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from __future__ import print_function
import logging
import string
import os
from math import log as mlog2
from collections import Counter, defaultdict
from seqcluster.libs.read import map_to_precursors, precursor_sequence, map_to_precursor_biopython
from seqcluster.libs.utils import safe_dirs
from progressbar import ProgressBar
from seqcluster.libs.utils import file_exists
from seqcluster.function.rnafold import run_rnafold
from seqcluster.html import HTML
from seqcluster import templates
logger = logging.getLogger('html')
def _get_ann(dbs, features):
"""
Gives format to annotation for html table output
"""
value = ""
for db, feature in zip(dbs, features):
value += db + ":" + feature
return value
def _parse(profile, size):
total = Counter()
for sample in profile:
for pos in range(len(size)):
total[pos] += profile[sample][pos]
return total.values()
def make_profile(data, out_dir, args):
"""
Make data report for each cluster
"""
safe_dirs(out_dir)
main_table = []
header = ['id', 'ann']
n = len(data[0])
bar = ProgressBar(maxval=n).start()
bar.update(0)
for itern, c in enumerate(data[0]):
bar.update(itern)
logger.debug("creating cluser: {}".format(c))
safe_dirs(os.path.join(out_dir, c))
valid, ann, pos_structure = _single_cluster(c, data, os.path.join(out_dir, c, "maps.tsv"), args)
data[0][c].update({'profile': pos_structure})
loci = data[0][c]['loci']
logger.debug("precursor_sequence")
data[0][c]['precursor'] = {"seq": precursor_sequence(loci[0][0:5], args.ref)}
logger.debug("parse alignments")
data[0][c]['precursor']["colors"] = list(_parse(data[0][c]['profile'], data[0][c]['precursor']["seq"]))
logger.debug("update rnafold")
data[0][c]['precursor'].update(run_rnafold(data[0][c]['precursor']['seq']))
return data
def _expand(dat, counts, start, end):
"""
expand the same counts from start to end
"""
for pos in range(start, end):
for s in counts:
dat[s][pos] += counts[s]
return dat
def _convert_to_df(in_file, freq, raw_file):
"""
convert data frame into table with pandas
"""
dat = defaultdict(Counter)
if isinstance(in_file, (str, bytes)):
with open(in_file) as in_handle:
for line in in_handle:
cols = line.strip().split("\t")
counts = freq[cols[3]]
dat = _expand(dat, counts, int(cols[1]), int(cols[2]))
else:
if raw_file:
out_handle = open(raw_file, "w")
for name in in_file:
counts = freq[name]
if raw_file:
print("%s\t%s\t%s\t%s\t%s\t%s" % ("chr", in_file[name][0], in_file[name][1], name, sum(counts.values()), "+"), file=out_handle, end="")
dat = _expand(dat, counts, in_file[name][0], in_file[name][1])
for s in dat:
for p in dat[s]:
dat[s][p] = mlog2(dat[s][p] + 1)
return dat
def _make(c):
"""
create html from template, adding figure,
annotation and sequences counts
"""
ann = defaultdict(list)
for pos in c['ann']:
for db in pos:
ann[db] += list(pos[db])
logger.debug(ann)
valid = [l for l in c['valid']]
ann_list = [", ".join(list(set(ann[feature]))) for feature in ann if feature in valid]
return valid, ann_list
def _single_cluster(c, data, out_file, args):
"""
Map sequences on precursors and create
expression profile
"""
valid, ann = 0, 0
raw_file = None
freq = defaultdict()
[freq.update({list(s.keys())[0]: list(s.values())[0]}) for s in data[0][c]['freq']]
names = [list(s.keys())[0] for s in data[0][c]['seqs']]
seqs = [list(s.values())[0] for s in data[0][c]['seqs']]
loci = data[0][c]['loci']
if loci[0][3] - loci[0][2] > 500:
logger.info("locus bigger > 500 nt, skipping: %s" % loci)
return valid, ann, {}
if not file_exists(out_file):
if args.razer:
logger.debug("map with razer all sequences to all loci %s " % loci)
map_to_precursors(seqs, names, {loci[0][0]: [loci[0][0:5]]}, out_file, args)
else:
logger.debug("map with biopython fn all sequences to all loci %s " % loci)
if args.debug:
raw_file = out_file
out_file = map_to_precursor_biopython(seqs, names, loci[0][0:5], args)
logger.debug("plot sequences on loci")
df = _convert_to_df(out_file, freq, raw_file)
if df:
logger.debug("create html")
valid, ann = _make(data[0][c])
logger.debug("done single cluster")
return valid, ann, df
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