def split_sff(sff_file_handles, map_file_handle, outdir="/tmp/"):
"""Splits a sff.txt file on barcode/mapping file."""
try:
(flowgrams, header) = cat_sff_files(sff_file_handles)
except ValueError:
# reading in the binary sff usually shows up as ValueError
raise FileFormatError(
'Wrong flogram file format. Make sure you pass the sff.txt format '
+ 'produced by sffinfo. The binary .sff will not work here.')
(inverse_map,
map_count) = build_inverse_barcode_map(parse_fasta(map_file_handle))
filenames = []
# we might have many barcodes and reach python open file limit
# therefor we go the slow way and open and close files each time
# First set up all files with the headers only
for barcode_id in map_count.keys():
fh = open(outdir + barcode_id, "w")
write_sff_header(header, fh, map_count[barcode_id])
fh.close()
filenames.append(outdir + barcode_id)
# Then direct each flowgram into its barcode file
for f in flowgrams:
if f.Name in inverse_map:
barcode_id = inverse_map[f.Name]
fh = open(outdir + barcode_id, "a")
fh.write(f.createFlowHeader() + "\n")
return filenames
def filter_sff_file(flowgrams, header, filter_list, out_fh):
"""Filters all flowgrams in handle with filter.
flowgrams: a list of flowgrams (or something similar)
header: the header for the flowgrams
filter_list: list of filters to be applied on sff.txt file
out_fh: output file handle
returns: number of flowgrams in filtered out file
"""
write_sff_header(header, out_fh)
l = 0
for f in flowgrams:
passed = True
for filter in filter_list:
passed = passed and filter(f)
if not passed:
# bail out
break
if (passed):
out_fh.write(f.createFlowHeader() + "\n")
l += 1
return l
def filter_sff_file(flowgrams, header, filter_list, out_fh):
"""Filters all flowgrams in handle with filter.
flowgrams: a list of flowgrams (or something similar)
header: the header for the flowgrams
filter_list: list of filters to be applied on sff.txt file
out_fh: output file handle
returns: number of flowgrams in filtered out file
"""
write_sff_header(header, out_fh)
l = 0
for f in flowgrams:
passed = True
for filter in filter_list:
passed = passed and filter(f)
if not passed:
#bail out
break
if (passed):
out_fh.write(f.createFlowHeader()+"\n")
l += 1
return l
def split_sff(sff_file_handles, map_file_handle, outdir="/tmp/"):
"""Splits a sff.txt file on barcode/mapping file."""
try:
(flowgrams, header) = cat_sff_files(sff_file_handles)
except ValueError:
#reading in the binary sff usually shows up as ValueError
raise FileFormatError, 'Wrong flogram file format. Make sure you pass the sff.txt format '+\
'produced by sffinfo. The binary .sff will not work here.'
(inverse_map, map_count) = build_inverse_barcode_map(MinimalFastaParser(map_file_handle))
filenames = []
#we might have many barcodes and reach python open file limit
# therefor we go the slow way and open and close files each time
#First set up all files with the headers only
for barcode_id in map_count.keys():
fh = open(outdir+barcode_id, "w")
write_sff_header(header, fh, map_count[barcode_id])
fh.close()
filenames.append(outdir+barcode_id)
#Then direct each flowgram into its barcode file
for f in flowgrams:
if inverse_map.has_key(f.Name):
barcode_id = inverse_map[f.Name]
fh = open(outdir+barcode_id, "a")
fh.write(f.createFlowHeader()+"\n")
return filenames
def truncate_flowgrams_in_SFF(flowgrams,
header,
outhandle=None,
outdir="/tmp/",
barcode_mapping=None,
primer=None,
allow_num_ambigous=4):
"""Truncate flowgrams at low quality 3' end and strip key+primers.
flowgrams: a list of flowgrams (or something similar)
header: the header for the flowgrams
outhandle: output file handle, can be None
outdir: directory where random file will be created if outhandle is None
barcode_mapping: dictionary mapping of read ids to barcode seqs.
