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https://github.com/benhg/pyblast3

Pyblast for python3
https://github.com/benhg/pyblast3

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Pyblast for python3

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README 

        
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pyblast

=======



Run `NCBI BLAST`_ with an easy-to-use Pythonic API.



Running NCBI BLAST manually is of course not rocket science, but this

module provides several benefits over doing so:



* Automatically runs a BLAST process for each CPU on the system;

  achieves far better throughput than the ``-num_threads`` option

* Provides an iterator API that emits native Python objects for each

  BLAST result `as they're produced`, rather than at the end

* ``Result`` and ``Hit`` objects obviate the need for manually parsing

  results; all values represented by their native Python types (e.g.

  ``Hit.evalue`` is a ``float``, etc)



.. _`NCBI BLAST`: http://blast.ncbi.nlm.nih.gov/



Example

-------



Here's a simple example with comments hilighting some relevant

features



::



    import pyblast

    

    with open('data.fasta') as f:

        # Use the pyblast.blastx() iterator function

        for r in pyblast.blastx(f, db='/path/to/swissprot'):

            msg = 'query {} has {} hits'.format(r.query_id, len(r.hits))

            if r.hits:

                # Use Hit.evalue as a float for comparison

                min_evalue = sorted([h.evalue for h in r.hits])[0]

                msg += '; minimum evalue {:f}'.format(min_evalue)



            print msg



This will produce output like the following



::



    query M00181:167:000000000-A4VBV:1:1101:11880:1874 1:N:0:6 has 6 hits; minimum evalue 0.310000

    query M00181:167:000000000-A4VBV:1:1101:17067:1875 1:N:0:6 has 14 hits; minimum evalue 0.200000

    query M00181:167:000000000-A4VBV:1:1101:15039:1878 1:N:0:6 has 4 hits; minimum evalue 4.400000

    query M00181:167:000000000-A4VBV:1:1101:17090:1895 1:N:0:6 has 6 hits; minimum evalue 1.700000

    query M00181:167:000000000-A4VBV:1:1101:15843:1907 1:N:0:6 has 2 hits; minimum evalue 1.800000



API

---



.. _`blastn`:



``blastn(input_file, *args, **kwargs)``



Iterator to process the contents of the FASTA ``input_file`` using

``blastn``; yields `Result`_ objects.



The ``*args`` and ``**kwargs`` arguments control how `blastn` is

invoked. The former are passed as options without values, while the

latter are passed as options with values. For example,

``blastn(some_file, 'ungapped', db='foo/bar')`` will run ``blastn``

with the ``-ungapped -db foo/bar`` options.



In addition, the following keyword arguments are handled specially and

are not passed on to BLAST:



- ``pb_num_processes``: number of BLAST processes to spawn; default is ``sysconf(SC_NPROCESSORS_ONLN)``

- ``pb_fields``: iterable of field names to retrieve for each hit; default is `DEFAULT_HIT_FIELDS`_. The list of valid field names (and their meanings) can be found in the ``*** Formatting options`` section of ``blastn -help``.



``blastp(input_file, *args, **kwargs)``



See documentation for `blastn`_.



``blastx(input_file, *args, **kwargs)``



See documentation for `blastn`_.



.. _`Result`:



``Result``



The result of BLAST processing a single query sequence. The set of

attributes on this object are:



- ``id``: identifier for the query sequence; can be ``None``

- ``description``: textual description of the query sequence; can be ``None``

- ``hits``: array of `Hit`_ objects



.. _`Hit`:



``Hit``



A single sequence hit in a `Result`_ object.



The attributes of this object are the names of the fields requested of

BLAST. For example, if ``blastn`` was run with ``pb_fields=['qseqid',

...]`` then one could access the ``qseqid`` value of the ``Hit``

object ``h`` like so: ``h.qseqid``. Fields referenced that were not

requested of BLAST have a ``None`` value.



In addition, BLAST fields are converted to their native Python types.

For example, ``evalue`` fields are automatically converted to floating

point values.



.. _`DEFAULT_HIT_FIELDS`:



``DEFAULT_HIT_FIELDS``



The default fields returned for each ``Hit`` object.



``VERSION``



The version of ``pyblast`` that's being used. This can be used to more

easily than feature detection to determine what features of the module

are available.