bandwagon 0.1.4

Simulate DNA band patterns for gel migration experiments

.. raw:: html

<p align="center">
<img alt="BandWagon Logo" title="BandWagon Logo" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/docs/_static/images/title.png" width="500">
<br /><br />
</p>

.. image:: https://travis-ci.org/Edinburgh-Genome-Foundry/BandWagon.svg?branch=master
:target: https://travis-ci.org/Edinburgh-Genome-Foundry/BandWagon
:alt: Travis 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
<http://edinburgh-genome-foundry.github.io/BandWagon/>`_) 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-enzymes 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

License = MIT
--------------

Bandwagon is an open-source software originally written at the `Edinburgh Genome Foundry
<http://edinburgh-genome-foundry.github.io/home.html>`_ by `Zulko <https://github.com/Zulko>`_
and `released on Github <https://github.com/Edinburgh-Genome-Foundry/Bandwagon>`_ under the MIT licence (¢ Edinburg Genome Foundry).

Everyone is welcome to contribute !

Installation
--------------

If you have pip installed, just type:

.. code:: shell

(sudo) pip install bandwagon

Bandwagon can be installed by unzipping the source code in one directory and using this command:

.. code:: shell

(sudo) python setup.py install

To create interactive bokeh plots you will need to additionally install Bokeh with

.. code:: shell

(sudo) pip install bokeh



Examples of use
----------------


Computing digestion bands 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 a 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 ````

.. code:: python

from bandwagon import plot_all_digestion_patterns, LADDER_100_to_4k

plot_records_digestions(
records=records,
digestions=[('BamHI', 'NcoI'), ('BsaI', 'XbaI'), ('StyI',)],
ladder=LADDER_100_to_4k,
target="records_digestions.pdf")

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 <https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/BandWagon/master/examples/plot_records_digestions_example.pdf>`_
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 dictionnary 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")