pyfasta 0.3.6

fast, memory-efficient, pythonic (and command-line) access to fasta sequence files

Author:

Brent Pedersen (brentp)

Email:

[e-mail]

License:

MIT

Contents

• Implementation

• Usage

  • Slicing

• Numpy

• Backends (Record class)

• Flattening

• Command Line Interface

• cleanup

• Testing

• Changes

  • 0.3.6

  • 0.3.5

  • 0.3.4

  • 0.3.3

  • 0.3.2

Implementation

Requires Python >= 2.5. Stores a flattened version of the fasta file without spaces or headers and uses either a mmap of numpy binary format or fseek/fread so the sequence data is never read into memory. Saves a pickle (.gdx) of the start, stop (for fseek/mmap) locations of each header in the fasta file for internal use.

Usage

>>> from pyfasta import Fasta

>>> f = Fasta('tests/data/three_chrs.fasta')
>>> sorted(f.keys())
['chr1', 'chr2', 'chr3']

>>> f['chr1']
NpyFastaRecord(0..80)

Slicing

>>> f['chr1'][:10]
'ACTGACTGAC'

# get the 1st basepair in every codon (it's python yo)
>>> f['chr1'][::3]
'AGTCAGTCAGTCAGTCAGTCAGTCAGT'

# can query by a 'feature' dictionary
>>> f.sequence({'chr': 'chr1', 'start': 2, 'stop': 9})
'CTGACTGA'

# same as:
>>> f['chr1'][1:9]
'CTGACTGA'

# with reverse complement (automatic for - strand)
>>> f.sequence({'chr': 'chr1', 'start': 2, 'stop': 9, 'strand': '-'})
'TCAGTCAG'

Numpy

The default is to use a memmaped numpy array as the backend. In which case it’s possible to get back an array directly…

>>> f['chr1'].tostring = False
>>> f['chr1'][:10] # doctest: +NORMALIZE_WHITESPACE
memmap(['A', 'C', 'T', 'G', 'A', 'C', 'T', 'G', 'A', 'C'], dtype='|S1')

>>> import numpy as np
>>> a = np.array(f['chr2'])
>>> a.shape[0] == len(f['chr2'])
True

>>> a[10:14] # doctest: +NORMALIZE_WHITESPACE
array(['A', 'A', 'A', 'A'], dtype='|S1')

mask a sub-sequence

>>> a[11:13] = np.array('N', dtype='c')
>>> a[10:14].tostring()
'ANNA'

Backends (Record class)

It’s also possible to specify another record class as the underlying work-horse for slicing and reading. Currently, there’s just the default:

• NpyFastaRecord which uses numpy memmap

• FastaRecord, which uses using fseek/fread

• MemoryRecord which reads everything into memory and must reparse the original fasta every time.

• TCRecord which is identical to NpyFastaRecord except that it saves the index in a TokyoCabinet hash database, for cases when there are enough records that loading the entire index from a pickle into memory is unwise. (NOTE: that the sequence is not loaded into memory in either case).

It’s possible to specify the class used with the record_class kwarg to the Fasta constructor:

>>> from pyfasta import FastaRecord # default is NpyFastaRecord
>>> f = Fasta('tests/data/three_chrs.fasta', record_class=FastaRecord)
>>> f['chr1']
FastaRecord('tests/data/three_chrs.fasta.flat', 0..80)

other than the repr, it should behave exactly like the Npy record class backend

it’s possible to create your own using a sub-class of FastaRecord. see the source in pyfasta/records.py for details.

Flattening

In order to efficiently access the sequence content, pyfasta saves a separate, flattened file with all newlines and headers removed from the sequence. In the case of large fasta files, one may not wish to save 2 copies of a 5GG+ file. In that case, it’s possible to flatten the file “inplace”, keeping all the headers, and retaining the validity of the fasta file – with the only change being that the new-lines are removed from each sequence. This can be specified via flatten_inplace = True

>>> import os
>>> os.unlink('tests/data/three_chrs.fasta.gdx') # cleanup non-inplace idx
>>> f = Fasta('tests/data/three_chrs.fasta', flatten_inplace=True)
>>> f['chr1']  # note the difference in the output from above.
NpyFastaRecord(6..86)

# sequence from is same as when requested from non-flat file above.
>>> f['chr1'][1:9]
'CTGACTGA'

# the flattened file is kept as a place holder without the sequence data.
>>> open('tests/data/three_chrs.fasta.flat').read()
'flattened'

Command Line Interface

there’s also a command line interface to manipulate / view fasta files. the pyfasta executable is installed via setuptools, running it will show help text.

split a fasta file into 6 new files of relatively even size:

$ pyfasta split -n 6 original.fasta

split the fasta file into one new file per header with “%(seqid)s” being filled into each filename.:

$ pyfasta split –header “%(seqid)s.fasta” original.fasta

create 1 new fasta file with the sequence split into 10K-mers:

$ pyfasta split -n 1 -k 10000 original.fasta

2 new fasta files with the sequence split into 10K-mers with 2K overlap:

$ pyfasta split -n 2 -k 10000 -o 2000 original.fasta

show some info about the file (and show gc content):

$ pyfasta info –gc test/data/three_chrs.fasta

extract sequence from the file. use the header flag to make a new fasta file. the args are a list of sequences to extract.

$ pyfasta extract –header –fasta test/data/three_chrs.fasta seqa seqb seqc

extract sequence from a file using a file containing the headers not wanted in the new file:

$ pyfasta extract –header –fasta input.fasta –exclude –file seqids_to_exclude.txt

flatten a file inplace, for faster later use by pyfasta, and without creating another copy. (Flattening)

$ pyfasta flatten input.fasta

cleanup

(though for real use these will remain for faster access)

>>> os.unlink('tests/data/three_chrs.fasta.gdx')
>>> os.unlink('tests/data/three_chrs.fasta.flat')

Testing

there is currently > 99% test coverage for the 2 modules and all included record classes. to run the tests:

$ python setup.py nosetests

Changes

0.3.6

• dont re-flatten the file every time!

0.3.5

• update docs in README.txt for new CLI stuff.

• allow flattening inplace.

• get rid of memmap (results in faster parsing).

0.3.4

• restore python2.5 compatiblity.

• CLI: add ability to exclude sequence from extract

• CLI: allow spliting based on header.

0.3.3

• include this file in the tar ball (thanks wen h.)

0.3.2

• separate out backends into records.py

• use nosetests (python setup.py nosetests)

• add a TCRecord backend for next-gen sequencing availabe if tc is (easy-)installed.

• improve test coverage.