pydna 5.2.0

Representing double stranded DNA and functions for simulating cloning and homologous recombination between DNA molecules.

pydna

Planning genetic constructs with many parts and assembly steps, such as recombinant metabolic pathways :petri_dish:, are often difficult to properly document as is evident from the poor state of documentation in the scientific literature :radioactive:.

The pydna python package provide a human-readable formal descriptions of :dna: cloning and genetic assembly strategies in Python :snake: which allow for simulation and verification.

Pydna can be used as executable documentation for cloning.

A cloning strategy expressed in pydna is complete, unambiguous and stable.

Pydna provides simulation of:

• Restriction digestion
• Ligation
• PCR
• Primer design
• Gibson assembly
• Golden gate assembly
• Homologous recombination
• Gel electrophoresis of DNA with generation of gel images

Virtually any sub-cloning experiment can be described in pydna, and its execution yield the sequences of intermediate and final DNA molecules.

Pydna has been designed with the goal of being understandable for biologists with only 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.

To get started, we have compiled some simple examples. For more elaborate use, look at some assembly strategies of D-xylose metabolic pathways MetabolicEngineeringGroupCBMA/ypk-xylose-pathways.

Usage

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:

>>> from pydna.dseq import Dseq
>>> seq = 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!
>>>

As the example above shows, pydna keeps track of sticky ends.

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 included gel module

Jupyter QtConsole 4.7.7
Python 3.8.5 | packaged by conda-forge | (default, Aug 29 2020, 01:22:49)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.18.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from pydna.gel import gel

In [2]: from pydna.ladders import PennStateLadder

In [3]: from pydna.dseqrecord import Dseqrecord

In [4]: gel([PennStateLadder,[Dseqrecord("A"*2000)]])
Out[4]:

Pydna can be very compact. The eleven 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.

from pydna.genbank import Genbank
gb = Genbank("[e-mail]") # Tell Genbank who you are!
gene = gb.nucleotide("X06997") # Kluyveromyces lactis LAC12 gene for lactose permease.
from pydna.parsers import parse_primers
primer_f,primer_r = parse_primers(''' >760_KlLAC12_rv (20-mer)
                                      ttaaacagattctgcctctg

                                      >759_KlLAC12_fw (19-mer)
                                      aaatggcagatcattcgag ''')
from pydna.amplify import pcr
pcr_prod = 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 can automate the simulation of sub cloning experiments using python. This is helpful to generate examples for teaching purposes.

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

Please post a message in the google group for pydna if you need help or have problems, questions or comments :sos:.

Feedback & suggestions are very welcome!

Who is using pydna?

Taylor, L. J., & Strebel, K. (2017). Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes. BMC Microbiology, 17(1), 12. PubMed

Wang, Y., Xue, H., Pourcel, C., Du, Y., & Gautheret, D. (2021). 2-kupl: mapping-free variant detection from DNA-seq data of matched samples. In Cold Spring Harbor Laboratory (p. 2021.01.17.427048). DOI PubMed

An Automated Protein Synthesis Pipeline with Transcriptic and Snakemake

and other projects on github

There is an open access paper in BMC Bioinformatics describing pydna:

Please reference the above paper:

Pereira, F., Azevedo, F., Carvalho, Â., Ribeiro, G. F., Budde, M. W., & Johansson, B. (2015). Pydna: a simulation and documentation tool for DNA assembly strategies using python. BMC Bioinformatics, 16(142), 142.

When using pydna.

Documentation

Documentation is built using Sphinx from docstrings in the code and displayed at readthedocs

The numpy docstring format is used.

Installation using pip

Pip is included in recent Python versions and is the officially recommended tool.

Pip installs the minimal installation requirements automatically, but not the optional requirements (see below).

pip install pydna

or use the --pre switch to get the latest version of pydna.

pip install pydna --pre

for optional functionality do:

pip install pydna[gel,download,express,gui]

Remove options inside the square brackets as required, but be sure not to leave spaces as pip will not recognize the options. See below under "Optional dependencies".

Windows:

You should be able to pip install pydna from the Windows terminal as biopython now can be installed with pip as well.

C:\> pip install pydna

By default python and pip are not on the PATH. You can re-install Python and select this option during installation, or give the full path for pip. Try something like this, depending on where your copy of Python is installed:

C:\Python37\Scripts\pip install pydna

Source Code

Pydna is developed on Github :octocat:.

Minimal installation dependencies

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.

• Python 3.8, 3.9, 3.10 or 3.11
• appdirs >=1.4.4
• biopython >= 1.80
• networkx >=2.8.8
• prettytable >=3.5.0
• pyperclip >=1.8.2
• pyfiglet >=0.8.post1

Optional dependencies

If the modules listed below in the first column are installed, they will provide the functionality listed in the second column.

Dependency	Function in pydna
scipy >=1.8.0	gel simulation with pydna.gel
matplotlib >=3.4.3	“
pillow >=8.4.0	“
numpy	"
pyparsing >=2.4.7	fix corrupt Genbank files with pydna.genbankfixer
requests >=2.26.0	download sequences with pydna.download
cai2 >=1.0.5	codon adaptation index calculations in several modules
pyqt5 >=5.15.0	codon adaptation index calculations in several modules

Requirements for running tests and analyzing code coverage

• pytest
• pytest-cov
• pytest-doctestplus
• coverage
• nbval
• requests-mock

Releases

See the releases for changes and releases.

Automatic testing & Release process

There are three github actions associated with this package:

• pydna_test_and_coverage_workflow.yml

The pydna_test_and_coverage_workflow.yml is triggered on all pushed non-tagged commits. This workflow run tests, doctests and a series of Jupyter notebooks using pytest.

The two other workflows build a setuptools wheel and packages for different Python versions on Linux, Windows and macOS.

These are triggered by publishing a github release manually from the github interface.

Building a PyPI package with Poetry

1. Commit changes to git

2. Tag the commit according to the Semantic Versioning format, for example "v2.0.1a3". Do not forget the "v" or poetry will not recognize the tag.

    git tag v2.0.1a3
   

3. Pydna uses the poetry poetry-dynamic-versioning plugin.

    poetry dynamic-versioning # This sets the version number in the source files
   

4. Verify the version

    poetry version
   

5. Build package:

    poetry build # run this command in the root directory where the pyproject.toml file is located
   

6. Verify the filename of the files in the dist/ folder, they should match

7. Publish to pypi

    poetry publish
   

History

Pydna was made public in 2012 on Google code.

:microbe:

:portugal: