def build(incl_map):
nm = "vg_construct_index_map" if incl_map else "vg_construct_index"
wf = dxpy.new_dxworkflow(title=nm,
name=nm,
description=nm,
project=project.get_id(),
folder=folder,
properties={"git_revision": git_revision})
construct_applet = find_applet("vg_construct")
construct_input = {
}
construct_stage_id = wf.add_stage(construct_applet, stage_input=construct_input, name="construct")
index_input = {
"vg_tar": dxpy.dxlink({"stage": construct_stage_id, "outputField": "vg_tar"})
}
index_stage_id = wf.add_stage(find_applet("vg_index"), stage_input=index_input, name="index")
if incl_map:
map_input = {
"vg_indexed_tar": dxpy.dxlink({"stage": index_stage_id, "outputField": "vg_indexed_tar"})
}
map_stage_id = wf.add_stage(find_applet("vg_map"), stage_input=map_input, name="map")
return wf
def main(project, folder, name):
# Build the applet
app_id, app_desc = upload_applet('.', None)
app_handler = dxpy.DXApplet(app_id)
# Build a workflow that uses that applet
workflow = dxpy.new_dxworkflow(name=name, project=project, folder=folder)
workflow.add_stage(app_id)
# Delete the applet, to break the workflow
app_handler.remove()
return workflow.get_id()
def new_workflow(args):
try_call(process_dataobject_args, args)
try_call(process_single_dataobject_output_args, args)
init_from = None
if args.init is not None:
if is_analysis_id(args.init):
init_from = args.init
else:
init_project, _init_folder, init_result = try_call(
resolve_existing_path, args.init, expected='entity')
init_from = dxpy.get_handler(init_result['id'],
project=init_project)
if args.output is None:
project = dxpy.WORKSPACE_ID
folder = dxpy.config.get("DX_CLI_WD", "/")
name = None
else:
project, folder, name = try_call(dxpy.utils.resolver.resolve_path,
args.output)
if args.output_folder is not None:
try:
# Try to resolve to a path in the project
_ignore, args.output_folder, _ignore = resolve_path(
args.output_folder, expected='folder')
except:
# But if not, just use the value directly
pass
try:
dxworkflow = dxpy.new_dxworkflow(title=args.title,
summary=args.summary,
description=args.description,
output_folder=args.output_folder,
project=project,
name=name,
tags=args.tags,
types=args.types,
hidden=args.hidden,
properties=args.properties,
details=args.details,
folder=folder,
parents=args.parents,
init_from=init_from)
if args.brief:
print(dxworkflow.get_id())
else:
dxpy.utils.describe.print_desc(
dxworkflow.describe(incl_properties=True, incl_details=True),
args.verbose)
except:
err_exit()
def createWorkflow(stepsToDo, priors, extras, resultsFolder, projectId, appProjectId=None):
'''This function will populate a workflow for the stepsToDo.'''
if len(stepsToDo) < 1:
return None
if appProjectId == None:
appProjectId = projectId
# create a workflow object
wf = dxpy.new_dxworkflow(title=extras['name'],name=extras['name'],folder=resultsFolder,
project=projectId,description=extras['description'])
# NOTE: prevStepResults dict contains links to result files to be generated by previous steps
prevStepResults = {}
for step in stepsToDo:
appName = STEPS[step]['app']
app = dxencode.find_applet_by_name(appName, appProjectId)
appInputs = {}
# file inputs
for fileToken in STEPS[step]['inputs'].keys():
appInp = STEPS[step]['inputs'][fileToken]
if fileToken in prevStepResults:
appInputs[ appInp ] = prevStepResults[fileToken]
elif fileToken in priors:
if isinstance(priors[fileToken], list):
appInputs[ appInp ] = []
for fid in priors[fileToken]:
appInputs[ appInp ] += [ dxencode.get_file_link(fid) ]
else:
appInputs[ appInp ] = dxencode.get_file_link(priors[fileToken])
else:
print "ERROR: step '"+step+"' can't find input '"+fileToken+"'!"
sys.exit(1)
# Non-file app inputs
if 'params' in STEPS[step]:
for param in STEPS[step]['params'].keys():
appParam = STEPS[step]['params'][param]
if param in extras:
appInputs[ appParam ] = extras[param]
else:
print "ERROR: unable to locate '"+param+"' in extras."
sys.exit(1)
# Add wf stage
stageId = wf.add_stage(app, stage_input=appInputs, folder=resultsFolder)
# outputs, which we will need to link to
for fileToken in STEPS[step]['results'].keys():
#appOut = STEPS[step]['results'][fileToken]
appOut = fileToken ## not the value
prevStepResults[ fileToken ] = dxpy.dxlink({ 'stage': stageId,'outputField': appOut })
wfRun = wf.run({})
return wfRun.describe()
def main():
args = get_args()
if len(args.replicates) < 1:
sys.exit('Need to have at least 1 replicate file.')
project = resolve_project(ENCODE_DNA_ME_PROJECT_NAME)
print 'Project: ' + project.describe()['name']
print 'Experiment to analyze: ' + args.experiment
if not project_has_folder(project, '/'+args.experiment):
project.new_folder('/'+args.experiment)
#TODO get all replicate ids from encoded DB from ENCSR (args.experiment)
#TODO error out if ENCSR not found, status not complete etc.
if args.test:
source_id = project.get_id()
else:
source_id = resolve_project(ENCODE_SNAPSHOT_PROJECT, level='VIEW').get_id()
replicates = []
for rep in args.replicates:
dx_rep = dxpy.find_data_objects(classname='file', name=rep,
name_mode='glob', project=source_id,
return_handler=False)
replicates.extend(dx_rep)
if not args.test:
replicates = copy_files(replicates, project.get_id(), "/"+args.experiment)
if not replicates:
print "No replicates found in project: " + project.name
print "Looking for " + ", ".join(args.replicates)
sys.exit(1)
paired = args.paired
gender = args.gender
organism = args.organism
#TODO determine paired or gender from ENCSR metadata
# Now create a new workflow ()
spec_name = args.experiment+'-'+'-'.join([ r.split('.')[0] for r in args.replicates])
wf = dxpy.new_dxworkflow(title='dx_dna_me_'+spec_name,
name='ENCODE Bismark DNA-ME pipeline: '+spec_name,
description='The ENCODE Bismark pipeline for WGBS shotgun methylation analysis for experiment' + args.experiment,
folder='/'+args.experiment,
project=project.get_id())
populate_workflow(wf, replicates, args.experiment, paired, gender, organism, project.id)
def new_workflow(args):
try_call(process_dataobject_args, args)
try_call(process_single_dataobject_output_args, args)
init_from = None
if args.init is not None:
if is_analysis_id(args.init):
init_from = args.init
else:
init_project, _init_folder, init_result = try_call(resolve_existing_path, args.init, expected="entity")
init_from = dxpy.get_handler(init_result["id"], project=init_project)
if args.output is None:
project = dxpy.WORKSPACE_ID
folder = get_env_var("DX_CLI_WD", "/")
name = None
else:
project, folder, name = dxpy.utils.resolver.resolve_path(args.output)
if args.output_folder is not None:
try:
# Try to resolve to a path in the project
_ignore, args.output_folder, _ignore = resolve_path(args.output_folder, expected="folder")
except:
# But if not, just use the value directly
pass
try:
dxworkflow = dxpy.new_dxworkflow(
title=args.title,
summary=args.summary,
description=args.description,
output_folder=args.output_folder,
project=project,
name=name,
tags=args.tags,
types=args.types,
hidden=args.hidden,
properties=args.properties,
details=args.details,
folder=folder,
parents=args.parents,
init_from=init_from,
)
if args.brief:
print(dxworkflow.get_id())
else:
dxpy.utils.describe.print_desc(dxworkflow.describe(incl_properties=True, incl_details=True), args.verbose)
except:
err_exit()
def new_workflow(args):
try_call(process_dataobject_args, args)
try_call(process_single_dataobject_output_args, args)
init_from = None
if args.init is not None:
try:
init_project, init_folder, init_result = try_call(resolve_existing_path,
args.init,
expected='entity')
init_from = dxpy.get_handler(init_result['id'], project=init_project)
except:
init_from = args.init
if args.output is None:
project = dxpy.WORKSPACE_ID
folder = os.environ.get('DX_CLI_WD', '/')
name = None
else:
project, folder, name = dxpy.utils.resolver.resolve_path(args.output)
if args.output_folder is not None:
try:
# Try to resolve to a path in the project
ignore, args.output_folder, ignore2 = resolve_path(args.output_folder, expected='folder')
except:
# But if not, just use the value directly
pass
try:
dxworkflow = dxpy.new_dxworkflow(title=args.title, summary=args.summary,
description=args.description,
output_folder=args.output_folder,
project=project, name=name,
tags=args.tags, types=args.types,
hidden=args.hidden, properties=args.properties,
details=args.details,
folder=folder,
parents=args.parents, init_from=init_from)
if args.brief:
print dxworkflow.get_id()
else:
dxpy.utils.describe.print_desc(dxworkflow.describe(incl_properties=True, incl_details=True),
args.verbose)
except:
err_exit()
def build_workflow():
if parameters["folder_provided"] == "false":
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
output_folder=parameters["Output"],
)
else:
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
)
wf_outputs = []
combine_counts_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["combine_counts"],
state="closed",
return_handler=True)
limma_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["limma"],
state="closed",
return_handler=True)
simple_DE_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["simple_DE"],
state="closed",
return_handler=True)
sample_num = 0
htseq_results = [dxpy.dxlink(count_id) for count_id in samples.values()]
combine_input = {
"count_files": htseq_results,
"name_value": "htseq",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_counts_stage_id = wf.add_stage(combine_counts_applet,
stage_input=combine_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE HTSEQ")
wf_outputs += [
{
"name": "combined_counts",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_counts_stage_id,
"outputField": "count_file"
}
}
},
]
if parameters["limma_DE_viewer"] != "None":
limma_viewer_project, limma_viewer_file = parameters[
"limma_DE_viewer"].split(":")
limma_viewer_link = dxpy.dxlink({
"project": limma_viewer_project,
"id": limma_viewer_file
})
if parameters["limma_runnable"] == "true":
limma_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"calcNormFactors_method":
parameters["calcNormFactors_method"],
"filter_count_type":
parameters["filter_count_type"],
"filter_count":
int(parameters["filter_count"]),
"p_value_adjust":
parameters["p_value_adjust"],
"contrasts_file":
dxpy.dxlink(comparisons_limma_id)
}
if parameters["limma_DE_viewer"] != "None":
limma_input["difex_viewer"] = limma_viewer_link
limma_stage_id = wf.add_stage(limma_applet,
stage_input=limma_input,
instance_type="azure:mem1_ssd1_x4",
name="LIMMA")
wf_outputs += [
{
"name": "limma_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "limma_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
simple_DE_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"contrasts_file":
dxpy.dxlink(comparisons_all_id)
}
if parameters["limma_DE_viewer"] != "None":
simple_DE_input["difex_viewer"] = limma_viewer_link
simple_DE_stage_id = wf.add_stage(simple_DE_applet,
stage_input=simple_DE_input,
instance_type="azure:mem1_ssd1_x4",
name="SIMPLE DIFFERENTIAL_EXPRESSION")
wf_outputs += [
{
"name": "simple_DE_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "simple_DE_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
wf.update(workflow_outputs=wf_outputs)
wf.close()
return wf.get_id()
def build_workflow(experiment, biorep_n, input_shield_stage_input, accession,
use_existing_folders):
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
mapping_applet = \
find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (mapping_applet.name))
input_shield_applet = \
find_applet_by_name(INPUT_SHIELD_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (input_shield_applet.name))
folders = ['workflows', 'fastqs', 'raw_bams', 'bams']
folder_paths = \
['/'.join([args.outf,
folder_name,
experiment.get('accession'),
'rep%d' % (biorep_n)])
for folder_name in folders]
paths_exist = \
[resolve_folder(output_project, folder_path)
for folder_path in folder_paths
if resolve_folder(output_project, folder_path)]
if any(paths_exist):
msg = "%s: output paths already exist: %s" % (
experiment.get('accession'), paths_exist)
if use_existing_folders:
logging.warning(msg)
else:
msg += "\nUse --use_existing_folders to supress but possibly create duplicate files"
logging.error(msg)
return None
workflow_output_folder, fastq_output_folder, mapping_output_folder, final_output_folder = \
tuple(create_folder(output_project, folder_path)
for folder_path in folder_paths)
if args.raw:
workflow_title = \
('Map %s rep%d to %s (no filter)'
% (experiment.get('accession'), biorep_n, args.assembly))
workflow_name = 'ENCODE raw mapping pipeline'
else:
workflow_title = \
('Map %s rep%d to %s and filter'
% (experiment.get('accession'), biorep_n, args.assembly))
workflow_name = 'ENCODE mapping pipeline'
if args.tag:
workflow_title += ': %s' % (args.tag)
workflow = dxpy.new_dxworkflow(title=workflow_title,
name=workflow_name,
project=output_project.get_id(),
folder=workflow_output_folder)
input_shield_stage_id = workflow.add_stage(
input_shield_applet,
name='Gather inputs %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=fastq_output_folder,
stage_input=input_shield_stage_input)
input_names = \
[name for name in ['reads1', 'reads2', 'crop_length', 'reference_tar',
'bwa_version', 'bwa_aln_params', 'samtools_version', 'debug']
if name in input_shield_stage_input]
logging.debug('input_names: %s' % (input_names))
mapping_stage_input = dict(
zip(input_names, [
dxpy.dxlink({
'stage': input_shield_stage_id,
'outputField': input_name
}) for input_name in input_names
]))
logging.debug('mapping_stage_input: %s' % (mapping_stage_input))
mapping_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s rep%d' % (experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input=mapping_stage_input)
if not args.raw:
filter_qc_applet = \
find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (filter_qc_applet.name))
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter and QC %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': mapping_stage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': mapping_stage_id,
'outputField': 'paired_end'
}),
'scrub':
args.scrub
})
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
logging.debug('Found applet %s' % (xcor_applet.name))
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Calculate cross-correlation %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': filter_qc_stage_id,
'outputField': 'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage': filter_qc_stage_id,
'outputField': 'paired_end'
}),
'spp_version':
args.spp_version
})
''' This should all be done in the shield's postprocess entrypoint
if args.accession_outputs:
derived_from = input_shield_stage_input.get('reads1')
if reads2:
derived_from.append(reads2)
files_json = {dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}) : {
'notes': 'Biorep%d | Mapped to %s' %(biorep_n, input_shield_stage_input.get('reference_tar')),
'lab': 'j-michael-cherry',
'award': 'U41HG006992',
'submitted_by': '*****@*****.**',
'file_format': 'bam',
'output_type': 'alignments',
'derived_from': derived_from,
'dataset': experiment.get('accession')}
}
output_shield_stage_id = workflow.add_stage(
output_shield_applet,
name='Accession outputs %s rep%d' %(experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={'files': [dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'})],
'files_json': files_json,
'key': input_shield_stage_input.get('key')}
)
'''
return workflow
def main():
args = get_args()
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if args.nomap and (args.rep1pe is None or args.rep2pe is None) and not blank_workflow:
logging.error("With --nomap, endedness of replicates must be specified with --rep1pe and --rep2pe")
raise ValueError
if not args.target:
target_type = 'default' # default
else:
target_type = args.target.lower()
if target_type not in WF.keys():
logging.error('Target type %s is not recognized')
sys.exit(2)
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
existing_folder = resolve_folder(output_project, args.outf)
if not existing_folder:
output_folder = create_folder(output_project, args.outf)
elif args.use_existing_folders:
output_folder = existing_folder
else:
assert (existing_folder and args.use_existing_folders), 'Output folder %s exists but --use_existing_folders is %s' % (existing_folder, args.use_existing_folders)
logging.debug('Using output folder %s' % (output_folder))
workflow = dxpy.new_dxworkflow(
name=args.name or WF[target_type]['wf_name'],
title=args.title or WF[target_type]['wf_title'],
description=args.description or WF[target_type]['wf_description'],
project=output_project.get_id(),
folder=output_folder,
properties={'pipeline_version': str(args.pipeline_version)})
unary_control = args.unary_control or (args.rep1 and args.rep2 and args.ctl1 and not args.ctl2)
if not args.genomesize:
genomesize = None
else:
genomesize = args.genomesize
if not args.chrom_sizes:
chrom_sizes = None
else:
chrom_sizes = dxpy.dxlink(resolve_file(args.chrom_sizes))
if not args.blacklist:
blacklist = None
else:
blacklist = dxpy.dxlink(resolve_file(args.blacklist))
run_idr = WF[target_type]['run_idr']
if not args.nomap:
