def glompX_blast_out(genomes, run_ref, blast_mode, r_root_dir, run_dirs,
run_id, fixed_dirs, blast_dtypes, references,
min_nt_match, min_nt_score, min_nt_idp, min_aa_match,
min_aa_score, min_aa_idp, capture_span, timestamp):
"""Collect Blast results and extract match contigs."""
# load inputs
ref_n = run_ref.name
run_root = r_root_dir+run_id+"/"
match_root = run_root+run_dirs['match_out_dir']+ref_n+"/"
capture_root = run_root+run_dirs['capture_dir']+ref_n+"/"
print " ", ref_n
# log
logstring = "".join(["\n\n# Collect Blast results @", timestamp, "\n\n"])
run_ref.log(logstring)
# collect results
ref_hits = {}
control_scores = []
run_ref.log("Segs/Gs\t")
run_ref.log("\t".join([genome['name'] for genome in genomes]))
for seg in run_ref.segs:
seg_n = seg['name']
print "\t", seg_n, "...",
run_ref.log("".join(["\n", seg_n]))
blast_dir = run_root+run_dirs['blast_out_dir']+ref_n+"/"+seg_n+"/"
capture_dir = capture_root+"/"+seg_n+"/"
ensure_dir([blast_dir, capture_dir])
ref_flag = True
for genome in genomes:
g_name = genome['name']
print "|",
# process
if g_name not in ref_hits.keys():
ref_hits[g_name] = {}
matches_dir = match_root+g_name+"/"
ensure_dir([matches_dir])
blast_infile = blast_dir+g_name+"_out.txt"
genome_ctg_dir = fixed_dirs['fas_contigs_dir']+g_name+"/"
rec_array = read_array(blast_infile, blast_dtypes)
if len(rec_array) > 0: # take qualified hits
p_cnt = 0
n_cnt = 0
if g_name in [ref['name'] for ref in references]:
copyfile(genome_ctg_dir+g_name+"_1.fas",
matches_dir+g_name+".fas")
if ref_flag:
# positive control TODO: better solution
control_scores.append(rec_array[0][11])
ref_flag = False
for line in rec_array:
idp = line[2]
q_start, q_stop = line[8], line[9]
score = line[11]
length = abs(q_stop-q_start)
# check the blast mode to use the right thresholds
if blast_mode == 'n' or blast_mode == 'tx':
min_match = min_nt_match
min_score = min_nt_score
min_idp = min_nt_idp
elif blast_mode == 'tn':
min_match = min_aa_match
min_score = min_aa_score
min_idp = min_aa_idp
else: # default to nucleotide mode
min_match = min_nt_match
min_score = min_nt_score
min_idp = min_nt_idp
if length>min_match and score>min_score and idp>min_idp:
print "+",
p_cnt +=1
contig_id = line[1]
if contig_id not in ref_hits[g_name].keys():
ref_hits[g_name][contig_id] = {seg_n: score}
else:
ref_hits[g_name][contig_id][seg_n] = score
pattern = re.compile(r'('+contig_id+')\.fas')
for item in listdir(genome_ctg_dir):
match = re.match(pattern, item)
if match:
fas_file = matches_dir+match.group(1)+".fas"
if not path.exists(fas_file):
copyfile(genome_ctg_dir+item, fas_file)
# context capture
capture_flag = False
while True:
try:
if int(seg_n) in run_ref.capture:
capture_flag = True
else:
break
except ValueError:
if seg_n in run_ref.capture:
capture_flag = True
else:
break
else:
break
if capture_flag:
# load the sequence
contig_file = matches_dir+contig_id+".fas"
contig_rec = load_fasta(contig_file)
# check orientation
if q_start < q_stop:
c_start = q_start-capture_span
c_stop = q_stop+capture_span
else:
c_start = q_stop-capture_span
c_stop = q_start+capture_span
print c_start, c_stop
# check limits
if c_start < 0:
c_start = 1
if c_stop > len(contig_rec.seq):
c_stop = len(contig_rec.seq)
# proceed
cxt_file = capture_dir+g_name+"_"+contig_id+".fas"
cxt_rec = SeqRecord(id=contig_id+"_"
+str(c_start)+"_"
+str(c_stop),
seq=contig_rec.seq
[c_start:c_stop])
write_fasta(cxt_file, cxt_rec)
else:
print "-",
n_cnt +=1
if n_cnt > 0:
logstring = "".join(["\t", str(p_cnt), " (",
str(n_cnt), ")"])
else:
logstring = "".join(["\t", str(p_cnt)])
run_ref.log(logstring)
else:
print "-",
run_ref.log("".join(["\t", "0"]))
print ""
return ref_hits, control_scores
def glomp_good_reads(dataset, bin_type):
"""Use matching reads lists to glomp good reads."""
