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 is also an open access paper in BMC Bioinformatics describing pydna:
Please make a reference to the above paper if you publish work where pydna was used.
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 besides 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.
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("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 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.
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 after each commit on:
OSX-64 using travis
Windows using appveyor
.
Ubuntu using drone
Documentation is built and displayed at readthedocs
Code coverage is
Dependencies are monitored by versioneye
Github stars:
Github issues:
Minimal installation requirements
Pydna is currently developed on and for Python 3.5. Pydna versions before 1.0.0 were compatible with python 2.7 only. The list below is the minimal requirements for installing pydna. Biopython has c-extensions, but the other modules are pure python.
Optional Requirements
Pydna has been designed to be used from the Jupyter notebook. If IPython and Jupyter are installed, importing ipython notebooks as modules among are supported among other things.
If the modules listed below are installed, gel simulation functionality is available.
The pydna conda package installs all optional requirements, currently:
Requirements for running tests
Installation using conda on Anaconda
The absolutely best way of installing and using pydna is to use the free Anaconda python distribution.
Once Anaconda is installed, the conda package manager can be used to install pydna. Pydna and its dependencies are available from the conda-forge and BjornFJohansson Anaconda.org channels. The first step is to add the channels:
conda config --append channels conda-forge conda config --append channels BjornFJohansson
Then pydna can be installed by simply:
conda install 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, the officially recommended tool.
Pip installs the minimal installation requirements automatically, but not the optional requirements (see above). These have to be installed manually.
Linux:
bjorn@bjorn-UL30A:~/pydna$ sudo pip install pydna
Windows:
C:\> pip install pydna
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 or from Github and extract the file. Open the pydna source code directory (containing the setup.py file) in terminal and type:
python setup.py install
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 these binary installers for Windows:
Dependency |
link |
|---|---|
Python (32,64) |
|
Biopython (32) |
|
Biopython (64) |
|
numpy (32,64) |
|
networkx (32,64) |
|
pint |
http://www.lfd.uci.edu/~gohlke/pythonlibs/Pint-0.6-py2.p y3-none-any.whl |
scipy (32,64) |
|
matplotlib (32,64) |
|
ipython>=4.0 |
|
jupyter |
Source Code
Pydna is developed on Github.
Changelog
See the change log for recent changes.
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