# a "superstage" is just a dict with a name, name(s) of input files,
# and then names and id's of stages that process that input
# each superstage here could be implemented as a stage in a more
# abstract workflow. That stage would then call the various applets
# that are separate
# stages here.
mapping_superstages = [ # the order of this list is important in that
{'name': 'Rep1', 'input_args': args.rep1},
{'name': 'Rep2', 'input_args': args.rep2},
{'name': 'Ctl1', 'input_args': args.ctl1}
]
if not unary_control:
mapping_superstages.append(
{'name': 'Ctl2', 'input_args': args.ctl2})
mapping_applet = find_applet_by_name(
MAPPING_APPLET_NAME, applet_project.get_id())
# mapping_output_folder = resolve_folder(
# output_project, output_folder + '/' + mapping_applet.name)
mapping_output_folder = mapping_applet.name
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(
FILTER_QC_APPLET_NAME, applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(
XCOR_APPLET_NAME, applet_project.get_id())
xcor_output_folder = mapping_output_folder
# in the first pass create the mapping stage id's so we can use JBOR's
# to link inputs
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' % (superstage_name),
folder=mapping_output_folder
)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
# in the second pass populate the stage inputs and build other stages
rep1_stage_id = next(ss.get('map_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1')
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
superstage_id = mapping_superstage.get('map_stage_id')
if mapping_superstage.get('input_args') or blank_workflow:
mapping_stage_input = {}
if superstage_name != "Rep1":
mapping_stage_input.update(
{'reference_tar': dxpy.dxlink(
{'stage': rep1_stage_id,
'inputField': 'reference_tar'})})
else:
if args.reference:
mapping_stage_input.update(
{'reference_tar': dxpy.dxlink(
reference_tar.get_id())})
if not blank_workflow:
for arg_index, input_arg in enumerate(mapping_superstage['input_args']): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update({'reads%d' %(arg_index+1): reads})
# this is now done in the first pass loop above
# mapped_stage_id = workflow.add_stage(
# mapping_applet,
# name='Map %s' %(superstage_name),
# folder=mapping_output_folder,
# stage_input=mapping_stage_input
# )
# mapping_superstage.update({'map_stage_id': mapped_stage_id})
workflow.update_stage(superstage_id, stage_input=mapping_stage_input)
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' %(superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': superstage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': superstage_id, 'outputField': 'paired_end'})
}
)
mapping_superstage.update({'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Xcor %s' %(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'}),
'spp_version': args.spp_version
}
)
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'})
exp_rep1_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'CC_scores_file'})
exp_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'})
exp_rep2_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'CC_scores_file'})
ctl_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'})
if unary_control:
ctl_rep2_ta = ctl_rep1_ta
else:
ctl_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'})
rep1_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'paired_end'})
rep2_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'paired_end'})
else: #skipped the mapping, so just bring in the inputs from arguments
if not blank_workflow:
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
exp_rep1_ta_desc = dxpy.describe(exp_rep1_ta)
exp_rep2_ta_desc = dxpy.describe(exp_rep2_ta)
exp_rep1_mapping_analysis_id = dxpy.describe(exp_rep1_ta_desc['createdBy']['job'])['analysis']
exp_rep2_mapping_analysis_id = dxpy.describe(exp_rep2_ta_desc['createdBy']['job'])['analysis']
exp_rep1_mapping_analysis = dxpy.describe(exp_rep1_mapping_analysis_id)
exp_rep2_mapping_analysis = dxpy.describe(exp_rep2_mapping_analysis_id)
exp_rep1_cc = next(
stage['execution']['output']['CC_scores_file']
for stage in exp_rep1_mapping_analysis.get('stages')
if stage['execution']['executableName'] == 'xcor')
exp_rep2_cc = next(
stage['execution']['output']['CC_scores_file']
for stage in exp_rep2_mapping_analysis.get('stages')
if stage['execution']['executableName'] == 'xcor')
else:
exp_rep1_ta = None
exp_rep2_ta = None
ctl_rep1_ta = None
ctl_rep2_ta = None
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
# #here we need to calculate the cc scores files, because we're only being supplied tagAligns
# #if we had mapped everything above we'd already have a handle to the cc file
# xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME, applet_project.get_id())
# # xcor_output_folder = resolve_folder(output_project, output_folder + '/' + xcor_only_applet.name)
# xcor_output_folder = xcor_only_applet.name
# xcor_only_stages = []
# exp_rep1_cc_stage_id = workflow.add_stage(
# xcor_only_applet,
# name="Rep1 cross-correlation",
# folder=xcor_output_folder,
# stage_input={
# 'input_tagAlign': exp_rep1_ta,
# 'paired_end': rep1_paired_end,
# 'spp_version': args.spp_version
# }
# )
# xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
# exp_rep1_cc = dxpy.dxlink(
# {'stage': exp_rep1_cc_stage_id,
# 'outputField': 'CC_scores_file'})
# exp_rep2_cc_stage_id = workflow.add_stage(
# xcor_only_applet,
# name="Rep2 cross-correlation",
# folder=xcor_output_folder,
# stage_input={
# 'input_tagAlign': exp_rep2_ta,
# 'paired_end': rep2_paired_end,
# 'spp_version': args.spp_version
# }
# )
# xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
# exp_rep2_cc = dxpy.dxlink(
# {'stage': exp_rep2_cc_stage_id,
# 'outputField': 'CC_scores_file'})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME, applet_project.get_id())
encode_macs2_stages = []
# peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_macs2_applet.name)
peaks_output_folder = encode_macs2_applet.name
macs2_stage_input = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as))
}
if genomesize:
macs2_stage_input.update({'genomesize': genomesize})
if chrom_sizes:
macs2_stage_input.update({'chrom_sizes': chrom_sizes})
encode_macs2_stage_id = workflow.add_stage(
encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input=macs2_stage_input
)
encode_macs2_stages.append({'name': 'ENCODE Peaks', 'stage_id': encode_macs2_stage_id})
if run_idr:
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME, applet_project.get_id())
encode_spp_stages = []
# idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_spp_applet.name)
idr_peaks_output_folder = encode_spp_applet.name
PEAKS_STAGE_NAME = 'SPP Peaks'
peaks_stage_input = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'idr_peaks': True,
'spp_version': args.spp_version
}
if chrom_sizes:
peaks_stage_input.update({'chrom_sizes': chrom_sizes})
else:
peaks_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_macs2_stage_id, 'inputField': 'chrom_sizes'})})
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input=peaks_stage_input
)
encode_spp_stages.append({'name': PEAKS_STAGE_NAME, 'stage_id': encode_spp_stage_id})
idr_applet = find_applet_by_name(IDR2_APPLET_NAME, applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME, applet_project.get_id())
idr_stages = []
# idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
idr_output_folder = idr_applet.name
if (args.rep1 and args.ctl1 and args.rep2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR True Replicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 1 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 2 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR Pooled Pseudoreplicates', 'stage_id': idr_stage_id})
final_idr_stage_input = {
'reps_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR True Replicates'),
'outputField': 'IDR_peaks'}),
'r1pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'r2pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'pooledpr_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'rep1_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_fc_signal'}),
'rep2_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_fc_signal'}),
'pooled_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_fc_signal'})
}
if blacklist:
final_idr_stage_input.update({'blacklist': blacklist})
if chrom_sizes:
final_idr_stage_input.update({'chrom_sizes': chrom_sizes})
else:
final_idr_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_spp_stage_id, 'inputField': 'chrom_sizes'})})
final_idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=final_idr_stage_input,
)
idr_stages.append({'name': 'Final IDR peak calls', 'stage_id': final_idr_stage_id})
if target_type == 'histone':
overlap_peaks_applet = find_applet_by_name(OVERLAP_PEAKS_APPLET_NAME, applet_project.get_id())
overlap_peaks_stages = []
for peaktype in ['narrowpeaks', 'gappedpeaks', 'broadpeaks']:
if peaktype == 'narrowpeaks':
as_file = dxpy.dxlink(resolve_file(args.narrowpeak_as))
peak_type_extension = 'narrowPeak'
elif peaktype == 'gappedpeaks':
as_file = dxpy.dxlink(resolve_file(args.gappedpeak_as))
peak_type_extension = 'gappedPeak'
elif peaktype == 'broadpeaks':
as_file = dxpy.dxlink(resolve_file(args.broadpeak_as))
peak_type_extension = 'broadPeak'
overlap_peaks_stage_input = {
'rep1_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_%s' %(peaktype)}),
'rep2_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_%s' %(peaktype)}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_%s' %(peaktype)}),
'pooledpr1_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr1_%s' %(peaktype)}),
'pooledpr2_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr2_%s' %(peaktype)}),
'as_file': as_file,
'peak_type': peak_type_extension,
'prefix': 'final',
'rep1_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_fc_signal'}),
'rep2_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_fc_signal'}),
'pooled_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_fc_signal'})
}
if chrom_sizes:
overlap_peaks_stage_input.update({'chrom_sizes': chrom_sizes})
else:
overlap_peaks_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_macs2_stage_id, 'inputField': 'chrom_sizes'})})
overlap_peaks_stage_id = workflow.add_stage(
overlap_peaks_applet,
name='Final %s' %(peaktype),
folder=peaks_output_folder,
stage_input=overlap_peaks_stage_input
)
overlap_peaks_stages.append({'name': 'Final %s' %(peaktype), 'stage_id': overlap_peaks_stage_id})
if args.accession:
accession_analysis_applet = find_applet_by_name(ACCESSION_ANALYSIS_APPLET_NAME, applet_project.get_id())
accession_output_folder = accession_analysis_applet.name
accession_stage_input = {
'analysis_ids': ['self'],
'force_patch': True,
'wait_on_files': []
}
if target_type == 'histone':
for stage in overlap_peaks_stages:
for output_field in ['overlapping_peaks', 'overlapping_peaks_bb']:
accession_stage_input['wait_on_files'].append(
dxpy.dxlink({'stage': stage.get('stage_id'), 'outputField': output_field})
)
elif run_idr:
for output_field in ['conservative_set', 'conservative_set_bb', 'optimal_set', 'optimal_set_bb']:
accession_stage_input['wait_on_files'].append(
dxpy.dxlink({'stage': final_idr_stage_id, 'outputField': output_field})
)
assert accession_stage_input['wait_on_files'], "ERROR: workflow has no wait_on_files defined, so --accession is not supported."
accession_stage_id = workflow.add_stage(
accession_analysis_applet,
name='Accession results',
folder=accession_output_folder,
stage_input=accession_stage_input
)
if args.yes:
if args.debug:
job_id = workflow.run({}, folder=output_folder, priority='high', debug={'debugOn': ['AppInternalError', 'AppError']}, delay_workspace_destruction=True, allow_ssh=['255.255.255.255'])
else:
job_id = workflow.run({}, folder=output_folder, priority='normal')
logging.info("Running as job %s" %(job_id))
def main():
args = get_args()
if len(args.replicates) < 1:
sys.exit('Need to have at least 1 replicate file.')
project = resolve_project(ENCODE_DNA_ME_PROJECT_NAME)
print 'Project: ' + project.describe()['name']
print 'Experiment to analyze: ' + args.experiment
if not project_has_folder(project, '/'+args.experiment):
project.new_folder('/'+args.experiment)
#TODO get all replicate ids from encoded DB from ENCSR (args.experiment)
#TODO error out if ENCSR not found, status not complete etc.
if args.test:
source_id = project.get_id()
else:
source_id = resolve_project(ENCODE_SNAPSHOT_PROJECT, level='VIEW').get_id()
replicates = []
for rep in args.replicates:
dx_rep = dxpy.find_data_objects(classname='file', name=rep,
name_mode='exact', project=source_id,
return_handler=False)
replicates.extend(dx_rep)
if not args.test:
replicates = copy_files(replicates, project.get_id(), "/"+args.experiment)
if not replicates:
print "No replicates found in project: " + project.name
print "Looking for " + ", ".join(args.replicates)
sys.exit(1)
inputs = {
'rnd_seed': 12345
}
inputs['paired'] = args.paired
inputs['gender']= args.gender
inputs['organism'] = args.organism
inputs['library_id'] = args.library
inputs['nthreads'] = args.nthreads
#TODO determine paired or gender from ENCSR metadata
# Now create a new workflow ()
inputs['spec_name'] = args.experiment+'-'+'-'.join([ r.split('.')[0] for r in args.replicates])
title_root = 'dx_long_rna_seq_'
name_root = 'ENCODE Long RNA Seq: '
desc = 'The ENCODE RNA Seq pipeline for long RNAs'
if args.paired:
title_root = title_root + '_paired_end '
name_root = name_root + '(paired-end) '
inputs['stranded'] = True
else:
title_root = title_root + '_single_end '
name_root = name_root + '(single-end) '
inputs['stranded'] = False
if args.export:
project_id = dxpy.find_one_project(name=ENCODE_PUBLIC_PROJECT, name_mode='exact', return_handler=False)['id']
wf = dxpy.new_dxworkflow(title=title_root,
name=name_root,
description=desc,
folder=PUBLIC_FOLDER,
project=project_id)
else:
project_id = project.get_id()
wf = dxpy.new_dxworkflow(title=title_root+inputs['spec_name'],
name=name_root+inputs['spec_name'],
description=desc+' for experiment:' + args.experiment,
folder='/'+args.experiment,
project=project.get_id())
populate_workflow(wf, replicates, args.experiment, inputs, project.id, args.export)
def main():
args = get_args()
## resolve projects
project = resolve_project(ENCODE_DNA_ME_PROJECT_NAME)
print 'Project: ' + project.describe()['name']
print 'Experiment to analyze: ' + args.experiment
if not project_has_folder(project, '/'+args.experiment):
project.new_folder('/'+args.experiment)
#TODO get all replicate ids from encoded DB from ENCSR (args.experiment)
#TODO error out if ENCSR not found, status not complete etc.
if args.test:
source_id = project.get_id()
else:
source_id = resolve_project(ENCODE_SNAPSHOT_PROJECT, level='VIEW').get_id()
## resolve replicates/fastq inputs
paired = args.paired
if not paired:
if len(args.replicates) < 1:
sys.exit('Need to have at least 1 replicate file (unpaired) use -r or --replicates')
replicates = find_replicates(args.replicates, source_id, project, args.experiment, args.test)
if not replicates:
print "No replicates found in project: " + project.name
print "Looking for " + ", ".join(args.replicates)
sys.exit(1)
dx_reps = {
'reads': [ dxpy.dxlink(r) for r in replicates ]
}
rnames = '-'.join([ r.split('.')[0] for r in args.replicates])
else:
if len(args.pair1) < 1 or len(args.pair2) < 1:
sys.exit("Need to have at least 1 replicate in pair1 (--r1/--pair1) and pair2 (--r2/--pair2")
pair1reps = find_replicates(args.pair1, source_id, project, args.experiment, args.test)
if not pair1reps:
print "No replicates for pair1 found in project: " + project.name
print "Looking for " + ", ".join(args.pair1)
sys.exit(1)
pair2reps = find_replicates(args.pair2, source_id, project, args.experiment, args.test)
if not pair2reps:
print "No replicates for pair2 found in project: " + project.name
print "Looking for " + ", ".join(args.pair2)
sys.exit(1)
dx_reps = {
'pair1_reads': [ dxpy.dxlink(r) for r in pair1reps ],
'pair2_reads': [ dxpy.dxlink(r) for r in pair2reps ]
}
rnames = '-'.join([ r.split('.')[0] for r in args.pair1+args.pair2])
gender = args.gender
organism = args.organism
#TODO determine paired or gender from ENCSR metadata
# Now create a new workflow ()
spec_name = args.experiment+'-'+rnames
title_root = 'dx_dna_me_'
name_root = 'ENCODE Bismark DNA-ME pipeline: '
desc = 'The ENCODE Bismark pipeline for WGBS shotgun methylation analysis for experiment'
if paired:
title_root = title_root + '_paired_end'
name_root = name_root + '(paired-end)'
else:
title_root = title_root + '_single_end'
name_root = name_root + '(single-end)'
if args.export:
project_id = dxpy.find_one_project(name=ENCODE_PUBLIC_PROJECT, name_mode='exact', return_handler=False)['id']
wf = dxpy.new_dxworkflow(title=title_root,
name=name_root,
description=desc,
folder=PUBLIC_FOLDER,
project=project_id)
else:
project_id = project.get_id()
wf = dxpy.new_dxworkflow(title='dx_dna_me_'+spec_name,
name='ENCODE Bismark DNA-ME pipeline: '+spec_name,
description='The ENCODE Bismark pipeline for WGBS shotgun methylation analysis for experiment' + args.experiment,
folder='/'+args.experiment,
project=project.get_id())
populate_workflow(wf, dx_reps, args.experiment, paired, gender, organism, project.id, args.export)
def test_workflow_completion(self):
dxworkflow = dxpy.new_dxworkflow(name="my workflow")
self.assert_completion("dx run my", "my workflow ")
dxworkflow.hide()
self.assert_no_completions("dx run my")
def main():
args = get_args()
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if args.nomap and (args.rep1pe is None
or args.rep2pe is None) and not blank_workflow:
logging.error(
"With --nomap, endedness of replicates must be specified with --rep1pe and --rep2pe"
)
raise ValueError
if not args.target:
target_type = 'default' # default
else:
target_type = args.target.lower()
if target_type not in WF.keys():
logging.error('Target type %s is not recognized')
sys.exit(2)
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
existing_folder = resolve_folder(output_project, args.outf)
if not existing_folder:
output_folder = create_folder(output_project, args.outf)
elif args.use_existing_folders:
output_folder = existing_folder
else:
assert (
existing_folder and args.use_existing_folders
), 'Output folder %s exists but --use_existing_folders is %s' % (
existing_folder, args.use_existing_folders)
logging.debug('Using output folder %s' % (output_folder))
workflow = dxpy.new_dxworkflow(name=args.name
or WF[target_type]['wf_name'],
title=args.title
or WF[target_type]['wf_title'],
description=args.description
or WF[target_type]['wf_description'],
project=output_project.get_id(),
folder=output_folder)
unary_control = args.unary_control or (args.rep1 and args.rep2
and args.ctl1 and not args.ctl2)
if not args.genomesize:
genomesize = None
else:
genomesize = args.genomesize
if not args.chrom_sizes:
chrom_sizes = None
else:
chrom_sizes = dxpy.dxlink(resolve_file(args.chrom_sizes))
if not args.blacklist:
blacklist = None
else:
blacklist = dxpy.dxlink(resolve_file(args.blacklist))
run_idr = WF[target_type]['run_idr']
if not args.nomap:
