Genomic intervals and genomic arrays


A genomic interval is a consecutive stretch on a genomic sequence such as a chromosome. It is represented by a GenomicInterval object.

class HTSeq.GenomicInterval(chrom, start, end, strand)
chrom (string)
The name of a sequence (i.e., chromosome, contig, or the like).
start (int)
The start of the interval. Even on the reverse strand, this is always the smaller of the two values ‘start’ and ‘end’. Note that all positions should be given and interpreted as 0-based value!
end (int)
The end of the interval. Following Python convention for ranges, this in one more than the coordinate of the last base that is considered part of the sequence.
strand (string)
The strand, as a single character, '+', '-', or '.'. '.' indicates that the strand is irrelevant.
Representation and string conversion

The class’s __str__ method gives a spcae-saving description of the interval, the __repr__ method is a bit more verbose:

>>> iv = HTSeq.GenomicInterval( "chr3", 123203, 127245, "+" )
>>> print iv
>>> iv
<GenomicInterval object 'chr3', [123203,127245), strand '+'>



as above


The “directional start” position. This is the position of the first base of the interval, taking the strand into account. Hence, this is the same as start except when strand == '-', in which case it is end-1.

Note that if you set start_d, both start and end are changed, such that the interval gets the requested new directional start and its length stays unchanged.


The “directional end”: The same as end, unless strand=='-', in which case it is start-1. This convention allows to go from start_d to end_d (not including, as usual in Python, the last value) and get all bases in “reading” direction.

end_d is not writable.


The length is calculated as end - start. If you set the length, start_d will be preserved, i.e., end is changed, unless the strand is -, in which case start is changed.


These attributes return GenomicPosition objects referring to the respective positions.

Directional instantiation
HTSeq.GenomicInterval_from_directional(chrom, start_d, length, strand=".")

This function allows to create a new GenomicInterval object specifying directional start and length instead of start and end.


These methods test whether the object is contained in, contains, or overlaps the second GenomicInterval object iv.

For any of of these conditions to be true, the start and end values have to be appropriate, and furthermore, the chrom values have to be equal and the strand values consistent. The latter means that the strands have to be the same if both intervals have strand information. However, if at least one of the objects has strand == '.', the strand information of the other object is disregarded.

Note that all three methods return True for identical intervals.

GenomicInterval.xrange(step = 1)
GenomicInterval.xrange_d(step = 1)

These methods yield iterators of :class:GenomicPosition objects from start to end (or, for xrange_d from start_d to end_d).


Change the object’s start end end values such that iv becomes contained.

Special methods

GenomicInterval implements the methods necessary for

  • obtaining a copy of the object (the copy method)
  • pickling the object
  • representing the object and converting it to a string (see above)
  • comparing two GenomicIntervals for equality and inequality
  • hashing the object


A GenomicPosition represents the position of a single base or base pair, i.e., it is an interval of length 1, and hence, the class is a subclass of :class:GenomicInterval.

class HTSeq.GenomicPosition(chrom, pos, strand='.')

The initialisation is as for a :class:GenomicInterval object, but no length argument is passed.



pos is an alias for GenomicInterval.start_d.

All other attributes of GenomicInterval are still exposed. Refrain from using them, unless you want to use the object as an interval, not as a position. Some of them are now read-only to prevent the length to be changed.


A GenomicArray is a collection of ChromVector objects, either one or two for each chromosome of a genome. It allows to access the data in these transparently via GenomicInterval objects.

Note: ``GenomicArray``’s interface changed significantly in version 0.5.0. Please see the Version History page.

class HTSeq.GenomicArray(chroms, stranded=True, typecode='d', storage='step', memmap_dir='')

Creates a GenomicArray.

If chroms is a list of chromosome names, two (or one, see below) ChromVector objects for each chromosome are created, with start index 0 and indefinite length. If chroms is a dict, the keys are used for the chromosome names and the values should be the lengths of the chromosome, i.e., the ChromVectors index ranges are then from 0 to these lengths. (Note that the term chromosome is used only for convenience. Of course, you can as well specify contig IDs or the like.) Finally, if chroms is the string "auto", the GenomicArray is created without any chromosomes but whenever the user attempts to assign a value to a yet unknown chromosome, a new one is automatically created with GenomicArray.add_chrom().

If stranded is True, two StepVector objects are created for each chromosome, one for the ‘+’ and one for the ‘-‘ strand. For stranded == False, only one StepVector per chromosome is used. In that case, the strand argument of all GenomicInterval objects that are used later to specify regions in the GenomicArray are ignored.

The typecode determines the data type and is as in numpy, i.e.:

  • 'd' for float values (C type ‘double’),
  • 'i' for int values,
  • 'b' for Boolean values,
  • 'O' for arbitrary Python objects as value.

The storage mode determines how the ChromVectors store the data internally:

  • mode 'step': A step vector is used. This is the default and useful for large amounts of data which may stay constant along a range of indices. Each such step is stored internally as a node in a red-black tree.
  • mode 'ndarray': A 1D numpy array is used internally. This is useful if the data changes a lot, and steps are hence inefficient. Using this mode requires that chromosome lengths are specified.
  • mode memmap: This is useful for large amounts of data with very short steps, where step is inefficient, but a numpy vectors would not fit into memory. A numpy memmap is used that stores the whole vector in a file on disk and transparently maps into memory windows of the data. This mode requires chromosome lengths, and specification of a directory, via the memmap_dir argument, to store the temporary files in. It is not suitable for type code O.



see above


a dict of dicts of ChromVector objects, using the chromosome names, and the strand as keys:

.. doctest::
>>> ga = HTSeq.GenomicArray( [ "chr1", "chr2" ], stranded=False )
>>> ga.chrom_vectors 
{'chr2': {'.': <ChromVector object, chr2:[0,Inf)/., step>},
 'chr1': {'.': <ChromVector object, chr1:[0,Inf)/., step>}}
>>> ga = HTSeq.GenomicArray( [ "chr1", "chr2" ], stranded=True )
>>> ga.chrom_vectors  
{'chr2': {'+': <ChromVector object, chr2:[0,Inf)/+, step>,
          '-': <ChromVector object, chr2:[0,Inf)/-, step>},
 'chr1': {'+': <ChromVector object, chr1:[0,Inf)/+, step>,
          '-': <ChromVector object, chr1:[0,Inf)/-, step>}}

A boolean. This attribute is set to True if the GenomicArray was created with the "auto" arguments for the chroms parameter. If it is true, an new chromosome will be added whenever needed.

