def set_probqv_from_model():
"""Set probablitiy values from a fixed model,
return probqv, log_info.
"""
msg = "Loading predefined probabilities model."
probqv = ProbFromModel(0.01, 0.07, 0.06)
return probqv, msg
def pickup_icec_job(pickle_filename, ccs_fofn, flnc_filename,
fasta_files_to_add, root_dir):
"""
Reconstruct an ICE object from a pickle file and restart this ICE job.
"""
log.info("Reading ICE pickle %s ....", pickle_filename)
icec_obj, icec_pickle_filename = ensure_pickle_goodness(
pickle_filename=pickle_filename,
root_dir=root_dir,
fasta_files_to_add=fasta_files_to_add)
c_fa = current_fasta(root_dir)
c_fq = current_fastq(root_dir)
log.info("Making current.fasta %s for ICE ....", c_fa)
make_current_fasta(icec_obj=icec_obj,
flnc_filename=flnc_filename,
root_dir=root_dir)
log.info("Loading prob QV information....")
probqv = None
if ccs_fofn is None:
logging.info("Loading probability from model (0.01,0.07,0.06)")
probqv = ProbFromModel(.01, .07, .06)
else:
#if use_finer_qv:
# probqv = ProbFromQV(input_fofn=ccs_fofn, fasta_filename=input_fasta)
# logging.info("Loading prob QVs from %s + %s took %s secs",
# ccs_fofn, input_fasta, time.time()-start_t)
logging.info("Converting %s to %s", c_fa, c_fq)
ice_fa2fq(c_fa, ccs_fofn, c_fq)
logging.info("Loading prob QVs from %s", c_fq)
probqv = ProbFromFastq(c_fq)
log.info("Starting ICE from pickle %s....", icec_pickle_filename)
icec = ice.IceIterative.from_pickle(icec_pickle_filename, probqv)
# first must RE-RUN gcon to get all the proper refs
icec.changes = set()
icec.refs = {}
icec.ccs_fofn = ccs_fofn
icec.all_fasta_filename = flnc_filename
todo = icec.uc.keys()
log.info("Re-run gcon for proper refs....")
icec.run_gcon_parallel(todo)
log.info("Re-calculating cluster prob, just to be safe....")
icec.calc_cluster_prob(True)
log.info("Sanity checking uc_refs now....")
icec.sanity_check_uc_refs()
log.info("Ensuring prob QV of new ids are consistent....")
icec.ensure_probQV_newid_consistency()
log.info("Sanity check done. Resuming ICE job.")
icec.run()
def build_uc_from_partial_daligner(input_fasta,
ref_fasta,
out_pickle,
ccs_fofn=None,
done_filename=None,
use_finer_qv=False,
cpus=24,
no_qv_or_aln_checking=True,
tmp_dir=None):
"""
Given an input_fasta file of non-full-length (partial) reads and
(unpolished) consensus isoforms sequences in ref_fasta, align reads to
consensus isoforms using BLASR, and then build up a mapping between
consensus isoforms and reads (i.e., assign reads to isoforms).
Finally, save
{isoform_id: [read_ids],
nohit: set(no_hit_read_ids)}
to an output pickle file.
ccs_fofn --- If None, assume no quality value is available,
otherwise, use QV from ccs_fofn.
tmp_dir - where to save intermediate files such as dazz files.
if None, writer dazz files to the same directory as query/target.
"""
input_fasta = realpath(input_fasta)
ref_fasta = realpath(ref_fasta)
out_pickle = realpath(out_pickle)
output_dir = op.dirname(out_pickle)
ice_opts = IceOptions()
ice_opts.detect_cDNA_size(ref_fasta)
# ice_partial is already being called through qsub, so run everything local!
runner = DalignerRunner(query_filename=input_fasta,
target_filename=ref_fasta,
is_FL=False,
same_strand_only=False,
query_converted=False,
target_converted=True,
dazz_dir=tmp_dir,
script_dir=op.join(output_dir, "script"),
use_sge=False,
sge_opts=None,
cpus=cpus)
runner.run(min_match_len=300,
output_dir=output_dir,
sensitive_mode=ice_opts.sensitive_mode)
if no_qv_or_aln_checking:
# not using QVs or alignment checking!
# this probqv is just a DUMMY to pass to daligner_against_ref, which won't be used
logging.info("Not using QV for partial_uc. Loading dummy QV.")