The barcode seq will be truncated of the 5' end of the read
primer: primer sequence that will be truncated of the 5' end of the read
allow_num_ambigous: number of 'N' allowed in flowgram
"""
out_filename = ""
if not outhandle:
fd, out_filename = mkstemp(dir=outdir,
prefix="trunc_sff",
suffix=".sff.txt")
close(fd)
outhandle = open(out_filename, "w")
write_sff_header(header, outhandle)
l = 0
for f in flowgrams:
qual_trimmed_flowgram = f.getQualityTrimmedFlowgram()
if barcode_mapping:
if f.Name in barcode_mapping:
trunc_flowgram = qual_trimmed_flowgram.getPrimerTrimmedFlowgram(
primerseq=DEFAULT_KEYSEQ + barcode_mapping[f.Name] +
primer)
else:
continue
else:
prim = DEFAULT_KEYSEQ
if primer:
prim += primer
trunc_flowgram = qual_trimmed_flowgram.getPrimerTrimmedFlowgram(
primerseq=prim)
if (trunc_flowgram is not None):
outhandle.write(trunc_flowgram.createFlowHeader() + "\n")
l += 1
return (out_filename, l)
def truncate_flowgrams_in_SFF(
flowgrams, header, outhandle=None, outdir="/tmp/",
barcode_mapping=None, primer=None,
allow_num_ambigous=4):
"""Truncate flowgrams at low quality 3' end and strip key+primers.
flowgrams: a list of flowgrams (or something similar)
header: the header for the flowgrams
outhandle: output file handle, can be None
outdir: directory where random file will be created if outhandle is None
barcode_mapping: dictionary mapping of read ids to barcode seqs.
The barcode seq will be truncated of the 5' end of the read
primer: primer sequence that will be truncated of the 5' end of the read
allow_num_ambigous: number of 'N' allowed in flowgram
"""
out_filename = ""
if not outhandle:
fd, out_filename = mkstemp(dir=outdir, prefix="trunc_sff",
suffix=".sff.txt")
close(fd)
outhandle = open(out_filename, "w")
write_sff_header(header, outhandle)
l = 0
for f in flowgrams:
qual_trimmed_flowgram = f.getQualityTrimmedFlowgram()
if barcode_mapping:
if f.Name in barcode_mapping:
trunc_flowgram = qual_trimmed_flowgram.getPrimerTrimmedFlowgram(
primerseq=DEFAULT_KEYSEQ + barcode_mapping[f.Name] + primer)
else:
continue
else:
prim = DEFAULT_KEYSEQ
if primer:
prim += primer
trunc_flowgram = qual_trimmed_flowgram.getPrimerTrimmedFlowgram(
primerseq=prim)
if(trunc_flowgram is not None):
outhandle.write(trunc_flowgram.createFlowHeader() + "\n")
l += 1
return (out_filename, l)
def build_averaged_flowgrams(mapping, sff_fp,
min_coverage=50, out_fp=None):
"""Build averaged flowgrams for each cluster in mapping.
mapping: a cluster mapping as dictionary of lists
sff_fp: pointer to sff.txt file, must be consistent with mapping
min_coverage: number of flowgrams to average over for each cluster
out_fp: ouput file name
NOTE: This function has no test code, since it is mostly IO around tested functions
"""
l = len(mapping)
(flowgrams, header) = lazy_parse_sff_handle(open(sff_fp))
# update some values in the sff header
header["# of Reads"] = l
header["Index Length"] = "NA"
if (out_fp):
out_filename = out_fp
else:
fd, out_filename = mkstemp(dir="/tmp/",
prefix="prefix_dereplicated",
suffix=".sff.txt")
close(fd)
outhandle = open(out_filename, "w")
# write out reduced flogram set
write_sff_header(header, outhandle)
seqs = {}
# get a random sample for each cluster
sample_keys = sample_mapped_keys(mapping, min_coverage)
for ave_f, id in _average_flowgrams(mapping, flowgrams, sample_keys):
outhandle.write(ave_f.createFlowHeader() + "\n")
ave_f.Bases = ave_f.toSeq()
seqs[id] = ave_f.Bases
outhandle.close()
return(out_filename, seqs)
def build_averaged_flowgrams(mapping, sff_fp,
min_coverage=50, out_fp=None):
"""Build averaged flowgrams for each cluster in mapping.
