https://github.com/edinburgh-genome-foundry/bandwagon
🎺 Plot DNA digestion band patterns with Python
https://github.com/edinburgh-genome-foundry/bandwagon
agarose-gel dna molecular-biology plotting synthetic-biology
Last synced: 5 months ago
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🎺 Plot DNA digestion band patterns with Python
- Host: GitHub
- URL: https://github.com/edinburgh-genome-foundry/bandwagon
- Owner: Edinburgh-Genome-Foundry
- License: mit
- Created: 2017-02-06T15:38:40.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-05-07T19:41:38.000Z (almost 3 years ago)
- Last Synced: 2024-11-08T16:17:40.988Z (6 months ago)
- Topics: agarose-gel, dna, molecular-biology, plotting, synthetic-biology
- Language: Python
- Homepage: http://edinburgh-genome-foundry.github.io/BandWagon/
- Size: 1.87 MB
- Stars: 19
- Watchers: 8
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
Awesome Lists containing this project
README
.. raw:: html
.. image:: https://github.com/Edinburgh-Genome-Foundry/BandWagon/actions/workflows/build.yml/badge.svg
:target: https://github.com/Edinburgh-Genome-Foundry/BandWagon/actions/workflows/build.yml
:alt: GitHub CI build status
.. image:: https://coveralls.io/repos/github/Edinburgh-Genome-Foundry/BandWagon/badge.svg?branch=master
:target: https://coveralls.io/github/Edinburgh-Genome-Foundry/BandWagon?branch=master
Bandwagon (full documentation `here
`_) is a Python library to predict and plot migration patterns
from DNA digestions. It supports hundreds of different enzymes (thanks to BioPython),
single- and multiple-enzyme digestions, and custom ladders.
It uses Matplotlib to produce plots like this one:
.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/mixed_digestions.png
:align: center
Bandwagon
=========
License = MIT
-------------
Bandwagon is an open-source software originally written at the `Edinburgh Genome Foundry
`_ by `Zulko `_
and `released on Github `_ under the MIT license (Copyright 2019 Edinburgh Genome Foundry).
Everyone is welcome to contribute!
Installation
------------
If you have pip installed, just type:
.. code:: shell
pip install bandwagon
Bandwagon can be installed by unzipping the source code in one directory and using this command:
.. code:: shell
python setup.py install
To create interactive bokeh plots you will need to additionally install Bokeh and Pandas with:
.. code:: shell
pip install bokeh pandas
Examples of use
---------------
Computing digestion band sizes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This first example shows how to compute digestion bands in the case of
a linear fragment, a circular fragment, and a multi-enzymes digestion:
.. code:: python
from bandwagon import compute_digestion_bands
# Read the sequence (a string of the form 'ATGTGTGGTA...' etc.)
with open("example_sequence.txt", "r") as f:
sequence = f.read()
# Compute digestion bands for a linear construct
print(compute_digestion_bands(sequence, ["EcoRI"], linear=True))
# Result >>> [400, 1017, 3583]
# Compute digestion bands for a circular construct
print(compute_digestion_bands(sequence, ["EcoRI"], linear=False))
# Result >>> [1017, 3983]
# Compute digestion bands for an enzymatic mix
print(compute_digestion_bands(sequence, ["EcoRI", "BamHI"]))
# Result >>> [400, 417, 600, 3583]
Plotting bands
~~~~~~~~~~~~~~
.. code:: python
from bandwagon import BandsPattern, BandsPatternsSet, LADDER_100_to_4k
ladder = LADDER_100_to_4k.modified(label="Ladder", background_color="#ffffaf")
patterns = [
BandsPattern([100, 500, 3500], ladder, label="C1"),
BandsPattern([300, 400, 1500], ladder, label="C2"),
BandsPattern([100, 1200, 1400, 3000], ladder, label="C3"),
BandsPattern([100, 700], ladder, label="C4"),
]
patterns_set = BandsPatternsSet(patterns=[ladder] + patterns, ladder=ladder,
label="Test pattern", ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig("simple_band_patterns.png", bbox_inches="tight", dpi=200)
.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/simple_band_patterns.png
:align: center
Plotting a gel simulation
~~~~~~~~~~~~~~~~~~~~~~~~~
Let us plot digestion patterns produced by different restriction enzymes on the same DNA sequence:
.. code:: python
from bandwagon import (BandsPattern, BandsPatternsSet, LADDER_100_to_4k,
compute_digestion_bands)
with open("example_sequence.txt", "r") as f:
sequence = f.read()
patterns = [
BandsPattern(compute_digestion_bands(sequence, [enzyme], linear=True),
ladder=LADDER_100_to_4k, label=enzyme)
for enzyme in ["BamHI", "EcoRI", "EcoRV", "PstI", "SpeI", "XbaI"]
]
patterns_set = BandsPatternsSet(patterns=[LADDER_100_to_4k] + patterns,
ladder=LADDER_100_to_4k,
label="Digestion results", ladder_ticks=3)
ax = patterns_set.plot()
ax.figure.savefig("digestion_results.png", bbox_inches="tight", dpi=200)
.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/simple_digestions.png
:align: center
If you have many sequences and digestions you can also use the utility ``plot_records_digestions``
.. code:: python
from bandwagon import plot_all_digestion_patterns, LADDER_100_to_4k
axes = plot_all_digestion_patterns(
records=records,
digestions=[('BamHI', 'NcoI'), ('BsaI', 'XbaI'), ('StyI',)],
ladder=LADDER_100_to_4k
)
axes[0].figure.savefig("plot_all_digestion_patterns.png")
.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/plot_all_digestion_patterns.png
:align: center
Plotting patterns alongside annotated records
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can also get a full report with indications of where in your sequences the
bands are formed (which is useful for troubleshooting) as follows:
.. code:: python
from bandwagon import plot_records_digestions, LADDER_100_to_4k
plot_records_digestions(
records=records,
digestions=[('BamHI', 'NcoI'), ('BsaI', 'XbaI'), ('StyI',)],
ladder=LADDER_100_to_4k,
target="records_digestions.pdf")
You get a `PDF report `_
with one page per construct and digestion, looking like this:
.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/plot_records_digestions_example.png
:align: center
Using a custom ladder
~~~~~~~~~~~~~~~~~~~~~
You can define a custom ladder by providing a dictionary of the form
.. code:: python
{ actual_size_of_the_fragment: observed_migration_distance }
For instance here is how the 100b-4kb ladder (provided with BandWagon) is defined:
.. code:: python
from bandwagon import custom_ladder
LADDER_100_to_4k = custom_ladder("100-4k", {
100: 205,
200: 186,
300: 171,
400: 158,
500: 149,
650: 139,
850: 128,
1000: 121,
1650: 100,
2000: 90,
3000: 73,
4000: 65
})
The unit of the "migration distance" from the starting point is not very important,
it could be millimeters on a gel, pixels in an image, etc.
If you are lucky enough to have an AATI automated fragment analyzer like us at the
Foundry, it will output a ``.csv`` calibration file after each run, from which you
can generate a ladder with:
.. code:: python
from bandwagon import ladder_from_aati_fa_calibration_table
ladder = ladder_from_aati_fa_calibration_table("Calibration.csv",
label="todays_ladder")