probqv = ProbFromModel(.01, .07, .06)
else:
if ccs_fofn is None:
logging.info("Loading probability from model (0.01,0.07,0.06)")
probqv = ProbFromModel(.01, .07, .06)
else:
start_t = time.time()
if use_finer_qv:
probqv = ProbFromQV(input_fofn=ccs_fofn,
fasta_filename=input_fasta)
logging.info("Loading QVs from %s + %s took %s secs", ccs_fofn,
input_fasta,
time.time() - start_t)
else:
input_fastq = input_fasta[:input_fasta.rfind('.')] + '.fastq'
logging.info("Converting %s + %s --> %s", input_fasta,
ccs_fofn, input_fastq)
ice_fa2fq(input_fasta, ccs_fofn, input_fastq)
probqv = ProbFromFastq(input_fastq)
logging.info("Loading QVs from %s took %s secs", input_fastq,
time.time() - start_t)
logging.info("Calling dalign_against_ref ...")
partial_uc = {} # Maps each isoform (cluster) id to a list of reads
# which can map to the isoform
seen = set() # reads seen
logging.info("Building uc from DALIGNER hits.")
for la4ice_filename in runner.la4ice_filenames:
start_t = time.time()
hitItems = daligner_against_ref(
query_dazz_handler=runner.query_dazz_handler,
target_dazz_handler=runner.target_dazz_handler,
la4ice_filename=la4ice_filename,
is_FL=False,
sID_starts_with_c=True,
qver_get_func=probqv.get_smoothed,
qvmean_get_func=probqv.get_mean,
ece_penalty=1,
ece_min_len=20,
same_strand_only=False,
no_qv_or_aln_checking=no_qv_or_aln_checking)
for h in hitItems:
if h.ece_arr is not None:
if h.cID not in partial_uc:
partial_uc[h.cID] = set()
partial_uc[h.cID].add(h.qID)
seen.add(h.qID)
logging.info("processing %s took %s sec", la4ice_filename,
str(time.time() - start_t))
for k in partial_uc:
partial_uc[k] = list(partial_uc[k])
allhits = set(r.name.split()[0]
for r in ContigSetReaderWrapper(input_fasta))
logging.info("Counting reads with no hit.")
nohit = allhits.difference(seen)
logging.info("Dumping uc to a pickle: %s.", out_pickle)
with open(out_pickle, 'w') as f:
if out_pickle.endswith(".pickle"):
dump({'partial_uc': partial_uc, 'nohit': nohit}, f)
elif out_pickle.endswith(".json"):
f.write(json.dumps({'partial_uc': partial_uc, 'nohit': nohit}))
else:
raise IOError("Unrecognized extension: %s" % out_pickle)
done_filename = realpath(done_filename) if done_filename is not None \
else out_pickle + '.DONE'
logging.debug("Creating %s.", done_filename)
touch(done_filename)
# remove all the .las and .las.out filenames
runner.clean_run()
def build_uc_from_partial(input_fasta,
ref_fasta,
out_pickle,
ccs_fofn=None,
done_filename=None,
blasr_nproc=12,
tmp_dir=None):
"""
Given an input_fasta file of non-full-length (partial) reads and
(unpolished) consensus isoforms sequences in ref_fasta, align reads to
consensus isoforms using BLASR, and then build up a mapping between
consensus isoforms and reads (i.e., assign reads to isoforms).
Finally, save
{isoform_id: [read_ids],
nohit: set(no_hit_read_ids)}
to an output pickle file.
ccs_fofn --- If None, assume no quality value is available,
otherwise, use QV from ccs_fofn.
blasr_nproc --- equivalent to blasr -nproc, number of CPUs to use
"""
input_fasta = _get_fasta_path(realpath(input_fasta))
m5_file = os.path.basename(input_fasta) + ".blasr"
if tmp_dir is not None:
m5_file = op.join(tmp_dir, m5_file)
out_pickle = realpath(out_pickle)
cmd = "blasr {i} ".format(i=real_upath(input_fasta)) + \
"{r} --bestn 5 ".format(r=real_upath(_get_fasta_path(ref_fasta))) + \
"--nproc {n} -m 5 ".format(n=blasr_nproc) + \
"--maxScore -1000 --minPctIdentity 85 " + \
"--out {o} ".format(o=real_upath(m5_file)) + \
"1>/dev/null 2>/dev/null"
execute(cmd)
if ccs_fofn is None:
logging.info("Loading probability from model")
probqv = ProbFromModel(.01, .07, .06)
else:
# FIXME this will not work with current CCS bam output, which lacks
# QV pulse features required - this is handled via a workaround in
# pbtranscript.tasks.ice_partial
logging.info("Loading probability from QV in %s", ccs_fofn)
probqv = ProbFromQV(input_fofn=ccs_fofn, fasta_filename=input_fasta)
logging.info("Calling blasr_against_ref ...")
