def _apply_vcf_filter_options(vcffilter, genotyping, sample):
filter_cfg = genotyping["VCF_Filter"]
apply_options(vcffilter.commands["filter"], filter_cfg)
if filter_cfg["MaxReadDepth"][sample]:
max_depth = filter_cfg["MaxReadDepth"][sample]
vcffilter.commands["filter"].set_option("--max-read-depth", max_depth)
return vcffilter.build_node()
def _apply_vcf_filter_options(vcffilter, genotyping, sample):
filter_cfg = genotyping["VCF_Filter"]
apply_options(vcffilter.commands["filter"], filter_cfg)
if filter_cfg["MaxReadDepth"][sample]:
max_depth = filter_cfg["MaxReadDepth"][sample]
vcffilter.commands["filter"].set_option("--max-read-depth", max_depth)
return vcffilter.build_node()
def _rescale_quality_scores(self, config, destination, prefix,
files_and_nodes):
# Generate plot / table files in internal tree in order to prevent the
# user from accidentially messing with them / causing re=runs
md_directory = "%s.mapDamage" % (destination, )
output_filename = destination + ".rescaled.bam"
# Generates basic plots / table files
plot = self._build_mapdamage_plot_node(config, md_directory, prefix,
files_and_nodes)
# Builds model of post-mortem DNA damage
model = MapDamageModelNode.customize(reference=prefix["Reference"],
directory=md_directory,
dependencies=plot)
apply_options(model.command, self.options["mapDamage"])
model = model.build_node()
# Rescales BAM quality scores using model built above
scale = MapDamageRescaleNode.customize(
config=config,
reference=prefix["Reference"],
input_files=files_and_nodes.keys(),
output_file=output_filename,
directory=md_directory,
dependencies=model)
apply_options(scale.command, self.options["mapDamage"])
scale = scale.build_node()
# Grab indexing and validation nodes
validate = index_and_validate_bam(config, prefix, scale)
return {output_filename: validate}, plot, model
def _rescale_quality_scores(self, config, destination, prefix, files_and_nodes):
# Generate plot / table files in internal tree in order to prevent the
# user from accidentially messing with them / causing re=runs
md_directory = "%s.mapDamage" % (destination,)
output_filename = destination + ".rescaled.bam"
# Generates basic plots / table files
plot = self._build_mapdamage_plot_node(config, md_directory, prefix, files_and_nodes)
# Builds model of post-mortem DNA damage
model = MapDamageModelNode.customize(reference = prefix["Reference"],
directory = md_directory,
dependencies = plot)
apply_options(model.command, self.options["mapDamage"])
model = model.build_node()
# Rescales BAM quality scores using model built above
scale = MapDamageRescaleNode.customize(config = config,
reference = prefix["Reference"],
input_files = files_and_nodes.keys(),
output_file = output_filename,
directory = md_directory,
dependencies = model)
apply_options(scale.command, self.options["mapDamage"])
scale = scale.build_node()
# Grab indexing and validation nodes
validate = IndexAndValidateBAMNode(config, prefix, scale).subnodes
node = MetaNode(description = "Rescale Qualities",
subnodes = (plot, model, scale) + tuple(validate),
dependencies = files_and_nodes.values())
return {output_filename : node}, plot, model
def _bowtie2_build_cl_tag(options):
# Build summary of parameters used by alignment, only including
# parameters that affect the output of bowtie2 (as far as possible)
cli_tag = ParamCollector("bowtie2", "...")
apply_options(cli_tag, _bowtie2_aln_parameters(options))
cli_tag = cli_tag.get_result()
_build_mapper_cl_tag(options["Aligners"]["Bowtie2"], cli_tag)
return " ".join(map(str, cli_tag)).replace("%", "%%")
def _bowtie2_build_nodes(config, parameters, tags, options):
params = Bowtie2Node.customize(threads=config.bowtie2_max_threads,
**parameters)
apply_options(params.commands["aln"], _bowtie2_aln_parameters(options))
pg_tags = "bowtie2:CL:%s" % (_bowtie2_build_cl_tag(options), )
_set_rg_tags(params.commands["convert"], tags, pg_tags)
return params
def _build_mapdamage_plot_node(self, config, destination, prefix, files_and_nodes):
plot = MapDamagePlotNode.customize(config = config,
reference = prefix["Path"],
input_files = files_and_nodes.keys(),
output_directory = destination,
title = "mapDamage plot for library %r" % (self.name,),
dependencies = files_and_nodes.values())
apply_options(plot.command, self.options["mapDamage"])
return plot.build_node()
def _bowtie2_build_cl_tag(options):
# Build summary of parameters used by alignment, only including
# parameters that affect the output of bowtie2 (as far as possible)
cli_tag = ParamCollector("bowtie2", "...")
