Metadata-Version: 1.1
Name: pydna
Version: 0.9.8
Summary: Contains classes and code for representing double
                     stranded DNA and functions for simulating homologous
                     recombination between DNA molecules.
Home-page: http://pypi.python.org/pypi/pydna/
Author: BjÃ¶rn Johansson
Author-email: bjorn_johansson@bio.uminho.pt
License: LICENSE.txt
Description: pydna
        =====
        
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        Planning genetic constructs with many parts, such as recombinant
        metabolic pathways is usually done manually using a DNA sequence editor
        which quickly becomes unfeasible as scale and complexity of the
        constructions increase.
        
        The Pydna python package provide a human-readable formal description of
        cloning and assembly strategies which also allows for automatic computer
        simulation and verification.
        
        Pydna provides simulation of:
        
        -  restriction digestion
        -  ligation
        -  PCR
        -  primer design
        -  Gibson assembly
        -  homologous recombination
        
        A cloning strategy expressed in pydna is complete, unambiguous and
        stable. Pydna has been designed to be understandable for biologists with
        limited programming skills.
        
        Pydna formalize planning and sharing of cloning strategies and is
        especially useful for complex or combinatorial DNA molecule
        constructions.
        
        Look at some assembly strategies made in the IPython notebook format
        `here <http://nbviewer.ipython.org/github/BjornFJohansson/ypk-xylose-pathways/blob/master/index.ipynb>`__.
        
        Pydna provide functions for molecular biology using python. Double
        stranded DNA sequence classes that make cut and paste cloning and PCR
        very simple is provided (see example below). Look at the open access BMC
        Bioinformatics publication describing pydna:
        
        |abstr|
        
        See an example of pydna usage at the command line below:
        
        ::
        
            >>> import pydna
            >>> seq = pydna.Dseq("GGATCCAAA","TTTGGATCC",ovhg=0)
            >>> seq
            Dseq(-9)
            GGATCCAAA
            CCTAGGTTT
            >>> from Bio.Restriction import BamHI
            >>> a,b = seq.cut(BamHI)
            >>> a
            Dseq(-5)
            G
            CCTAG
            >>> b
            Dseq(-8)
            GATCCAAA
                GTTT
            >>> a+b
            Dseq(-9)
            GGATCCAAA
            CCTAGGTTT
            >>> b+a
            Dseq(-13)
            GATCCAAAG
                GTTTCCTAG
            >>> b+a+b
            Dseq(-17)
            GATCCAAAGGATCCAAA
                GTTTCCTAGGTTT
            >>> b+a+a
            Traceback (most recent call last):
              File "<stdin>", line 1, in <module>
              File "/usr/local/lib/python2.7/dist-packages/pydna/dsdna.py", line 217, in __add__
                raise TypeError("sticky ends not compatible!")
            TypeError: sticky ends not compatible!
            >>>
        
        Notably, homologous recombination and Gibson assembly between linear DNA
        fragments can be easily simulated.
        
        Most functionality is implemented as methods for the double stranded DNA
        sequence record classes Dseq and Dseqrecord, which are subclasses of the
        `Biopython <http://biopython.org/wiki/Main_Page>`__
        `Seq <http://biopython.org/wiki/Seq>`__ and
        `SeqRecord <http://biopython.org/wiki/SeqRecord>`__ classes.
        
        Pydna was designed to provide a form of executable documentation
        describing a subcloning or DNA assembly experiment. The pydna code
        unambiguously describe a sub cloning experiment, and can be executed to
        yield the sequence of the of the resulting DNA molecule.
        
        Pydna was designed to semantically imitate how sub cloning experiments
        are typically documented in Scientific literature. Pydna code describing
        a sub cloning is reasonably compact and meant to be easily readable.
        
        The nine lines of Python below, simulates the construction of a
        recombinant plasmid. DNA sequences are downloaded from Genbank by
        accession numbers that are guaranteed to be stable.
        
        ::
        
            import pydna
        
            gb = pydna.Genbank("myself@email.com") # Tell Genbank who you are!
        
            gene = gb.nucleotide("X06997") # Kluyveromyces lactis LAC12 gene for lactose permease.
        
            primer_f,primer_r = pydna.parse(''' >760_KlLAC12_rv (20-mer)
                                                ttaaacagattctgcctctg
        
                                                >759_KlLAC12_fw (19-mer)
                                                aaatggcagatcattcgag
                                                ''', ds=False)
        
            pcr_prod = pydna.pcr(primer_f,primer_r, gene)
        
            vector = gb.nucleotide("AJ001614") # pCAPs cloning vector
        
            from Bio.Restriction import EcoRV
        
            lin_vector = vector.linearize(EcoRV)
        
            rec_vec =  ( lin_vector + pcr_prod ).looped()
        
        Pydna might also be useful to automate the simulation of `sub
        cloning <http://en.wikipedia.org/wiki/Subcloning>`__ experiments using
        python. This could be helpful to generate examples for teaching
        purposes. Read the
        `documentation <https://pydna.readthedocs.org/en/latest>`__ or the
        `cookbook <https://www.dropbox.com/sh/4re9a0wk03m95z4/AABpu4zwq4IuKUvK0Iy9Io0Fa?dl=0>`__
        with example files for further information.
        
        An `on-line <http://pydna-shell.appspot.com>`__ shell running Python
        with pydna is available for experimentation.
        