# a "superstage" is just a dict with a name, name(s) of input files,
# and then names and id's of stages that process that input
# each superstage here could be implemented as a stage in a more
# abstract workflow. That stage would then call the various applets
# that are separate
# stages here.
mapping_superstages = [ # the order of this list is important in that
{
'name': 'Rep1',
'input_args': args.rep1
}, {
'name': 'Rep2',
'input_args': args.rep2
}, {
'name': 'Ctl1',
'input_args': args.ctl1
}
]
if not unary_control:
mapping_superstages.append({
'name': 'Ctl2',
'input_args': args.ctl2
})
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME,
applet_project.get_id())
# mapping_output_folder = resolve_folder(
# output_project, output_folder + '/' + mapping_applet.name)
mapping_output_folder = mapping_applet.name
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME,
applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = mapping_output_folder
# in the first pass create the mapping stage id's so we can use JBOR's
# to link inputs
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
mapped_stage_id = workflow.add_stage(mapping_applet,
name='Map %s' %
(superstage_name),
folder=mapping_output_folder)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
# in the second pass populate the stage inputs and build other stages
rep1_stage_id = next(
ss.get('map_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1')
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
superstage_id = mapping_superstage.get('map_stage_id')
if mapping_superstage.get('input_args') or blank_workflow:
mapping_stage_input = {}
if superstage_name != "Rep1":
mapping_stage_input.update({
'reference_tar':
dxpy.dxlink({
'stage': rep1_stage_id,
'inputField': 'reference_tar'
})
})
else:
if args.reference:
mapping_stage_input.update({
'reference_tar':
dxpy.dxlink(reference_tar.get_id())
})
if not blank_workflow:
for arg_index, input_arg in enumerate(
mapping_superstage['input_args']
): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update(
{'reads%d' % (arg_index + 1): reads})
# this is now done in the first pass loop above
# mapped_stage_id = workflow.add_stage(
# mapping_applet,
# name='Map %s' %(superstage_name),
# folder=mapping_output_folder,
# stage_input=mapping_stage_input
# )
# mapping_superstage.update({'map_stage_id': mapped_stage_id})
workflow.update_stage(superstage_id,
stage_input=mapping_stage_input)
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' % (superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': superstage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': superstage_id,
'outputField': 'paired_end'
})
})
mapping_superstage.update(
{'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(xcor_applet,
name='Xcor %s' %
(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'paired_end'
})
})
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'tagAlign_file'
})
exp_rep1_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'CC_scores_file'
})
exp_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'tagAlign_file'
})
exp_rep2_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'CC_scores_file'
})
ctl_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl1'),
'outputField':
'tagAlign_file'
})
if unary_control:
ctl_rep2_ta = ctl_rep1_ta
else:
ctl_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl2'),
'outputField':
'tagAlign_file'
})
rep1_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'paired_end'
})
rep2_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'paired_end'
})
else: #skipped the mapping, so just bring in the inputs from arguments
if not blank_workflow:
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
else:
exp_rep1_ta = None
exp_rep2_ta = None
ctl_rep1_ta = None
ctl_rep2_ta = None
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME,
applet_project.get_id())
# xcor_output_folder = resolve_folder(output_project, output_folder + '/' + xcor_only_applet.name)
xcor_output_folder = xcor_only_applet.name
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
})
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink({
'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'
})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
})
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink({
'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'
})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME,
applet_project.get_id())
encode_macs2_stages = []
# peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_macs2_applet.name)
peaks_output_folder = encode_macs2_applet.name
macs2_stage_input = {
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as))
}
if genomesize:
macs2_stage_input.update({'genomesize': genomesize})
if chrom_sizes:
macs2_stage_input.update({'chrom_sizes': chrom_sizes})
encode_macs2_stage_id = workflow.add_stage(encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input=macs2_stage_input)
encode_macs2_stages.append({
'name': 'ENCODE Peaks',
'stage_id': encode_macs2_stage_id
})
if run_idr:
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME,
applet_project.get_id())
encode_spp_stages = []
# idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_spp_applet.name)
idr_peaks_output_folder = encode_spp_applet.name
PEAKS_STAGE_NAME = 'SPP Peaks'
peaks_stage_input = {
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'idr_peaks': True
}
if chrom_sizes:
peaks_stage_input.update({'chrom_sizes': chrom_sizes})
else:
peaks_stage_input.update({
'chrom_sizes':
dxpy.dxlink({
'stage': encode_macs2_stage_id,
'inputField': 'chrom_sizes'
})
})
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input=peaks_stage_input)
encode_spp_stages.append({
'name': PEAKS_STAGE_NAME,
'stage_id': encode_spp_stage_id
})
idr_applet = find_applet_by_name(IDR2_APPLET_NAME,
applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME,
applet_project.get_id())
idr_stages = []
# idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
idr_output_folder = idr_applet.name
if (args.rep1 and args.ctl1 and args.rep2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
})
})
idr_stages.append({
'name': 'IDR True Replicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
})
})
idr_stages.append({
'name': 'IDR Rep 1 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
})
})
idr_stages.append({
'name': 'IDR Rep 2 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
})
})
idr_stages.append({
'name': 'IDR Pooled Pseudoreplicates',
'stage_id': idr_stage_id
})
final_idr_stage_input = {
'reps_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR True Replicates'),
'outputField':
'IDR_peaks'
}),
'r1pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'r2pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'pooledpr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'as_file':
dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'rep1_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_fc_signal'
}),
'rep2_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_fc_signal'
}),
'pooled_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_fc_signal'
})
}
if blacklist:
final_idr_stage_input.update({'blacklist': blacklist})
if chrom_sizes:
final_idr_stage_input.update({'chrom_sizes': chrom_sizes})
else:
final_idr_stage_input.update({
'chrom_sizes':
dxpy.dxlink({
'stage': encode_spp_stage_id,
'inputField': 'chrom_sizes'
})
})
idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=final_idr_stage_input,
)
idr_stages.append({
'name': 'Final IDR peak calls',
'stage_id': idr_stage_id
})
if target_type == 'histone':
overlap_peaks_applet = find_applet_by_name(OVERLAP_PEAKS_APPLET_NAME,
applet_project.get_id())
overlap_peaks_stages = []
for peaktype in ['narrowpeaks', 'gappedpeaks', 'broadpeaks']:
if peaktype == 'narrowpeaks':
as_file = dxpy.dxlink(resolve_file(args.narrowpeak_as))
peak_type_extension = 'narrowPeak'
elif peaktype == 'gappedpeaks':
as_file = dxpy.dxlink(resolve_file(args.gappedpeak_as))
peak_type_extension = 'gappedPeak'
elif peaktype == 'broadpeaks':
as_file = dxpy.dxlink(resolve_file(args.broadpeak_as))
peak_type_extension = 'broadPeak'
overlap_peaks_stage_input = {
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_%s' % (peaktype)
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_%s' % (peaktype)
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_%s' % (peaktype)
}),
'pooledpr1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr1_%s' % (peaktype)
}),
'pooledpr2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr2_%s' % (peaktype)
}),
'as_file':
as_file,
'peak_type':
peak_type_extension,
'prefix':
'final',
'rep1_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_fc_signal'
}),
'rep2_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_fc_signal'
}),
'pooled_signal':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_fc_signal'
})
}
if chrom_sizes:
overlap_peaks_stage_input.update({'chrom_sizes': chrom_sizes})
else:
overlap_peaks_stage_input.update({
'chrom_sizes':
dxpy.dxlink({
'stage': encode_macs2_stage_id,
'inputField': 'chrom_sizes'
})
})
overlap_peaks_stage_id = workflow.add_stage(
overlap_peaks_applet,
name='Final %s' % (peaktype),
folder=peaks_output_folder,
stage_input=overlap_peaks_stage_input)
overlap_peaks_stages.append({
'name': 'Final %s' % (peaktype),
'stage_id': overlap_peaks_stage_id
})
if args.yes:
if args.debug:
job_id = workflow.run(
{},
folder=output_folder,
priority='high',
debug={'debugOn': ['AppInternalError', 'AppError']},
delay_workspace_destruction=True,
allow_ssh=['255.255.255.255'])
else:
job_id = workflow.run({}, folder=output_folder, priority='normal')
logging.info("Running as job %s" % (job_id))
def main():
args = get_args()
## resolve projects
project = resolve_project(ENCODE_DNA_ME_PROJECT_NAME)
print 'Project: ' + project.describe()['name']
print 'Experiment to analyze: ' + args.experiment
if not project_has_folder(project, '/' + args.experiment):
project.new_folder('/' + args.experiment)
#TODO get all replicate ids from encoded DB from ENCSR (args.experiment)
#TODO error out if ENCSR not found, status not complete etc.
if args.test:
source_id = project.get_id()
else:
source_id = resolve_project(ENCODE_SNAPSHOT_PROJECT,
level='VIEW').get_id()
## resolve replicates/fastq inputs
paired = args.paired
if not paired:
if len(args.replicates) < 1:
sys.exit(
'Need to have at least 1 replicate file (unpaired) use -r or --replicates'
)
replicates = find_replicates(args.replicates, source_id, project,
args.experiment, args.test)
if not replicates:
print "No replicates found in project: " + project.name
print "Looking for " + ", ".join(args.replicates)
sys.exit(1)
dx_reps = {'reads': [dxpy.dxlink(r) for r in replicates]}
rnames = '-'.join([r.split('.')[0] for r in args.replicates])
else:
if len(args.pair1) < 1 or len(args.pair2) < 1:
sys.exit(
"Need to have at least 1 replicate in pair1 (--r1/--pair1) and pair2 (--r2/--pair2"
)
pair1reps = find_replicates(args.pair1, source_id, project,
args.experiment, args.test)
if not pair1reps:
print "No replicates for pair1 found in project: " + project.name
print "Looking for " + ", ".join(args.pair1)
sys.exit(1)
pair2reps = find_replicates(args.pair2, source_id, project,
args.experiment, args.test)
if not pair2reps:
print "No replicates for pair2 found in project: " + project.name
print "Looking for " + ", ".join(args.pair2)
sys.exit(1)
dx_reps = {
'pair1_reads': [dxpy.dxlink(r) for r in pair1reps],
'pair2_reads': [dxpy.dxlink(r) for r in pair2reps]
}
rnames = '-'.join([r.split('.')[0] for r in args.pair1 + args.pair2])
gender = args.gender
organism = args.organism
#TODO determine paired or gender from ENCSR metadata
# Now create a new workflow ()
spec_name = args.experiment + '-' + rnames
title_root = 'dx_dna_me_'
name_root = 'ENCODE Bismark DNA-ME pipeline: '
desc = 'The ENCODE Bismark pipeline for WGBS shotgun methylation analysis for experiment'
if paired:
title_root = title_root + '_paired_end'
name_root = name_root + '(paired-end)'
else:
title_root = title_root + '_single_end'
name_root = name_root + '(single-end)'
if args.export:
project_id = dxpy.find_one_project(name=ENCODE_PUBLIC_PROJECT,
name_mode='exact',
return_handler=False)['id']
wf = dxpy.new_dxworkflow(title=title_root,
name=name_root,
description=desc,
folder=PUBLIC_FOLDER,
project=project_id)
else:
project_id = project.get_id()
wf = dxpy.new_dxworkflow(
title='dx_dna_me_' + spec_name,
name='ENCODE Bismark DNA-ME pipeline: ' + spec_name,
description=
'The ENCODE Bismark pipeline for WGBS shotgun methylation analysis for experiment'
+ args.experiment,
folder='/' + args.experiment,
project=project.get_id())
populate_workflow(wf, dx_reps, args.experiment, paired, gender, organism,
project.id, args.export)
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.debug('Using output folder %s' % (output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
workflow = dxpy.new_dxworkflow(name=args.name,
title=args.title,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not args.genomesize:
genomesize = None
else:
genomesize = args.genomesize
if not args.chrom_sizes:
chrom_sizes = None
else:
chrom_sizes = dxpy.dxlink(resolve_file(args.chrom_sizes))
if not args.blacklist:
blacklist = None
else:
blacklist = dxpy.dxlink(resolve_file(args.blacklist))
if not args.nomap:
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [ # the order of this list is important in that
{
'name': 'Rep1',
'input_args': args.rep1
}, {
'name': 'Rep2',
'input_args': args.rep2
}, {
'name': 'Ctl1',
'input_args': args.ctl1
}
]
if not args.unary_control:
mapping_superstages.append({
'name': 'Ctl2',
'input_args': args.ctl2
})
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME,
applet_project.get_id())
mapping_output_folder = resolve_folder(
output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME,
applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = mapping_output_folder
# in the first pass create the mapping stage id's so we can use JBOR's
# to link inputs
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
mapped_stage_id = workflow.add_stage(mapping_applet,
name='Map %s' %
(superstage_name),
folder=mapping_output_folder)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
# in the second pass populate the stage inputs and build other stages
rep1_stage_id = next(
ss.get('map_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1')
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
superstage_id = mapping_superstage.get('map_stage_id')
if mapping_superstage.get('input_args') or blank_workflow:
mapping_stage_input = {}
if superstage_name != "Rep1":
mapping_stage_input.update({
'reference_tar':
dxpy.dxlink({
'stage': rep1_stage_id,
'inputField': 'reference_tar'
})
})
else:
if args.reference:
mapping_stage_input.update({
'reference_tar':
dxpy.dxlink(reference_tar.get_id())
})
if not blank_workflow:
for arg_index, input_arg in enumerate(
mapping_superstage['input_args']
): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update(
{'reads%d' % (arg_index + 1): reads})
# this is now done in the first pass loop above
# mapped_stage_id = workflow.add_stage(
# mapping_applet,
# name='Map %s' %(superstage_name),
# folder=mapping_output_folder,
# stage_input=mapping_stage_input
# )
# mapping_superstage.update({'map_stage_id': mapped_stage_id})
workflow.update_stage(superstage_id,
stage_input=mapping_stage_input)
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' % (superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': superstage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': superstage_id,
'outputField': 'paired_end'
})
})
mapping_superstage.update(
{'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(xcor_applet,
name='Xcor %s' %
(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'paired_end'
})
})
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'tagAlign_file'
})
exp_rep1_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'CC_scores_file'
})
exp_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'tagAlign_file'
})
exp_rep2_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'CC_scores_file'
})
ctl_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl1'),
'outputField':
'tagAlign_file'
})
if not args.unary_control:
ctl_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl2'),
'outputField':
'tagAlign_file'
})
else:
ctl_rep2_ta = ctl_rep1_ta
rep1_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'paired_end'
})
rep2_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'paired_end'
})
else: #skipped the mapping, so just bring in the inputs from arguments
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = resolve_folder(
output_project, output_folder + '/' + xcor_only_applet.name)
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
})
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink({
'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'
})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
})
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink({
'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'
})
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME,
applet_project.get_id())
encode_spp_stages = []
idr_peaks_output_folder = resolve_folder(
output_project, output_folder + '/' + encode_spp_applet.name)
PEAKS_STAGE_NAME = 'SPP Peaks'
peaks_stage_input = {
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'idr_peaks': args.idr
}
if chrom_sizes:
peaks_stage_input.update({'chrom_sizes': chrom_sizes})
encode_spp_stage_id = workflow.add_stage(encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input=peaks_stage_input)
encode_spp_stages.append({
'name': PEAKS_STAGE_NAME,
'stage_id': encode_spp_stage_id
})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME,
applet_project.get_id())
encode_macs2_stages = []
peaks_output_folder = resolve_folder(
output_project, output_folder + '/' + encode_macs2_applet.name)
macs2_stage_input = {
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as))
}
if genomesize:
macs2_stage_input.update({'genomesize': genomesize})
if chrom_sizes:
macs2_stage_input.update({'chrom_sizes': chrom_sizes})
else:
macs2_stage_input.update({
'chrom_sizes':
dxpy.dxlink({
'stage': encode_spp_stage_id,
'inputField': 'chrom_sizes'
})
})
encode_macs2_stage_id = workflow.add_stage(encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input=macs2_stage_input)
encode_macs2_stages.append({
'name': 'ENCODE Peaks',
'stage_id': encode_macs2_stage_id
})
if args.idr:
# if args.idrversion == "1":
# idr_applet = find_applet_by_name(IDR_APPLET_NAME, applet_project.get_id())
# elif args.idrversion == "2":
# idr_applet = find_applet_by_name(IDR2_APPLET_NAME, applet_project.get_id())
# else:
# logging.error("Invalid IDR version: %s" %(args.idrversion))
# idr_applet = None
idr_applet = find_applet_by_name(IDR2_APPLET_NAME,
applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME,
applet_project.get_id())
idr_stages = []
idr_output_folder = resolve_folder(
output_project, output_folder + '/' + idr_applet.name)
if (args.rep1 and args.ctl1 and args.rep2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
})
})
idr_stages.append({
'name': 'IDR True Replicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
})
})
idr_stages.append({
'name': 'IDR Rep 1 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
})
})
idr_stages.append({
'name': 'IDR Rep 2 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
})
})
idr_stages.append({
'name': 'IDR Pooled Pseudoreplicates',
'stage_id': idr_stage_id
})
stage_input = {
'reps_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR True Replicates'),
'outputField':
'IDR_peaks'
}),
'r1pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'r2pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'pooledpr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'as_file':
dxpy.dxlink(resolve_file(args.narrowpeak_as))
}
if blacklist:
stage_input.update({'blacklist': blacklist})
if chrom_sizes:
stage_input.update({'chrom_sizes': chrom_sizes})
else:
stage_input.update({
'chrom_sizes':
dxpy.dxlink({
'stage': encode_spp_stage_id,
'inputField': 'chrom_sizes'
})
})
idr_stage_id = workflow.add_stage(encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=stage_input)
idr_stages.append({
'name': 'Final IDR peak calls',
'stage_id': idr_stage_id
})
if not (args.nomap):
logging.debug("Mapping stages: %s" % (mapping_superstages))
else:
logging.debug("xcor only stages: %s" % (xcor_only_stages))
# if not args.idronly:
# logging.debug("Peak stages: %s" %(spp_stages))
logging.debug("Peak stages: %s" % (encode_spp_stages))
if args.idr:
logging.debug("IDR stages: %s" % (idr_stages))
if args.yes:
if args.debug:
job_id = workflow.run(
{},
priority='high',
debug={'debugOn': ['AppInternalError', 'AppError']},
delay_workspace_destruction=True,
allow_ssh=['255.255.255.255'])
else:
job_id = workflow.run({}, priority='high')
logging.info("Running as job %s" % (job_id))
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.info('Found output project %s' %(output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.info('Using output folder %s' %(output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.info('Found applet project %s' %(applet_project.name))
workflow = dxpy.new_dxworkflow(
title=WF_TITLE,
name=args.name,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
#this whole strategy is fragile and unsatisfying
#subsequent code assumes reps come before contols
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [
{'name': 'Rep1', 'input_args': args.rep1},
{'name': 'Rep2', 'input_args': args.rep2},
{'name': 'Ctl1', 'input_args': args.ctl1},
{'name': 'Ctl2', 'input_args': args.ctl2}
# {'name': 'Pooled Reps', 'input_args': (args.rep1 and args.rep2)},
# {'name': 'Pooled Controls', 'input_args': (args.ctl1 and args.ctl2)} ##idea is to create a "stub" stage and then populate it's input with the output of the pool stage, defined below
]
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
mapping_output_folder = resolve_folder(output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME, applet_project.get_id())
xcor_output_folder = mapping_output_folder
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
if mapping_superstage.get('input_args') or blank_workflow:
if blank_workflow:
mapping_stage_input = None
else:
mapping_stage_input = {'reference_tar' : dxpy.dxlink(reference_tar.get_id())}
for arg_index,input_arg in enumerate(mapping_superstage['input_args']): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update({'reads%d' %(arg_index+1): reads})
