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 = index_and_validate_bam(config, prefix, scale)
return {output_filename: validate}, plot, model
def _build_raw_bam(self, config, prefix, files_and_bams):
output_filename = os.path.join(self.folder, "%s.%s.bam" % (self.target, prefix["Name"]))
validated_filename = os.path.join(self.folder, self.target, prefix["Name"] + ".validated")
node = MergeSamFilesNode(config = config,
input_bams = files_and_bams.keys(),
output_bam = output_filename,
dependencies = self.datadup_check)
validated_node = index_and_validate_bam(config, prefix, node, validated_filename)
return {output_filename : validated_node}
def _build_raw_bam(self, config, prefix, files_and_bams):
output_filename = os.path.join(
self.folder, "%s.%s.bam" % (self.target, prefix["Name"]))
validated_filename = os.path.join(self.folder, self.target,
prefix["Name"] + ".validated")
node = MergeSamFilesNode(config=config,
input_bams=files_and_bams.keys(),
output_bam=output_filename,
dependencies=self.datadup_check)
validated_node = index_and_validate_bam(config, prefix, node,
validated_filename)
return {output_filename: validated_node}
def _build_realigned_bam(self, config, prefix, bams):
output_filename = os.path.join(self.folder, "%s.%s.realigned.bam" % (self.target, prefix["Name"]))
intervals_filename = os.path.join(self.folder, self.target, prefix["Name"] + ".intervals")
validated_filename = os.path.join(self.folder, self.target, prefix["Name"] + ".realigned.validated")
node = IndelRealignerNode(config = config,
reference = prefix["Reference"],
infiles = bams.keys(),
outfile = output_filename,
intervals = intervals_filename,
dependencies = self.datadup_check)
validated_node = index_and_validate_bam(config, prefix, node, validated_filename)
return {output_filename : validated_node}
def _init_pre_aligned_lane(self, config, prefix, record):
if prefix["Name"] not in record["Data"]:
return
input_filename = record["Data"][prefix["Name"]]
output_filename = os.path.join(self.folder, "processed.bam")
node = CleanupBAMNode(config=config,
reference=prefix["Reference"],
input_bam=input_filename,
output_bam=output_filename,
tags=self.tags,
dependencies=prefix["Node"])
validated_node = index_and_validate_bam(config, prefix, node)
self.bams["Processed"] = {output_filename: validated_node}
def _init_pre_aligned_lane(self, config, prefix, record):
if prefix["Name"] not in record["Data"]:
return
input_filename = record["Data"][prefix["Name"]]
output_filename = os.path.join(self.folder, "processed.bam")
node = CleanupBAMNode(config = config,
reference = prefix["Reference"],
input_bam = input_filename,
output_bam = output_filename,
tags = self.tags,
dependencies = prefix["Node"])
validated_node = index_and_validate_bam(config, prefix, node)
self.bams["Processed"] = {output_filename : validated_node}
def _build_realigned_bam(self, config, prefix, bams):
output_filename = os.path.join(
self.folder, "%s.%s.realigned.bam" % (self.target, prefix["Name"]))
intervals_filename = os.path.join(self.folder, self.target,
prefix["Name"] + ".intervals")
validated_filename = os.path.join(
self.folder, self.target, prefix["Name"] + ".realigned.validated")
node = IndelRealignerNode(config=config,
reference=prefix["Reference"],
infiles=bams.keys(),
outfile=output_filename,
intervals=intervals_filename,
dependencies=self.datadup_check)
validated_node = index_and_validate_bam(config, prefix, node,
validated_filename)
return {output_filename: validated_node}
def _init_unaligned_lane(self, config, prefix, record):
aln_key, aln_func = _select_aligner(record["Options"])
prefix_key = "Node:%s" % (aln_key, )
postfix = [
"minQ%i" % record["Options"]["Aligners"][aln_key]["MinQuality"]
]
if not record["Options"]["Aligners"][aln_key].get("UseSeed", True):
postfix.append("noSeed")