# Identify dataset
nickname = dataset['nickname']
# Identify input directory and filenames root
match_dir_root = dirs['match_dir']+nickname+"/"
# Signal process start
print "-- Glomping matches against all references for", nickname, "--"
print datetime.now()
filter_files = []
rescue_files = []
for ref_nick in dataset['ref_nicks']:
infile = match_dir_root+ref_nick+"/"+nickname+bin_type+"_match.npy"
ref_type = [reference['type'] for reference in references if
reference['nickname'] is ref_nick]
if ref_type[0] is 'filter':
filter_files.append({'ref_nick': ref_nick, 'matches': infile})
elif ref_type[0] is 'rescue':
rescue_files.append({'ref_nick': ref_nick, 'matches': infile})
# process filter files
print "\tprocessing filter references", [filter_file['ref_nick'] for
filter_file in filter_files]
filter_arrays = []
for file in filter_files:
data_array = numpy.load(file['matches'])
filter_arrays.append(data_array)
array_index = 0
filter_IRA = filter_arrays[array_index]
filter_URA = filter_arrays[array_index]
while array_index < len(filter_arrays)-1:
array_index +=1
filter_IRA = numpy.intersect1d(filter_IRA, filter_arrays[array_index])
filter_URA = numpy.union1d(filter_URA, filter_arrays[array_index])
print "\t\t"+str(len(filter_IRA)), "present in all filter references"
print "\t\t"+str(len(filter_URA)), "matching reads all together (union)"
# process rescue files
print "\tprocessing rescue references", [rescue_file['ref_nick'] for
rescue_file in rescue_files]
rescue_arrays = []
for file in rescue_files:
data_array = numpy.load(file['matches'])
rescue_arrays.append(data_array)
array_index = 0
rescue_IRA = rescue_arrays[array_index]
rescue_URA = rescue_arrays[array_index]
while array_index < len(rescue_arrays)-1:
array_index +=1
rescue_IRA = numpy.intersect1d(rescue_IRA, rescue_arrays[array_index])
rescue_URA = numpy.union1d(rescue_URA, rescue_arrays[array_index])
print "\t\t"+str(len(rescue_IRA)), "present in all rescue references"
print "\t\t"+str(len(rescue_URA)), "matching reads all together (union)"
# prepare for masking
print "\tpreparing selection masks"
q2a_file = dirs['mft_dir']+nickname+"/"+nickname+bin_type+"_track.txt"
dtype = numpy.dtype([('title', 'S50'), ('bincode', 'S15')])
pair_array = read_array(q2a_file, dtype, separator='\t')
# create masking arrays
mask = numpy.zeros(len(pair_array), bool)
mask = numpy.invert(mask)
# filter out baddies - flip to False
for item in filter_URA:
mask[item-1] = False # False means reject
if item%2==0:
mask[item-2] = False # even numbers are /2, flip previous
else:
mask[item] = False # odd numbers are /1, flip next
# rescue goodies - flip to True
for item in rescue_URA:
mask[item-1] = True # True means accept
if item%2==0:
mask[item-2] = True # even numbers are /2, flip previous
else:
mask[item] = True # odd numbers are /1, flip next
# save mask to file (where True means keep, False means reject)
mask_file = dirs['trim_dir']+nickname+bin_type+"_mask.npy"
numpy.save(mask_file, mask)
# separate the two sets and write to file
bin_accept_titles = pair_array[mask]
accept_file = dirs['select_dir']+nickname+bin_type+"_accept.npy"
numpy.save(accept_file, bin_accept_titles)
inv_mask = numpy.invert(mask)
bin_reject_titles = pair_array[inv_mask]
reject_file = dirs['select_dir']+nickname+bin_type+"_reject.npy"
numpy.save(reject_file, bin_reject_titles)
print "\t\t"+str(len(bin_accept_titles)), "reads to accept"
print "\t\t"+str(len(bin_reject_titles)), "reads to reject"
print "-- Done! --"
print datetime.now()
def glompX_blast_out(genomes, run_ref, blast_mode, r_root_dir, run_dirs,
run_id, fixed_dirs, blast_dtypes, references,
min_nt_match, min_nt_score, min_nt_idp, min_aa_match,
min_aa_score, min_aa_idp, capture_span, timestamp):
"""Collect Blast results and extract match contigs."""