apply_options(cli_tag, _bowtie2_aln_parameters(options))
cli_tag = cli_tag.get_result()
_build_mapper_cl_tag(options["Aligners"]["Bowtie2"], cli_tag)
return " ".join(map(str, cli_tag)).replace("%", "%%")
def _bowtie2_build_nodes(config, parameters, tags, options):
params = Bowtie2Node.customize(threads = config.bowtie2_max_threads,
**parameters)
apply_options(params.commands["aln"], _bowtie2_aln_parameters(options))
pg_tags = "bowtie2:CL:%s" % (_bowtie2_build_cl_tag(options),)
_set_rg_tags(params.commands["convert"], tags, pg_tags)
return params
def test_apply_options__single_option__user_pred__set_when_pred_is_true():
was_called = []
def _user_pred(key):
was_called.append(key)
return True
mock = flexmock()
mock.should_receive('set_option').with_args('FOO_BAR', 17).once()
apply_options(mock, {"FOO_BAR": 17}, _user_pred)
assert_equal(was_called, ["FOO_BAR"])
def _build_mapdamage_plot_node(self, config, destination, prefix,
files_and_nodes):
plot = MapDamagePlotNode.customize(
config=config,
reference=prefix["Path"],
input_files=files_and_nodes.keys(),
output_directory=destination,
title="mapDamage plot for library %r" % (self.name, ),
dependencies=files_and_nodes.values())
apply_options(plot.command, self.options["mapDamage"])
return plot.build_node()
def _bwa_build_cl_tag(options):
# Build summary of parameters used by alignment, only including
# parameters that affect the output of BWA (as far as possible)
algorithm = options["Aligners"]["BWA"]["Algorithm"].lower()
algorithm = "aln" if algorithm == "backtrack" else algorithm
cli_tag = ParamCollector(("bwa", algorithm), "...")
apply_options(cli_tag, _bwa_aln_parameters(options))
cli_tag = cli_tag.get_result()
_build_mapper_cl_tag(options["Aligners"]["BWA"], cli_tag)
return " ".join(map(str, cli_tag)).replace("%", "%%")
def _bwa_build_cl_tag(options):
# Build summary of parameters used by alignment, only including
# parameters that affect the output of BWA (as far as possible)
algorithm = options["Aligners"]["BWA"]["Algorithm"].lower()
algorithm = "aln" if algorithm == "backtrack" else algorithm
cli_tag = ParamCollector(("bwa", algorithm), "...")
