def load_rational_designs():
df = densiometry.load_cleavage_data_from_xlsx_dir('densiometry')
df = densiometry.calc_mean_change(df)
df = df.apply(name_to_strategy, axis='columns')
df['strategy'] = pd.Categorical(df.strategy, [e.value for e in Strategy])
df['domain'] = pd.Categorical(df.domain, [e.value for e in Domain])
df['active'] = abs(df.mean_change) > 0.15
df['sequence'] = df.apply(name_to_sequence, axis='columns')
#df['aligned_sequence'] = align_sequences(df.sequence)
df = discard_modified_aptamers(df)
df = get_pretty_sequences(df)
priority = [
'align_group', 'strategy', 'algorithm', 'domain', 'earliest_date',
'earliest_order'
]
df = df.sort_values(priority).reset_index(drop=True)
df['id'] = df.index.values + 1
return df
def get_construct(x):
names = {
'on': 'pos',
'off': 'neg',
'mhf 30': r'\ligrnaF{}',
'rxb 11,1': r'\ligrnaB{}',
}
return names[x]
args = docopt.docopt(__doc__)
# Parse the densiometry data.
df = densiometry.load_cleavage_data_from_xlsx_dir(DATA_DIR)
df = densiometry.calc_mean_change(df)
# Add NaNs for the experiments that didn't work.
df = df.append({'spacer': 'd9', 'design': 'on'}, ignore_index=True)
# Add a column for the names I want to display.
df = df[df.design.isin(DESIGN_ORDER)]
df['construct'] = df.design.apply(get_construct)
# Put the rows in the order I want to display them in.
spacer_order = densiometry.sort_spacers_by_activity(df, ['mhf 30', 'rxb 11,1'])
df['spacer'] = pd.Categorical(df.spacer, spacer_order)
df['design'] = pd.Categorical(df.design, DESIGN_ORDER)
df = df.sort_values(['spacer', 'design']).reset_index()
def calc_energy_from_row(row):
spacer = sgrna_sensor.spacer(row.spacer).seq
insert = inserts[row.design]
return calc_energy(spacer, insert)
if args['<spacer>'] and args['<design>']:
spacer = sgrna_sensor.spacer(args['<spacer>'],
species=args['--species']).rna
insert = inserts[args['<design>']]
print(f"Spacer: {spacer}")
print(f"Insert: {insert}")
print(f"ΔG: {calc_energy(spacer, insert)}")
else:
df = densiometry.load_cleavage_data_from_xlsx_dir('densiometry')
df = densiometry.calc_mean_change(df)
# Drop all the controls
df = df[df.design.isin(inserts.keys())]
# Calculate the minimum free energy (MFE) for binding between the spacer
# and the aptamer insert.
df['binding_energy'] = df.apply(calc_energy_from_row, axis='columns')
def plot_design(design, label, color): #
i = df.design == design
x = 100 * abs(
df[i].max_change if args['--max-change'] else df[i].mean_change)
y = df[i].binding_energy
m, b, R, p, err = linregress(x, y)