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' %(superstage_name),
folder=mapping_output_folder,
stage_input=mapping_stage_input,
instance_type=args.instance_type
)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' %(superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': mapped_stage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': mapped_stage_id, 'outputField': 'paired_end'})
},
instance_type=args.instance_type
)
mapping_superstage.update({'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Xcor %s' %(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'})
},
instance_type=args.instance_type
)
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
spp_applet = find_applet_by_name(SPP_APPLET_NAME, applet_project.get_id())
spp_stages = []
peaks_output_folder = resolve_folder(output_project, output_folder + '/' + spp_applet.name)
if (args.rep1 and args.ctl1) or blank_workflow:
rep1_spp_stage_id = workflow.add_stage(
spp_applet,
name='Peaks Rep1',
folder=peaks_output_folder,
stage_input={
'experiment' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'}),
'control': dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'}),
'xcor_scores_input': dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'CC_scores_file'})
},
instance_type=args.instance_type
)
spp_stages.append({'name': 'Peaks Rep1', 'stage_id': rep1_spp_stage_id})
if (args.rep2 and args.ctl2) or blank_workflow:
rep2_spp_stage_id = workflow.add_stage(
spp_applet,
name='Peaks Rep2',
folder=peaks_output_folder,
stage_input={
'experiment' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'}),
'control': dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'}),
'xcor_scores_input': dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'CC_scores_file'})
},
instance_type=args.instance_type
)
spp_stages.append({'name': 'Peaks Rep2', 'stage_id': rep2_spp_stage_id})
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME, applet_project.get_id())
encode_spp_stages = []
if args.idr:
idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_spp_applet.name)
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name='Peaks for IDR',
folder=idr_peaks_output_folder,
stage_input={
'rep1_ta' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'}),
'rep2_ta' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'}),
'ctl1_ta': dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'}),
'ctl2_ta' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'}),
'rep1_xcor' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': '"CC_scores_file"'}),
'rep2_xcor' : dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': '"CC_scores_file"'}),
'paired_end': dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'paired_end'}) #here we're assuming if rep1 is PE it's a PE experiment - need better error checking
},
instance_type=args.instance_type
)
encode_spp_stages.append({'name': 'Peaks for IDR', 'stage_id': encode_spp_stage_id})
idr_applet = find_applet_by_name(IDR_APPLET_NAME, applet_project.get_id())
idr_stages = []
if args.idr:
idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'pooled_peaks'})
},
instance_type=args.instance_type
)
idr_stages.append({'name': 'IDR True Replicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep1pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep1pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep1_peaks'})
},
instance_type=args.instance_type
)
idr_stages.append({'name': 'IDR Rep 1 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep2pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep2pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'rep2_peaks'})
},
instance_type=args.instance_type
)
idr_stages.append({'name': 'IDR Rep 2 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoeplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'pooledpr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'pooledpr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == 'Peaks for IDR'),
'outputField': 'pooled_peaks'})
},
instance_type=args.instance_type
)
idr_stages.append({'name': 'IDR Pooled Pseudoeplicates', 'stage_id': idr_stage_id})
logging.debug("Mapping stages: %s" %(mapping_superstages))
logging.debug("Peak stages: %s" %(spp_stages))
logging.debug("Peaks for IDR stages: %s" %(encode_spp_stages))
logging.debug("IDR stages: %s" %(idr_stages))
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.info('Found output project %s' % (output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.info('Using output folder %s' % (output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.info('Found applet project %s' % (applet_project.name))
workflow = dxpy.new_dxworkflow(name=args.name,
title=args.title,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not args.nomap:
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [
{
'name': 'Rep1',
'input_args': args.rep1
}, {
'name': 'Rep2',
'input_args': args.rep2
}, {
'name': 'Ctl1',
'input_args': args.ctl1
}, {
'name': 'Ctl2',
'input_args': args.ctl2
}
# {'name': 'Pooled Reps', 'input_args': (args.rep1 and args.rep2)},
# {'name': 'Pooled Controls', 'input_args': (args.ctl1 and args.ctl2)} ##idea is to create a "stub" stage and then populate it's input with the output of the pool stage, defined below
]
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME,
applet_project.get_id())
mapping_output_folder = resolve_folder(
output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME,
applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = mapping_output_folder
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
if mapping_superstage.get('input_args') or blank_workflow:
if blank_workflow:
if args.reference:
mapping_stage_input = {
'reference_tar':
dxpy.dxlink(reference_tar.get_id())
}
else:
mapping_stage_input = None
else:
mapping_stage_input = {
'reference_tar': dxpy.dxlink(reference_tar.get_id())
}
for arg_index, input_arg in enumerate(
mapping_superstage['input_args']
): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update(
{'reads%d' % (arg_index + 1): reads})
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' % (superstage_name),
folder=mapping_output_folder,
stage_input=mapping_stage_input)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' % (superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': mapped_stage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': mapped_stage_id,
'outputField': 'paired_end'
})
})
mapping_superstage.update(
{'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(xcor_applet,
name='Xcor %s' %
(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'paired_end'
})
})
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'tagAlign_file'
})
exp_rep1_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'CC_scores_file'
})
exp_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'tagAlign_file'
})
exp_rep2_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'CC_scores_file'
})
ctl_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl1'),
'outputField':
'tagAlign_file'
})
ctl_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl2'),
'outputField':
'tagAlign_file'
})
rep1_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'paired_end'
})
rep2_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'paired_end'
})
else: #skipped the mapping, so just bring in the inputs from arguments
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = resolve_folder(
output_project, output_folder + '/' + xcor_only_applet.name)
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
})
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink({
'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'
})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
})
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink({
'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'
})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME,
applet_project.get_id())
encode_macs2_stages = []
peaks_output_folder = resolve_folder(
output_project, output_folder + '/' + encode_macs2_applet.name)
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
encode_macs2_stage_id = workflow.add_stage(
encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input={
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as)),
'genomesize': args.genomesize
})
encode_macs2_stages.append({
'name': 'ENCODE Peaks',
'stage_id': encode_macs2_stage_id
})
#new applet here, similar to IDR, to do naive peak processing
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
overlap_peaks_applet = find_applet_by_name(OVERLAP_PEAKS_APPLET_NAME,
applet_project.get_id())
overlap_peaks_stages = []
for peaktype in ['narrowpeaks', 'gappedpeaks', 'broadpeaks']:
if peaktype == 'narrowpeaks':
as_file = dxpy.dxlink(resolve_file(args.narrowpeak_as))
peak_type_extension = 'narrowPeak'
elif peaktype == 'gappedpeaks':
as_file = dxpy.dxlink(resolve_file(args.gappedpeak_as))
peak_type_extension = 'gappedPeak'
elif peaktype == 'broadpeaks':
as_file = dxpy.dxlink(resolve_file(args.broadpeak_as))
peak_type_extension = 'broadPeak'
overlap_peaks_stage_id = workflow.add_stage(
overlap_peaks_applet,
name='Overlap %s' % (peaktype),
folder=peaks_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_%s' % (peaktype)
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_%s' % (peaktype)
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_%s' % (peaktype)
}),
'pooledpr1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr1_%s' % (peaktype)
}),
'pooledpr2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr2_%s' % (peaktype)
}),
'chrom_sizes':
dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file':
as_file,
'peak_type':
peak_type_extension
})
overlap_peaks_stages.append({
'name': 'Overlap %s' % (peaktype),
'stage_id': overlap_peaks_stage_id
})
#TODO - IDR on gapped and broad peaks
if args.idr:
idr_applet = find_applet_by_name(IDR_APPLET_NAME,
applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME,
applet_project.get_id())
idr_peaks_output_folder = resolve_folder(
output_project, output_folder + '/' + idr_applet.name)
idr_output_folder = resolve_folder(
output_project, output_folder + '/' + idr_applet.name)
idr_stages = []
if (args.rep1 and args.ctl1 and args.rep2
and args.ctl2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_narrowpeaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_narrowpeaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_narrowpeaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR True Replicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1pr1_narrowpeaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1pr2_narrowpeaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep1_narrowpeaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Rep 1 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2pr1_narrowpeaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2pr2_narrowpeaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'rep2_narrowpeaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Rep 2 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr1_narrowpeaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooledpr2_narrowpeaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_macs2_stages
if ss['name'] == 'ENCODE Peaks'),
'outputField':
'pooled_narrowpeaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Pooled Pseudoreplicates',
'stage_id': idr_stage_id
})
final_idr_stage_input = {
'reps_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR True Replicates'),
'outputField':
'IDR_peaks'
}),
'r1pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'r2pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'pooledpr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'chrom_sizes':
dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file':
dxpy.dxlink(resolve_file(args.narrowpeak_as))
}
if args.blacklist:
final_idr_stage_input.update(
{'blacklist': dxpy.dxlink(resolve_file(args.blacklist))})
idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=final_idr_stage_input)
idr_stages.append({
'name': 'Final IDR peak calls',
'stage_id': idr_stage_id
})
if not (args.nomap):
logging.debug("Mapping stages: %s" % (mapping_superstages))
else:
logging.debug("xcor only stages: %s" % (xcor_only_stages))
logging.debug("Peaks for ENCODE stages: %s" % (encode_macs2_stages))
logging.debug("Peak overlap stages: %s" % (overlap_peaks_stages))
if args.idr:
logging.debug("IDR stages: %s" % (idr_stages))
if args.yes:
if args.debug:
job_id = workflow.run(
{},
priority='high',
debug={'debugOn': ['AppInternalError', 'AppError']},
delay_workspace_destruction=True,
allow_ssh=['255.255.255.255'])
else:
job_id = workflow.run({}, priority='high')
logging.info("Running as job %s" % (job_id))
def main():
args = get_args()
if len(args.replicates) < 1:
sys.exit('Need to have at least 1 replicate file.')
project = resolve_project(ENCODE_DNA_ME_PROJECT_NAME)
print 'Project: ' + project.describe()['name']
print 'Experiment to analyze: ' + args.experiment
if not project_has_folder(project, '/' + args.experiment):
project.new_folder('/' + args.experiment)
#TODO get all replicate ids from encoded DB from ENCSR (args.experiment)
#TODO error out if ENCSR not found, status not complete etc.
if args.test:
source_id = project.get_id()
else:
source_id = resolve_project(ENCODE_SNAPSHOT_PROJECT,
level='VIEW').get_id()
replicates = []
for rep in args.replicates:
dx_rep = dxpy.find_data_objects(classname='file',
name=rep,
name_mode='exact',
project=source_id,
return_handler=False)
replicates.extend(dx_rep)
if not args.test:
replicates = copy_files(replicates, project.get_id(),
"/" + args.experiment)
if not replicates:
print "No replicates found in project: " + project.name
print "Looking for " + ", ".join(args.replicates)
sys.exit(1)
inputs = {'rnd_seed': 12345}
inputs['paired'] = args.paired
inputs['gender'] = args.gender
inputs['organism'] = args.organism
inputs['library_id'] = args.library
inputs['nthreads'] = args.nthreads
#TODO determine paired or gender from ENCSR metadata
# Now create a new workflow ()
inputs['spec_name'] = args.experiment + '-' + '-'.join(
[r.split('.')[0] for r in args.replicates])
title_root = 'dx_long_rna_seq_'
name_root = 'ENCODE Long RNA Seq: '
desc = 'The ENCODE RNA Seq pipeline for long RNAs'
if args.paired:
title_root = title_root + '_paired_end '
name_root = name_root + '(paired-end) '
inputs['stranded'] = True
else:
title_root = title_root + '_single_end '
name_root = name_root + '(single-end) '
inputs['stranded'] = False
if args.export:
project_id = dxpy.find_one_project(name=ENCODE_PUBLIC_PROJECT,
name_mode='exact',
return_handler=False)['id']
wf = dxpy.new_dxworkflow(title=title_root,
name=name_root,
description=desc,
folder=PUBLIC_FOLDER,
project=project_id)
else:
project_id = project.get_id()
wf = dxpy.new_dxworkflow(title=title_root + inputs['spec_name'],
name=name_root + inputs['spec_name'],
description=desc + ' for experiment:' +
args.experiment,
folder='/' + args.experiment,
project=project.get_id())
populate_workflow(wf, replicates, args.experiment, inputs, project.id,
args.export)
def build_workflow():
if parameters["folder_provided"] == "false":
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
output_folder=parameters["Output"],
)
else:
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
)
wf_outputs = []
fastqc_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["fastqc"],
state="closed",
return_handler=True)
star_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["star"],
state="closed",
return_handler=True)
combine_sj_tab_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["combine_sj_out"],
state="closed",
return_handler=True)
sort_bam_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["sort_bam"],
state="closed",
return_handler=True)
htseq_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["htseq"],
state="closed",
return_handler=True)
genome_cov_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["genome_coverage"],
state="closed",
return_handler=True)
bigwig_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["bigwig"],
state="closed",
return_handler=True)
combine_counts_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["combine_counts"],
state="closed",
return_handler=True)
combine_flagstat_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["combine_flagstat"],
state="closed",
return_handler=True)
limma_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["limma"],
state="closed",
return_handler=True)
simple_DE_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["simple_DE"],
state="closed",
return_handler=True)
bw_viewer_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["bw_viewer"],
state="closed",
return_handler=True)
sample_num = 0
htseq_results = []
bigwig_files = []
flagstat_files_arr = []
index_project, index_id = parameters["index_file"].split(":")
gtf_project, gtf_id = parameters["gtf_file"].split(":")
genome_length_project, genome_length_id = parameters[
"genome_sizes_file"].split(":")
gene_length_project, gene_length_id = parameters["gene_length_file"].split(
":")
fpkm_results = []
fpkm_log2_results = []
sj_out_files_arr = []
star_alignment_opts = {
"outSAMunmapped": parameters["outSAMunmapped"],
"outSAMattributes": parameters["outSAMattributes"],
"outFilterMultimapNmax": int(parameters["outFilterMultimapNmax"]),
"outFilterMismatchNmax": int(parameters["outFilterMismatchNmax"]),
"alignIntronMax": int(parameters["alignIntronMax"]),
"outSAMstrandField": parameters["outSAMstrandField"],
"chimSegmentMin": int(parameters["chimSegmentMin"]),
"sjdbOverhang": int(parameters["sjdbOverhang"]),
"chimJunctionOverhangMin": int(parameters["chimJunctionOverhangMin"]),
"subsample_target": int(parameters["STAR_subsample_n_reads"]),
}
if parameters["two_pass_alignment"] == 'true':
for sample_name in samples:
forward_id = samples[sample_name][0]
forward_link = dxpy.dxlink(forward_id)
align_input = {}
align_input["first_pass"] = True
align_input.update(star_alignment_opts)
align_input["read_file1"] = forward_link
star_instance = parameters["star_instance"]
align_input["mark_duplicates"] = False
align_input["generate_transcriptome_BAM"] = False
align_input["star_index_archive"] = dxpy.dxlink({
"project": index_project,
"id": index_id
})
if parameters["sjdbFileChrStartEnd"] != "null" and parameters[
"sjdbFileChrStartEnd"] != '':
sjdbFileChrStartEnd_project, sjdbFileChrStartEnd_id = parameters[
"sjdbFileChrStartEnd"].split(":")
align_input["sjdbFileChrStartEnd"] = dxpy.dxlink({
"project":
sjdbFileChrStartEnd_project,
"id":
sjdbFileChrStartEnd_id
})
if parameters["indexed_with_gtf"] != "true":
align_input["transcriptome_gtf"] = dxpy.dxlink({
"project": gtf_project,
"id": gtf_id
})
align_input["output_prefix"] = sample_name
align_stage_id = ""
if samples[sample_name][1] != "-":
reverse_id = samples[sample_name][1]
reverse_link = dxpy.dxlink(reverse_id)
align_input["read_file2"] = reverse_link
first_align_stage_id = wf.add_stage(
star_applet,
stage_input=align_input,
instance_type=star_instance,
folder="ALIGN_Pass1",
name=sample_name + ":ALIGN_Pass1")
else:
first_align_stage_id = wf.add_stage(
star_applet,
stage_input=align_input,
instance_type=star_instance,
folder="ALIGN_Pass1",
name=sample_name + ":ALIGN_Pass1")
sj_out_files_arr.append(
dxpy.dxlink({
"stage": first_align_stage_id,
"outputField": "sj_tab_out"
}))
combine_sj_out_input = {"sj_out_files": sj_out_files_arr}
combine_sj_out_stage_pass1_id = wf.add_stage(
combine_sj_tab_applet,
stage_input=combine_sj_out_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE SJ OUT PASS1",
folder="COMBINED_JUNCTIONS_PASS1")
parameters["pass1_sj_out"] = dxpy.dxlink({
"stage":
combine_sj_out_stage_pass1_id,
"outputField":
"combined_sj_out"
})
for sample_name in samples:
forward_id = samples[sample_name][0]
forward_link = dxpy.dxlink(forward_id)
if parameters["run_FastQC"] == 'true':
forward_input = {"fastq_input": forward_link}
fq_stage_id = wf.add_stage(fastqc_applet,
stage_input=forward_input,
instance_type="azure:mem2_ssd1_x2",
folder="FASTQC",
name=sample_name + ":Forward FASTQC")
wf_outputs += [{
"name": sample_name + "_forward_fastqc_html",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": fq_stage_id,
"outputField": "html_file"
}
}
}, {
"name": sample_name + "_forward_fastqc_zip",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": fq_stage_id,
"outputField": "zip_file"
}
}
}]
align_input = {}
align_input.update(star_alignment_opts)
mark_duplicates = parameters["mark_duplicates"]
if mark_duplicates == "false":
mark_duplicates = False
else:
mark_duplicates = True
align_input["mark_duplicates"] = mark_duplicates
if parameters["generate_transcriptome_BAM"] == "true":
align_input["generate_transcriptome_BAM"] = True
else:
align_input["generate_transcriptome_BAM"] = False
star_instance = parameters["star_instance"]
align_input["read_file1"] = forward_link
align_input["star_index_archive"] = dxpy.dxlink({
"project": index_project,
"id": index_id
})
if parameters["indexed_with_gtf"] != "true":
align_input["transcriptome_gtf"] = dxpy.dxlink({
"project": gtf_project,
"id": gtf_id
})
if "pass1_sj_out" in parameters:
align_input["sjdbFileChrStartEnd"] = parameters["pass1_sj_out"]
elif parameters["sjdbFileChrStartEnd"] != "null" and parameters[
"sjdbFileChrStartEnd"] != '':
sjdbFileChrStartEnd_project, sjdbFileChrStartEnd_id = parameters[
"sjdbFileChrStartEnd"].split(":")
align_input["sjdbFileChrStartEnd"] = dxpy.dxlink({
"project":
sjdbFileChrStartEnd_project,
"id":
sjdbFileChrStartEnd_id
})
align_input["output_prefix"] = sample_name
align_stage_id = ""
if samples[sample_name][1] != "-":
reverse_id = samples[sample_name][1]
reverse_link = dxpy.dxlink(reverse_id)
if parameters["run_FastQC"] == 'true':
rev_input = {"fastq_input": reverse_link}
rev_fq_stage_id = wf.add_stage(
fastqc_applet,
stage_input=rev_input,
instance_type="azure:mem2_ssd1_x2",
folder="FASTQC",
name=sample_name + ":Reverse FASTQC")
wf_outputs += [{
"name": sample_name + "_reverse_fastqc_html",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": rev_fq_stage_id,
"outputField": "html_file"
}
}
}, {
"name": sample_name + "_reverse_fastqc_zip",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": rev_fq_stage_id,
"outputField": "zip_file"
}
}
}]
align_input["read_file2"] = reverse_link
align_stage_id = wf.add_stage(star_applet,
stage_input=align_input,
instance_type=star_instance,
folder="STAR",
name=sample_name + ":ALIGN")
else:
align_stage_id = wf.add_stage(star_applet,