apply_options(cli_tag, _bwa_aln_parameters(options))
cli_tag = cli_tag.get_result()
_build_mapper_cl_tag(options["Aligners"]["BWA"], cli_tag)
return " ".join(map(str, cli_tag)).replace("%", "%%")
def build_genotyping_nodes(options, genotyping, taxa, interval, dependencies):
prefix = "{0}.{Genome}.{Name}".format(taxa["Name"], **interval)
reference = os.path.join(options.genomes_root, interval["Genome"] + ".fasta")
fasta = os.path.join(options.destination, "genotypes", prefix + ".fasta")
calls = os.path.join(options.destination, "genotypes", prefix + ".vcf.bgz")
pileups = os.path.join(options.destination, "genotypes", prefix + ".vcf.pileup.bgz")
filtered = os.path.join(options.destination, "genotypes", prefix + ".filtered.vcf.bgz")
padding = genotyping["Padding"]
infile = os.path.join(options.samples_root, "%s.%s.bam" % (taxa["Name"], interval["Genome"]))
slop, node = build_interval_nodes(options, taxa, interval, padding, dependencies)
genotype = GenotypeNode.customize(reference = reference,
regions = slop,
infile = infile,
outfile = calls,
dependencies = node)
apply_options(genotype.commands["pileup"], genotyping.get("MPileup", {}))
apply_options(genotype.commands["genotype"], genotyping.get("BCFTools", {}))
genotype = genotype.build_node()
vcfpileup = VCFPileupNode.customize(reference = reference,
in_bam = infile,
in_vcf = calls,
outfile = pileups,
dependencies = genotype)
apply_options(vcfpileup.commands["pileup"], genotyping.get("MPileup", {}))
vcfpileup = vcfpileup.build_node()
vcffilter = VCFFilterNode.customize(infile = calls,
pileup = pileups,
outfile = filtered,
interval = interval,
dependencies = vcfpileup)
filter_cfg = genotyping.get("VCF_Filter", {})
apply_options(vcffilter.commands["filter"], filter_cfg)
if "MaxReadDepth" in filter_cfg:
max_depth = filter_cfg["MaxReadDepth"]
if isinstance(max_depth, dict):
max_depth = max_depth[taxa["Name"]]
vcffilter.commands["filter"].set_option("--max-read-depth", max_depth)
if filter_cfg.get("Mappability"):
vcffilter.commands["filter"].set_option("--filter-by-mappability", "%(IN_MAPPABILITY)s")
vcffilter.commands["filter"].set_kwargs(IN_MAPPABILITY = filter_cfg["Mappability"])
vcffilter = vcffilter.build_node()
tabix = TabixIndexNode(infile = filtered,
preset = "vcf",
dependencies = vcffilter)
builder = BuildRegionsNode(options = options,
infile = filtered,
interval = interval,
outfile = fasta,
padding = padding,
dependencies = tabix)
return (builder,)
def _init_raw_reads(self, config, record):
version = VERSION_14
if record["Options"]["AdapterRemoval"]["Version"] == "v1.5+":
version = VERSION_15
output_format = record["Options"]["CompressionFormat"]
output_prefix = os.path.join(self.folder, "reads")
files = record["Data"]
if ("SE" in files):
# This returns a named tuple containing the parameters for the AdapterRemoval Node
command = SE_AdapterRemovalNode.customize(
input_files=files["SE"],
output_prefix=output_prefix,
output_format=output_format,
version=version)
self.files[
"Single"] = output_prefix + ".truncated." + output_format
else:
command = PE_AdapterRemovalNode.customize(
input_files_1=files["PE_1"],
input_files_2=files["PE_2"],
output_prefix=output_prefix,
output_format=output_format,
version=version)
self.files[
"Paired"] = output_prefix + ".pair{Pair}.truncated." + output_format
if version is VERSION_14:
self.files[
"Single"] = output_prefix + ".singleton.unaln.truncated." + output_format
self.files[
"Collapsed"] = output_prefix + ".singleton.aln.truncated." + output_format
else:
self.files[
"Single"] = output_prefix + ".singleton.truncated." + output_format
self.files[
"Collapsed"] = output_prefix + ".collapsed." + output_format
self.files[
"CollapsedTruncated"] = output_prefix + ".collapsed.truncated." + output_format
self.stats = output_prefix + ".settings"
quality_offset = self.quality_offset # record["Options"]["QualityOffset"]
if quality_offset == "Solexa":
quality_offset = 64
command.command.set_option("--qualitybase", quality_offset)
apply_options(command.command, record["Options"]["AdapterRemoval"])
