bandwagon 0.1.1

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:

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

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)

Plotting a gel simulation

Let us plot digestion patterns produced by different restriction enzymes on a same DNA sequence:

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)

Using a custom ladder

You can define a custom ladder by providing a dictionnary of the form

{ actual_size_of_the_fragment: observed_migration_distance }

For instance here is how the 100b-4kb ladder (provided with BandWagon) is defined:

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:

from bandwagon import ladder_from_aati_fa_calibration_table

ladder = ladder_from_aati_fa_calibration_table("Calibration.csv",
                                               label="todays_ladder")