skbio.metadata.IntervalMetadata#

class skbio.metadata.IntervalMetadata(upper_bound, copy_from=None)[source]#

Stores the interval features.

IntervalMetadata object allows storage, modification, and querying of interval features covering a region of a single coordinate system. For instance, this can be used to store functional annotations about genes across a genome. This object is also applied to the sequence alignment.

This object is typically coupled with another object, such as a Sequence object (or its child class), or a TabularMSA object.

Parameters:

upper_boundint or None: Defines the exclusive upper bound of the interval features. No coordinate can be greater than it. None means that the coordinate space is unbounded.
copy_fromIntervalMetadata or None, optional: Create a new object from the input IntervalMetadata object by shallow copying if it is not None. The upper bound of the new object will be updated with the upper_bound parameter specified.

See also

skbio.metadata.Interval

Notes

This class stores coordinates of all feature bounds into a interval tree. It allows the speed up of query-by-bound. The building of interval tree is deferred until necessary to save computation. It is updated from all coordinates only when you need to fetch info from the interval tree.

When you add a method into this class and if you method need to fetch info from IntervalMetadata._interval_tree, you should decorate it with _rebuild_tree. This decorator will check if the current interval tree is stale and will update it if so. Additionally, if your method add, delete, or changes the coordinates of any interval features, you should set self._is_stale_tree to True at the end of your method to indicate the interval tree becomes stale.

Examples

Let’s say we have a sequence of length 10 and want to add annotation to it. Create an IntervalMetadata object:

>>> from skbio.metadata import Interval, IntervalMetadata
>>> im = IntervalMetadata(10)

Let’s add annotations of 3 genes:

>>> im.add(bounds=[(3, 9)],
...        metadata={'gene': 'sagB'})  
Interval(interval_metadata=..., bounds=[(3, 9)], fuzzy=[(False, False)], metadata={'gene': 'sagB'})
>>> im.add(bounds=[(3, 7)],
...        metadata={'gene': 'sagC'})  
Interval(interval_metadata=..., bounds=[(3, 7)], fuzzy=[(False, False)], metadata={'gene': 'sagC'})
>>> im.add(bounds=[(1, 2), (4, 7)],
...        metadata={'gene': 'sagA'})  
Interval(interval_metadata=..., bounds=[(1, 2), (4, 7)], fuzzy=[(False, False), (False, False)], metadata={'gene': 'sagA'})

Show the object representation:

>>> im    
3 interval features
-------------------
Interval(interval_metadata=..., bounds=[(3, 9)], fuzzy=[(False, False)], metadata={'gene': 'sagB'})
Interval(interval_metadata=..., bounds=[(3, 7)], fuzzy=[(False, False)], metadata={'gene': 'sagC'})
Interval(interval_metadata=..., bounds=[(1, 2), (4, 7)], fuzzy=[(False, False), (False, False)], metadata={'gene': 'sagA'})

We can sort the genes by their bounds:

>>> im.sort()
>>> im    
3 interval features
-------------------
Interval(interval_metadata=..., bounds=[(1, 2), (4, 7)], fuzzy=[(False, False), (False, False)], metadata={'gene': 'sagA'})
Interval(interval_metadata=..., bounds=[(3, 7)], fuzzy=[(False, False)], metadata={'gene': 'sagC'})
Interval(interval_metadata=..., bounds=[(3, 9)], fuzzy=[(False, False)], metadata={'gene': 'sagB'})

Query the genes by bound and/or metadata:

>>> intvls = im.query([(1, 2)], metadata={'gene': 'foo'})
>>> list(intvls)
[]
>>> intvls = im.query([(7, 9)])
>>> list(intvls)  
[Interval(interval_metadata=..., bounds=[(3, 9)], fuzzy=[(False, False)], metadata={'gene': 'sagB'})]
>>> intvls = im.query(metadata={'gene': 'sagA'})
>>> intvls = list(intvls)
>>> intvls  
[Interval(interval_metadata=..., bounds=[(1, 2), (4, 7)], fuzzy=[(False, False), (False, False)], metadata={'gene': 'sagA'})]

Drop the gene(s) we get from query:

>>> im.drop(intvls)
>>> im.sort()
>>> im   
2 interval features
-------------------
Interval(interval_metadata=..., bounds=[(3, 7)], fuzzy=[(False, False)], metadata={'gene': 'sagC'})
Interval(interval_metadata=..., bounds=[(3, 9)], fuzzy=[(False, False)], metadata={'gene': 'sagB'})

Attributes

`default_write_format`
`lower_bound`	The inclusive lower bound of interval features.
`num_interval_features`	The total number of interval features.
`upper_bound`	The exclusive upper bound of interval features.

Methods

`add`(bounds[, fuzzy, metadata])	Create and add an `Interval` to this `IntervalMetadata`.
`concat`(interval_metadata)	Concatenate an iterable of `IntervalMetadata` objects.
`drop`(intervals[, negate])	Drop Interval objects.
`merge`(other)	Merge the interval features of another `IntervalMetadata` object.
`query`([bounds, metadata])	Yield `Interval` object with the bounds and attributes.
`read`([format])	Create a new `IntervalMetadata` instance from a file.
`sort`([ascending])	Sort interval features by their coordinates.
`write`(file[, format])	Write an instance of `IntervalMetadata` to a file.

Special methods

`__copy__`()	Return a shallow copy.
`__deepcopy__`(memo)	Return a deep copy.
`__eq__`(other)	Test if this object is equal to another.
`__ne__`(other)	Test if this object is not equal to another.

Special methods (inherited)

`__ge__`(value, /)	Return self>=value.
`__getstate__`(/)	Helper for pickle.
`__gt__`(value, /)	Return self>value.
`__le__`(value, /)	Return self<=value.
`__lt__`(value, /)	Return self<value.
`__str__`(/)	Return str(self).

Details

default_write_format = 'gff3'#

lower_bound#: The inclusive lower bound of interval features.

num_interval_features#: The total number of interval features.

upper_bound#: The exclusive upper bound of interval features.

__copy__()[source]#

Return a shallow copy.

See also

__deepcopy__

Notes

The IntervalMetadata copy will have copies of the Interval objects present in this object. The metadata dictionary of each Interval object will be a shallow copy.

__deepcopy__(memo)[source]#

Return a deep copy.

See also

__copy__

Notes

The IntervalMetadata copy will have copies of the Interval objects present in this object. The metadata dictionary of each Interval object will be a deep copy.

__eq__(other)[source]#

Test if this object is equal to another.

It checks if the coordinate spaces are the same between the two objects. If so, then check if all the interval features are equal between the two objects after sorting them by bounds.

Parameters:

otherIntervalMetadata: Interval metadata to test for equality against.

Returns:

bool: Indicates if the two objects are equal.

__ne__(other)[source]#

Test if this object is not equal to another.

Parameters:

otherIntervalMetadata: Interval metadata to test for inequality against.

Returns:

bool: Indicates if the two objects are not equal.

See also

skbio.metadata.IntervalMetadata.__eq__