def chainSamFileJobFunction(job, config, aln_struct):
# Cull the files from the job store that we want
if config["chain"] is None and config["realign"] is None:
job.fileStore.logToMaster("[chainSamFileJobFunction]Nothing to do.")
return
if config["chain"] is not None:
sam_file = job.fileStore.readGlobalFile(aln_struct.FileStoreID())
reference = job.fileStore.readGlobalFile(config["reference_FileStoreID"])
reads = job.fileStore.readGlobalFile(config["sample_FileStoreID"])
workdir = job.fileStore.getLocalTempDir()
output_sam = LocalFile(workdir=workdir, filename="{}_chained.bam".format(config["sample_label"]))
if config["debug"]:
job.fileStore.logToMaster("[chainSamFileJobFunction] chaining {bwa_out} (locally: {sam})"
"".format(bwa_out=aln_struct.FileStoreID(), sam=sam_file))
chainSamFile(parent_job=job,
samFile=sam_file,
outputSamFile=output_sam.fullpathGetter(),
readFastqFile=reads,
referenceFastaFile=reference)
chainedSamFileId = job.fileStore.writeGlobalFile(output_sam.fullpathGetter())
deliverOutput(job, output_sam, config["output_dir"])
job.addFollowOnJobFn(realignmentRootJobFunction, config, chainedSamFileId)
else:
job.fileStore.logToMaster("[chainSamFileJobFunction]Not chaining SAM, passing alignment "
"on to realignment")
job.addFollowOnJobFn(realignmentRootJobFunction, config, aln_struct.FileStoreID())
def makeNanoporeRead(f5_path):
# here we load the NanoporeRead and write it to a file
np = NanoporeRead(fast_five_file=f5_path, twoD=False) # make this a config arg
ok = np.Initialize(job)
if not ok:
return None
_l = np.read_label
tF = job.fileStore.getLocalTempFile()
fH = open(tF, "w")
ok = np.Write(job, fH, initialize=False)
if not ok:
fH.close()
return None
fH.close()
# then we gzip it and deliver it to the readstore and return the ledger line
fn = LocalFile(workdir=workdir, filename="%s.np.gz" % _l)
fH = open(tF, "rb")
gz = gzip.open(fn.fullpathGetter(), "wb")
shutil.copyfileobj(fH, gz)
fH.close()
gz.close()
try:
deliverOutput(job, fn, readstore_dir)
except RuntimeError:
job.fileStore.logToMaster("[makeNanoporeReadsJobFunction]Read %s failed to upload" % _l)
return None
return (_l, "%s%s\n" % (readstore_dir, fn.filenameGetter()))
def deliverLedgerJobFunction(job, config, ledger_fids):
fHs = [open(job.fileStore.readGlobalFile(f), "r") for f in ledger_fids]
ls = [pickle.load(f) for f in fHs]
ledger = ls[0]
[ledger.update(d) for d in ls[1:]]
fn = LocalFile(workdir=job.fileStore.getLocalTempDir(), filename="%s_ledger.pkl" % config["ledger_name"])
_h = open(fn.fullpathGetter(), "w")
pickle.dump(ledger, _h)
_h.close()
deliverOutput(job, fn, config["readstore_ledger_dir"])
def consolidateMethylationCallsJobFunction(job, config, methylation_prob_fids):
outfile = LocalFile(workdir=job.fileStore.getLocalTempDir(),
filename="%s_%s.tsv" %
(config["sample_label"], config["degenerate"]))
_handle = open(outfile.fullpathGetter(), "w")
files = fileinput.input(
[job.fileStore.readGlobalFile(fid) for fid in methylation_prob_fids])
map(lambda l: _handle.write(l), files)
files.close()
_handle.close()
deliverOutput(job, outfile, config["output_dir"])
return
def _sumExpectationsOverColumns():
f = LocalFile(workdir=workdir)
_h = open(f.fullpathGetter(), "w")
for pos, pos_df in aligned_pairs.groupby(["ref_pos"]):
for base, base_df in pos_df.groupby("base"):
marginal_prob = base_df["posterior"].sum()
coverage = len(base_df["read_label"].unique())
l = "%s\t%s\t%s\t%s\t%s\n" % (cPecan_config["contig_name"],
pos, base, marginal_prob,
coverage)
_h.write(l)
_h.close()
return f
