF or GTF data source.
This function is useful for figuring out the different featuretypes found
in a file (for potential removal before creating a FeatureDB).
Returns a dictionary with a key for each item in `look_for` and
a corresponding value that is a dictionary of how many of each unique item
were found.
There will always be a `feature_count` key, indicating how many features
were looked at (if `limit` is provided, then `feature_count` will be the
same as `limit`).
For example, if `look_for` is ['chrom', 'featuretype'], then the result
will be a dictionary like::
{
'chrom': {
'chr1': 500,
'chr2': 435,
'chr3': 200,
...
...
}.
'featuretype': {
'gene': 150,
'exon': 324,
...
},
'feature_count': 5000
}
Parameters
----------
data : str, FeatureDB instance, or iterator of Features
If `data` is a string, assume it's a GFF or GTF filename. If it's
a FeatureDB instance, then its `all_features()` method will be
automatically called. Otherwise, assume it's an iterable of Feature
objects.
look_for : list
List of things to keep track of. Options are:
- any attribute of a Feature object, such as chrom, source, start,
stop, strand.
- "attribute_keys", which will look at all the individual
attribute keys of each feature
limit : int
Number of features to look at. Default is no limit.
verbose : bool
Report how many features have been processed.
Returns
-------
dict
"""
results = {}
obj_attrs = []
for i in look_for:
if i not in ['attribute_keys', 'feature_count']:
obj_attrs.append(i)
results[i] = Counter()
attr_keys = 'attribute_keys' in look_for
d = iterators.DataIterator(data)
feature_count = 0
for f in d:
if verbose:
sys.stderr.write('\r%s features inspected' % feature_count)
sys.stderr.flush()
for obj_attr in obj_attrs:
results[obj_attr].update([getattr(f, obj_attr)])
if attr_keys:
results['attribute_keys'].update(f.attributes.keys())
feature_count += 1
if limit and feature_count == limit:
break
new_results = {}
for k, v in results.items():
new_results[k] = dict(v)
new_results['feature_count'] = feature_count
return new_results