def generate_sequences(fg_bins, bg_bins, bg_dir, nfold, random_seed):
"""
Choose randomly the background sequences in each bin of %GC.
Follow the same distribution as the one of foreground sequences with a
nfold ratio.
Return the list of %GC and length distrib.
"""
random.seed(random_seed)
lengths = []
gc_list = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for percent in range(0, 101):
if fg_bins[percent]:
try:
nb = fg_bins[percent] * nfold
if bg_bins:
bin_seq = bg_bins[percent]
else:
bin_seq = get_bins_from_bg_dir(bg_dir, percent)
sample = random.sample(bin_seq, nb)
gc_list.extend([percent] * nb)
except ValueError:
sys.stderr.write("""*** WARNING ***
Sample larger than population for {0:d}% G+C content:
{1:d} needed and {2:d} obtained\n""".format(
percent, fg_bins[percent] * nfold, len(bin_seq)))
sample = bin_seq
gc_list.extend([percent] * len(bin_seq))
for r in sample:
print(r.format("fasta"), end='')
lengths.append(len(r.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(r.seq))]
return gc_list, lengths, dinuc
def fg_len_GC_bins(fg_file):
"""
Compute G+C content for all sequences in the foreground.
Computes %GC contant and store the information in a list. To each G+C
percentage bin, we associate the number of sequences falling in the
corresponding bin.
Return lists of GC contents, GC bins, and lengths distrib.
"""
with open_for_parsing(fg_file) as stream:
gc_bins = []
for _ in range(0, 101):
gc_bins.append({})
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for record in SeqIO.parse(stream, "fasta"):
gc = GC(record.seq)
gc_list.append(gc)
length = len(record)
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
lengths.append(length)
if length in gc_bins[gc]:
gc_bins[gc][length] += 1
else:
gc_bins[gc][length] = 1
return gc_list, gc_bins, lengths, dinuc
def bg_GC_bins(bg_file, bg_dir):
"""
Compute G+C content for all sequences in the background.
Compute and store the GC information in a list. To each G+C percentage bin,
we associate the corresponding sequence names.
Files representing the binning are stored in the "bg_dir" directory.
Return lists of GC contents, GC bins, and lengths distrib.
"""
with open_for_parsing(bg_file) as stream:
gc_bins = []
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for _ in range(0, 101):
gc_bins.append([])
for record in SeqIO.parse(stream, "fasta"):
gc = GC(record.seq)
gc_list.append(gc)
gc = int(round(gc)) # python3 fix
gc_bins[gc].append(record)
lengths.append(len(record.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
print_in_bg_dir(gc_bins, bg_dir)
return gc_list, gc_bins, lengths, dinuc
def bg_len_GC_bins(bg_file, bg_dir):
"""
Compute G+C content for all sequences in the background.
Compute and store the %GC information in a list. To each G+C percentage
bin, we associate the corresponding sequence names.
Return lists of GC contents, GC bins, and lengths distrib.
"""
with open_for_parsing(bg_file) as stream:
gc_bins = []
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for _ in range(0, 101):
gc_bins.append({})
for record in SeqIO.parse(stream, "fasta"):
gc = GC(record.seq)
gc_list.append(gc)
if len(record) in gc_bins[gc]:
gc_bins[gc][len(record)].append(record)
else:
gc_bins[gc][len(record)] = [record]
lengths.append(len(record.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
print_in_bg_dir(gc_bins, bg_dir, True)
return gc_list, gc_bins, lengths, dinuc
def fg_GC_bins(fg, winlen, step):
"""
Get %GC info for foreground sequences.
Compute G+C content for all sequences in the foreground and store the
information in a list. To each G+C percentage bin, we associate the number
of sequences falling in the corresponding bin
Return the corresponding lists.
