def main():
# Parse command-line arguments
parser = ArgumentParser(
prog="hic-pipeline",
description="Hi-C pipeline."
)
parser = arg_parser(parser)
parser = pypiper.add_pypiper_args(parser, groups=["ngs", "looper", "resource", "pypiper"])
args = parser.parse_args()
# Read in yaml configs
series = pd.Series(yaml.load(open(args.sample_config, "r")))
# looper 0.6/0.7 compatibility:
if "protocol" in series.index:
key = "protocol"
elif "library" in series.index:
key = "library"
else:
raise KeyError(
"Sample does not contain either a 'protocol' or 'library' attribute!")
# Create Sample object
if series[key] != "HiChIP":
sample = HiCSample(series)
else:
sample = HiChIPSample(series)
# Check if merged
if len(sample.data_path.split(" ")) > 1:
sample.merged = True
else:
sample.merged = False
sample.prj = AttributeDict(sample.prj)
sample.paths = AttributeDict(sample.paths.__dict__)
# Check read type if not provided
if not hasattr(sample, "ngs_inputs"):
sample.ngs_inputs = [sample.data_source]
if not hasattr(sample, "read_type"):
sample.set_read_type()
# Shorthand for read_type
if sample.read_type == "paired":
sample.paired = True
else:
sample.paired = False
# Set file paths
sample.set_file_paths()
# sample.make_sample_dirs() # should be fixed to check if values of paths are strings and paths indeed
# Start Pypiper object
# Best practice is to name the pipeline with the name of the script;
# or put the name in the pipeline interface.
pipe_manager = pypiper.PipelineManager(name="hic", outfolder=sample.paths.sample_root, args=args)
pipe_manager.config.tools.scripts_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "tools")
# Start main function
process(sample, pipe_manager, args)
def test_no_default_env_settings_provided(
self, minimal_project_conf_path,
explicit_null, compute_env_attname):
""" Project doesn't require default environment settings. """
kwargs = {"default_compute": None} if explicit_null else {}
project = Project(minimal_project_conf_path, **kwargs)
observed_attribute = getattr(project, compute_env_attname)
expected_attribute = \
self.default_compute_settings(project)[compute_env_attname]
if compute_env_attname == "compute":
# 'compute' refers to a section in the default environment
# settings file and also to a Project attribute. A Project
# instance selects just one of the options in the 'compute'
# section of the file as the value for its 'compute' attribute.
expected_attribute = expected_attribute["default"]
observed_attribute = _compute_paths_to_names(observed_attribute)
elif compute_env_attname == "environment":
envs_with_reduced_filepaths = \
_env_paths_to_names(observed_attribute["compute"])
observed_attribute = AttributeDict(
{"compute": envs_with_reduced_filepaths})
assert expected_attribute == observed_attribute
def main():
# Parse command-line arguments
parser = ArgumentParser(prog="rnaseq-pipeline",
description="RNA-seq pipeline.")
parser = arg_parser(parser)
parser = pypiper.add_pypiper_args(parser, groups=["all"])
args = parser.parse_args()
# Read in yaml configs
sample = RNASeqSample(pd.Series(yaml.load(open(args.sample_config, "r"))))
# Check if merged
if len(sample.data_path.split(" ")) > 1:
sample.merged = True
else:
sample.merged = False
sample.prj = AttributeDict(sample.prj)
sample.paths = AttributeDict(sample.paths.__dict__)
# Check read type if not provided
if not hasattr(sample, "ngs_inputs"):
sample.ngs_inputs = [sample.data_source]
if not hasattr(sample, "read_type"):
sample.set_read_type()
# Shorthand for read_type
if sample.read_type == "paired":
sample.paired = True
else:
sample.paired = False
# Set file paths
sample.set_file_paths()
# sample.make_sample_dirs() # should be fixed to check if values of paths are strings and paths indeed
# Start Pypiper object
# Best practice is to name the pipeline with the name of the script;
# or put the name in the pipeline interface.
pipe_manager = pypiper.PipelineManager(name="rnaseq",
outfolder=sample.paths.sample_root,
args=args)
pipe_manager.config.tools.scripts_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "tools")
# Start main function
process(sample, pipe_manager, args)
def main():
# Parse command-line arguments
parser = ArgumentParser(prog="dropseq-pipeline",
description="Drop-seq pipeline.")
parser = arg_parser(parser)
parser = pypiper.add_pypiper_args(parser, groups=["all"])
args = parser.parse_args()
if args.sample_config is None:
parser.print_help()
return 1
# Read in yaml configs
sample = AttributeDict(yaml.load(open(args.sample_config, "r")))
pipeline_config = AttributeDict(
yaml.load(
open(os.path.join(os.path.dirname(__file__), args.config_file),
"r")))
# Start main function
process(sample, pipeline_config, args)
def main():
# Parse command-line arguments
parser = ArgumentParser(prog="chipseq-pipeline",
description="ChIP-seq pipeline.")
parser = arg_parser(parser)
parser = pypiper.add_pypiper_args(parser, groups=["all"])
args = parser.parse_args()
if args.sample_config is None:
parser.print_help()
return 1
# Read in yaml configs
series = pd.Series(yaml.load(open(args.sample_config, "r")))
# looper 0.6/0.7 compatibility:
if "protocol" in series.index:
key = "protocol"
elif "library" in series.index:
key = "library"
else:
raise KeyError(
"Sample does not contain either a 'protocol' or 'library' attribute!"
)
# Create Sample object
if series[key] != "ChIPmentation":
sample = ChIPseqSample(series)
else:
sample = ChIPmentation(series)
# Check if merged
if len(sample.data_path.split(" ")) > 1:
sample.merged = True
else:
sample.merged = False
sample.prj = AttributeDict(sample.prj)
sample.paths = AttributeDict(sample.paths.__dict__)
# Check read type if not provided
if not hasattr(sample, "ngs_inputs"):
sample.ngs_inputs = [sample.data_source]
if not hasattr(sample, "read_type"):
sample.set_read_type()
# Shorthand for read_type
if sample.read_type == "paired":
sample.paired = True
else:
sample.paired = False
# Set file paths
sample.set_file_paths()
# sample.make_sample_dirs() # should be fixed to check if values of paths are strings and paths indeed
# Start Pypiper object
# Best practice is to name the pipeline with the name of the script;
# or put the name in the pipeline interface.
pipe_manager = pypiper.PipelineManager(name="chipseq",
outfolder=sample.paths.sample_root,
args=args)
# Start main function
if not args.only_peaks:
pipe_manager = process(sample, pipe_manager, args)
else:
print("Skipped processing sample '{}'.".format(sample.name))
# If sample does not have "ctrl" attribute, finish processing it.
if not hasattr(sample, "compare_sample"):
pipe_manager.stop_pipeline()
print("Finished processing sample '{}'.".format(sample.name))
return
# The pipeline will now wait for the comparison sample file to be completed
pipe_manager._wait_for_file(
sample.filtered.replace(sample.name, sample.compare_sample))
# Start peak calling function
call_peaks(sample, pipe_manager, args)
def test_AttributeDict_representations_smoke(self, data, funcname):
""" Text representation of base AttributeDict doesn't fail. """
attrdict = AttributeDict(data)
getattr(attrdict, funcname).__call__()