skbio.metadata.IntervalMetadata

class skbio.metadata.IntervalMetadata(upper_bound)[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_bound : int

Defines the exclusive upper bound of the interval features. No coordinate can be greater than it.

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

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

copy.copy(im) Return a shallow copy.
copy.deepcopy(im) Return a deep copy.
im1 == im2 Test if this object is equal to another.
im1 != im2 Test if this object is not equal to another.
add(bounds[, fuzzy, metadata]) Create and add an Interval to this IntervalMetadata.
concat(interval_metadata) Concatenate an iterable of IntervalMetadata objects.
drop(intervals) Drops Interval objects.
merge(other) Merge the interval features of another IntervalMetadata object.
query([bounds, metadata]) Yield Interval object with the bounds and attributes.
sort([ascending]) Sort interval features by their coordinates.