self.nodes = (command.build_node(), )
def _bwa_build_nodes(config, parameters, tags, options):
algorithm = options["Aligners"]["BWA"]["Algorithm"].lower()
params = BWANode(threads=config.bwa_max_threads,
algorithm=algorithm,
**parameters)
parameters = dict(_bwa_aln_parameters(options))
# "aln" is used by SE backtrack, mem, and sw; _1 and _2 by PE backtrack
for aln_key in ("aln", "aln_1", "aln_2"):
if aln_key in params.commands:
apply_options(params.commands[aln_key], parameters)
pg_tags = "bwa:CL:%s" % (_bwa_build_cl_tag(options), )
_set_rg_tags(params.commands["convert"], tags, pg_tags)
return params
def _bwa_build_nodes(config, parameters, tags, options):
algorithm = options["Aligners"]["BWA"]["Algorithm"].lower()
params = BWANode(threads=config.bwa_max_threads,
algorithm=algorithm,
**parameters)
parameters = dict(_bwa_aln_parameters(options))
# "aln" is used by SE backtrack, mem, and sw; _1 and _2 by PE backtrack
for aln_key in ("aln", "aln_1", "aln_2"):
if aln_key in params.commands:
apply_options(params.commands[aln_key], parameters)
pg_tags = "bwa:CL:%s" % (_bwa_build_cl_tag(options),)
_set_rg_tags(params.commands["convert"], tags, pg_tags)
return params
def _init_raw_reads(self, record):
# Support for older versions of the pipeline, which used ARv1.0 - 1.4
version = VERSION_14
if record["Options"]["AdapterRemoval"]["Version"] == "v1.5+":
version = VERSION_15
quality_offset = self.quality_offset
if quality_offset == "Solexa":
quality_offset = 64
ar_options = dict(record["Options"]["AdapterRemoval"])
# Setup of "--collapsed" is handled by the node itself
collapse_reads = ar_options.pop("--collapse")
collapse_reads = collapse_reads or collapse_reads is None
init_args = {
"output_prefix": os.path.join(self.folder, "reads"),
"output_format": record["Options"]["CompressionFormat"],
"quality_offset": quality_offset,
"version": version
}
output_tmpl = "{output_prefix}.%s.{output_format}".format(**init_args)
if ("SE" in record["Data"]):
self.files["Single"] = output_tmpl % ("truncated", )
init_args["input_files"] = record["Data"]["SE"]
command = SE_AdapterRemovalNode.customize(**init_args)
else:
if version is VERSION_14:
self._set_adapterrm_v14_files(self.files, output_tmpl)
else:
self._set_adapterrm_v15_files(self.files, output_tmpl,
collapse_reads)
init_args["collapse"] = collapse_reads
init_args["input_files_1"] = record["Data"]["PE_1"]
init_args["input_files_2"] = record["Data"]["PE_2"]
command = PE_AdapterRemovalNode.customize(**init_args)
apply_options(command.command, ar_options)
self.stats = os.path.join(self.folder, "reads.settings")
self.nodes = (command.build_node(), )
def build_msa_nodes(options, settings, regions, filtering, dependencies):
if settings["Program"].lower() != "mafft":
raise RuntimeError("Only MAFFT support has been implemented!")
sequencedir = os.path.join(options.destination, "alignments",
regions["Name"])
# Run on full set of sequences
sequences = regions["Sequences"][None]
node = CollectSequencesNode(fasta_files=regions["Genotypes"],
destination=sequencedir,
sequences=sequences,
dependencies=dependencies)
fasta_files = dict((filename, node) for filename in node.output_files)
if settings["Enabled"]:
fasta_files = {}
algorithm = settings["MAFFT"]["Algorithm"]
for sequence in sequences:
input_file = os.path.join(sequencedir, sequence + ".fasta")
output_file = os.path.join(sequencedir, sequence + ".afa")
mafft = MAFFTNode.customize(input_file=input_file,
output_file=output_file,
algorithm=algorithm,
dependencies=node)
apply_options(mafft.command, settings["MAFFT"])
fasta_files[output_file] = mafft.build_node()
node = MetaNode(description="MAFFT",
subnodes=fasta_files.values(),
dependencies=node)
if any(filtering.itervalues()):
node = FilterSingletonsMetaNode(input_files=fasta_files,
destination=sequencedir + ".filtered",
filter_by=filtering,
dependencies=node)
return node
def build_msa_nodes(options, settings, regions, filtering, dependencies):
if settings["Program"].lower() != "mafft":
raise RuntimeError("Only MAFFT support has been implemented!")