"""
with open_for_parsing(fg) as stream:
tmp_gc_bins = []
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for _ in range(0, 101):
tmp_gc_bins.append([])
for record in SeqIO.parse(stream, "fasta"):
gc, min_gc, max_gc, sd_gc, cv_gc = GC_info(record.seq, winlen,
step)
gc_list.append(gc)
# python 3 fix
gc = int(round(gc))
tmp_gc_bins[gc].append((min_gc, max_gc, sd_gc, cv_gc))
lengths.append(len(record.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
return gc_list, avg_and_sd_gc_info(tmp_gc_bins), lengths, dinuc
def bg_len_GC_bins(bg, bg_dir):
"""
Get lengths info for background sequences.
Compute G+C content for all sequences in the background and store the
information in a list. To each G+C percentage bin, we associate the
corresponding sequence names with information about GC composition within
sliding windows.
Return info in lists.
"""
with open_for_parsing(bg) as stream:
gc_bins = []
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for _ in range(0, 101):
gc_bins.append({})
for record in SeqIO.parse(stream, "fasta"):
gc = GC(record.seq)
gc_list.append(gc)
if len(record) in gc_bins[gc]:
gc_bins[gc][len(record)].append(record)
else:
gc_bins[gc][len(record)] = [record]
lengths.append(len(record.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
print_in_bg_dir(gc_bins, bg_dir, True)
return gc_list, gc_bins, lengths, dinuc
def fg_len_GC_bins(fg, winlen, step):
"""
Get needed lengths info for foreground sequences.
Compute G+C content for all sequences in the foreground and store the
information in a list. To each G+C percentage bin, we associate the number
of sequences falling in the corresponding bin.
Return the corresponding info in lists.
"""
with open_for_parsing(fg) as stream:
tmp_gc_bins = []
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
l_dic = []
for _ in range(0, 101):
tmp_gc_bins.append([])
l_dic.append({})
for record in SeqIO.parse(stream, "fasta"):
gc, min_gc, max_gc, sd_gc, cv_gc = GC_info(record.seq, winlen,
step)
gc_list.append(gc)
tmp_gc_bins[gc].append((min_gc, max_gc, sd_gc, cv_gc))
length = len(record)
if length in l_dic[gc]:
l_dic[gc][length] += 1
else:
l_dic[gc][length] = 1
lengths.append(length)
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
return gc_list, avg_and_sd_len_gc_info(l_dic, tmp_gc_bins), lengths, dinuc
def generate_len_sequences(fg_bins, bg_bins, bg_dir, deviation, winlen, step,
nfold):
"""
Choose randomly the background sequences in each bin of GC%.
with the same distribution as the one of foreground sequences with a nfold
ratio.
Return the sequences and their lengths.
"""
sys.setrecursionlimit(10000)
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for percent in range(0, 101):
if fg_bins[percent][0]:
nb = sum(fg_bins[percent][0][0].values()) * nfold
if bg_bins:
bin_seq = bg_bins[percent]
else:
bin_seq = get_bins_len_from_bg_dir(bg_dir, percent)
sequences = []
bg_keys = sorted(bin_seq.keys())
for size in fg_bins[percent][0][0].keys():
nb_to_retrieve = fg_bins[percent][0][0][size] * nfold
seqs, _, bg_keys = extract_seq_rec(size, nb_to_retrieve,
bg_keys, bin_seq, [], 0,
fg_bins[percent], deviation,
winlen, step)
sequences.extend(seqs)
nb_match = len(sequences)
if nb_match != nb:
sys.stderr.write("""\n*** WARNING ***
Sample larger than population for {0:d}% G+C content:
{1:d} needed and {2:d} obtained\n""".format(
percent, nb, nb_match))
gc_list.extend([percent] * (nb_match))
for r in sequences:
print("{0:s}".format(r.format("fasta")), end='')
lengths.append(len(r))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(r.seq))]
return gc_list, lengths, dinuc
def generate_sequences(seqs, kmer, winlen, step, nfold, random_seed):
set_seed(random_seed)
cpt = 1
bg_gc_list = []
bg_lengths = []
dinuc = [0] * len(IUPAC_DINUC)
for record in seqs:
seq = record.seq.__str__()
descr = "Background sequence for {0:s}".format(record.name, cpt)
for n in range(0, nfold):
new_sequence = shuffle_window(seq, kmer, winlen, step)
new_seq = SeqRecord(Seq(new_sequence,
IUPACData.ambiguous_dna_letters),
id="background_seq_{0:d}".format(cpt),
description=descr)
print(new_seq.format("fasta"), end='')
bg_gc_list.append(GC(new_sequence))
bg_lengths.append(len(new_sequence))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(new_sequence))]
cpt += 1
return bg_gc_list, bg_lengths, dinuc
def generate_len_sequences(fg, bg, bg_dir, nfold):
"""
Extract the sequences from the bg with similar sizes as in the fg.