hitItems = blasr_against_ref(output_filename=m5_file,
is_FL=False,
sID_starts_with_c=True,
qver_get_func=probqv.get_smoothed,
qvmean_get_func=probqv.get_mean,
ece_penalty=1,
ece_min_len=10,
same_strand_only=False)
partial_uc = {} # Maps each isoform (cluster) id to a list of reads
# which can map to the isoform
seen = set() # reads seen
logging.info("Building uc from BLASR hits.")
for h in hitItems:
if h.ece_arr is not None:
if h.cID not in partial_uc:
partial_uc[h.cID] = set()
partial_uc[h.cID].add(h.qID)
seen.add(h.qID)
for k in partial_uc:
partial_uc[k] = list(partial_uc[k])
allhits = set(r.name.split()[0]
for r in ContigSetReaderWrapper(input_fasta))
logging.info("Counting reads with no hit.")
nohit = allhits.difference(seen)
logging.info("Dumping uc to a pickle: %s.", out_pickle)
with open(out_pickle, 'w') as f:
if out_pickle.endswith(".pickle"):
dump({'partial_uc': partial_uc, 'nohit': nohit}, f)
elif out_pickle.endswith(".json"):
f.write(json.dumps({'partial_uc': partial_uc, 'nohit': nohit}))
else:
raise IOError("Unrecognized extension: %s" % out_pickle)
os.remove(m5_file)
done_filename = realpath(done_filename) if done_filename is not None \
else out_pickle + '.DONE'
logging.debug("Creating %s.", done_filename)
touch(done_filename)
def _test_daligner_against_ref(self,
test_name,
use_sge,
sge_opts,
prob_model_from="fake"):
"""Test daligner_against_ref with and without using sge."""
copy_dir = op.join(self.dataDir, "test_daligner_against_ref")
output_dir = op.join(self.outDir, test_name)
mknewdir(output_dir)
qname, tname = "test_daligner_query.fasta", "test_daligner_target.fasta"
query_filename = op.join(output_dir, qname)
target_filename = op.join(output_dir, tname)
prob_model = None
if prob_model_from == "fake":
prob_model = ProbFromModel(0.01, 0.07, 0.06)
elif prob_model_from == "fastq":
fastq_fn = op.join(copy_dir, "test_daligner_reads.fastq")
prob_model = ProbFromFastq(fastq_fn)
else:
self.assertTrue(False)
qver_get_func = prob_model.get_smoothed
qvmean_get_func = prob_model.get_mean
dummy_o, c, dummy_m = backticks(
"cp %s %s" % (op.join(copy_dir, qname), query_filename))
self.assertTrue(c == 0)
dummy_o, c, dummy_m = backticks(
"cp %s %s" % (op.join(copy_dir, tname), target_filename))
self.assertTrue(c == 0)
old_dir = os.getcwd()
os.chdir(output_dir)
runner = DalignerRunner(query_filename=query_filename,
target_filename=target_filename,
is_FL=True,
same_strand_only=True,
use_sge=use_sge,
sge_opts=sge_opts)
runner.run(output_dir=op.join(self.outDir, test_name))
hits = []
for la4ice_filename in runner.la4ice_filenames:
hits.extend(
daligner_against_ref(
query_dazz_handler=runner.query_dazz_handler,
target_dazz_handler=runner.target_dazz_handler,
la4ice_filename=la4ice_filename,
is_FL=True,
sID_starts_with_c=False,
qver_get_func=qver_get_func,
qvmean_get_func=qvmean_get_func))
# Num of hits may change when daligner or parameters change.
self.assertTrue(len(hits), 706)
self.assertEqual(
str(hits[0]),
"m54007_160109_025449/27984844/29_646_CCS/0_617 aligns to m54007_160109_025449/28836279/631_54_CCS"
)
os.chdir(output_dir)