bx-python handles a number of these formats, including BigWig and BigBed which are nice ways to store your interval and score data compressed on disk but still have random access:

• wiggle
• BED
• BigWig
• BigBed

Most libraries will be iterative parsers to help be memory conscious on large files. BigWig and BigBed get the closest to your dream API without requiring the entire file be loaded in memory.

There is also a Bed parser in BedTools which you can access from the python/cython wrapper bedtools-python

There's another python wrapper pybedtools that's mostly for calling the bedtools executables, but it does have a feature module (that could be stand-alone) for Bed, GFF, GTF, and other feature "parsing".

And, there's a Bed module here that puts the file into a numpy array and allows you to do things like your list comprehension above

   b = Bed('some.bed')
   small_genes = b[b['end'] - b['start'] < 100]

Shameless plug: pybedtools now does almost everything you asked (with the exception of the chromosome attribute access), and it's in Cython for speed.

For example, say we have this BED file:

$ cat a.bed
chr1    1    100    feature1    0    +
chr1    100    200    feature2    0    +
chr1    150    500    feature3    0    -
chr1    900    950    feature4    0    +

And this GFF file:

$ cat d.gff
chr1    fake    gene    50    300    .    +    .    ID=gene1
chr1    fake    mRNA    50    300    .    +    .    ID=mRNA1;Parent=gene1;
chr1    fake    CDS    75    150    .    +    .    ID=CDS1;Parent=mRNA1;
chr1    fake    CDS    200    275    .    +    .    ID=CDS2;Parent=mRNA1;
chr1    fake    rRNA    1200    1275    .    +    .    ID=rRNA1;

Then:

>>> from pybedtools import BedTool

pybedtools has auto-detection of BED/GFF/VCF based on position of string and integer fields:

>>> a = BedTool('a.bed')
>>> d = BedTool('d.gff')

>>> a.file_type
'bed'

>>> d.file_type
'gff'

BedTools are iterable, and you get an Interval for each feature. So you can do list comprehension:

>>> [i.name, i.strand) for i in a if len(i) < 150]
[('feature1', '+'), ('feature2', '+'), ('feature4', '+')]

or indexing:

>>> feature = d[0]

You have GFF attribute access (lazily parsed):

>>> feature['ID']
'gene1'

And arbitrary access of fields:

>>> d[1].fields[1]
'mRNA'

...not to mention access to all of BEDTools:

>>> not_in_d = a.intersect(d, v=True)
>>> print not_in_d
chr1    900    950    feature4    0    +

At least a GFF parser is available with the newer versions of BioPython. For code examples see their wiki.

There is a question about converting BED to GFF and some tools for other conversions have also been suggested here by Brad.

I just finished work on a python package that can read most of the genomic file formats such as BED, WIG, GFF, BedGraph in a simple and standard way.

It's free and open-source. It will read many different kinds of formats in the same simple syntax:

import track
with track.load('tracks/rp_genes.bed') as rp:
    data = rp.read('chr3')

You install it by typing:

$ sudo easy_install track

Particular attention was brought to the documentation:

http://xapple.github.com/track/

Please tell me what you think about it !

Maybe the following helps

• BED Classes provided by Taos in this python-forum thread
• BED module discussion on stackoverflow

Never tried them myself though.

Andreas