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
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.
There at the open access BMC Bioinformatics publication describing pydna:
Double stranded DNA sequence classes that make cut and paste cloning and PCR very simple is provided.
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 without any additional information other than the primary sequence of the fragments.
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.
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 experiments using python. This could be 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 rinning pydna on your own computer.
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
The test suit is run automatically on OSX-64 using travis 
Installation requirements
This package was developed on and for Python 2.7. Other versions have not been tested.
Requirements for running tests
Optional Requirements
Pydna has been designed to be used from the IPython notebook. If you have IPython installed, there are functions in pydna for importing ipython notebooks as modules among other things.
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
There is a conda 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 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
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. 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) |
|
Biopython (32) |
|
Biopython (64) |
|
networkx (32,64) |
Source Code Repository
Pydna is developed on Github
TODO
[ ] IPython 4 (Jupyter) support
[ ] Add agarose gel electrophoresis simulation
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