def test_read_denoiser_mapping(self):
"""read_denoiser_mapping reads correctly"""
mapping = """1:\t2\t3
4:\t5\t6
7:""".split("\n")
expected = {'1': ['2', '3'], '4': ['5', '6'], '7': []}
self.assertEqual(read_denoiser_mapping(mapping), expected)
# empty mapping gives empty result
self.assertEqual(read_denoiser_mapping([]), {})
def test_read_denoiser_mapping(self):
"""read_denoiser_mapping reads correctly"""
mapping = """1:\t2\t3
4:\t5\t6
7:""".split(
"\n"
)
expected = {"1": ["2", "3"], "4": ["5", "6"], "7": []}
self.assertEqual(read_denoiser_mapping(mapping), expected)
# empty mapping gives empty result
self.assertEqual(read_denoiser_mapping([]), {})
def test_read_denoiser_mapping(self):
"""read_denoiser_mapping reads correctly"""
mapping ="""1:\t2\t3
4:\t5\t6
7:""".split("\n")
expected = {'1':['2','3'],
'4':['5','6'],
'7':[]}
self.assertEqual(read_denoiser_mapping(mapping),
expected)
# empty mapping gives empty result
self.assertEqual(read_denoiser_mapping([]), {})
def combine_mappings(fasta_fh, mapping_fh, denoised_seqs_fh,
otu_picker_otu_map_fh, out_dir):
"""Combine denoiser and OTU picker mapping file, replace flowgram IDs.
fasta_fh: a fasta file with labels as produced by Qiime's split_libraries.py
used to replace flowgram id with the unique se_sample_id
mapping_fh: The cluster mapping from the denoiser.py
denoised_seqs_fh: the Fasta output files from denoiser.py
otu_picker_map_fh: cluster map from otu picker on denoised_seqs_fh
out_dir: output directory
"""
# read in mapping from split_library file
labels = imap(lambda a_b: a_b[0], MinimalFastaParser(fasta_fh))
# mapping from seq_id to sample_id
sample_id_mapping = extract_read_to_sample_mapping(labels)
denoiser_mapping = read_denoiser_mapping(mapping_fh)
# read in cd_hit otu map
# and write out combined otu_picker+denoiser map
otu_fh = open(out_dir + "/denoised_otu_map.txt", "w")
for otu_line in otu_picker_otu_map_fh:
otu_split = otu_line.split()
otu = otu_split[0]
ids = otu_split[1:]
get_sample_id = sample_id_mapping.get
# concat lists
# make sure the biggest one is first for pick_repr
all_ids = sort_ids(ids, denoiser_mapping)
all_ids.extend(sum([denoiser_mapping[id] for id in ids], []))
try:
otu_fh.write("%s\t" % otu +
"\t".join(map(get_sample_id, all_ids)) + "\n")
except TypeError:
# get returns Null if denoiser_mapping id not present in
# sample_id_mapping
print "Found id in denoiser output, which was not found in split_libraries " +\
"output FASTA file. Wrong file?"
exit()
fasta_out_fh = open(out_dir + "/denoised_all.fasta", "w")
for label, seq in MinimalFastaParser(denoised_seqs_fh):
id = label.split()[0]
newlabel = "%s %s" % (sample_id_mapping[id], id)
fasta_out_fh.write(Sequence(name=newlabel, seq=seq).toFasta() + "\n")
def combine_mappings(fasta_fh, mapping_fh, denoised_seqs_fh,
otu_picker_otu_map_fh, out_dir):
"""Combine denoiser and OTU picker mapping file, replace flowgram IDs.
fasta_fh: a fasta file with labels as produced by Qiime's split_libraries.py
used to replace flowgram id with the unique se_sample_id
mapping_fh: The cluster mapping from the denoiser.py
denoised_seqs_fh: the Fasta output files from denoiser.py
otu_picker_map_fh: cluster map from otu picker on denoised_seqs_fh
out_dir: output directory
"""
# read in mapping from split_library file
labels = imap(lambda a_b: a_b[0], parse_fasta(fasta_fh))
# mapping from seq_id to sample_id
sample_id_mapping = extract_read_to_sample_mapping(labels)
denoiser_mapping = read_denoiser_mapping(mapping_fh)
# read in cd_hit otu map
# and write out combined otu_picker+denoiser map
otu_fh = open(out_dir + "/denoised_otu_map.txt", "w")
for otu_line in otu_picker_otu_map_fh:
otu_split = otu_line.split()
otu = otu_split[0]
ids = otu_split[1:]
get_sample_id = sample_id_mapping.get
# concat lists
# make sure the biggest one is first for pick_repr
all_ids = sort_ids(ids, denoiser_mapping)
all_ids.extend(sum([denoiser_mapping[id] for id in ids], []))
try:
otu_fh.write("%s\t" % otu +
"\t".join(map(get_sample_id, all_ids)) + "\n")
except TypeError:
# get returns Null if denoiser_mapping id not present in
# sample_id_mapping
print "Found id in denoiser output, which was not found in split_libraries " +\
"output FASTA file. Wrong file?"