        Please post a message in the `google
        group <https://groups.google.com/d/forum/pydna>`__ for pydna if you have
        problems, questions or comments. Feedback in the form of questions,
        comments or criticism is very welcome! ## Installation requirements
        
        This package was developed on and for Python 2.7. Other versions have
        not been tested.
        
        -  `Python 2.7 <http://www.python.org>`__
        -  `ipython>=4.0.0 <https://pypi.python.org/pypi/ipython>`__
        -  `biopython >= 1.65 <http://pypi.python.org/pypi/biopython>`__
        -  `networkx >= 1.8.1 <http://pypi.python.org/pypi/networkx>`__
        -  `appdirs >=1.3.0 <https://pypi.python.org/pypi/appdir>`__
        -  `prettytable>=0.7.2 <https://pypi.python.org/pypi/PrettyTable>`__
        
        Requirements for running tests
        ------------------------------
        
        -  `nose>=1.3.4 <https://pypi.python.org/pypi/nose>`__
        -  `coverage>=3.7.1 <https://pypi.python.org/pypi/coverage>`__
        
        Python 3
        --------
        
        This code has not been tried with Python 3. If there is sufficient
        interest, there might be a Python 3 version in the future.
        
        Installation using conda on Anaconda
        ------------------------------------
        
        The best way of using Python in general is to use a free distribution
        such as `Anaconda <https://store.continuum.io/cshop/anaconda>`__
        
        There is a `conda <https://anaconda.org/bjornfjohansson/pydna>`__
        package available for pydna, which is easily installed at the command
        line using the conda package manager.
        
        ::
        
            conda install -c https://conda.anaconda.org/bjornfjohansson pydna
        
        This works on Windows, MacOSX and Linux, and installs all dependencies
        automatically in one go.
        
        Installation using pip
        ----------------------
        
        The second best way of installing pydna is with pip. Pip is the
        officially
        `recommended <http://python-packaging-user-guide.readthedocs.org/en/latest>`__
        tool for installation of Python packages from PyPi. Pip installs
        dependencies automatically.
        
        Linux:
        ~~~~~~
        
        ::
        
            bjorn@bjorn-UL30A:~/Dropbox/pydna$ sudo pip install pydna
        
        Windows:
        ~~~~~~~~
        
        ::
        
            C:\> pip install pydna
        
        If you do not have pip, you can get it by following these
        `instructions <http://www.pip-installer.org/en/latest/installing.html>`__
        
        Installation from Source
        ------------------------
        
        If you install from source, you need to install the dependencies
        separately (listed above). Download one of the source installers from
        the pypi site and extract the file. Open the pydna source code directory
        (containing the setup.py file) in terminal and type:
        
        ::
        
            python setup.py install
        
        Installation from binary distributions
        --------------------------------------
        
        There is a 64 bit windows executable and a windows wheel
        `here <https://ci.appveyor.com/project/BjornFJohansson/pydna/build/artifacts>`__.
        Note that these will not install required dependencies (see below).
        
        Windows dependencies
        ~~~~~~~~~~~~~~~~~~~~
        
        Sometimes the dependecies can be difficult to install on windows,
        especially Biopython as a C compiler is necessary. If dependencies have
        to be installed separately, this can be done using the binary installers
        for Windows:
        
        +--------------------+--------------------------------------------------------+
        | Dependency         | link                                                   |
        +====================+========================================================+
        | Python (32,64)     | http://www.python.org/download                         |
        +--------------------+--------------------------------------------------------+
        | Biopython (32)     | http://biopython.org/wiki/Download                     |
        +--------------------+--------------------------------------------------------+
        | Biopython (64)     | http://www.lfd.uci.edu/~gohlke/pythonlibs/#biopython   |
        +--------------------+--------------------------------------------------------+
        | networkx (32,64)   | http://www.lfd.uci.edu/~gohlke/pythonlibs/#networkx    |
        +--------------------+--------------------------------------------------------+
        
        Source Code Repository
        ----------------------
        
        Pydna is developed on
        `Github <https://github.com/BjornFJohansson/pydna>`__
        
        .. |icon1| image:: https://travis-ci.org/BjornFJohansson/pydna.svg
           :target: https://travis-ci.org/BjornFJohansson/pydna
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           :target: https://ci.appveyor.com/project/BjornFJohansson/pydna
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           :target: https://drone.io/github.com/BjornFJohansson/pydna/latest
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           :target: https://binstar.org/bjornfjohansson/pydna
        .. |icon6| image:: https://coveralls.io/repos/BjornFJohansson/pydna/badge.svg?branch=master
           :target: https://coveralls.io/r/BjornFJohansson/pydna?branch=master
        .. |icon7| image:: https://readthedocs.org/projects/pydna/badge/?version=latest
           :target: https://readthedocs.org/projects/pydna/?badge=latest
        .. |icon8| image:: https://img.shields.io/pypi/v/pydna.png
           :target: https://pypi.python.org/pypi/pydna
        .. |icon9| image:: https://img.shields.io/github/stars/BjornFJohansson/pydna.svg
           :target: https://github.com/BjornFJohansson/pydna/stargazers
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           :target: https://pypi.python.org/pypi/pydna
        .. |icon11| image:: https://www.versioneye.com/user/projects/553174c010e714f9e50010bb/badge.svg
           :target: https://www.versioneye.com/user/projects/553174c010e714f9e50010bb
        .. |abstr| image:: https://raw.githubusercontent.com/BjornFJohansson/pydna/master/BMC_resized.png
           :target: http://www.biomedcentral.com/1471-2105/16/142/abstract
        
Keywords: bioinformatics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