Data access

To access the data, use :class:GenomicInterval objects.

To set an single position or an interval, use:

>>> ga[ HTSeq.GenomicPosition( "chr1", 100, "+" ) ] = 7
>>> ga[ HTSeq.GenomicInterval( "chr1", 250, 400, "+" ) ] = 20

To read a single position:

>>> ga[ HTSeq.GenomicPosition( "chr1", 300, "+" ) ]

To read an interval, use a GenomicInterval object as index, and obtain a ChromVector with a sub-view:

>>> iv = HTSeq.GenomicInterval( "chr1", 250, 450, "+" )
>>> v = ga[ iv ]
>>> v
<ChromVector object, chr1:[250,450)/+, step>
>>> list( v.steps() )  
[(<GenomicInterval object 'chr1', [250,400), strand '+'>, 20.0),
 (<GenomicInterval object 'chr1', [400,450), strand '+'>, 0.0)]

Note that you get ( interval, value ) pairs , i.e., you can conveniently cycle through them with:

>>> for iv, value in ga[ iv ].steps():
...    print iv, value
chr1:[250,400)/+ 20.0
chr1:[400,450)/+ 0.0

You can get all steps from all chromosomes by calling the arrays own steps method.

Modifying values

ChromVector implements the __iadd__ method. Hence you can use +=:

>>> ga[ HTSeq.GenomicInterval( "chr1", 290, 310, "+" ) ] += 1000
>>> list( ga[ HTSeq.GenomicInterval( "chr1", 250, 450, "+" ) ].steps() )  
[(<GenomicInterval object 'chr1', [250,290), strand '+'>, 20.0),
 (<GenomicInterval object 'chr1', [290,310), strand '+'>, 1020.0),
 (<GenomicInterval object 'chr1', [310,400), strand '+'>, 20.0),
 (<GenomicInterval object 'chr1', [400,450), strand '+'>, 0.0)]

To do manipulations other than additions, use Chromvector’s apply method:

>>> ga[ HTSeq.GenomicInterval( "chr1", 290, 300, "+" ) ].apply( lambda x: x * 2 )
>>> list( ga[ HTSeq.GenomicInterval( "chr1", 250, 450, "+" ) ].steps() ) 
[(<GenomicInterval object 'chr1', [250,290), strand '+'>, 20.0),
 (<GenomicInterval object 'chr1', [290,300), strand '+'>, 2040.0),
 (<GenomicInterval object 'chr1', [300,310), strand '+'>, 1020.0),
 (<GenomicInterval object 'chr1', [310,400), strand '+'>, 20.0),
 (<GenomicInterval object 'chr1', [400,450), strand '+'>, 0.0)]
Writing to a file
GenomicArray.write_bedgraph_file(file_or_filename, strand=".", track_options="")

Write out the data in the GenomicArray as a BedGraph track. This is a subtype of the Wiggle format (i.e., the file extension is usually ”.wig”) and such files can be conveniently viewed in a genome browser, e.g., with IGB.

This works only for numerical data, i.e., datatype 'i' or 'd'. As a bedgraph track cannot store strand information, you have to specify either '+' or '-' as the strand argument if your GenomicArray is stranded (stranded==True). Typically, you will write two wiggle files, one for each strand, and display them together.

Adding a chromosome
GenomicArray.add_chrom(chrom, length=sys.maxint, start_index=0)

Adds step vector(s) for a further chromosome. This is useful if you do not have a full list of chromosome names yet when instantiating the GenomicArray.


A GenomicArrayOfSets is a sub-class of GenomicArray that deal with the common special case of overlapping features. This is best explained by an example: Let’s say, we have two features, "geneA" and "geneB", that are at overlapping positions:

>>> ivA = HTSeq.GenomicInterval( "chr1", 100, 300, "." )
>>> ivB = HTSeq.GenomicInterval( "chr1", 200, 400, "." )

In a GenomicArrayOfSets, the value of each step is a set and so can hold more than one object. The __iadd__ method is overloaded to add elements to the sets:

>>> gas = HTSeq.GenomicArrayOfSets( ["chr1", "chr2"], stranded=False )
>>> gas[ivA] += "gene A"
>>> gas[ivB] += "gene B"
>>> list( gas[ HTSeq.GenomicInterval( "chr1", 0, 500, "." ) ].steps() ) 
[(<GenomicInterval object 'chr1', [0,100), strand '.'>,   set([])),
 (<GenomicInterval object 'chr1', [100,200), strand '.'>, set(['gene A'])),
 (<GenomicInterval object 'chr1', [200,300), strand '.'>, set(['gene A', 'gene B'])),
 (<GenomicInterval object 'chr1', [300,400), strand '.'>, set(['gene B'])),
 (<GenomicInterval object 'chr1', [400,500), strand '.'>, set([]))]
class HTSeq.GenomicArrayOfSets(chroms, stranded = True)

Instantiation is as in GenomicArray, only that datatype is always 'O'.

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