stage_input=align_input,
instance_type=star_instance,
folder="STAR",
name=sample_name + ":ALIGN")
flagstat_files_arr.append(
dxpy.dxlink({
"stage": align_stage_id,
"outputField": "flagstat_out"
}))
sj_out_files_arr.append(
dxpy.dxlink({
"stage": align_stage_id,
"outputField": "sj_tab_out"
}))
wf_outputs += [
{
"name": sample_name + "_star_bam",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "sorted_by_coord_bam"
}
}
},
{
"name": sample_name + "_star_log",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "log_final_out"
}
}
},
{
"name": sample_name + "_flagstat",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "flagstat_out"
}
}
},
{
"name": sample_name + "_star_splice_junctions",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "sj_tab_out"
}
}
},
{
"name": sample_name + "_star_chimeric_bam",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "chimeric_bam"
}
}
},
{
"name": sample_name + "_star_chimeric_junction",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "chimeric_junction"
}
}
},
]
if parameters["generate_transcriptome_BAM"] == "true":
wf_outputs += [
{
"name": sample_name + "_star_transcriptome_bam",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": align_stage_id,
"outputField": "to_transcriptome_bam"
}
}
},
]
if parameters["generate_name_sorted_BAM"] == "true":
sort_input = {
"input_bam":
dxpy.dxlink({
"stage": align_stage_id,
"outputField": "sorted_by_coord_bam"
})
}
sort_stage_id = wf.add_stage(sort_bam_applet,
stage_input=sort_input,
instance_type="azure:mem2_ssd1_x2",
name=sample_name + ":NAME SORT BAM",
folder="STAR")
wf_outputs += [
{
"name": sample_name + "_name_sorted_bam",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": sort_stage_id,
"outputField": "output_bam"
}
}
},
]
htseq_input = {
"input_bam":
dxpy.dxlink({
"stage": sort_stage_id,
"outputField": "output_bam"
})
}
htseq_input["order"] = "name"
else:
htseq_input = {
"input_bam":
dxpy.dxlink({
"stage": align_stage_id,
"outputField": "sorted_by_coord_bam"
})
}
htseq_input["order"] = "pos"
htseq_input["annotation_file"] = dxpy.dxlink({
"project": gtf_project,
"id": gtf_id
})
htseq_input["gene_length_file"] = dxpy.dxlink({
"project": gene_length_project,
"id": gene_length_id
})
htseq_input["prefix"] = sample_name
htseq_input["strand"] = parameters["strandedness"]
htseq_input["feature_type"] = parameters["feature_type"]
htseq_input["id_attribute"] = parameters["id_attribute"]
htseq_input["mode"] = parameters["mode"]
htseq_input["nonunique"] = parameters["nonunique"]
htseq_input["secondary_alignments"] = parameters[
"secondary_alignments"]
htseq_input["supplementary_alignments"] = parameters[
"supplementary_alignments"]
htseq_stage_id = wf.add_stage(
htseq_applet,
stage_input=htseq_input,
instance_type=parameters["htseq_instance"],
name=sample_name + ":HTSEQ COUNT",
folder="HTSEQ")
htseq_results.append(
dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "htseq_counts"
}))
wf_outputs += [
{
"name": sample_name + "_htseqcounts",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "htseq_counts"
}
}
},
]
if parameters["id_attribute"] == "gene_name":
fpkm_results.append((dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "fpkm"
})))
fpkm_log2_results.append((dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "fpkm_log2"
})))
wf_outputs += [
{
"name": sample_name + "_fpkm",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "fpkm"
}
}
},
{
"name": sample_name + "_fpkm_log2",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "fpkm_log2"
}
}
},
]
if parameters["run_coverage"] == 'true':
gcb_input = {}
gcb_input["input_bam"] = dxpy.dxlink({
"stage":
align_stage_id,
"outputField":
"sorted_by_coord_bam"
})
gcb_input["genome_sizes_file"] = dxpy.dxlink({
"project": genome_length_project,
"id": genome_length_id
})
gcb_input["strandedness"] = parameters["strandedness"]
gcb_input["output_prefix"] = sample_name
gcb_stage_id = wf.add_stage(genome_cov_applet,
stage_input=gcb_input,
instance_type="azure:mem3_ssd1_x8",
name=sample_name + ":COVERAGE",
folder="COVERAGE")
bg2bw_all_input = {}
bg2bw_all_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"all_coverage_file"
})
bg2bw_all_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_all_input["output_prefix"] = sample_name
bg2bw_all_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_all_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-ALL",
folder="BIGWIG")
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_all_stage_id,
"outputField": "bigwig"
}))
wf_outputs += [
{
"name": sample_name + "_all_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_all_stage_id,
"outputField": "bigwig"
}
}
},
]
if parameters["strandedness"] != "no":
bg2bw_pos_input = {}
bg2bw_pos_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"pos_coverage_file"
})
bg2bw_pos_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_pos_input["output_prefix"] = sample_name
bg2bw_pos_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_pos_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-POS",
folder="BIGWIG")
wf_outputs += [
{
"name": sample_name + "_pos_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_pos_stage_id,
"outputField": "bigwig"
}
}
},
]
bg2bw_neg_input = {}
bg2bw_neg_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"neg_coverage_file"
})
bg2bw_neg_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_neg_input["output_prefix"] = sample_name
bg2bw_neg_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_neg_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-NEG",
folder="BIGWIG")
wf_outputs += [
{
"name": sample_name + "_neg_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_neg_stage_id,
"outputField": "bigwig"
}
}
},
]
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_pos_stage_id,
"outputField": "bigwig"
}))
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_neg_stage_id,
"outputField": "bigwig"
}))
sample_num += 1
combine_input = {
"count_files": htseq_results,
"name_value": "htseq",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_counts_stage_id = wf.add_stage(combine_counts_applet,
stage_input=combine_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE HTSEQ")
wf_outputs += [
{
"name": "combined_counts",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_counts_stage_id,
"outputField": "count_file"
}
}
},
]
if parameters["id_attribute"] == "gene_name":
combine_fpkm_input = {
"count_files": fpkm_results,
"name_value": "fpkm",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_fpkm_stage_id = wf.add_stage(
combine_counts_applet,
stage_input=combine_fpkm_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE FPKM")
combine_fpkm_log2_input = {
"count_files": fpkm_log2_results,
"name_value": "fpkm.log2",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_fpkm_log2_stage_id = wf.add_stage(
combine_counts_applet,
stage_input=combine_fpkm_log2_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE FPKMlog2")
wf_outputs += [
{
"name": "combined_fpkm",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_fpkm_stage_id,
"outputField": "count_file"
}
}
},
{
"name": "combined_fpkm_log2",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_fpkm_log2_stage_id,
"outputField": "count_file"
}
}
},
]
combine_flagstat_input = {
"flagstat_files": flagstat_files_arr,
"sample_list": dxpy.dxlink(final_sample_list_id)
}
combine_flagstat_stage_id = wf.add_stage(
combine_flagstat_applet,
stage_input=combine_flagstat_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE FLAGSTAT",
folder="STAR")
wf_outputs += [
{
"name": "combined_flagstat",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_flagstat_stage_id,
"outputField": "combined_flagstat"
}
}
},
]
if parameters["BW_VIEWER"] != "None" and parameters[
"run_coverage"] == 'true':
bw_project, bw_file = parameters["BW_VIEWER"].split(":")
viewer_link = dxpy.dxlink({"project": bw_project, "id": bw_file})
bw_viewer_input = {"viewer": viewer_link, "bigwig_files": bigwig_files}
bw_viewer_stage_id = wf.add_stage(bw_viewer_applet,
stage_input=bw_viewer_input,
instance_type="azure:mem2_ssd1_x1",
name="BIGWIG_VIEWER",
folder="BIGWIG")
wf_outputs += [
{
"name": "bw_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": bw_viewer_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
if parameters["limma_DE_viewer"] != "None":
limma_viewer_project, limma_viewer_file = parameters[
"limma_DE_viewer"].split(":")
limma_viewer_link = dxpy.dxlink({
"project": limma_viewer_project,
"id": limma_viewer_file
})
if parameters["run_limma"] == 'true' and parameters[
"limma_runnable"] == "true":
limma_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"calcNormFactors_method":
parameters["calcNormFactors_method"],
"filter_count_type":
parameters["filter_count_type"],
"filter_count":
int(parameters["filter_count"]),
"p_value_adjust":
parameters["p_value_adjust"],
"contrasts_file":
dxpy.dxlink(comparisons_limma_id)
}
if parameters["limma_DE_viewer"] != "None":
limma_input["difex_viewer"] = limma_viewer_link
limma_stage_id = wf.add_stage(limma_applet,
stage_input=limma_input,
instance_type="azure:mem1_ssd1_x4",
name="LIMMA")
wf_outputs += [
{
"name": "limma_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "limma_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
if parameters["run_simple_dif_ex"] == 'true':
simple_DE_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"contrasts_file":
dxpy.dxlink(comparisons_all_id),
"difex_viewer":
limma_viewer_link
}
if parameters["limma_DE_viewer"] != "None":
simple_DE_input["difex_viewer"] = limma_viewer_link
simple_DE_stage_id = wf.add_stage(
simple_DE_applet,
stage_input=simple_DE_input,
instance_type="azure:mem1_ssd1_x4",
name="SIMPLE DIFFERENTIAL_EXPRESSION")
wf_outputs += [
{
"name": "simple_DE_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "simple_DE_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
wf.update(workflow_outputs=wf_outputs)
wf.close()
return wf.get_id()
def build_workflow():
wf = dxpy.new_dxworkflow(title='tcga_mc3_full_run',
name='tcga_mc3_full_run',
description='TCGA mc3 variant calling pipeline',
project=args.project,
folder=args.folder,
properties={"git_revision": git_revision})
# variant calling tools
pindel_applet = find_applet("pindel-tool")
pindel_stage_id = wf.add_stage(pindel_applet)
radia_applet = find_applet("radia-tool")
radia_input = {
"dnaNormalBam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBamFile"
}),
"dnaTumorBam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"fasta":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
radia_stage_id = wf.add_stage(radia_applet, stage_input=radia_input)
somaticsniper_applet = find_applet("somaticsniper-tool")
somaticsniper_input = {
"normal":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBamFile"
}),
"tumor":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
somaticsniper_stage_id = wf.add_stage(somaticsniper_applet,
stage_input=somaticsniper_input,
instance_type="mem2_hdd2_x1")
samtools_pileup_applet = find_applet("samtools-pileup-tool")
samtools_pileup_normal_input = {
"input1":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBamFile"
}),
"input1_index":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBaiFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
samtools_pileup_normal_stage_id = wf.add_stage(
samtools_pileup_applet,
stage_input=samtools_pileup_normal_input,
instance_type="mem2_hdd2_x1")
samtools_pileup_tumor_input = {
"input1":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"input1_index":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBaiFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
samtools_pileup_tumor_stage_id = wf.add_stage(
samtools_pileup_applet,
stage_input=samtools_pileup_tumor_input,
instance_type="mem2_hdd2_x2")
muse_applet = find_applet("muse-tool")
muse_input = {
"tumor_bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"tumor_bai":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBaiFile"
}),
"normal_bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBamFile"
}),
"normal_bai":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBaiFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
}),
"dbsnp":
dxpy.dxlink("file-Bj1V0400kF9Z3GqJY4ZbYbYj")
}
muse_stage_id = wf.add_stage(muse_applet, stage_input=muse_input)
varscan_applet = find_applet("varscan-tool")
varscan_input = {
"normal_pileup":
dxpy.dxlink({
"stage": samtools_pileup_normal_stage_id,
"outputField": "pileup"
}),
"tumor_pileup":
dxpy.dxlink({
"stage": samtools_pileup_tumor_stage_id,
"outputField": "pileup"
})
}
varscan_stage_id = wf.add_stage(varscan_applet,
stage_input=varscan_input,
instance_type="mem2_hdd2_x2")
mutect_applet = find_applet("mutect-tool")
mutect_input = {
"tumor_bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"tumor_bai":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBaiFile"
}),
"normal_bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBamFile"
}),
"normal_bai":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "normalInputBaiFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
}),
"dbsnp":
dxpy.dxlink("file-Bj1V0400kF9Z3GqJY4ZbYbYj"),
"cosmic":
dxpy.dxlink("file-Bk9g2kQ0kF9f9XG6VZf7VGKQ"),
}
mutect_stage_id = wf.add_stage(mutect_applet, stage_input=mutect_input)
# fpfilter (somaticSniper, Varscan)
fpfilter_applet = find_applet("fpfilter-tool")
somatcisniper_fpfilter_input = {
"vcf":
dxpy.dxlink({
"stage": somaticsniper_stage_id,
"outputField": "vcf"
}),
"bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
somaticsniper_fpfilter_stage_id = wf.add_stage(
fpfilter_applet,
stage_input=somatcisniper_fpfilter_input,
name="fpfilter-tool(somaticSniper)",
folder="fpfiltered")
varscan_snp_fpfilter_input = {
"vcf":
dxpy.dxlink({
"stage": varscan_stage_id,
"outputField": "snp_vcf"
}),
"bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
varscan_snp_fpfilter_stage_id = wf.add_stage(
fpfilter_applet,
stage_input=varscan_snp_fpfilter_input,
name="fpfilter-tool(varscan SNP)",
folder="fpfiltered")
varscan_indel_fpfilter_input = {
"vcf":
dxpy.dxlink({
"stage": varscan_stage_id,
"outputField": "indel_vcf"
}),
"bam":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "tumorInputBamFile"
}),
"reference":
dxpy.dxlink({
"stage": pindel_stage_id,
"inputField": "inputReferenceFile"
})
}
varscan_indel_fpfilter_stage_id = wf.add_stage(
fpfilter_applet,
stage_input=varscan_indel_fpfilter_input,
name="fpfilter-tool(varscan INDEL)",
folder="fpfiltered")
# vcf_filter (All variant callers)
vcf_filter_applet = find_applet("tcga-vcf-filter-tool")
radia_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": radia_stage_id,
"outputField": "filtered_output_vcf"
}),
"filterRejects":
False
}
radia_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=radia_vcf_filter_input,
name="vcffilter-tool(radia)",
folder="final_filtered")
somaticsniper_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": somaticsniper_fpfilter_stage_id,
"outputField": "annotated_output"
}),
"filterRejects":
False
}
somaticsniper_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=somaticsniper_vcf_filter_input,
name="vcffilter-tool(somaticsniper)",
folder="final_filtered")
varscan_snp_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": varscan_snp_fpfilter_stage_id,
"outputField": "annotated_output"
}),
"filterRejects":
True
}
varscan_snp_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=varscan_snp_vcf_filter_input,
name="vcffilter-tool(varscan SNP)",
folder="final_filtered")
varscan_indel_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": varscan_indel_fpfilter_stage_id,
"outputField": "annotated_output"
}),
"filterRejects":
True
}
varscan_indel_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=varscan_indel_vcf_filter_input,
name="vcffilter-tool(varscan INDEL)",
folder="final_filtered")
muse_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": muse_stage_id,
"outputField": "mutations"
}),
"filterRejects":
False
}
muse_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=muse_vcf_filter_input,
name="vcffilter-tool(muse)",
folder="final_filtered")
pindel_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": pindel_stage_id,
"outputField": "outputSomaticVcf"
}),
"filterRejects":
False
}
pindel_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=pindel_vcf_filter_input,
name="vcffilter-tool(pindel)",
folder="final_filtered")
mutect_vcf_filter_input = {
"input_vcf":
dxpy.dxlink({
"stage": mutect_stage_id,
"outputField": "mutations"
}),
"filterRejects":
True
}
mutect_vcf_filter_stage_id = wf.add_stage(
vcf_filter_applet,
stage_input=mutect_vcf_filter_input,
name="vcffilter-tool(mutect)",
folder="final_filtered")
vcf_reheader_applet = find_applet("tcga-vcf-reheader")
radia_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": radia_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"radia",
"software_version":
"1",
"software_params":
"--dnaNormalMinTotalBases 4 --dnaNormalMinAltBases 2 --dnaNormalBaseQual 10 --dnaNormalMapQual 10 --dnaTumorDescription TumorDNASample --dnaTumorMinTotalBases 4 --dnaTumorMinAltBases 2 --dnaTumorBaseQual 10 --dnaTumorMapQual 10 --dnaNormalMitochon=MT --dnaTumorMitochon=MT --genotypeMinDepth 2 --genotypeMinPct 0.100",
"center":
"ucsc.edu"
}
radia_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=radia_vcf_reheader_input,
name="vcf-reheader(radia)",
folder="final_reheadered")
"""
sample_params = {
"platform": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "platform"}),
"participant_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "participant_uuid"}),
"disease_code": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "disease_code"}),
"normal_analysis_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_analysis_uuid"}),
"normal_bam_name": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_bam_name"}),
"normal_aliquot_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_aliquot_id"}),
"normal_aliquot_barcode": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_aliquot_barcode"}),
"tumor_analysis_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_analysis_uuid"}),
"tumor_bam_name": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_bam_name"}),
"tumor_aliquot_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_aliquot_uuid"}),
"tumor_aliquot_barcode": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_aliquot_barcode"})
}
"""
somaticsniper_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": somaticsniper_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"somaticsniper",
"software_version":
"v1.0.5.0",
"software_params":
"-Q 40 -n NORMAL -q 1 -s 0.01 -r 0.001",
"center":
"wustl.edu"
}
#somaticsniper_vcf_reheader_input.update(sample_params)
somaticsniper_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=somaticsniper_vcf_reheader_input,
name="vcf-reheader(somaticsniper)",
folder="final_reheadered")
varscan_snp_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": varscan_snp_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"varscan",
"software_version":
"2.3.9",
"software_params":
"--output-vcf 1 --min-coverage 3 --normal-purity 1 --p-value 0.99 --min-coverage-normal 8 --min-freq-for-hom 0.75 --min-var-freq 0.08 --somatic-p-value 0.05 --min-coverage-tumor 6 --tumor-purity 1",
"center":
"wustl.edu"
}
#varscan_snp_vcf_reheader_input.update(sample_params)
varscan_snp_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=varscan_snp_vcf_reheader_input,
name="vcf-reheader(varscan SNP)",
folder="final_reheadered")
varscan_indel_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": varscan_indel_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"varscan",
"software_version":
"2.3.9",
"software_params":
"--output-vcf 1 --min-coverage 3 --normal-purity 1 --p-value 0.99 --min-coverage-normal 8 --min-freq-for-hom 0.75 --min-var-freq 0.08 --somatic-p-value 0.05 --min-coverage-tumor 6 --tumor-purity 1",
"center":
"wustl.edu"
}
#varscan_indel_vcf_reheader_input.update(sample_params)
varscan_indel_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=varscan_indel_vcf_reheader_input,
name="vcf-reheader(varscan INDEL)",
folder="final_reheadered")
muse_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": muse_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"muse",
"software_version":
"v1.0rc",
"software_params":
"--mode wxs",
"center":
"mdanderson.org"
}
#muse_vcf_reheader_input.update(sample_params)
muse_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=muse_vcf_reheader_input,
name="vcf-reheader(muse)",
folder="final_reheadered")
pindel_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": pindel_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"pindel",
"software_version":
"v0.2.5b8",
"software_params":
"--max_range_index 1 --window_size 5 --sequencing_error_rate 0.010000 --sensitivity 0.950000 --maximum_allowed_mismatch_rate 0.020000 --NM 2 --additional_mismatch 1 --min_perfect_match_around_BP 3 --min_inversion_size 50 --min_num_matched_bases 30 --balance_cutoff 0 --anchor_quality 0 --minimum_support_for_event 3 --report_long_insertions --report_duplications --report_inversions --report_breakpoints",
"center":
"wustl.edu"
}
#pindel_vcf_reheader_input.update(sample_params)
pindel_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=pindel_vcf_reheader_input,
name="vcf-reheader(pindel)",
folder="final_reheadered")
mutect_vcf_reheader_input = {
"input_vcf":
dxpy.dxlink({
"stage": mutect_vcf_filter_stage_id,
"outputField": "output_vcf"
}),
"software_name":
"mutect",
"software_version":
"1.1.5",
"software_params":
"--initial_tumor_lod 4.0 --tumor_lod 10.0",
"center":
"broad.org"
}
mutect_vcf_reheader_stage_id = wf.add_stage(
vcf_reheader_applet,
stage_input=mutect_vcf_reheader_input,
name="vcf-reheader(mutect)",
folder="final_reheadered")
return wf
return_handler=False)
replicates = [dxpy.dxlink(r) for r in replicates]
controls = dxpy.find_data_objects(classname='file',
name='*.bam',
name_mode='glob',
project=project.get_id(),
folder=CONTROLS_FOLDER,
return_handler=False)
controls = [dxpy.dxlink(c) for c in controls]
else:
if (len(args.replicates) < 1) or (len(args.controls) < 1):
sys.exit(
'Need to have at least 1 replicate file and 1 control file.')
project.new_folder(REPLICATES_FOLDER, True)
project.new_folder(CONTROLS_FOLDER, True)
replicates = copy_files(args.replicates, project, REPLICATES_FOLDER)
controls = copy_files(args.controls, project, CONTROLS_FOLDER)
if (len(replicates) < 1) or (len(controls) < 1):
sys.exit('Need to have at least 1 replicate file and 1 control file.')