def consolidateVariantCallsJobFunction(job, config, posterior_prob_fids):
variants = getVariantCallFunctions(config["degenerate"])
parser = variants.parseVariantCalls
file_iter = (job.fileStore.readGlobalFile(fid)
for fid in posterior_prob_fids)
table = pd.concat([parser(f)
for f in file_iter]).sort_values(["contig", "ref_pos"])
outfile = LocalFile(workdir=job.fileStore.getLocalTempDir(),
filename="%s_%s.tsv" %
(config["sample_label"], config["degenerate"]))
_handle = open(outfile.fullpathGetter(), "w")
variants.writeVariantCalls(table, _handle)
_handle.close()
deliverOutput(job, outfile, config["output_dir"])
def processReferenceSequence(ref_seq,
workdir,
motif_key=None,
sub_char="X",
parent_job=None):
# make the forward and backward sequences, substituting the necessary motifs
if motif_key is not None:
motif, ok = getMotif(motif_key, ref_seq)
require(
ok,
"[processReferenceSequence]Illegal motif_key given %s" % motif_key)
if parent_job is not None:
parent_job.fileStore.logToMaster(
"[processReferenceSequence]Made %s substitutions" %
motif.substitutionPositionCount())
try:
fw_refseq = motif.forwardSubstitutedSequence(sub_char)
bw_refseq = motif.complementSubstitutedSequence(sub_char)
except AssertionError:
return None, None, False
else:
fw_refseq = ref_seq.upper()
bw_refseq = _reverseComplement(fw_refseq,
reverse=False,
complement=True)
fw_refseqfile = LocalFile(workdir=workdir)
bw_refseqfile = LocalFile(workdir=workdir)
sequences = [fw_refseq, bw_refseq]
sequence_files = [fw_refseqfile, bw_refseqfile]
for f, s in zip(sequence_files, sequences):
_h = open(f.fullpathGetter(), "w")
_h.write(s + "\n")
_h.close()
[
require(os.path.exists(f.fullpathGetter()),
"[processReferenceSequence]Missing %s" % f.filenameGetter())
for f in sequence_files
]
return fw_refseqfile, bw_refseqfile, True
def signalAlignRootJobFunction(job, config, sample):
# download the reference
config["reference_FileStoreID"] = job.addChildJobFn(urlDownlodJobFunction,
config["ref"],
disk=config["ref_size"]).rv()
# download the BAM, and shard by region
alignment_fid = job.addChildJobFn(urlDownlodJobFunction, sample.URL, disk=sample.size).rv()
# download the models
config["HMM_fid"] = job.addChildJobFn(urlDownlodJobFunction, config["HMM_file"], disk="10M").rv()
config["HDP_fid"] = job.addChildJobFn(urlDownlodJobFunction, config["HDP_file"], disk="250M").rv()
# setup labels
config["sample_label"] = sample.sample_label
# download and load the ledger
# TODO use new function here
ledger = LocalFile(workdir=job.fileStore.getLocalTempDir(), filename="%s.tmp" % uuid.uuid4().hex)
urlDownload(job, config["ledger_url"], ledger)
config["ledger"] = cPickle.load(open(ledger.fullpathGetter(), "r"))
job.addFollowOnJobFn(shardAlignmentJobNode, config, alignment_fid)
def prepareFast5Tarfile(job, split_tars_bigger_than_this, batchsize, download_slots, part_size, rs_sample):
job.fileStore.logToMaster("[prepareFast5Tarfile]Working on sample %s" % rs_sample.sample_label)
workdir = job.fileStore.getLocalTempDir()
archive = LocalFile(workdir=workdir, filename="%s.tar" % uuid.uuid4().hex)
urlDownload(job, rs_sample.URL, archive, download_slots=str(download_slots), part_size=str(part_size))
_handle = tarfile.open(archive.fullpathGetter(), "r")
members = _handle.getmembers()[1:] # the first member is often just the directory with the fast5s
paths = [os.path.join(workdir, m.name) for m in members]
_handle.extractall(path=workdir)
if rs_sample.size >= split_tars_bigger_than_this:
_iter = [paths[i:i + batchsize] for i in range(0, len(paths), batchsize)]