mapping: a cluster mapping as dictionary of lists
sff_fp: pointer to sff.txt file, must be consistent with mapping
min_coverage: number of flowgrams to average over for each cluster
out_fp: ouput file name
NOTE: This function has no test code, since it is mostly IO around tested functions
"""
l = len(mapping)
(flowgrams, header) = lazy_parse_sff_handle(open(sff_fp))
# update some values in the sff header
header["# of Reads"] = l
header["Index Length"] = "NA"
if (out_fp):
out_filename = out_fp
else:
fd, out_filename = mkstemp(dir="/tmp/",
prefix="prefix_dereplicated",
suffix=".sff.txt")
close(fd)
outhandle = open(out_filename, "w")
# write out reduced flogram set
write_sff_header(header, outhandle)
seqs = {}
# get a random sample for each cluster
sample_keys = sample_mapped_keys(mapping, min_coverage)
for ave_f, id in _average_flowgrams(mapping, flowgrams, sample_keys):
outhandle.write(ave_f.createFlowHeader() + "\n")
ave_f.Bases = ave_f.toSeq()
seqs[id] = ave_f.Bases
outhandle.close()
return(out_filename, seqs)
def greedy_clustering(sff_fp, seqs, cluster_mapping, outdir, num_flows,
log_fh, num_cpus=1, on_cluster=False,
bail_out=1, pair_id_thresh=0.97, verbose=False,
threshold=3.75, fast_method=True,
error_profile=DENOISER_DATA_DIR +
'FLX_error_profile.dat',
max_num_rounds=None, checkpoint_fp=None):
"""second clustering phase of denoiser.
sff_fp: flowgram file
seqs: fasta seqs corresponding to sff_fp
cluster_mapping: preliminary cluster mapping from phase I
outdir: output directory
num_flows: number of flowgrams in sff_fp (need to now before parsing sff_fp)
log_fh: write verbose info to log_fh if set
num_cpus:number of cpus to use of on_cluster ==True
on_cluster: run in paralell if True
bail_out: stop clustering with first cluster having bail_out members
pair_id_thresh: always cluster flowgrams whose flowgram alignment implies a seq
identity of pair_id_thresh or higher
verbose: be verbose or not
threshold: low clustering threshold for phase II
fast_method: use more memory intensive but faster method
error_profile: path to error profile *.dat file
max_num_rounds: If set, will stop clustering after this many rounds
"""
(flowgrams, header) = lazy_parse_sff_handle(open(sff_fp))
l = num_flows
spread = [1.0 for x in range(num_cpus)]
(client_sockets, workers) = (None, None)
if on_cluster:
(client_sockets, workers, server_socket) = \
setup_cluster(num_cpus, outdir, verbose, error_profile)
if checkpoint_fp:
(checkpoint_key, round_ctr, cluster_mapping, ids, bestscores, sorted_keys) = \
read_checkpoint(checkpoint_fp)
skipping = True
else:
# ids stores all the active sequences
# we initialize it with the ids from the seqs dict here,
# as it starts with all active flows.
ids = dict.fromkeys(seqs)
sorted_keys = sort_mapping_by_size(cluster_mapping)
bestscores = {}
round_ctr = 1
# this is the main clustering loop, where most of the compute time is spent
for key in sorted_keys:
# skip until we reach the checkpoint
if checkpoint_fp:
if (checkpoint_key == key):
if log_fh:
log_fh.write("Resume denoising with %s\n" % key)
skipping = False
if (skipping):
continue
if(key not in cluster_mapping):
# this guy already has been clustered
continue
if (max_num_rounds and round_ctr > max_num_rounds):
if log_fh:
log_fh.write("Max number of rounds reached. " +
"Aborting clustering phase II and continuing with phase III.\n")
break
prefix_clustersize = len(cluster_mapping[key])
# abort greedy first phase
if(prefix_clustersize < bail_out):
break
# Do not take bad sequences as cluster seeds, as this will break the
# code
if('N' in seqs[key]):
continue
# check and delete workers if no longer needed
if on_cluster:
num_cpus = adjust_workers(l, num_cpus, client_sockets, log_fh)
# check for dead workers
check_workers(workers, client_sockets, log_fh)
if num_cpus != len(spread):
spread = [1.0 for x in range(num_cpus)]
# write checkpoint right before expensive computation starts
# Currently, write checkpint every 50 rounds,
# could easily be changed here or exposed to command line
if (round_ctr % 50) == 0:
write_checkpoint(key, round_ctr, cluster_mapping, ids, bestscores,
sorted_keys, outdir)
if log_fh:
log_fh.write("Round %d:\n" % round_ctr)
log_remaining_rounds(ids, cluster_mapping, bail_out, log_fh)
ideal_flow = seq_to_flow(seqs[key])
(new_flowgrams, newl) = filter_with_flowgram(key, ideal_flow, flowgrams, header, ids,
l, bestscores, log_fh, outdir,
on_cluster=on_cluster,
num_cpus=num_cpus,
fast_method=fast_method,
mapping=cluster_mapping,
verbose=verbose,
threshold=threshold,
pair_id_thresh=pair_id_thresh,
client_sockets=client_sockets,
error_profile=error_profile, spread=spread)
l = newl
flowgrams = new_flowgrams
round_ctr += 1
if(newl == 0):
# all flowgrams clustered
break
# JR: I think this is too much info for the regular user, I leave it in, so
# we can simply turn it on for debugging
# if log_fh:
# log_fh.write("Throughput Spread %s\n" % str(spread))
if on_cluster:
stop_workers(client_sockets, log_fh)
server_socket.close()
# write all remaining flowgrams into file for next step
# TODO: might use abstract FlowgramContainer here as well
fd, non_clustered_filename = mkstemp(dir=outdir, prefix="ff",
suffix=".sff.txt")
close(fd)
non_clustered_fh = open(non_clustered_filename, "w")
write_sff_header(header, non_clustered_fh)
for f in flowgrams:
if (f.Name in ids):
non_clustered_fh.write(f.createFlowHeader() + "\n")
return(non_clustered_filename, bestscores, cluster_mapping)
def greedy_clustering(sff_fp,
seqs,
cluster_mapping,
outdir,
num_flows,
log_fh,
num_cpus=1,
on_cluster=False,
bail_out=1,
pair_id_thresh=0.97,
verbose=False,
threshold=3.75,
fast_method=True,
error_profile=DENOISER_DATA_DIR +
'FLX_error_profile.dat',
max_num_rounds=None,
checkpoint_fp=None):
"""second clustering phase of denoiser.