# Now create a new workflow
wf = dxpy.new_dxworkflow(title='dx_chip_seq',
name='ENCODE ChIP-Seq 2.0',
description='The ENCODE ChIP-Seq Pipeline 2.0',
project=project.get_id())
populate_workflow(wf, replicates, controls,
project.describe()['name'],
args.sort_filter_and_remove_dups,
args.duplicates_removed, args.gender, applets_project_id)
def build_workflow(experiment, biorep_n, input_shield_stage_input, key):
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME,
applet_project.get_id())
logging.debug('Found applet %s' % (mapping_applet.name))
input_shield_applet = find_applet_by_name(INPUT_SHIELD_APPLET_NAME,
applet_project.get_id())
logging.debug('Found applet %s' % (input_shield_applet.name))
workflow_output_folder = resolve_folder(
output_project, args.outf + '/workflows/' +
experiment.get('accession') + '/' + 'rep%d' % (biorep_n))
fastq_output_folder = resolve_folder(
output_project, args.outf + '/fastqs/' + experiment.get('accession') +
'/' + 'rep%d' % (biorep_n))
mapping_output_folder = resolve_folder(
output_project, args.outf + '/raw_bams/' +
experiment.get('accession') + '/' + 'rep%d' % (biorep_n))
if args.raw:
workflow_title = 'Map %s rep%d to %s (no filter)' % (
experiment.get('accession'), biorep_n, args.assembly)
workflow_name = 'ENCODE raw mapping pipeline'
else:
workflow_title = 'Map %s rep%d to %s and filter' % (
experiment.get('accession'), biorep_n, args.assembly)
workflow_name = 'ENCODE mapping pipeline'
if args.tag:
workflow_title += ': %s' % (args.tag)
workflow = dxpy.new_dxworkflow(title=workflow_title,
name=workflow_name,
project=output_project.get_id(),
folder=workflow_output_folder)
input_shield_stage_id = workflow.add_stage(
input_shield_applet,
name='Gather inputs %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=fastq_output_folder,
stage_input=input_shield_stage_input)
mapping_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s rep%d' % (experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={
'input_JSON':
dxpy.dxlink({
'stage': input_shield_stage_id,
'outputField': 'output_JSON'
})
})
if not args.raw:
final_output_folder = resolve_folder(
output_project, args.outf + '/bams/' +
experiment.get('accession') + '/' + 'rep%d' % (biorep_n))
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME,
applet_project.get_id())
logging.debug('Found applet %s' % (filter_qc_applet.name))
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter and QC %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': mapping_stage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': mapping_stage_id,
'outputField': 'paired_end'
})
})
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
logging.debug('Found applet %s' % (xcor_applet.name))
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Calculate cross-correlation %s rep%d' %
(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': filter_qc_stage_id,
'outputField': 'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage': filter_qc_stage_id,
'outputField': 'paired_end'
})
})
''' This should all be done in the shield's postprocess entrypoint
if args.accession_outputs:
derived_from = input_shield_stage_input.get('reads1')
if reads2:
derived_from.append(reads2)
files_json = {dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}) : {
'notes': 'Biorep%d | Mapped to %s' %(biorep_n, input_shield_stage_input.get('reference_tar')),
'lab': 'j-michael-cherry',
'award': 'U41HG006992',
'submitted_by': '*****@*****.**',
'file_format': 'bam',
'output_type': 'alignments',
'derived_from': derived_from,
'dataset': experiment.get('accession')}
}
output_shield_stage_id = workflow.add_stage(
output_shield_applet,
name='Accession outputs %s rep%d' %(experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={'files': [dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'})],
'files_json': files_json,
'key': input_shield_stage_input.get('key')}
)
'''
return workflow
def build_workflow(experiment, biorep_n, input_shield_stage_input, accession, use_existing_folders):
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
mapping_applet = \
find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (mapping_applet.name))
input_shield_applet = \
find_applet_by_name(INPUT_SHIELD_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (input_shield_applet.name))
folders = ['workflows', 'fastqs', 'raw_bams', 'bams']
folder_paths = \
['/'.join([args.outf,
folder_name,
experiment.get('accession'),
'rep%d' % (biorep_n)])
for folder_name in folders]
paths_exist = \
[resolve_folder(output_project, folder_path)
for folder_path in folder_paths
if resolve_folder(output_project, folder_path)]
if any(paths_exist):
msg = "%s: output paths already exist: %s" % (experiment.get('accession'), paths_exist)
if use_existing_folders:
logging.warning(msg)
else:
msg += "\nUse --use_existing_folders to supress but possibly create duplicate files"
logging.error(msg)
return None
workflow_output_folder, fastq_output_folder, mapping_output_folder, final_output_folder = \
tuple(create_folder(output_project, folder_path)
for folder_path in folder_paths)
if args.raw:
workflow_title = \
('Map %s rep%d to %s (no filter)'
% (experiment.get('accession'), biorep_n, args.assembly))
workflow_name = 'ENCODE raw mapping pipeline'
else:
workflow_title = \
('Map %s rep%d to %s and filter'
% (experiment.get('accession'), biorep_n, args.assembly))
workflow_name = 'ENCODE mapping pipeline'
if args.tag:
workflow_title += ': %s' % (args.tag)
workflow = dxpy.new_dxworkflow(
title=workflow_title,
name=workflow_name,
project=output_project.get_id(),
folder=workflow_output_folder
)
input_shield_stage_id = workflow.add_stage(
input_shield_applet,
name='Gather inputs %s rep%d' % (experiment.get('accession'), biorep_n),
folder=fastq_output_folder,
stage_input=input_shield_stage_input
)
input_names = \
[name for name in ['reads1', 'reads2', 'crop_length', 'reference_tar',
'bwa_version', 'bwa_aln_params', 'samtools_version', 'debug']
if name in input_shield_stage_input]
logging.debug('input_names: %s' % (input_names))
mapping_stage_input = dict(zip(
input_names,
[dxpy.dxlink(
{'stage': input_shield_stage_id, 'outputField': input_name})
for input_name in input_names]))
logging.debug('mapping_stage_input: %s' % (mapping_stage_input))
mapping_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s rep%d' % (experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input=mapping_stage_input
)
if not args.raw:
filter_qc_applet = \
find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' % (filter_qc_applet.name))
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter and QC %s rep%d' % (experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'paired_end'}),
'scrub': args.scrub
}
)
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' %(xcor_applet.name))
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Calculate cross-correlation %s rep%d' %(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'}),
'spp_version': args.spp_version
}
)
''' This should all be done in the shield's postprocess entrypoint
if args.accession_outputs:
derived_from = input_shield_stage_input.get('reads1')
if reads2:
derived_from.append(reads2)
files_json = {dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}) : {
'notes': 'Biorep%d | Mapped to %s' %(biorep_n, input_shield_stage_input.get('reference_tar')),
'lab': 'j-michael-cherry',
'award': 'U41HG006992',
'submitted_by': '*****@*****.**',
'file_format': 'bam',
'output_type': 'alignments',
'derived_from': derived_from,
'dataset': experiment.get('accession')}
}
output_shield_stage_id = workflow.add_stage(
output_shield_applet,
name='Accession outputs %s rep%d' %(experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={'files': [dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'})],
'files_json': files_json,
'key': input_shield_stage_input.get('key')}
)
'''
return workflow
def main():
args = get_args()
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not blank_workflow:
assert args.rep1, "Reads are required for rep1"
assert args.ctl1, "Reads are required for ctl1"
assert not args.nomap or args.rep1pe is not None, "With --nomap, endedness of rep1 must be specified witn --rep1pe"
assert not args.nomap or (not args.rep2 or args.rep2pe is not None), "With --nomap, endedness of rep2 must be specified with --rep2pe"
if not args.target:
target_type = 'default' # default
else:
target_type = args.target.lower()
if target_type not in WF.keys():
logging.error('Target type %s is not recognized')
sys.exit(2)
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' % (output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' % (applet_project.name))
existing_folder = resolve_folder(output_project, args.outf)
if not existing_folder:
output_folder = create_folder(output_project, args.outf)
elif args.use_existing_folders:
output_folder = existing_folder
else:
assert (existing_folder and args.use_existing_folders), 'Output folder %s exists but --use_existing_folders is %s' % (existing_folder, args.use_existing_folders)
logging.debug('Using output folder %s' % (output_folder))
workflow = dxpy.new_dxworkflow(
name=args.name or WF[target_type]['wf_name'],
title=args.title or WF[target_type]['wf_title'],
description=args.description or WF[target_type]['wf_description'],
project=output_project.get_id(),
folder=output_folder,
properties={'pipeline_version': str(args.pipeline_version)})
unary_control = args.unary_control or (not blank_workflow and args.ctl2 is None)
simplicate_experiment = args.simplicate_experiment or (args.rep1 and not args.rep2)
if not args.genomesize:
genomesize = None
else:
genomesize = args.genomesize
if not args.chrom_sizes:
chrom_sizes = None
else:
chrom_sizes = dxpy.dxlink(resolve_file(args.chrom_sizes))
if not args.blacklist:
blacklist = None
else:
blacklist = dxpy.dxlink(resolve_file(args.blacklist))
run_idr = WF[target_type]['run_idr']
if not args.nomap:
# a "superstage" is just a dict with a name, name(s) of input files,
# and then names and id's of stages that process that input
# each superstage here could be implemented as a stage in a more
# abstract workflow. That stage would then call the various applets
# that are separate
# stages here.
mapping_superstages = [ # the order of this list is important in that
{'name': 'Rep1', 'input_args': args.rep1}
]
if not simplicate_experiment:
mapping_superstages.append(
{'name': 'Rep2', 'input_args': args.rep2})
mapping_superstages.append(
{'name': 'Ctl1', 'input_args': args.ctl1})
if not unary_control and not simplicate_experiment:
mapping_superstages.append(
{'name': 'Ctl2', 'input_args': args.ctl2})
mapping_applet = find_applet_by_name(
MAPPING_APPLET_NAME, applet_project.get_id())
# mapping_output_folder = resolve_folder(
# output_project, output_folder + '/' + mapping_applet.name)
mapping_output_folder = mapping_applet.name
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(
FILTER_QC_APPLET_NAME, applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(
XCOR_APPLET_NAME, applet_project.get_id())
xcor_output_folder = mapping_output_folder
# in the first pass create the mapping stage id's so we can use JBOR's
# to link inputs
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' % (superstage_name),
folder=mapping_output_folder
)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
# in the second pass populate the stage inputs and build other stages
rep1_stage_id = next(ss.get('map_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1')
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
superstage_id = mapping_superstage.get('map_stage_id')
if mapping_superstage.get('input_args') or blank_workflow:
mapping_stage_input = {}
if superstage_name != "Rep1":
mapping_stage_input.update(
{'reference_tar': dxpy.dxlink(
{'stage': rep1_stage_id,
'inputField': 'reference_tar'})})
else:
if args.reference:
mapping_stage_input.update(
{'reference_tar': dxpy.dxlink(
reference_tar.get_id())})
if not blank_workflow:
for arg_index, input_arg in enumerate(mapping_superstage['input_args']): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update({'reads%d' %(arg_index+1): reads})
# this is now done in the first pass loop above
# mapped_stage_id = workflow.add_stage(
# mapping_applet,
# name='Map %s' %(superstage_name),
# folder=mapping_output_folder,
# stage_input=mapping_stage_input
# )
# mapping_superstage.update({'map_stage_id': mapped_stage_id})
workflow.update_stage(superstage_id, stage_input=mapping_stage_input)
filter_qc_stage_input = {
'input_bam': dxpy.dxlink({'stage': superstage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': superstage_id, 'outputField': 'paired_end'})
}
if args.scrub is not None:
filter_qc_stage_input.update({'scrub': args.scrub})
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' %(superstage_name),
folder=filter_qc_output_folder,
stage_input=filter_qc_stage_input
)
mapping_superstage.update({'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Xcor %s' %(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'}),
'spp_version': args.spp_version
}
)
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'})
exp_rep1_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'CC_scores_file'})
rep1_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'paired_end'})
if not simplicate_experiment:
exp_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'})
exp_rep2_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'CC_scores_file'})
rep2_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'paired_end'})
else:
exp_rep2_ta = None
exp_rep2_cc = None
rep2_paired_end = None
ctl_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'})
if not unary_control and not simplicate_experiment:
ctl_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'})
else:
ctl_rep2_ta = None
else: # skipped the mapping, so just bring in the inputs from arguments
if not blank_workflow:
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep1_ta_desc = dxpy.describe(exp_rep1_ta)
exp_rep1_mapping_analysis_id = dxpy.describe(exp_rep1_ta_desc['createdBy']['job'])['analysis']
exp_rep1_mapping_analysis = dxpy.describe(exp_rep1_mapping_analysis_id)
rep1_xcor_stage_description = next(
stage
for stage in exp_rep1_mapping_analysis.get('stages')
if stage['execution']['executableName'] == 'xcor')
exp_rep1_cc = rep1_xcor_stage_description['execution']['output']['CC_scores_file']
if args.rep1pe is None:
print("Inferring rep1 PE-ness from analysis")
rep1_paired_end = rep1_xcor_stage_description['execution']['output']['paired_end']
else:
rep1_paired_end = args.rep1pe
if not simplicate_experiment:
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
exp_rep2_ta_desc = dxpy.describe(exp_rep2_ta)
exp_rep2_mapping_analysis_id = dxpy.describe(exp_rep2_ta_desc['createdBy']['job'])['analysis']
exp_rep2_mapping_analysis = dxpy.describe(exp_rep2_mapping_analysis_id)
rep2_xcor_stage_description = next(
stage
for stage in exp_rep2_mapping_analysis.get('stages')
if stage['execution']['executableName'] == 'xcor')
exp_rep2_cc = rep2_xcor_stage_description['execution']['output']['CC_scores_file']
if args.rep2pe is None:
print("Inferring rep2 PE-ness from analysis")
rep2_paired_end = rep1_xcor_stage_description['execution']['output']['paired_end']
else:
rep2_paired_end = args.rep1pe
else:
exp_rep2_ta = None
exp_rep2_cc = None
rep2_paired_end = None
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
if not unary_control and not simplicate_experiment:
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
else:
ctl_rep2_ta = None
else: # blank workflow
ctl_rep1_ta = None
ctl_rep2_ta = None
# here we need to calculate the cc scores files, because we're only
# being supplied tagAligns
# if we had mapped everything above we'd already have a handle to
# the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME, applet_project.get_id())
# xcor_output_folder = resolve_folder(output_project, output_folder + '/' + xcor_only_applet.name)
xcor_output_folder = xcor_only_applet.name
xcor_only_stages = []
rep1_xcor_input = {'spp_version': args.spp_version}
if args.rep1pe is not None:
rep1_xcor_input.update({'paired_end': args.rep1pe})
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input=rep1_xcor_input
)
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink(
{'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'})
rep1_paired_end = dxpy.dxlink(
{'stage': exp_rep1_cc_stage_id,
'outputField': 'paired_end'})
exp_rep1_ta = dxpy.dxlink(
{'stage': exp_rep1_cc_stage_id,
'inputField': 'input_tagAlign'})
if not simplicate_experiment:
rep2_xcor_input = {'spp_version': args.spp_version}
if args.rep2pe is not None:
rep2_xcor_input.update({'paired_end': args.rep2pe})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input=rep2_xcor_input
)
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink(
{'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'})
rep2_paired_end = dxpy.dxlink(
{'stage': exp_rep2_cc_stage_id,
'outputField': 'paired_end'})
exp_rep2_ta = dxpy.dxlink(
{'stage': exp_rep2_cc_stage_id,
'inputField': 'input_tagAlign'})
else:
exp_rep2_cc = None
exp_rep2_ta = None
rep2_paired_end = None
if not args.maponly:
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME, applet_project.get_id())
encode_macs2_stages = []
# peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_macs2_applet.name)
peaks_output_folder = encode_macs2_applet.name
# for simplicate experiments and/or unary controls, some of the ta inputs
# will have the value None
macs2_stage_input_mapping = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as)),
'genomesize': genomesize,
'chrom_sizes': chrom_sizes
}
# have to prune out any arguments with value None because DX will error
# with arguments with null values
macs2_stage_input = dict([(k,v) for k,v in macs2_stage_input_mapping.iteritems() if v is not None])
encode_macs2_stage_id = workflow.add_stage(
encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input=macs2_stage_input
)
encode_macs2_stages.append({'name': 'ENCODE Peaks', 'stage_id': encode_macs2_stage_id})
if run_idr:
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME, applet_project.get_id())
encode_spp_stages = []
# idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_spp_applet.name)
idr_peaks_output_folder = encode_spp_applet.name
PEAKS_STAGE_NAME = 'SPP Peaks'
# for simplicate experiments and/or unary controls, some of the ta inputs
# will have the value None
peaks_stage_input_mapping = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'idr_peaks': True,
'spp_version': args.spp_version,
'spp_instance': args.spp_instance
}
if chrom_sizes:
peaks_stage_input_mapping.update({'chrom_sizes': chrom_sizes})
else:
peaks_stage_input_mapping.update({'chrom_sizes': dxpy.dxlink({'stage': encode_macs2_stage_id, 'inputField': 'chrom_sizes'})})
# have to prune out any arguments with value None because DX will error
# with arguments with null values
peaks_stage_input = dict([(k,v) for k,v in peaks_stage_input_mapping.iteritems() if v is not None])
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input=peaks_stage_input
)
encode_spp_stages.append({'name': PEAKS_STAGE_NAME, 'stage_id': encode_spp_stage_id})
# TODO here I think we should abstract out all the IDR to one step like the two peak-calling steps
idr_applet = find_applet_by_name(IDR2_APPLET_NAME, applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME, applet_project.get_id())
idr_stages = []
# idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
idr_output_folder = idr_applet.name
if (args.rep1 and args.ctl1 and args.rep2) or blank_workflow or simplicate_experiment:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 1 Self-pseudoreplicates', 'stage_id': idr_stage_id})
if not simplicate_experiment:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 2 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR True Replicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR Pooled Pseudoreplicates', 'stage_id': idr_stage_id})
final_idr_stage_input = {
'r1pr_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'rep1_ta': exp_rep1_ta,
'rep1_xcor': exp_rep1_cc,
'paired_end': rep1_paired_end, # applies to replicated experiments, too
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'rep1_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_fc_signal'})
}
if not simplicate_experiment:
final_idr_stage_input.update({
'reps_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR True Replicates'),
'outputField': 'IDR_peaks'}),
'r2pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'pooledpr_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'rep2_ta': exp_rep2_ta,
'rep2_xcor': exp_rep2_cc,
'rep2_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_fc_signal'}),
'pooled_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_fc_signal'})
})
if blacklist:
final_idr_stage_input.update({'blacklist': blacklist})
if chrom_sizes:
final_idr_stage_input.update({'chrom_sizes': chrom_sizes})
else:
final_idr_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_spp_stage_id, 'inputField': 'chrom_sizes'})})
final_idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=final_idr_stage_input,
)
idr_stages.append({'name': 'Final IDR peak calls', 'stage_id': final_idr_stage_id})
if target_type == 'histone':
PEAKS_STAGE_NAME = "ENCODE Peaks"
overlap_peaks_applet = find_applet_by_name(OVERLAP_PEAKS_APPLET_NAME, applet_project.get_id())
overlap_peaks_stages = []
for peaktype in ['narrowpeaks', 'gappedpeaks', 'broadpeaks']:
if peaktype == 'narrowpeaks':
as_file = dxpy.dxlink(resolve_file(args.narrowpeak_as))
peak_type_extension = 'narrowPeak'
elif peaktype == 'gappedpeaks':
as_file = dxpy.dxlink(resolve_file(args.gappedpeak_as))
peak_type_extension = 'gappedPeak'
elif peaktype == 'broadpeaks':
as_file = dxpy.dxlink(resolve_file(args.broadpeak_as))
peak_type_extension = 'broadPeak'
overlap_peaks_stage_input = {
'rep1_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_%s' % (peaktype)}),
'rep2_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_%s' % (peaktype)}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_%s' % (peaktype)}),
'pooledpr1_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr1_%s' % (peaktype)}),
'pooledpr2_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr2_%s' % (peaktype)}),
'rep1_ta': exp_rep1_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_ta': exp_rep2_ta,
'rep2_xcor': exp_rep2_cc,
'paired_end': rep1_paired_end, # applies to replicated experiments, too
'as_file': as_file,
'peak_type': peak_type_extension,
'prefix': 'final',
'rep1_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_fc_signal'}),
'rep2_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_fc_signal'}),
'pooled_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_fc_signal'})
} if not simplicate_experiment else {
'rep1_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr1_%s' % (peaktype)}),
'rep2_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr2_%s' % (peaktype)}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_%s' % (peaktype)}),
'rep1_ta': exp_rep1_ta,
'rep1_xcor': exp_rep1_cc,
'paired_end': rep1_paired_end, # applies to replicated experiments, too
'as_file': as_file,
'peak_type': peak_type_extension,
'prefix': 'final',
'rep1_signal': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_fc_signal'})
}
if chrom_sizes:
overlap_peaks_stage_input.update({'chrom_sizes': chrom_sizes})
else:
overlap_peaks_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_macs2_stage_id, 'inputField': 'chrom_sizes'})})
overlap_peaks_stage_id = workflow.add_stage(
overlap_peaks_applet,
name='Final %s' % (peaktype),
folder=peaks_output_folder,
stage_input=overlap_peaks_stage_input
)
overlap_peaks_stages.append({'name': 'Final %s' %(peaktype), 'stage_id': overlap_peaks_stage_id})
if args.yes:
if args.debug:
analysis = workflow.run({}, folder=output_folder, priority='high', debug={'debugOn': ['AppInternalError', 'AppError']}, delay_workspace_destruction=True, allow_ssh=['*'])
else:
analysis = workflow.run({}, folder=output_folder, priority='normal')
analysis.set_properties({
"target_type": target_type,
"unreplicated_experiment": str(simplicate_experiment),
"unary_control": str(unary_control)
})
print("Running %s as %s" % (analysis.name, analysis.get_id()))
if args.accession:
accession_analysis_applet = find_applet_by_name(ACCESSION_ANALYSIS_APPLET_NAME, applet_project.get_id())
accession_output_folder = '/' + accession_analysis_applet.name
accession_job_input = {
'analysis_ids': [analysis.get_id()],
'wait_on_files': []
}
if args.fqcheck is not None:
accession_job_input.update({'fqcheck' : args.fqcheck})
if args.skip_control is not None:
accession_job_input.update({'skip_control' : args.skip_control})
if args.force_patch is not None:
accession_job_input.update({'force_patch': args.force_patch})
# assert accession_stage_input['wait_on_files'], "ERROR: workflow has no wait_on_files defined, so --accession is not supported."
