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

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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-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 <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 license (Copyright 2019 Edinburgh Genome Foundry, University of Edinburgh).

Everyone is welcome to contribute!

Installation

BandWagon can be installed with pip:

.. code:: shell

pip install bandwagon

To create interactive Bokeh plots, install additional packages with:

.. code:: shell

pip install bandwagon[bokeh]

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 <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 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")