sff_fp: flowgram file
seqs: fasta seqs corresponding to sff_fp
cluster_mapping: preliminary cluster mapping from phase I
outdir: output directory
num_flows: number of flowgrams in sff_fp (need to now before parsing sff_fp)
log_fh: write verbose info to log_fh if set
num_cpus:number of cpus to use of on_cluster ==True
on_cluster: run in paralell if True
bail_out: stop clustering with first cluster having bail_out members
pair_id_thresh: always cluster flowgrams whose flowgram alignment implies a seq
identity of pair_id_thresh or higher
verbose: be verbose or not
threshold: low clustering threshold for phase II
fast_method: use more memory intensive but faster method
error_profile: path to error profile *.dat file
max_num_rounds: If set, will stop clustering after this many rounds
"""
(flowgrams, header) = lazy_parse_sff_handle(open(sff_fp))
l = num_flows
spread = [1.0 for x in range(num_cpus)]
(client_sockets, workers) = (None, None)
if on_cluster:
(client_sockets, workers, server_socket) = \
setup_cluster(num_cpus, outdir, verbose, error_profile)
if checkpoint_fp:
(checkpoint_key, round_ctr, cluster_mapping, ids, bestscores, sorted_keys) = \
read_checkpoint(checkpoint_fp)
skipping = True
else:
# ids stores all the active sequences
# we initialize it with the ids from the seqs dict here,
# as it starts with all active flows.
ids = dict.fromkeys(seqs)
sorted_keys = sort_mapping_by_size(cluster_mapping)
bestscores = {}
round_ctr = 1
# this is the main clustering loop, where most of the compute time is spent
for key in sorted_keys:
# skip until we reach the checkpoint
if checkpoint_fp:
if (checkpoint_key == key):
if log_fh:
log_fh.write("Resume denoising with %s\n" % key)
skipping = False
if (skipping):
continue
if (key not in cluster_mapping):
# this guy already has been clustered
continue
if (max_num_rounds and round_ctr > max_num_rounds):
if log_fh:
log_fh.write(
"Max number of rounds reached. " +
"Aborting clustering phase II and continuing with phase III.\n"
)
break
prefix_clustersize = len(cluster_mapping[key])
# abort greedy first phase
if (prefix_clustersize < bail_out):
break
# Do not take bad sequences as cluster seeds, as this will break the
# code
if ('N' in seqs[key]):
continue
# check and delete workers if no longer needed
if on_cluster:
num_cpus = adjust_workers(l, num_cpus, client_sockets, log_fh)
# check for dead workers
check_workers(workers, client_sockets, log_fh)
if num_cpus != len(spread):
spread = [1.0 for x in range(num_cpus)]
# write checkpoint right before expensive computation starts
# Currently, write checkpint every 50 rounds,
# could easily be changed here or exposed to command line
if (round_ctr % 50) == 0:
write_checkpoint(key, round_ctr, cluster_mapping, ids, bestscores,
sorted_keys, outdir)
if log_fh:
log_fh.write("Round %d:\n" % round_ctr)
log_remaining_rounds(ids, cluster_mapping, bail_out, log_fh)
ideal_flow = seq_to_flow(seqs[key])
(new_flowgrams,
newl) = filter_with_flowgram(key,
ideal_flow,
flowgrams,
header,
ids,
l,
bestscores,
log_fh,
outdir,
on_cluster=on_cluster,
num_cpus=num_cpus,
fast_method=fast_method,
mapping=cluster_mapping,
verbose=verbose,
threshold=threshold,
pair_id_thresh=pair_id_thresh,
client_sockets=client_sockets,
error_profile=error_profile,
spread=spread)
l = newl
flowgrams = new_flowgrams
round_ctr += 1
if (newl == 0):
# all flowgrams clustered
break
# JR: I think this is too much info for the regular user, I leave it in, so
# we can simply turn it on for debugging
# if log_fh:
# log_fh.write("Throughput Spread %s\n" % str(spread))
if on_cluster:
stop_workers(client_sockets, log_fh)
server_socket.close()
# write all remaining flowgrams into file for next step
# TODO: might use abstract FlowgramContainer here as well
fd, non_clustered_filename = mkstemp(dir=outdir,
prefix="ff",
suffix=".sff.txt")
close(fd)
non_clustered_fh = open(non_clustered_filename, "w")
write_sff_header(header, non_clustered_fh)
for f in flowgrams:
if (f.Name in ids):
non_clustered_fh.write(f.createFlowHeader() + "\n")
return (non_clustered_filename, bestscores, cluster_mapping)
