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company name:
Paul Joseph Davis
license: Freeware
minimum requirements:
functional limitations:
nebseq description

nebseq is a Python module for basic biological sequence manipulations.

Import as usual

>>> import nebseq

Reverse complements

The only note here is that revcomp does not check the input sequence to see if it looks like DNA or RNA.

>>> nebseq.revcomp('ACGT')
>>> nebseq.revcomp('TTACC')

And if we give it garbage it just gives us garbage back.

>>> nebseq.revcomp('ZQ')


The translation function should allow for full support of sequence translation. This includes things like trimming the first couple bases and using alternate translation tables. There is also support for the more esoteric post translational modifications that can be found in some Genbank files as well as translating partial peptides (for things like fuzzy coordinates).

Basic translation:

>>> nebseq.translate('TTGGCCAAGGAACGA', table=11)

Showing the effects of a partial peptide translation. By default the first codon should be a start codon according to the selected translation table, if not then its converted to an 'X'

>>> nebseq.translate('GCCAAG')
>>> nebseq.translate('GCCAAG', partial=True)

Or we can remove the first couple of bases for fuzzy coordinates.

>>> nebseq.translate('TTGCCAAG', start=2, partial=True)

Modifications are specified as an (index, amino_acid) two-tuple. Notice that modification indexes are specified as one-based indexes into the amino acid sequence.

>>> nebseq.translate('ATGAAGGAA', modifications=[(2, 'U')])


Sequence extraction is for when you want to slice out part of a larger sequence. This is useful if you use the nebgb module and its definition of locations parsed from strings like join(1..5,9..100).

>>> location = {'type': 'span', 'from': 4, 'to': 10}
>>> nebseq.extract('ACCGTACCATAGTT', location)
('GTACCAT', (False, False))
>>> location = {
... "type": "complement",
... "segment": {
... "type": "join",
... "segments": [
... {"type": "span", "from": 3, "to": 8},
... {"type": "span", "from": 10, "to": 14}. .

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>>> import nebgb
>>> rec = nebgb.parse_file("./test/data/").next()
>>> rec.keywords["source"]["name"]
'house mouse'
>>> rec.features[1]["properties"]["product"]
'interferon beta, fibroblast'
>>> for seq in rec.seqiter:
... print seq

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