pydna 1.0.0

Contains classes and code for representing double stranded DNA and functions for simulating homologous recombination between DNA molecules.

Planning genetic constructs with many parts, such as recombinant metabolic pathways is usually done manually using a DNA sequence editor, a task 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

• Gel electrophoresis of DNA (NEW Feature)

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. A cloning strategy expressed in pydna is complete, unambiguous and stable. Pydna has been designed to be understandable for biologists with some basic understanding of Python.

Pydna can 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 Jupyter notebook format here.

There at the open access BMC Bioinformatics publication describing pydna:

Most pydna functionality is implemented as methods for the double stranded DNA sequence record classes Dseq and Dseqrecord, which are subclasses of the Biopython Seq and SeqRecord classes.

These classes make cut and paste cloning and PCR very simple:

>>> 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 without any additional information other than the primary sequence of the fragments.

Gel electrophoresis of DNA fragments can be simulated using the gel.py module by Bruno Silva:

alt text

Look at an example notebook with a gel simulation here

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 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 over time.

import pydna

gb = pydna.Genbank("[e-mail]") # 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 is also be useful to automate the simulation of sub cloning experiments using python. This is helpful to generate examples for teaching purposes.

Read the documentation or the cookbook with example files for further information.

An on-line shell running Python with pydna is available for simple experimentation. It is slower than running pydna on your own computer locally.

Please post a message in the google group for pydna if you have problems, questions or comments. Feedback in the form of questions, comments or criticism is very welcome!

Automatic testing and builds

An anaconda package is automatically built on Anaconda cloud .

The test suit is run automatically after each commit on OSX-64 using travis and on Windows using appveyor.

Source distributions (gztar,zip) and a python wheel are built on drone and uploaded to pypi

Documentation is built and displayed at readthedocs,

Code coverage is .

Dependencies are monitored by versioneye

Minimal installation requirements

Pydna was developed on and for Python 2.7. Other versions have not been tested. The list below is the minimal requirements for installing pydna.

• Python 2.7

• biopython >= 1.65

• networkx >= 1.8.1

• appdirs >=1.3.0

• prettytable>=0.7.2

• pint >=0.6

Optional Requirements

Pydna has been designed to be used from the Jupyter notebook. If you have IPython and Jupyter installed, there are functions in pydna for importing ipython notebooks as modules among other things.

If scipy, numpy, matplotlib and mpldatacursor are installed, the gel simulation functionality is available.

• ipython>=4

• scipy>=0.16.0

• numpy>=1.10.1

• matplotlib>=1.5.0

• mpldatacursor>=0.6.1

Requirements for running tests

• nose>=1.3.4

• coverage>=3.7.1

Python 3

This code has not been tested with Python 3.

Installation using conda on Anaconda

The absolutely best way of installing and using pydna is to use a the free Anaconda python distribution.

There is a conda package available for pydna, which is easily installed from 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 necessary and optional dependencies automatically in one go.

Installation using pip

The second best way of installing pydna is with pip. Pip is the officially recommended tool for installation of Python packages from PyPi. Pip installs the minimal installation requirements automatically, but not the optional requirements (see above).

Linux:

bjorn@bjorn-UL30A:~/pydna$ sudo pip install pydna

Windows:

C:\> pip install pydna

If you do not have pip, you can get it by following these instructions.

Installation from Source

If you install from source, you need to install all 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. Note that these will not install dependencies (see below).

Windows dependencies

Sometimes dependencies can be difficult to install on windows, 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
numpy (32,64)	http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
networkx (32,64)	http://www.lfd.uci.edu/~gohlke/pythonlibs/#networkx
pint	http://www.lfd.uci.edu/~gohlke/pythonlibs/Pint-0.6-py2.py3-none-any.whl
scipy (32,64)	http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
matplotlib (32,64)	http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib
ipython>=4.0	http://www.lfd.uci.edu/~gohlke/pythonlibs/#ipython
jupyter	http://www.lfd.uci.edu/~gohlke/pythonlibs/#jupyter

Source Code

Pydna is developed on Github.