'A': 'EVD68_SWE_039_160831-1_NFLG',
'B': 'EVD68_SWE_039_160831-2_NFLG'
}
}
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]
from create_allele_counts import load_allele_counts, nuc_alpha
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)
import matplotlib.pyplot as plt
#import seaborn as sns
from create_allele_counts import load_allele_counts
from coverage_consensus_diversity import coverage, consensus
from minor_variant import trim_ac
from helpers import add_panel_label
plt.ion()
if __name__ == '__main__':
freqs = {}
major_freqs = {}
cov = {}
for sample in ['JA-A', 'JA-B', 'QC_JA-A', 'QC_JA-B']:
ac, ins = load_allele_counts('mapped_data/' + sample)
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()
}
fs = 24
fig, axs = plt.subplots(1, 2, figsize=(12, 6))
axs[0].plot([0.001, 1.0], [0.001, 1.0], c='k')
min_cov = 2000
for s in 'AB':
for ref in major_freqs['JA-' + s]:
good_ind = (cov['JA-' + s] > min_cov) & (cov['QC_JA-' + s] >
min_cov)
# Script
if __name__ == '__main__':
# Parse input args
parser = argparse.ArgumentParser(description='plot coverage, diversity and output consensus sequence',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--sample', required=True, type=str, help='the sample to analyze')
parser.add_argument('--out_dir', required=True, type=str, help='directory to output')
parser.add_argument('--min_cov', type=int, default=1000, help='minimal coverage to call consensus')
parser.add_argument('--primers', type=str, help='file with primers to mask in diversity calculation')
parser.add_argument('--min_freq', type=float, default=0.05, help='minimal frequency to accept minor variant')
args = parser.parse_args()
stats = {}
ac,ins = load_allele_counts(args.sample)
if args.primers:
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)
from create_allele_counts import load_allele_counts, nuc_alpha
from coverage_consensus_diversity import coverage, consensus, get_fragment_boundaries
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'}
from helpers import add_panel_label, name_translations
if __name__ == '__main__':
sample_location = "samples_by_strain2/"
freqs = {}
major_freqs = {}
major_seqs = {}
cov = {}
#snames = ['16CA514285', '16CA403717', '14CA515617']
#snames = ["EVD68_SWE_045_160831_NFLG", "EVD68_SWE_046_160904_NFLG", "EVD68_SWE_007_140908_NFLG"]
#ntt = name_translations('name_translation_table.tsv')
snames = ["EVD68_BEL_009_18XXXX_NFLG"]
for sname in snames:
ac, ins = load_allele_counts(sample_location + 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'}
from create_allele_counts import load_allele_counts
from coverage_consensus_diversity import coverage, consensus
from minor_variant import trim_ac
from helpers import add_panel_label
plt.ion()
if __name__ == '__main__':
freqs = {}
major_freqs = {}
cov = {}
samples = glob.glob('mapped_data/1*')
for sample in samples:
ac,ins = load_allele_counts(sample)
sname = sample.rstrip('/').split('/')[-1]
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()}
#cutoffs = [0.01, 0.03, 0.1]
cutoffs = [0.0005,0.001, 0.002, 0.005, 0.01, 0.02, 0.03, 0.05, 0.1]
display_cutoffs = [0.01, 0.03, 0.1]
min_cov = 1000
n_minor = []
snames = sorted(list(major_freqs.keys()))
ref='KX675261.1'
for sname in snames:
good_ind = cov[sname]>min_cov
tmp = []
type=str,
help='file with primers to mask in diversity calculation')
parser.add_argument('--min_cov',
type=int,
default=100,
help='minimal coverage to call consensus')
parser.add_argument(
'--all_counts',
action="store_true",
default=False,
help="plot coverage/diversity for all count files found")
args = parser.parse_args()
stats = {}
ac, ins = load_allele_counts(args.sample, allCounts=args.all_counts)
primer_masks = get_primer_mask(args.primers, ac)
primer_boundaries = get_fragment_boundaries(args.primers, ac)
sample = args.sample.split('/')[-1]
stats = plot_coverage_concatenated(sample,
ac,
args.out_dir + '/figures/coverage.png',
primer_boundaries=primer_boundaries)
div = plot_diversity(sample,
ac,
args.out_dir + "/figures/diversity.png",
primer_masks,
primer_boundaries=primer_boundaries)
for k, v in list(div.items()):
stats[k].update(v)
import matplotlib.pyplot as plt
#import seaborn as sns
from create_allele_counts import load_allele_counts, nuc_alpha
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 = ### redacted sample name
name = ntt[labid]
fs=16
ac, ins = load_allele_counts('mapped_data/'+labid)
primer_boundaries = get_fragment_boundaries('primers.csv', ac)
cov = coverage(ac[0][1])
ref='KX675261.1'
plt.figure()
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)