sequencedir = os.path.join(options.destination, "alignments", regions["Name"])
# Run on full set of sequences
sequences = regions["Sequences"][None]
node = CollectSequencesNode(fasta_files = regions["Genotypes"],
destination = sequencedir,
sequences = sequences,
dependencies = dependencies)
fasta_files = dict((filename, node) for filename in node.output_files)
if settings["Enabled"]:
fasta_files = {}
algorithm = settings["MAFFT"]["Algorithm"]
for sequence in sequences:
input_file = os.path.join(sequencedir, sequence + ".fasta")
output_file = os.path.join(sequencedir, sequence + ".afa")
mafft = MAFFTNode.customize(input_file = input_file,
output_file = output_file,
algorithm = algorithm,
dependencies = node)
apply_options(mafft.command, settings["MAFFT"])
fasta_files[output_file] = mafft.build_node()
node = MetaNode(description = "MAFFT",
subnodes = fasta_files.values(),
dependencies = node)
if any(filtering.itervalues()):
node = FilterSingletonsMetaNode(input_files = fasta_files,
destination = sequencedir + ".filtered",
filter_by = filtering,
dependencies = node)
return node
def _init_raw_reads(self, config, record):
version = VERSION_14
if record["Options"]["AdapterRemoval"]["Version"] == "v1.5+":
version = VERSION_15
output_format = record["Options"]["CompressionFormat"]
output_prefix = os.path.join(self.folder, "reads")
files = record["Data"]
if ("SE" in files):
# This returns a named tuple containing the parameters for the AdapterRemoval Node
command = SE_AdapterRemovalNode.customize(input_files = files["SE"],
output_prefix = output_prefix,
output_format = output_format,
version = version)
self.files["Single"] = output_prefix + ".truncated." + output_format
else:
command = PE_AdapterRemovalNode.customize(input_files_1 = files["PE_1"],
input_files_2 = files["PE_2"],
output_prefix = output_prefix,
output_format = output_format,
version = version)
self.files["Paired"] = output_prefix + ".pair{Pair}.truncated." + output_format
if version is VERSION_14:
self.files["Single"] = output_prefix + ".singleton.unaln.truncated." + output_format
self.files["Collapsed"] = output_prefix + ".singleton.aln.truncated." + output_format
else:
self.files["Single"] = output_prefix + ".singleton.truncated." + output_format
self.files["Collapsed"] = output_prefix + ".collapsed." + output_format
self.files["CollapsedTruncated"] = output_prefix + ".collapsed.truncated." + output_format
self.stats = output_prefix + ".settings"
quality_offset = self.quality_offset # record["Options"]["QualityOffset"]
if quality_offset == "Solexa":
quality_offset = 64
command.command.set_option("--qualitybase", quality_offset)
apply_options(command.command, record["Options"]["AdapterRemoval"])
self.nodes = (command.build_node(),)
def test_apply_options__single_option__user_pred__set_when_pred_is_true():
mock = flexmock()
mock.should_receive('set_option').with_args('FOO_BAR', 17).once()
apply_options(mock, {"FOO_BAR": 17}, _user_pred)
def test_apply_options__single_option__user_pred__ignore_when_pred_is_false():
mock = flexmock()
apply_options(mock, {"BAR_FOO": 17}, _user_pred)
def test_apply_options__single_option__default_pred__set_when_pred_is_true():
mock = flexmock()
mock.should_receive('set_option').with_args('--foo', 17).once()
apply_options(mock, {"--foo": 17})
def test_apply_options__single_option__default_pred__ignore_when_pred_is_false(
):
mock = flexmock()
apply_options(mock, {"Other": None})
def test_apply_options__single_option__user_pred__ignore_when_pred_is_false():
mock = flexmock()
apply_options(mock, {"BAR_FOO" : 17}, _user_pred)
def test_apply_options__single_option__boolean__set_when_value_is_true():
mock = flexmock()
mock.should_receive("set_option").with_args("-v")
apply_options(mock, {"-v": True})
def test_apply_options__single_option__default_pred__set_when_pred_is_true():
mock = flexmock()
mock.should_receive('set_option').with_args('--foo', 17).once()
apply_options(mock, {"--foo" : 17})
def test_apply_options__multiple_option():
mock = flexmock()
mock.should_receive('add_option').with_args('--foo', 3).once()