time.sleep(5)
max_retries = 10
retries = max_retries
while retries:
try:
accession_job = accession_analysis_applet.run(
accession_job_input,
name='Accession %s' % (analysis.name),
folder=accession_output_folder,
depends_on=analysis.describe()['dependsOn']
)
except Exception as e:
logging.error("%s launching auto-accession ... %d retries left" % (e, retries))
time.sleep(5)
retries -= 1
continue
else:
logging.info("Auto-accession will run as %s %s" % (accession_job.name, accession_job.get_id()))
break
else:
logging.error("Auto-accession failed with %s" % ())
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' %(output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.debug('Using output folder %s' %(output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' %(applet_project.name))
workflow = dxpy.new_dxworkflow(
name=args.name,
title=args.title,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not args.genomesize:
genomesize = None
else:
genomesize = args.genomesize
if not args.chrom_sizes:
chrom_sizes = None
else:
chrom_sizes = dxpy.dxlink(resolve_file(args.chrom_sizes))
if not args.blacklist:
blacklist = None
else:
blacklist = dxpy.dxlink(resolve_file(args.blacklist))
if not args.nomap:
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [ # the order of this list is important in that
{'name': 'Rep1', 'input_args': args.rep1},
{'name': 'Rep2', 'input_args': args.rep2},
{'name': 'Ctl1', 'input_args': args.ctl1}
]
if not args.unary_control:
mapping_superstages.append({'name': 'Ctl2', 'input_args': args.ctl2})
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
mapping_output_folder = resolve_folder(output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME, applet_project.get_id())
xcor_output_folder = mapping_output_folder
# in the first pass create the mapping stage id's so we can use JBOR's
# to link inputs
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' %(superstage_name),
folder=mapping_output_folder
)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
# in the second pass populate the stage inputs and build other stages
rep1_stage_id = next(ss.get('map_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1')
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
superstage_id = mapping_superstage.get('map_stage_id')
if mapping_superstage.get('input_args') or blank_workflow:
mapping_stage_input = {}
if superstage_name != "Rep1":
mapping_stage_input.update({'reference_tar': dxpy.dxlink({'stage': rep1_stage_id, 'inputField': 'reference_tar'})})
else:
if args.reference:
mapping_stage_input.update({'reference_tar' : dxpy.dxlink(reference_tar.get_id())})
if not blank_workflow:
for arg_index, input_arg in enumerate(mapping_superstage['input_args']): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update({'reads%d' %(arg_index+1): reads})
# this is now done in the first pass loop above
# mapped_stage_id = workflow.add_stage(
# mapping_applet,
# name='Map %s' %(superstage_name),
# folder=mapping_output_folder,
# stage_input=mapping_stage_input
# )
# mapping_superstage.update({'map_stage_id': mapped_stage_id})
workflow.update_stage(superstage_id, stage_input=mapping_stage_input)
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' %(superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': superstage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': superstage_id, 'outputField': 'paired_end'})
}
)
mapping_superstage.update({'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Xcor %s' %(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'})
}
)
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'})
exp_rep1_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'CC_scores_file'})
exp_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'})
exp_rep2_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'CC_scores_file'})
ctl_rep1_ta = dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'})
if not args.unary_control:
ctl_rep2_ta = dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'})
else:
ctl_rep2_ta = ctl_rep1_ta
rep1_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'paired_end'})
rep2_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'paired_end'})
else: #skipped the mapping, so just bring in the inputs from arguments
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME, applet_project.get_id())
xcor_output_folder = resolve_folder(output_project, output_folder + '/' + xcor_only_applet.name)
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
}
)
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink(
{'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
}
)
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink(
{'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'})
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME, applet_project.get_id())
encode_spp_stages = []
idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_spp_applet.name)
PEAKS_STAGE_NAME = 'SPP Peaks'
peaks_stage_input = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'idr_peaks': args.idr
}
if chrom_sizes:
peaks_stage_input.update({'chrom_sizes': chrom_sizes})
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input=peaks_stage_input
)
encode_spp_stages.append({'name': PEAKS_STAGE_NAME, 'stage_id': encode_spp_stage_id})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME, applet_project.get_id())
encode_macs2_stages = []
peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_macs2_applet.name)
macs2_stage_input = {
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as))
}
if genomesize:
macs2_stage_input.update({'genomesize': genomesize})
if chrom_sizes:
macs2_stage_input.update({'chrom_sizes': chrom_sizes})
else:
macs2_stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_spp_stage_id, 'inputField': 'chrom_sizes'})})
encode_macs2_stage_id = workflow.add_stage(
encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input=macs2_stage_input
)
encode_macs2_stages.append({'name': 'ENCODE Peaks', 'stage_id': encode_macs2_stage_id})
if args.idr:
# if args.idrversion == "1":
# idr_applet = find_applet_by_name(IDR_APPLET_NAME, applet_project.get_id())
# elif args.idrversion == "2":
# idr_applet = find_applet_by_name(IDR2_APPLET_NAME, applet_project.get_id())
# else:
# logging.error("Invalid IDR version: %s" %(args.idrversion))
# idr_applet = None
idr_applet = find_applet_by_name(IDR2_APPLET_NAME, applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME, applet_project.get_id())
idr_stages = []
idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
if (args.rep1 and args.ctl1 and args.rep2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR True Replicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep1_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 1 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2pr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'rep2_peaks'})
}
)
idr_stages.append({'name': 'IDR Rep 2 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr1_peaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooledpr2_peaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_spp_stages if ss['name'] == PEAKS_STAGE_NAME),
'outputField': 'pooled_peaks'})
}
)
idr_stages.append({'name': 'IDR Pooled Pseudoreplicates', 'stage_id': idr_stage_id})
stage_input = {
'reps_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR True Replicates'),
'outputField': 'IDR_peaks'}),
'r1pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'r2pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'pooledpr_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as))
}
if blacklist:
stage_input.update({'blacklist': blacklist})
if chrom_sizes:
stage_input.update({'chrom_sizes': chrom_sizes})
else:
stage_input.update({'chrom_sizes': dxpy.dxlink({'stage': encode_spp_stage_id, 'inputField': 'chrom_sizes'})})
idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=stage_input
)
idr_stages.append({'name': 'Final IDR peak calls', 'stage_id': idr_stage_id})
if not (args.nomap):
logging.debug("Mapping stages: %s" %(mapping_superstages))
else:
logging.debug("xcor only stages: %s" %(xcor_only_stages))
# if not args.idronly:
# logging.debug("Peak stages: %s" %(spp_stages))
logging.debug("Peak stages: %s" %(encode_spp_stages))
if args.idr:
logging.debug("IDR stages: %s" %(idr_stages))
if args.yes:
if args.debug:
job_id = workflow.run({}, priority='high', debug={'debugOn': ['AppInternalError', 'AppError']}, delay_workspace_destruction=True, allow_ssh=['255.255.255.255'])
else:
job_id = workflow.run({}, priority='high')
logging.info("Running as job %s" %(job_id))
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.info('Found output project %s' % (output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.info('Using output folder %s' % (output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.info('Found applet project %s' % (applet_project.name))
workflow = dxpy.new_dxworkflow(name=WF_NAME,
title=args.name,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not args.nomap:
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [
{
'name': 'Rep1',
'input_args': args.rep1
}, {
'name': 'Rep2',
'input_args': args.rep2
}, {
'name': 'Ctl1',
'input_args': args.ctl1
}, {
'name': 'Ctl2',
'input_args': args.ctl2
}
# {'name': 'Pooled Reps', 'input_args': (args.rep1 and args.rep2)},
# {'name': 'Pooled Controls', 'input_args': (args.ctl1 and args.ctl2)} ##idea is to create a "stub" stage and then populate it's input with the output of the pool stage, defined below
]
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME,
applet_project.get_id())
mapping_output_folder = resolve_folder(
output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME,
applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = mapping_output_folder
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
if mapping_superstage.get('input_args') or blank_workflow:
if blank_workflow:
mapping_stage_input = None
else:
mapping_stage_input = {
'reference_tar': dxpy.dxlink(reference_tar.get_id())
}
for arg_index, input_arg in enumerate(
mapping_superstage['input_args']
): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update(
{'reads%d' % (arg_index + 1): reads})
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' % (superstage_name),
folder=mapping_output_folder,
stage_input=mapping_stage_input)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' % (superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage': mapped_stage_id,
'outputField': 'mapped_reads'
}),
'paired_end':
dxpy.dxlink({
'stage': mapped_stage_id,
'outputField': 'paired_end'
})
})
mapping_superstage.update(
{'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(xcor_applet,
name='Xcor %s' %
(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'filtered_bam'
}),
'paired_end':
dxpy.dxlink({
'stage':
filter_qc_stage_id,
'outputField':
'paired_end'
})
})
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'tagAlign_file'
})
exp_rep1_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'CC_scores_file'
})
exp_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'tagAlign_file'
})
exp_rep2_cc = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'CC_scores_file'
})
ctl_rep1_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl1'),
'outputField':
'tagAlign_file'
})
ctl_rep2_ta = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Ctl2'),
'outputField':
'tagAlign_file'
})
rep1_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep1'),
'outputField':
'paired_end'
})
rep2_paired_end = dxpy.dxlink({
'stage':
next(
ss.get('xcor_stage_id') for ss in mapping_superstages
if ss['name'] == 'Rep2'),
'outputField':
'paired_end'
})
else: #skipped the mapping, so just bring in the inputs from arguments
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME,
applet_project.get_id())
xcor_output_folder = resolve_folder(
output_project, output_folder + '/' + xcor_only_applet.name)
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
})
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink({
'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'
})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
})
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink({
'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'
})
# if not args.idronly:
# spp_applet = find_applet_by_name(SPP_APPLET_NAME, applet_project.get_id())
# peaks_output_folder = resolve_folder(output_project, output_folder + '/' + spp_applet.name)
# spp_stages = []
# if (args.rep1 and args.ctl1) or blank_workflow:
# rep1_spp_stage_id = workflow.add_stage(
# spp_applet,
# name='Peaks Rep1',
# folder=peaks_output_folder,
# stage_input={
# 'experiment': exp_rep1_ta,
# 'control': ctl_rep1_ta,
# 'xcor_scores_input': exp_rep1_cc,
# 'bigbed': True,
# 'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
# 'as_file': dxpy.dxlink(resolve_file(args.as_file))
# }
# )
# spp_stages.append({'name': 'Peaks Rep1', 'stage_id': rep1_spp_stage_id})
# if (args.rep2 and args.ctl2) or blank_workflow:
# rep2_spp_stage_id = workflow.add_stage(
# spp_applet,
# name='Peaks Rep2',
# folder=peaks_output_folder,
# stage_input={
# 'experiment': exp_rep2_ta,
# 'control': ctl_rep2_ta,
# 'xcor_scores_input': exp_rep2_cc,
# 'bigbed': True,
# 'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
# 'as_file': dxpy.dxlink(resolve_file(args.as_file))
# }
# )
# spp_stages.append({'name': 'Peaks Rep2', 'stage_id': rep2_spp_stage_id})
encode_spp_applet = find_applet_by_name(ENCODE_SPP_APPLET_NAME,
applet_project.get_id())
encode_spp_stages = []
idr_peaks_output_folder = resolve_folder(
output_project, output_folder + '/' + encode_spp_applet.name)
PEAKS_STAGE_NAME = 'SPP Peaks'
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
encode_spp_stage_id = workflow.add_stage(
encode_spp_applet,
name=PEAKS_STAGE_NAME,
folder=idr_peaks_output_folder,
stage_input={
'rep1_ta': exp_rep1_ta,
'rep2_ta': exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta': ctl_rep2_ta,
'rep1_xcor': exp_rep1_cc,
'rep2_xcor': exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file': dxpy.dxlink(resolve_file(args.as_file)),
'idr_peaks': args.idr
})
encode_spp_stages.append({
'name': PEAKS_STAGE_NAME,
'stage_id': encode_spp_stage_id
})
if args.idr:
idr_applet = find_applet_by_name(IDR_APPLET_NAME,
applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME,
applet_project.get_id())
idr_stages = []
idr_output_folder = resolve_folder(
output_project, output_folder + '/' + idr_applet.name)
if (args.rep1 and args.ctl1 and args.rep2
and args.ctl2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR True Replicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep1_peaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Rep 1 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2pr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'rep2_peaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Rep 2 Self-pseudoreplicates',
'stage_id': idr_stage_id
})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoeplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr1_peaks'
}),
'rep2_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooledpr2_peaks'
}),
'pooled_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in encode_spp_stages
if ss['name'] == PEAKS_STAGE_NAME),
'outputField':
'pooled_peaks'
}),
'idr_version':
int(args.idrversion)
})
idr_stages.append({
'name': 'IDR Pooled Pseudoreplicates',
'stage_id': idr_stage_id
})
blacklist = resolve_file(args.blacklist)
idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input={
'reps_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR True Replicates'),
'outputField':
'IDR_peaks'
}),
'r1pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages if
ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'r2pr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages if
ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'pooledpr_peaks':
dxpy.dxlink({
'stage':
next(
ss.get('stage_id') for ss in idr_stages
if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField':
'IDR_peaks'
}),
'blacklist':
dxpy.dxlink(blacklist.get_id()),
'chrom_sizes':
dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file':
dxpy.dxlink(resolve_file(args.as_file))
})
idr_stages.append({
'name': 'Final IDR peak calls',
'stage_id': idr_stage_id
})
if not (args.nomap):
logging.debug("Mapping stages: %s" % (mapping_superstages))
else:
logging.debug("xcor only stages: %s" % (xcor_only_stages))
# if not args.idronly:
# logging.debug("Peak stages: %s" %(spp_stages))
logging.debug("Peak stages: %s" % (encode_spp_stages))
if args.idr:
logging.debug("IDR stages: %s" % (idr_stages))
if args.yes:
job_id = workflow.run({}, delay_workspace_destruction=True)
logging.info("Running as job %s" % (job_id))
def main():
args = get_args()
output_project = resolve_project(args.outp, 'w')
logging.info('Found output project %s' %(output_project.name))
output_folder = resolve_folder(output_project, args.outf)
logging.info('Using output folder %s' %(output_folder))
applet_project = resolve_project(args.applets, 'r')
logging.info('Found applet project %s' %(applet_project.name))
workflow = dxpy.new_dxworkflow(
title=WF_TITLE,
name=args.name,
description=WF_DESCRIPTION,
project=output_project.get_id(),
folder=output_folder)
blank_workflow = not (args.rep1 or args.rep2 or args.ctl1 or args.ctl2)
if not args.nomap:
#a "superstage" is just a dict with a name, name(s) of input files, and then names and id's of stages that process that input
#each superstage here could be implemented as a stage in a more abstract workflow. That stage would then call the various applets that are separate
#stages here.