Return the %GC list and length distrib.
"""
sys.setrecursionlimit(10000)
lengths = []
gc_list = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for percent in range(0, 101):
if fg[percent]:
nb = sum(fg[percent].values()) * nfold
sequences = []
for size in fg[percent].keys():
nb_to_retrieve = fg[percent][size] * nfold
if bg:
bg_bins = bg[percent]
else:
bg_bins = get_bins_len_from_bg_dir(bg_dir, percent)
bg_keys = sorted(bg_bins.keys())
seqs, _ = extract_seq_rec(size, nb_to_retrieve, bg_keys,
bg_bins, [], 0)
sequences.extend(seqs)
nb_match = len(sequences)
gc_list.extend([percent] * nb_match)
if nb_match != nb:
sys.stderr.write("""*** WARNING ***
Sample larger than population for {0:d}% G+C content:
{1:d} needed and {2:d} obtained\n""".format(percent,
nb,
nb_match))
for s in sequences:
lengths.append(len(s))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(s.seq))]
print("{0:s}".format(s.format("fasta")), end='')
return gc_list, lengths, dinuc
def generate_sequences(fg_bins, bg_bins, bg_dir, deviation, winlen, step,
nfold, random_seed):
"""
Choose randomly the background sequences in each bin of GC%.
The same distribution as the one of foreground sequences with a nfold ratio
is asked.
Return the sequences with their lengths.
"""
random.seed(random_seed)
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for percent in range(0, 101):
if fg_bins[percent][0]:
nb = fg_bins[percent][0][0] * nfold
if bg_bins:
bin_seq = bg_bins[percent]
else:
bin_seq = get_bins_from_bg_dir(bg_dir, percent)
left, sample = extract_random_sample(bin_seq, fg_bins[percent], nb,
deviation, winlen, step)
if left:
sys.stderr.write("""\n*** WARNING ***
Sample larger than population for {0:d}% G+C content:
{1:d} needed and {2:d} obtained\n""".format(
percent, nb, nb - left))
gc_list.extend([percent] * (nb - left))
else:
gc_list.extend([percent] * nb)
for r in sample:
print(r.format("fasta"), end='')
lengths.append(len(r.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(r.seq))]
return gc_list, lengths, dinuc
def fg_GC_bins(fg_file):
"""
Compute G+C content for all sequences in the foreground.
It computes the %GC content and store the information in a list. To each
G+C percentage bin, we associate the number of sequences falling in the
corresponding bin.
Return lists of GC contents, GC bins, lengths distrib, and dinuc compo.
"""
with open_for_parsing(fg_file) as stream:
gc_bins = [0] * 101
gc_list = []
lengths = []
dinuc = [0] * len(IUPAC) * len(IUPAC)
for record in SeqIO.parse(stream, "fasta"):
gc = GC(record.seq)
gc_list.append(gc)
# python 3 fix
gc = int(round(gc))
gc_bins[gc] += 1
lengths.append(len(record.seq))
dinuc = [x + y for x, y in zip(dinuc, dinuc_count(record.seq))]
return gc_list, gc_bins, lengths, dinuc