tar_fids = [archiveBatchAndUploadToFileStore(job, b, workdir) for b in _iter]
_handle.close()
job.fileStore.logToMaster("[prepareFast5Tarfile]Split %s into %s smaller tars"
% (rs_sample.sample_label, len(tar_fids)))
return tar_fids
else:
tar_fid = archiveBatchAndUploadToFileStore(job, paths, workdir)
_handle.close()
return [tar_fid]
def _SignalMachine(read_label, cigar, nanopore_read):
guide_aln = LocalFile(workdir=workdir)
_handle = open(guide_aln.fullpathGetter(), "w")
_handle.write(cigar)
_handle.close()
require(os.path.exists(guide_aln.fullpathGetter()),
"NO guide aln file")
signalMachine_args = [
"--sm3Hdp",
"-s",
"1",
"-o",
"%s" % degenerate_enum,
"-L",
"%s" % read_label,
"-T",
"%s%s" % (DOCKER_DIR, models.localFileName(hmmfid)),
"-q",
"%s%s" % (DOCKER_DIR, nanopore_read.filenameGetter()),
"-f",
"%s%s" % (DOCKER_DIR, fw_seqfile.filenameGetter()),
"-b",
"%s%s" % (DOCKER_DIR, bw_seqfile.filenameGetter()),
"-p",
"%s%s" % (DOCKER_DIR, guide_aln.filenameGetter()),
"-u",
"%s%s" % (DOCKER_DIR, posteriors.filenameGetter()),
"-v",
"%s%s" % (DOCKER_DIR, models.localFileName(hdpfid)),
]
try:
docker_call(job=job,
tool=signalMachine_image,
parameters=signalMachine_args,
work_dir=(workdir + "/"))
except subprocess.CalledProcessError:
pass
def getFastqFromBam(job,
bam_sample,
samtools_image="quay.io/ucsc_cgl/samtools"):
# n.b. this is NOT a jobFunctionWrappingJob, it just takes the parent job as
# an argument to have access to the job store
# download the BAM to the local directory, use a uid to aviod conflicts
uid = uuid.uuid4().hex
work_dir = job.fileStore.getLocalTempDir()
local_bam = LocalFile(workdir=work_dir, filename="bam_{}.bam".format(uid))
fastq_reads = LocalFile(workdir=work_dir,
filename="fastq_reads{}.fq".format(uid))
urlDownload(parent_job=job,
source_url=bam_sample.URL,
destination_file=local_bam)
require(not os.path.exists(fastq_reads.fullpathGetter()),
"[getFastqFromBam]fastq file already exists")
# run samtools to get the reads from the BAM
# TODO use DOCKER_DIR and clean this up. idea: make globls.py or something
samtools_parameters = [
"fastq", "/data/{}".format(local_bam.filenameGetter())
]
with open(fastq_reads.fullpathGetter(), 'w') as fH:
docker_call(job=job,
tool=samtools_image,
parameters=samtools_parameters,
work_dir=work_dir,
outfile=fH)
require(os.path.exists(fastq_reads.fullpathGetter()),
"[getFastqFromBam]didn't generate reads")
# upload fastq to fileStore
return job.fileStore.writeGlobalFile(fastq_reads.fullpathGetter())
def calculateMethylationProbabilityJobFunction(
job,
config,
cPecan_config,
ignore_hmm,
batch_number,
signalMachine_image="quay.io/artrand/signalmachine"):
def _get_url(read_label):
try:
return ledger[read_label]
except KeyError:
return None
def _SignalMachine(read_label, cigar, nanopore_read):
guide_aln = LocalFile(workdir=workdir)
_handle = open(guide_aln.fullpathGetter(), "w")
_handle.write(cigar)
_handle.close()
require(os.path.exists(guide_aln.fullpathGetter()),
"NO guide aln file")
signalMachine_args = [
"--sm3Hdp",
"-s",
"1",
"-o",
"%s" % degenerate_enum,
"-L",
"%s" % read_label,
"-T",
"%s%s" % (DOCKER_DIR, models.localFileName(hmmfid)),
"-q",
"%s%s" % (DOCKER_DIR, nanopore_read.filenameGetter()),
"-f",
"%s%s" % (DOCKER_DIR, fw_seqfile.filenameGetter()),
"-b",
"%s%s" % (DOCKER_DIR, bw_seqfile.filenameGetter()),
"-p",
"%s%s" % (DOCKER_DIR, guide_aln.filenameGetter()),
"-u",
"%s%s" % (DOCKER_DIR, posteriors.filenameGetter()),
"-v",
"%s%s" % (DOCKER_DIR, models.localFileName(hdpfid)),
]
try:
docker_call(job=job,
tool=signalMachine_image,
parameters=signalMachine_args,
work_dir=(workdir + "/"))
except subprocess.CalledProcessError:
pass
def _parse_probabilities():
return pd.read_table(
posteriors.fullpathGetter(),
usecols=(1, 2, 3, 6),
names=["ref_pos", "base", "posterior", "read_label"],
dtype={
"ref_pos": np.int,
"base": np.str,
"posterior": np.float64,
"read_label": np.str
})
def _sumExpectationsOverColumns():
f = LocalFile(workdir=workdir)
_h = open(f.fullpathGetter(), "w")
for pos, pos_df in aligned_pairs.groupby(["ref_pos"]):
for base, base_df in pos_df.groupby("base"):
marginal_prob = base_df["posterior"].sum()
coverage = len(base_df["read_label"].unique())
l = "%s\t%s\t%s\t%s\t%s\n" % (cPecan_config["contig_name"],
pos, base, marginal_prob,
coverage)
_h.write(l)
_h.close()
return f
job.fileStore.logToMaster(
"[calculateMethylationProbabilityJobFunction]Running on batch %s" %
batch_number)
workdir = job.fileStore.getLocalTempDir()
fw_seqfile, bw_seqfile, ok = processReferenceSequence(
cPecan_config["contig_seq"], workdir, config["motif_key"],
config["substitute_char"])
if not ok:
raise RuntimeError(
"[calculateMethylationProbabilityJobFunction]ERROR processing reference sequences"
)
# get the models
hmmfid = config["HMM_fid"]
hdpfid = config["HDP_fid"]
try:
models = LocalFileManager(job=job,
fileIds_to_get=[hmmfid, hdpfid],
workdir=workdir)
except AssertionError:
raise RuntimeError(
"[calculateMethylationProbabilityJobFunction]ERROR getting models locally"
)
# download the npRead files
ledger = config["ledger"]
url_iter = (_get_url(l.strip()) for l in cPecan_config["query_labels"])
read_urls = [u for u in url_iter if u is not None]
if config["debug"]:
job.fileStore.logToMaster(
"[calculateMethylationProbabilityJobFunction]Got %s URLs" %
len(read_urls))
npReads = [
unzipLocalFile(f) for f in
[urlDownloadToLocalFile(job, workdir, url) for url in read_urls]
if f is not None
]
failed = len(read_urls) - len(npReads)
if failed > 0 and config["stop_at_failed_reads"]:
raise RuntimeError(
"[calculateMethylationProbabilityJobFunction]Got %s failed npRead"
"downloads and stop_at_failed_reads is True" % failed)
else:
if config["debug"]:
job.fileStore.logToMaster(
"[calculateMethylationProbabilityJobFunction]"
"Failed to download and upzip %s NanoporeReads" % failed)
# file to collect the posterior probs
posteriors = LocalFile(workdir=workdir,
filename="%s_%s.dat" %
(config["sample_label"], uuid.uuid4()))
degenerate_enum = getVariantCallFunctions(config["degenerate"]).enum()
# do the signal alignment, and get the posterior probabilities
map(
lambda (l, c, n): _SignalMachine(l.strip(), c, n),
zip(cPecan_config["query_labels"], cPecan_config["exonerate_cigars"],
npReads))
# the reads may not produce any posteriors, if, for example, they don't align to a region where
# there are any ambiguity characters the posteriors file will be empty and we just return
# None, which is the convention
if not os.path.exists(posteriors.fullpathGetter()) or os.stat(
posteriors.fullpathGetter()).st_size == 0:
return None
# reminder: the convention is that 'expectations' are un-normalized posterior probabilities
# so this file is a table of expectatiosn, I also use the convention that the trailing
# underscore means `file` or `file-path`
aligned_pairs = _parse_probabilities()
expectations_ = _sumExpectationsOverColumns()
if config["probs_output_dir"] is not None:
deliverOutput(job, posteriors, config["probs_output_dir"])
return job.fileStore.writeGlobalFile(expectations_.fullpathGetter())
