from coverage_consensus_diversity import coverage, consensus, get_fragment_boundaries
from minor_variant import trim_ac
from helpers import name_translations
if __name__ == '__main__':
#ntt = name_translations('name_translation_table.tsv')
#labid = '16CA403716'
#name = ntt[labid]
sample_location = 'samples_by_strain/'
name = "EVD68_SWE_029_160904_NFLG"
fs=16
ac, ins = load_allele_counts(sample_location+name+"/")
primer_boundaries = get_fragment_boundaries('primers.csv', ac)
cov = coverage(ac[0][1])
ref='KX675261.1'
plt.figure(figsize=(8,4))
plt.plot(cov, lw=3)
for p in primer_boundaries[ref]:
y = 50 if int(p[1])%2 else 70
plt.plot([primer_boundaries[ref][p]['start'], primer_boundaries[ref][p]['end']],[y,y], lw=7, c=(0.7, 0.7, 0.7))
plt.xlabel('position in genome', fontsize=fs)
plt.ylabel('coverage', fontsize=fs)
plt.ylim(30,10000)
plt.yscale('log')
plt.tick_params(labelsize=0.8*fs)
plt.title(name, fontsize=fs)
plt.tight_layout()
primer_masks = get_primer_mask(args.primers, ac)
freqs = trim_ac(ac)
sample = args.sample.split('/')[-1]
major_freq = {ref:np.max(x, axis=0) for ref, x in freqs.items()}
minor_seqs = {}
any_minors = False
seqs = []
from Bio import SeqIO, SeqRecord, Seq
for ref, counts in ac:
print("ref", ref)
consensus_seq = consensus(counts, min_cov=args.min_cov)
cov = coverage(counts)
div_pos = np.where((major_freq[ref]<1.0-args.min_freq)&(cov>args.min_cov))[0]
alterations = []
insertions_to_include = []
for pos in div_pos:
tmp_freqs = freqs[ref][:, pos]
if sorted(tmp_freqs)[-2]>args.min_freq:
ii = np.argsort(tmp_freqs)[-2]
alterations.append([pos, nuc_alpha[ii], tmp_freqs[ii]])
if alterations:
print(sample, ref, 'minor variants', alterations)
consensus_seq[[p for p,n,f in alterations]] = [n for p,n,f in alterations]
any_minors = True
for pos in ins[ref]:
}
}
days = {
'SWE_012': '2 days',
'SWE_021': '7 days',
'SWE_024': '1 day',
'SWE_037': '1 day',
'SWE_039': '0 days'
}
for pt in samps:
for key in samps[pt]:
ac, ins = load_allele_counts(sample_location + samps[pt][key])
sample = pt + '-' + key
cov[sample] = coverage(ac[0][1])
freqs[sample] = trim_ac(ac, n_states=5)
major_freqs[sample] = {
ref: np.max(x, axis=0)
for ref, x in freqs[sample].items()
}
##################################
##################################
#positions not to plot
#These positions are just before CDS, and show up in
#4/5 of these samples....
exclude = [690, 694]
fs = 12
from minor_variant import trim_ac
from helpers import add_panel_label, name_translations
if __name__ == '__main__':
freqs = {}
major_freqs = {}
major_seqs = {}
cov = {}
snames = ['16CA514285', '16CA403717', '14CA515617']
ntt = name_translations('name_translation_table.tsv')
for sname in snames:
ac, ins = load_allele_counts('mapped_data/' + sname)
primer_boundaries = get_fragment_boundaries('primers.csv', ac)
cov[sname] = coverage(ac[0][1])
freqs[sname] = trim_ac(ac, n_states=5)
major_freqs[sname] = {
ref: np.max(x, axis=0)
for ref, x in freqs[sname].items()
}
major_seqs[sname] = {
ref: nuc_alpha[np.argmax(x, axis=0)]
for ref, x in freqs[sname].items()
}
min_cov = 1000
ref = 'KX675261.1'
cp_labels = {0: 'non coding', 1: '1st', 2: '2nd', 3: '3rd'}
variable_sites = {}
fig, axs = plt.subplots(len(snames),