exit()
fasta_out_fh = open(out_dir + "/denoised_all.fasta", "w")
for label, seq in parse_fasta(denoised_seqs_fh):
id = label.split()[0]
newlabel = "%s %s" % (sample_id_mapping[id], id)
fasta_out_fh.write(BiologicalSequence(seq, id=newlabel).to_fasta())
def read_preprocessed_data(out_fp="/tmp/"):
"""Read data of a previous preprocessing run.
out_fp: output directory of previous preprocess run.
Supposed to contain two files:
- prefix_dereplicated.fasta
- prefix_mapping.txt
"""
# read mapping, and extract seqs
# mapping has fasta_header like this:
# > id: count
seqs = dict([(a.split(":")[0], b) for (a, b) in (parse_fasta(open(out_fp + "/prefix_dereplicated.fasta")))])
mapping = read_denoiser_mapping(open(out_fp + "/prefix_mapping.txt"))
return (out_fp + "/prefix_dereplicated.sff.txt", len(mapping), mapping, seqs)
def read_preprocessed_data(out_fp="/tmp/"):
"""Read data of a previous preprocessing run.
out_fp: output directory of previous preprocess run.
Supposed to contain two files:
- prefix_dereplicated.fasta
- prefix_mapping.txt
"""
# read mapping, and extract seqs
# mapping has fasta_header like this:
# > id: count
seqs = dict([(a.split(':')[0],b) for (a,b) in
(MinimalFastaParser(open(out_fp+"/prefix_dereplicated.fasta")))])
mapping = read_denoiser_mapping(open(out_fp+"/prefix_mapping.txt"))
return(out_fp+"/prefix_dereplicated.sff.txt", len(mapping), mapping, seqs)
def denoise_per_sample(sff_fps, fasta_fp, tmpoutdir, cluster=False,
num_cpus=1, squeeze=True, percent_id=0.97, bail=1,
primer="", low_cutoff=3.75, high_cutoff=4.5,
log_fp="denoiser.log", low_memory=False, verbose=False,
error_profile=DENOISER_DATA_DIR +
'FLX_error_profile.dat',
max_num_rounds=None, titanium=False):
"""Denoise each sample separately"""
# abort early if binary is missing
check_flowgram_ali_exe()
log_fh = None
if log_fp:
# switch of buffering for global log file
log_fh = open(tmpoutdir + "/" + log_fp, "w", 0)
# overwrite settings if titanium is set
# This flag is only used from qiime. Remove after qiime integration
if titanium:
error_profile = DENOISER_DATA_DIR + "Titanium_error_profile.dat"
low_cutoff = 4
high_cutoff = 5
if verbose:
log_fh.write("Denoiser version: %s\n" % __version__)
log_fh.write("SFF files: %s\n" % ', '.join(sff_fps))
log_fh.write("Fasta file: %s\n" % fasta_fp)
log_fh.write("Cluster: %s\n" % cluster)
log_fh.write("Num CPUs: %d\n" % num_cpus)
log_fh.write("Squeeze Seqs: %s\n" % squeeze)
log_fh.write("tmpdir: %s\n\n" % tmpoutdir)
log_fh.write("percent_id threshold: %.2f\n" % percent_id)
log_fh.write("Minimal sequence coverage for first phase: %d\n" % bail)
log_fh.write("Error profile: %s\n" % error_profile)
log_fh.write("Maximal number of iteration: %s\n\n" % max_num_rounds)
