pyfasta 0.3.1

fast, memory-efficient, pythonic access to fasta sequence files

Author:

Brent Pedersen (brentp)

Email:

[e-mail]

License:

MIT

Contents

• Implementation

• Usage

  • Slicing

• Numpy

• Backends (Record class)

• Command Line Interface

• cleanup

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.

it’s possible to create your own using a sub-class of FastaRecord. see the source for details. Next addition will be a pytables/hdf5 backend.

>>> 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

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

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

cleanup

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

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