# load inputs
ref_n = run_ref.name
run_root = r_root_dir + run_id + "/"
match_root = run_root + run_dirs['match_out_dir'] + ref_n + "/"
capture_root = run_root + run_dirs['capture_dir'] + ref_n + "/"
print " ", ref_n
# log
logstring = "".join(["\n\n# Collect Blast results @", timestamp, "\n\n"])
run_ref.log(logstring)
# collect results
ref_hits = {}
control_scores = []
run_ref.log("Segs/Gs\t")
run_ref.log("\t".join([genome['name'] for genome in genomes]))
for seg in run_ref.segs:
seg_n = seg['name']
print "\t", seg_n, "...",
run_ref.log("".join(["\n", seg_n]))
blast_dir = run_root + run_dirs[
'blast_out_dir'] + ref_n + "/" + seg_n + "/"
capture_dir = capture_root + "/" + seg_n + "/"
ensure_dir([blast_dir, capture_dir])
ref_flag = True
for genome in genomes:
g_name = genome['name']
print "|",
# process
if g_name not in ref_hits.keys():
ref_hits[g_name] = {}
matches_dir = match_root + g_name + "/"
ensure_dir([matches_dir])
blast_infile = blast_dir + g_name + "_out.txt"
genome_ctg_dir = fixed_dirs['fas_contigs_dir'] + g_name + "/"
rec_array = read_array(blast_infile, blast_dtypes)
if len(rec_array) > 0: # take qualified hits
p_cnt = 0
n_cnt = 0
if g_name in [ref['name'] for ref in references]:
copyfile(genome_ctg_dir + g_name + "_1.fas",
matches_dir + g_name + ".fas")
if ref_flag:
# positive control TODO: better solution
control_scores.append(rec_array[0][11])
ref_flag = False
for line in rec_array:
idp = line[2]
q_start, q_stop = line[8], line[9]
score = line[11]
length = abs(q_stop - q_start)
# check the blast mode to use the right thresholds
if blast_mode == 'n' or blast_mode == 'tx':
min_match = min_nt_match
min_score = min_nt_score
min_idp = min_nt_idp
elif blast_mode == 'tn':
min_match = min_aa_match
min_score = min_aa_score
min_idp = min_aa_idp
else: # default to nucleotide mode
min_match = min_nt_match
min_score = min_nt_score
min_idp = min_nt_idp
if length > min_match and score > min_score and idp > min_idp:
print "+",
p_cnt += 1
contig_id = line[1]
if contig_id not in ref_hits[g_name].keys():
ref_hits[g_name][contig_id] = {seg_n: score}
else:
ref_hits[g_name][contig_id][seg_n] = score
pattern = re.compile(r'(' + contig_id + ')\.fas')
for item in listdir(genome_ctg_dir):
match = re.match(pattern, item)
if match:
fas_file = matches_dir + match.group(
1) + ".fas"
if not path.exists(fas_file):
copyfile(genome_ctg_dir + item, fas_file)
# context capture
capture_flag = False
while True:
try:
if int(seg_n) in run_ref.capture:
capture_flag = True
else:
break
except ValueError:
if seg_n in run_ref.capture:
capture_flag = True
else:
break
else:
break
if capture_flag:
# load the sequence
contig_file = matches_dir + contig_id + ".fas"
contig_rec = load_fasta(contig_file)
# check orientation
if q_start < q_stop:
c_start = q_start - capture_span
c_stop = q_stop + capture_span
else:
c_start = q_stop - capture_span
c_stop = q_start + capture_span
print c_start, c_stop
# check limits
if c_start < 0:
c_start = 1
if c_stop > len(contig_rec.seq):
c_stop = len(contig_rec.seq)
# proceed
cxt_file = capture_dir + g_name + "_" + contig_id + ".fas"
cxt_rec = SeqRecord(
id=contig_id + "_" + str(c_start) + "_" +
str(c_stop),
seq=contig_rec.seq[c_start:c_stop])
write_fasta(cxt_file, cxt_rec)
else:
print "-",
n_cnt += 1
if n_cnt > 0:
logstring = "".join(
["\t", str(p_cnt), " (",
str(n_cnt), ")"])
else:
logstring = "".join(["\t", str(p_cnt)])
run_ref.log(logstring)
else:
print "-",
run_ref.log("".join(["\t", "0"]))
print ""
return ref_hits, control_scores
def glomp_blast_out(dataset, ref_nick):
"""Consolidate Blast output files."""