mock.should_receive('add_option').with_args('--foo', 17).once()
apply_options(mock, {"--foo": [3, 17]})
def _apply_aln_user_parameters(mkfile_params, params, aligners):
for aligner_key in aligners:
apply_options(params.commands[aligner_key], mkfile_params)
def build_genotyping_nodes(options, genotyping, taxa, interval, dependencies):
prefix = "{0}.{Genome}.{Name}".format(taxa["Name"], **interval)
reference = os.path.join(options.genomes_root,
interval["Genome"] + ".fasta")
fasta = os.path.join(options.destination, "genotypes", prefix + ".fasta")
calls = os.path.join(options.destination, "genotypes", prefix + ".vcf.bgz")
pileups = os.path.join(options.destination, "genotypes",
prefix + ".vcf.pileup.bgz")
filtered = os.path.join(options.destination, "genotypes",
prefix + ".filtered.vcf.bgz")
padding = genotyping["Padding"]
infile = os.path.join(options.samples_root,
"%s.%s.bam" % (taxa["Name"], interval["Genome"]))
slop, node = build_interval_nodes(options, taxa, interval, padding,
dependencies)
genotype = GenotypeNode.customize(reference=reference,
regions=slop,
infile=infile,
outfile=calls,
dependencies=node)
apply_options(genotype.commands["pileup"], genotyping.get("MPileup", {}))
apply_options(genotype.commands["genotype"],
genotyping.get("BCFTools", {}))
genotype = genotype.build_node()
vcfpileup = VCFPileupNode.customize(reference=reference,
in_bam=infile,
in_vcf=calls,
outfile=pileups,
dependencies=genotype)
apply_options(vcfpileup.commands["pileup"], genotyping.get("MPileup", {}))
vcfpileup = vcfpileup.build_node()
vcffilter = VCFFilterNode.customize(infile=calls,
pileup=pileups,
outfile=filtered,
interval=interval,
dependencies=vcfpileup)
filter_cfg = genotyping.get("VCF_Filter", {})
apply_options(vcffilter.commands["filter"], filter_cfg)
if "MaxReadDepth" in filter_cfg:
max_depth = filter_cfg["MaxReadDepth"]
if isinstance(max_depth, dict):
max_depth = max_depth[taxa["Name"]]
vcffilter.commands["filter"].set_option("--max-read-depth", max_depth)
if filter_cfg.get("Mappability"):
vcffilter.commands["filter"].set_option("--filter-by-mappability",
"%(IN_MAPPABILITY)s")
vcffilter.commands["filter"].set_kwargs(
IN_MAPPABILITY=filter_cfg["Mappability"])
vcffilter = vcffilter.build_node()
tabix = TabixIndexNode(infile=filtered,
preset="vcf",
dependencies=vcffilter)
builder = BuildRegionsNode(options=options,
infile=filtered,
interval=interval,
outfile=fasta,
padding=padding,
dependencies=tabix)
return (builder, )
def test_apply_options__multiple_option():
mock = flexmock()
mock.should_receive('add_option').with_args('--foo', 3).once()
mock.should_receive('add_option').with_args('--foo', 17).once()
apply_options(mock, {"--foo" : [3, 17]})
def test_apply_options__single_option__boolean__pop_when_value_is_false():
mock = flexmock()
mock.should_receive('pop_option').with_args('-v')
apply_options(mock, {"-v" : False})
def test_apply_options__single_option__boolean__set_when_value_is_none():
mock = flexmock()
mock.should_receive('set_option').with_args('-v')
apply_options(mock, {"-v" : None})
def test_apply_options__single_option__boolean__set_when_value_is_none():
mock = flexmock()
mock.should_receive('set_option').with_args('-v')
apply_options(mock, {"-v": None})
def test_apply_options__single_option__user_pred__ignore_when_pred_is_false():
def _user_pred(key):
return key.startswith("FOO")
mock = flexmock()
apply_options(mock, {"BAR_FOO": 17})
def test_apply_options__single_option__boolean__pop_when_value_is_false():
mock = flexmock()
mock.should_receive('pop_option').with_args('-v')
apply_options(mock, {"-v": False})
def _apply_aln_user_parameters(mkfile_params, params, aligners):
for aligner_key in aligners:
apply_options(params.commands[aligner_key], mkfile_params)
def test_apply_options__single_option__default_pred__ignore_when_pred_is_false():
mock = flexmock()
apply_options(mock, {"Other" : None})
def test_apply_options__single_option__user_pred__set_when_pred_is_true():
mock = flexmock()
mock.should_receive('set_option').with_args('FOO_BAR', 17).once()
apply_options(mock, {"FOO_BAR" : 17}, _user_pred)