# here we go ...
sff_files = split_sff(map(open, sff_fps), open(fasta_fp), tmpoutdir)
combined_mapping = {}
result_centroids = []
result_singletons_files = []
# denoise each sample separately
for i, sff_file in enumerate(sff_files):
if not exists(tmpoutdir + ("/%d" % i)):
makedirs(tmpoutdir + ("/%d" % i))
out_fp = tmpoutdir + ("/%d/" % i)
denoise_seqs([sff_file], fasta_fp, out_fp, None, cluster,
num_cpus, squeeze, percent_id, bail, primer,
low_cutoff, high_cutoff, log_fp, low_memory,
verbose, error_profile, max_num_rounds)
# collect partial results
this_rounds_mapping = read_denoiser_mapping(
open(out_fp + "/denoiser_mapping.txt"))
combined_mapping.update(this_rounds_mapping)
result_centroids.append(
parse_fasta(open(out_fp + "/centroids.fasta")))
result_singletons_files.append(out_fp + "/singletons.fasta")
# write the combined files
store_mapping(combined_mapping, tmpoutdir, "denoiser")
seqs = chain(*result_centroids)
fasta_fh = open(tmpoutdir + "/denoised.fasta", "w")
# write centroids sorted by clustersize
write_Fasta_from_name_seq_pairs(
sort_seqs_by_clustersize(seqs, combined_mapping),
fasta_fh)
for singleton_file in result_singletons_files:
write_Fasta_from_name_seq_pairs(
parse_fasta(open(singleton_file, "r")),
fasta_fh)
fasta_fh.close()
# return outdir for tests/test_denoiser
return tmpoutdir
def denoise_per_sample(sff_fps,
fasta_fp,
tmpoutdir,
cluster=False,
num_cpus=1,
squeeze=True,
percent_id=0.97,
bail=1,
primer="",
low_cutoff=3.75,
high_cutoff=4.5,
log_fp="denoiser.log",
low_memory=False,
verbose=False,
error_profile=DENOISER_DATA_DIR +
'FLX_error_profile.dat',
max_num_rounds=None,
titanium=False):
"""Denoise each sample separately"""
# abort early if binary is missing
check_flowgram_ali_exe()
log_fh = None
if log_fp:
# switch of buffering for global log file
log_fh = open(tmpoutdir + "/" + log_fp, "w", 0)
# overwrite settings if titanium is set
# This flag is only used from qiime. Remove after qiime integration
if titanium:
error_profile = DENOISER_DATA_DIR + "Titanium_error_profile.dat"
low_cutoff = 4
high_cutoff = 5
if verbose:
log_fh.write("Denoiser version: %s\n" % __version__)
log_fh.write("SFF files: %s\n" % ', '.join(sff_fps))
log_fh.write("Fasta file: %s\n" % fasta_fp)
log_fh.write("Cluster: %s\n" % cluster)
log_fh.write("Num CPUs: %d\n" % num_cpus)
log_fh.write("Squeeze Seqs: %s\n" % squeeze)
log_fh.write("tmpdir: %s\n\n" % tmpoutdir)
log_fh.write("percent_id threshold: %.2f\n" % percent_id)
log_fh.write("Minimal sequence coverage for first phase: %d\n" % bail)
log_fh.write("Error profile: %s\n" % error_profile)
log_fh.write("Maximal number of iteration: %s\n\n" % max_num_rounds)
# here we go ...
sff_files = split_sff(map(open, sff_fps), open(fasta_fp), tmpoutdir)
combined_mapping = {}
result_centroids = []
result_singletons_files = []
# denoise each sample separately
for i, sff_file in enumerate(sff_files):
if not exists(tmpoutdir + ("/%d" % i)):
makedirs(tmpoutdir + ("/%d" % i))
out_fp = tmpoutdir + ("/%d/" % i)
denoise_seqs([sff_file], fasta_fp, out_fp, None, cluster, num_cpus,
squeeze, percent_id, bail, primer, low_cutoff,
high_cutoff, log_fp, low_memory, verbose, error_profile,
max_num_rounds)
# collect partial results
this_rounds_mapping = read_denoiser_mapping(
open(out_fp + "/denoiser_mapping.txt"))
combined_mapping.update(this_rounds_mapping)
result_centroids.append(parse_fasta(open(out_fp + "/centroids.fasta")))
result_singletons_files.append(out_fp + "/singletons.fasta")
# write the combined files
store_mapping(combined_mapping, tmpoutdir, "denoiser")
seqs = chain(*result_centroids)
fasta_fh = open(tmpoutdir + "/denoised.fasta", "w")
# write centroids sorted by clustersize
write_Fasta_from_name_seq_pairs(
sort_seqs_by_clustersize(seqs, combined_mapping), fasta_fh)
for singleton_file in result_singletons_files:
write_Fasta_from_name_seq_pairs(parse_fasta(open(singleton_file, "r")),
fasta_fh)
fasta_fh.close()
# return outdir for tests/test_denoiser
return tmpoutdir