# Identify the genome
nickname = dataset['nickname']
# Determine the input file root
root_dir = dirs['blast_out_dir']+nickname+"/"+ref_nick+"/"
file_root = root_dir+nickname
# Signal process start
print "-- Consolidating B_out for", nickname, "against", ref_nick, "--"
print datetime.now()
# Cycle through bin types
series_index = 0
averages = [] # for comparing series later
binned_pos = []
for bin_type in bin_types:
index = 1
bin_arrays =[]
while os.path.isfile(file_root+bin_type+"_"+str(index)+"_blast.out"):
infile = file_root+bin_type+"_"+str(index)+"_blast.out"
rec_array = read_array(infile, blast_dtypes)
if len(rec_array) > 0:
bin_arrays.append(rec_array)
index +=1
print "\t\t"+str(len(bin_arrays)), "arrays for", \
nickname+bin_type, "series"
if len(bin_arrays) > 0:
series = numpy.hstack(bin_arrays)
else:
series = []
print "\t\t"+str(len(series)), "total records in", \
nickname+bin_type, "series"
# Save to file
cons_outfile = file_root+bin_type+"_cons_out.npy"
numpy.save(cons_outfile, series)
# Evaluate match positions on reference
positions = []
match_read = []
for row in series:
# collect match read info while we're at it
# use regex to extract query index
query_pattern = re.compile(r'\w*_(\d*)')
query_match = query_pattern.match(row[0])
query_index = int(query_match.group(1))
match_read.append(query_index)
# use regex to extract ref coords
ref_pattern = re.compile(r'\w*_\d*_(\d*)')
ref_match = ref_pattern.match(row[1])
ref_pos = int(ref_match.group(1))
pos_scaled = ref_pos/cpm['size'] # adjust to db segment length
positions.append(pos_scaled)
# uniquify the match read array
unique_matches = numpy.unique(match_read)
print "\t"+str(len(unique_matches)), "unique matches for", bin_type
# write to file for future use
match_dir_root = dirs['match_dir']+nickname+"/"+ref_nick+"/"
ensure_dir(match_dir_root)
match_outfile = match_dir_root+nickname+bin_type+"_match.npy"
numpy.save(match_outfile, unique_matches)
# now count ocurrences per position
pos_np = numpy.array(positions)
binned = numpy.bincount(pos_np)
binned_pos.append(binned)
pos_count_average = numpy.average(binned)
averages.append((pos_count_average, series_index))
series_index +=1
# compare series
averages.sort()
averages.reverse()
order_indices = []
for pair in averages:
order_indices.append(pair[1])
# identify reference
ref_name = [reference['full_name'] for reference in references if
reference['nickname'] is ref_nick]
# prep directory & file
fig_root = dirs['reports_dir']+"match_figs/"
fig_file = fig_root+nickname+"_"+ref_nick+".png"
ensure_dir(fig_root)
# generate a figure
pylot.autoscale(enable=True, axis='both', tight=True)
pylot.xlabel('Position on the chromosome (/'+str(cpm['size'])+')')
pylot.ylabel('Number of matches (includes multiples)')
pylot.title(nickname+' matches to '+ref_name)
pylot.grid(True)
for index in order_indices:
label_root = nickname+bin_types[index]
label_str = label_root+" ("+str(numpy.sum(binned_pos[index]))+")"
pylot.plot(binned_pos[index], label=label_str)
pylot.legend(loc=1)
pylot.savefig(fig_file, dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None)
pylot.clf()
print "\t"+str(series_index), "series consolidated and parsed"
print "-- Done, see plot --"
print datetime.now()
return "OK"