for (key, input_filename) in self.reads.files.iteritems():
# Common parameters between BWA / Bowtie2
output_filename = os.path.join(
self.folder, "%s.%s.bam" % (key.lower(), ".".join(postfix)))
parameters = {
"output_file": output_filename,
"prefix": prefix["Path"],
"reference": prefix["Reference"],
"input_file_1": input_filename,
"input_file_2": None,
"dependencies": self.reads.nodes + (prefix[prefix_key], )
}
if paths.is_paired_end(input_filename):
parameters["input_file_1"] = input_filename.format(Pair=1)
parameters["input_file_2"] = input_filename.format(Pair=2)
alignment_obj = aln_func(config=config,
parameters=parameters,
tags=self.tags,
options=record["Options"])
alignment_opts = record["Options"]["Aligners"][aln_key]
alignment_obj.commands["convert"].set_option(
'-q', alignment_opts["MinQuality"])
if alignment_opts["FilterUnmappedReads"]:
alignment_obj.commands["convert"].set_option('-F', "0x4")
alignment_node = alignment_obj.build_node()
validated_node = index_and_validate_bam(config, prefix,
alignment_node)
self.bams[key] = {output_filename: validated_node}
def _remove_pcr_duplicates(self, config, prefix, bams, strategy):
rmdup_cls = {"collapsed" : FilterCollapsedBAMNode,
"normal" : MarkDuplicatesNode}
keep_duplicates = False
if isinstance(strategy, types.StringTypes) and (strategy.lower() == "mark"):
keep_duplicates = True
results = {}
for (key, files_and_nodes) in bams.items():
output_filename = self.folder + ".rmdup.%s.bam" % key
node = rmdup_cls[key](config = config,
input_bams = files_and_nodes.keys(),
output_bam = output_filename,
keep_dupes = keep_duplicates,
dependencies = files_and_nodes.values())
validated_node = index_and_validate_bam(config, prefix, node)
results[key] = {output_filename : validated_node}
return results
def _remove_pcr_duplicates(self, config, prefix, bams, strategy):
rmdup_cls = {
"collapsed": FilterCollapsedBAMNode,
"normal": MarkDuplicatesNode
}
keep_duplicates = False
if isinstance(strategy, types.StringTypes) and (strategy.lower()
== "mark"):
keep_duplicates = True
results = {}
for (key, files_and_nodes) in bams.items():
output_filename = self.folder + ".rmdup.%s.bam" % key
node = rmdup_cls[key](config=config,
input_bams=files_and_nodes.keys(),
output_bam=output_filename,
keep_dupes=keep_duplicates,
dependencies=files_and_nodes.values())
validated_node = index_and_validate_bam(config, prefix, node)
results[key] = {output_filename: validated_node}
return results
def _init_unaligned_lane(self, config, prefix, record):
aln_key, aln_func = _select_aligner(record["Options"])
prefix_key = "Node:%s" % (aln_key,)
postfix = ["minQ%i" % record["Options"]["Aligners"][aln_key]["MinQuality"]]
if not record["Options"]["Aligners"][aln_key].get("UseSeed", True):
postfix.append("noSeed")
for (key, input_filename) in self.reads.files.iteritems():
# Common parameters between BWA / Bowtie2
output_filename = os.path.join(self.folder, "%s.%s.bam" % (key.lower(), ".".join(postfix)))
parameters = {"output_file" : output_filename,
"prefix" : prefix["Path"],
"reference" : prefix["Reference"],
"input_file_1" : input_filename,
"input_file_2" : None,
"dependencies" : self.reads.nodes + (prefix[prefix_key],)}
if paths.is_paired_end(input_filename):
parameters["input_file_1"] = input_filename.format(Pair = 1)
parameters["input_file_2"] = input_filename.format(Pair = 2)
alignment_obj = aln_func(config = config,
parameters = parameters,
tags = self.tags,
options = record["Options"])
alignment_opts = record["Options"]["Aligners"][aln_key]
alignment_obj.commands["convert"].set_option('-q', alignment_opts["MinQuality"])
if alignment_opts["FilterUnmappedReads"]:
alignment_obj.commands["convert"].set_option('-F', "0x4")
alignment_node = alignment_obj.build_node()
validated_node = index_and_validate_bam(config, prefix, alignment_node)
self.bams[key] = {output_filename : validated_node}