mapping_superstages = [
{'name': 'Rep1', 'input_args': args.rep1},
{'name': 'Rep2', 'input_args': args.rep2},
{'name': 'Ctl1', 'input_args': args.ctl1},
{'name': 'Ctl2', 'input_args': args.ctl2}
# {'name': 'Pooled Reps', 'input_args': (args.rep1 and args.rep2)},
# {'name': 'Pooled Controls', 'input_args': (args.ctl1 and args.ctl2)} ##idea is to create a "stub" stage and then populate it's input with the output of the pool stage, defined below
]
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
mapping_output_folder = resolve_folder(output_project, output_folder + '/' + mapping_applet.name)
reference_tar = resolve_file(args.reference)
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
filter_qc_output_folder = mapping_output_folder
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME, applet_project.get_id())
xcor_output_folder = mapping_output_folder
for mapping_superstage in mapping_superstages:
superstage_name = mapping_superstage.get('name')
if mapping_superstage.get('input_args') or blank_workflow:
if blank_workflow:
mapping_stage_input = None
else:
mapping_stage_input = {'reference_tar' : dxpy.dxlink(reference_tar.get_id())}
for arg_index,input_arg in enumerate(mapping_superstage['input_args']): #read pairs assumed be in order read1,read2
reads = dxpy.dxlink(resolve_file(input_arg).get_id())
mapping_stage_input.update({'reads%d' %(arg_index+1): reads})
mapped_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s' %(superstage_name),
folder=mapping_output_folder,
stage_input=mapping_stage_input
)
mapping_superstage.update({'map_stage_id': mapped_stage_id})
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter_QC %s' %(superstage_name),
folder=filter_qc_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': mapped_stage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': mapped_stage_id, 'outputField': 'paired_end'})
}
)
mapping_superstage.update({'filter_qc_stage_id': filter_qc_stage_id})
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Xcor %s' %(superstage_name),
folder=xcor_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'})
}
)
mapping_superstage.update({'xcor_stage_id': xcor_stage_id})
exp_rep1_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'tagAlign_file'})
exp_rep1_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'CC_scores_file'})
exp_rep2_ta = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'tagAlign_file'})
exp_rep2_cc = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'CC_scores_file'})
ctl_rep1_ta = dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl1'),
'outputField': 'tagAlign_file'})
ctl_rep2_ta = dxpy.dxlink(
{'stage' : next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Ctl2'),
'outputField': 'tagAlign_file'})
rep1_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep1'),
'outputField': 'paired_end'})
rep2_paired_end = dxpy.dxlink(
{'stage': next(ss.get('xcor_stage_id') for ss in mapping_superstages if ss['name'] == 'Rep2'),
'outputField': 'paired_end'})
else: #skipped the mapping, so just bring in the inputs from arguments
exp_rep1_ta = dxpy.dxlink(resolve_file(args.rep1[0]).get_id())
exp_rep2_ta = dxpy.dxlink(resolve_file(args.rep2[0]).get_id())
ctl_rep1_ta = dxpy.dxlink(resolve_file(args.ctl1[0]).get_id())
ctl_rep2_ta = dxpy.dxlink(resolve_file(args.ctl2[0]).get_id())
rep1_paired_end = args.rep1pe
rep2_paired_end = args.rep2pe
#here we need to calculate the cc scores files, because we're only being supplied tagAligns
#if we had mapped everything above we'd already have a handle to the cc file
xcor_only_applet = find_applet_by_name(XCOR_ONLY_APPLET_NAME, applet_project.get_id())
xcor_output_folder = resolve_folder(output_project, output_folder + '/' + xcor_only_applet.name)
xcor_only_stages = []
exp_rep1_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep1 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep1_ta,
'paired_end': rep1_paired_end
}
)
xcor_only_stages.append({'xcor_only_rep1_id': exp_rep1_cc_stage_id})
exp_rep1_cc = dxpy.dxlink(
{'stage': exp_rep1_cc_stage_id,
'outputField': 'CC_scores_file'})
exp_rep2_cc_stage_id = workflow.add_stage(
xcor_only_applet,
name="Rep2 cross-correlation",
folder=xcor_output_folder,
stage_input={
'input_tagAlign': exp_rep2_ta,
'paired_end': rep2_paired_end
}
)
xcor_only_stages.append({'xcor_only_rep2_id': exp_rep2_cc_stage_id})
exp_rep2_cc = dxpy.dxlink(
{'stage': exp_rep2_cc_stage_id,
'outputField': 'CC_scores_file'})
encode_macs2_applet = find_applet_by_name(ENCODE_MACS2_APPLET_NAME, applet_project.get_id())
encode_macs2_stages = []
peaks_output_folder = resolve_folder(output_project, output_folder + '/' + encode_macs2_applet.name)
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
encode_macs2_stage_id = workflow.add_stage(
encode_macs2_applet,
name='ENCODE Peaks',
folder=peaks_output_folder,
stage_input={
'rep1_ta' : exp_rep1_ta,
'rep2_ta' : exp_rep2_ta,
'ctl1_ta': ctl_rep1_ta,
'ctl2_ta' : ctl_rep2_ta,
'rep1_xcor' : exp_rep1_cc,
'rep2_xcor' : exp_rep2_cc,
'rep1_paired_end': rep1_paired_end,
'rep2_paired_end': rep2_paired_end,
'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
'narrowpeak_as': dxpy.dxlink(resolve_file(args.narrowpeak_as)),
'gappedpeak_as': dxpy.dxlink(resolve_file(args.gappedpeak_as)),
'broadpeak_as': dxpy.dxlink(resolve_file(args.broadpeak_as)),
'genomesize': args.genomesize
}
)
encode_macs2_stages.append({'name': 'ENCODE Peaks', 'stage_id': encode_macs2_stage_id})
#new applet here, similar to IDR, to do naive peak processing
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
overlap_peaks_applet = find_applet_by_name(OVERLAP_PEAKS_APPLET_NAME, applet_project.get_id())
overlap_peaks_stages = []
for peaktype in ['narrowpeaks', 'gappedpeaks', 'broadpeaks']:
if peaktype == 'narrowpeaks':
as_file = dxpy.dxlink(resolve_file(args.narrowpeak_as))
peak_type_extension = 'narrowPeak'
elif peaktype == 'gappedpeaks':
as_file = dxpy.dxlink(resolve_file(args.gappedpeak_as))
peak_type_extension = 'gappedPeak'
elif peaktype == 'broadpeaks':
as_file = dxpy.dxlink(resolve_file(args.broadpeak_as))
peak_type_extension = 'broadPeak'
overlap_peaks_stage_id = workflow.add_stage(
overlap_peaks_applet,
name='Overlap %s' %(peaktype),
folder=peaks_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_%s' %(peaktype)}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_%s' %(peaktype)}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_%s' %(peaktype)}),
'pooledpr1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr1_%s' %(peaktype)}),
'pooledpr2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr2_%s' %(peaktype)}),
'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file': as_file,
'peak_type': peak_type_extension
}
)
overlap_peaks_stages.append({'name': 'Overlap %s' %(peaktype), 'stage_id': overlap_peaks_stage_id})
#TODO - IDR on gapped and broad peaks
if args.idr:
idr_peaks_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
idr_applet = find_applet_by_name(IDR_APPLET_NAME, applet_project.get_id())
encode_idr_applet = find_applet_by_name(ENCODE_IDR_APPLET_NAME, applet_project.get_id())
idr_stages = []
idr_output_folder = resolve_folder(output_project, output_folder + '/' + idr_applet.name)
if (args.rep1 and args.ctl1 and args.rep2 and args.ctl2) or blank_workflow:
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR True Replicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_narrowpeaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_narrowpeaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_narrowpeaks'}),
'idr_version': int(args.idrversion)
}
)
idr_stages.append({'name': 'IDR True Replicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 1 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1pr1_narrowpeaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1pr2_narrowpeaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep1_narrowpeaks'}),
'idr_version': int(args.idrversion)
}
)
idr_stages.append({'name': 'IDR Rep 1 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Rep 2 Self-pseudoreplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2pr1_narrowpeaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2pr2_narrowpeaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'rep2_narrowpeaks'}),
'idr_version': int(args.idrversion)
}
)
idr_stages.append({'name': 'IDR Rep 2 Self-pseudoreplicates', 'stage_id': idr_stage_id})
idr_stage_id = workflow.add_stage(
idr_applet,
name='IDR Pooled Pseudoeplicates',
folder=idr_output_folder,
stage_input={
'rep1_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr1_narrowpeaks'}),
'rep2_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooledpr2_narrowpeaks'}),
'pooled_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in encode_macs2_stages if ss['name'] == 'ENCODE Peaks'),
'outputField': 'pooled_narrowpeaks'}),
'idr_version': int(args.idrversion)
}
)
idr_stages.append({'name': 'IDR Pooled Pseudoreplicates', 'stage_id': idr_stage_id})
final_idr_stage_input = {
'reps_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR True Replicates'),
'outputField': 'IDR_peaks'}),
'r1pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 1 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'r2pr_peaks' : dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Rep 2 Self-pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'pooledpr_peaks': dxpy.dxlink(
{'stage': next(ss.get('stage_id') for ss in idr_stages if ss['name'] == 'IDR Pooled Pseudoreplicates'),
'outputField': 'IDR_peaks'}),
'chrom_sizes': dxpy.dxlink(resolve_file(args.chrom_sizes)),
'as_file': dxpy.dxlink(resolve_file(args.narrowpeak_as))
}
if args.blacklist:
final_idr_stage_input.update({'blacklist': dxpy.dxlink(resolve_file(args.blacklist))})
idr_stage_id = workflow.add_stage(
encode_idr_applet,
name='Final IDR peak calls',
folder=idr_output_folder,
stage_input=final_idr_stage_input
)
idr_stages.append({'name': 'Final IDR peak calls', 'stage_id': idr_stage_id})
if not (args.nomap):
logging.debug("Mapping stages: %s" %(mapping_superstages))
else:
logging.debug("xcor only stages: %s" %(xcor_only_stages))
logging.debug("Peaks for ENCODE stages: %s" %(encode_macs2_stages))
logging.debug("Peak overlap stages: %s" %(overlap_peaks_stages))
if args.idr:
logging.debug("IDR stages: %s" %(idr_stages))
if args.yes:
job_id = workflow.run({}, delay_workspace_destruction=True)
logging.info("Running as job %s" %(job_id))
def build_workflow():
wf = dxpy.new_dxworkflow(title='tcga_mc3_full_run',
name='tcga_mc3_full_run',
description='TCGA mc3 variant calling pipeline',
project=args.project,
folder=args.folder,
properties={"git_revision": git_revision})
# variant calling tools
pindel_applet = find_applet("pindel-tool")
pindel_stage_id = wf.add_stage(pindel_applet)
radia_applet = find_applet("radia-tool")
radia_input = {
"dnaNormalBam": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBamFile"}),
"dnaTumorBam": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"fasta": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
radia_stage_id = wf.add_stage(radia_applet, stage_input=radia_input)
somaticsniper_applet = find_applet("somaticsniper-tool")
somaticsniper_input = {
"normal": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBamFile"}),
"tumor": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
somaticsniper_stage_id = wf.add_stage(somaticsniper_applet, stage_input=somaticsniper_input, instance_type="mem2_hdd2_x1")
samtools_pileup_applet = find_applet("samtools-pileup-tool")
samtools_pileup_normal_input = {
"input1" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBamFile"}),
"input1_index" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBaiFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
samtools_pileup_normal_stage_id = wf.add_stage(samtools_pileup_applet, stage_input=samtools_pileup_normal_input, instance_type="mem2_hdd2_x1")
samtools_pileup_tumor_input = {
"input1" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"input1_index" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBaiFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
samtools_pileup_tumor_stage_id = wf.add_stage(samtools_pileup_applet, stage_input=samtools_pileup_tumor_input, instance_type="mem2_hdd2_x2")
muse_applet = find_applet("muse-tool")
muse_input = {
"tumor_bam" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"tumor_bai" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBaiFile"}),
"normal_bam" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBamFile"}),
"normal_bai" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBaiFile"}),
"reference" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"}),
"dbsnp": dxpy.dxlink("file-Bj1V0400kF9Z3GqJY4ZbYbYj")
}
muse_stage_id = wf.add_stage(muse_applet, stage_input=muse_input)
varscan_applet = find_applet("varscan-tool")
varscan_input = {
"normal_pileup": dxpy.dxlink({"stage": samtools_pileup_normal_stage_id, "outputField": "pileup"}),
"tumor_pileup": dxpy.dxlink({"stage": samtools_pileup_tumor_stage_id, "outputField": "pileup"})
}
varscan_stage_id = wf.add_stage(varscan_applet, stage_input=varscan_input, instance_type="mem2_hdd2_x2")
mutect_applet = find_applet("mutect-tool")
mutect_input = {
"tumor_bam" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"tumor_bai" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBaiFile"}),
"normal_bam" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBamFile"}),
"normal_bai" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "normalInputBaiFile"}),
"reference" : dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"}),
"dbsnp": dxpy.dxlink("file-Bj1V0400kF9Z3GqJY4ZbYbYj"),
"cosmic": dxpy.dxlink("file-Bk9g2kQ0kF9f9XG6VZf7VGKQ"),
}
mutect_stage_id = wf.add_stage(mutect_applet, stage_input=mutect_input)
# fpfilter (somaticSniper, Varscan)
fpfilter_applet = find_applet("fpfilter-tool")
somatcisniper_fpfilter_input = {
"vcf": dxpy.dxlink({"stage": somaticsniper_stage_id, "outputField": "vcf"}),
"bam": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
somaticsniper_fpfilter_stage_id = wf.add_stage(fpfilter_applet,
stage_input=somatcisniper_fpfilter_input,
name="fpfilter-tool(somaticSniper)",
folder="fpfiltered")
varscan_snp_fpfilter_input = {
"vcf": dxpy.dxlink({"stage": varscan_stage_id, "outputField": "snp_vcf"}),
"bam": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
varscan_snp_fpfilter_stage_id = wf.add_stage(fpfilter_applet,
stage_input=varscan_snp_fpfilter_input,
name="fpfilter-tool(varscan SNP)",
folder="fpfiltered")
varscan_indel_fpfilter_input = {
"vcf": dxpy.dxlink({"stage": varscan_stage_id, "outputField": "indel_vcf"}),
"bam": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "tumorInputBamFile"}),
"reference": dxpy.dxlink({"stage": pindel_stage_id, "inputField": "inputReferenceFile"})
}
varscan_indel_fpfilter_stage_id = wf.add_stage(fpfilter_applet,
stage_input=varscan_indel_fpfilter_input,
name="fpfilter-tool(varscan INDEL)",
folder="fpfiltered")
# vcf_filter (All variant callers)
vcf_filter_applet = find_applet("tcga-vcf-filter-tool")
radia_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": radia_stage_id, "outputField": "filtered_output_vcf"}),
"filterRejects": False
}
radia_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=radia_vcf_filter_input,
name="vcffilter-tool(radia)",
folder="final_filtered")
somaticsniper_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": somaticsniper_fpfilter_stage_id, "outputField": "annotated_output"}),
"filterRejects": False
}
somaticsniper_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=somaticsniper_vcf_filter_input,
name="vcffilter-tool(somaticsniper)",
folder="final_filtered")
varscan_snp_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": varscan_snp_fpfilter_stage_id, "outputField": "annotated_output"}),
"filterRejects": True
}
varscan_snp_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=varscan_snp_vcf_filter_input,
name="vcffilter-tool(varscan SNP)",
folder="final_filtered")
varscan_indel_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": varscan_indel_fpfilter_stage_id, "outputField": "annotated_output"}),
"filterRejects": True
}
varscan_indel_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=varscan_indel_vcf_filter_input,
name="vcffilter-tool(varscan INDEL)",
folder="final_filtered")
muse_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": muse_stage_id, "outputField": "mutations"}),
"filterRejects": False
}
muse_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=muse_vcf_filter_input,
name="vcffilter-tool(muse)",
folder="final_filtered")
pindel_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": pindel_stage_id, "outputField": "outputSomaticVcf"}),
"filterRejects": False
}
pindel_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=pindel_vcf_filter_input,
name="vcffilter-tool(pindel)",
folder="final_filtered")
mutect_vcf_filter_input = {
"input_vcf": dxpy.dxlink({"stage": mutect_stage_id, "outputField": "mutations"}),
"filterRejects": True
}
mutect_vcf_filter_stage_id = wf.add_stage(vcf_filter_applet,
stage_input=mutect_vcf_filter_input,
name="vcffilter-tool(mutect)",
folder="final_filtered")
vcf_reheader_applet = find_applet("tcga-vcf-reheader")
radia_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": radia_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "radia",
"software_version": "1",
"software_params": "--dnaNormalMinTotalBases 4 --dnaNormalMinAltBases 2 --dnaNormalBaseQual 10 --dnaNormalMapQual 10 --dnaTumorDescription TumorDNASample --dnaTumorMinTotalBases 4 --dnaTumorMinAltBases 2 --dnaTumorBaseQual 10 --dnaTumorMapQual 10 --dnaNormalMitochon=MT --dnaTumorMitochon=MT --genotypeMinDepth 2 --genotypeMinPct 0.100",
"center": "ucsc.edu"
}
radia_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=radia_vcf_reheader_input,
name="vcf-reheader(radia)",
folder="final_reheadered")
"""
sample_params = {
"platform": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "platform"}),
"participant_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "participant_uuid"}),
"disease_code": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "disease_code"}),
"normal_analysis_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_analysis_uuid"}),
"normal_bam_name": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_bam_name"}),
"normal_aliquot_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_aliquot_id"}),
"normal_aliquot_barcode": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "normal_aliquot_barcode"}),
"tumor_analysis_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_analysis_uuid"}),
"tumor_bam_name": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_bam_name"}),
"tumor_aliquot_uuid": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_aliquot_uuid"}),
"tumor_aliquot_barcode": dxpy.dxlink({"stage": radia_vcf_reheader_stage_id, "inputField": "tumor_aliquot_barcode"})
}
"""
somaticsniper_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": somaticsniper_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "somaticsniper",
"software_version": "v1.0.5.0",
"software_params": "-Q 40 -n NORMAL -q 1 -s 0.01 -r 0.001",
"center": "wustl.edu"
}
#somaticsniper_vcf_reheader_input.update(sample_params)
somaticsniper_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=somaticsniper_vcf_reheader_input,
name="vcf-reheader(somaticsniper)",
folder="final_reheadered")
varscan_snp_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": varscan_snp_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "varscan",
"software_version": "2.3.9",
"software_params": "--output-vcf 1 --min-coverage 3 --normal-purity 1 --p-value 0.99 --min-coverage-normal 8 --min-freq-for-hom 0.75 --min-var-freq 0.08 --somatic-p-value 0.05 --min-coverage-tumor 6 --tumor-purity 1",
"center": "wustl.edu"
}
#varscan_snp_vcf_reheader_input.update(sample_params)
varscan_snp_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=varscan_snp_vcf_reheader_input,
name="vcf-reheader(varscan SNP)",
folder="final_reheadered")
varscan_indel_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": varscan_indel_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "varscan",
"software_version": "2.3.9",
"software_params": "--output-vcf 1 --min-coverage 3 --normal-purity 1 --p-value 0.99 --min-coverage-normal 8 --min-freq-for-hom 0.75 --min-var-freq 0.08 --somatic-p-value 0.05 --min-coverage-tumor 6 --tumor-purity 1",
"center": "wustl.edu"
}
#varscan_indel_vcf_reheader_input.update(sample_params)
varscan_indel_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=varscan_indel_vcf_reheader_input,
name="vcf-reheader(varscan INDEL)",
folder="final_reheadered")
muse_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": muse_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "muse",
"software_version": "v1.0rc",
"software_params": "--mode wxs",
"center": "mdanderson.org"
}
#muse_vcf_reheader_input.update(sample_params)
muse_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=muse_vcf_reheader_input,
name="vcf-reheader(muse)",
folder="final_reheadered")
pindel_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": pindel_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "pindel",
"software_version": "v0.2.5b8",
"software_params": "--max_range_index 1 --window_size 5 --sequencing_error_rate 0.010000 --sensitivity 0.950000 --maximum_allowed_mismatch_rate 0.020000 --NM 2 --additional_mismatch 1 --min_perfect_match_around_BP 3 --min_inversion_size 50 --min_num_matched_bases 30 --balance_cutoff 0 --anchor_quality 0 --minimum_support_for_event 3 --report_long_insertions --report_duplications --report_inversions --report_breakpoints",
"center": "wustl.edu"
}
#pindel_vcf_reheader_input.update(sample_params)
pindel_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=pindel_vcf_reheader_input,
name="vcf-reheader(pindel)",
folder="final_reheadered")
mutect_vcf_reheader_input = {
"input_vcf": dxpy.dxlink({"stage": mutect_vcf_filter_stage_id, "outputField": "output_vcf"}),
"software_name": "mutect",
"software_version": "1.1.5",
"software_params": "--initial_tumor_lod 4.0 --tumor_lod 10.0",
"center": "broad.org"
}
mutect_vcf_reheader_stage_id = wf.add_stage(vcf_reheader_applet,
stage_input=mutect_vcf_reheader_input,
name="vcf-reheader(mutect)",
folder="final_reheadered")
return wf
def build_workflow(experiment, biorep_n, input_shield_stage_input, key):
output_project = resolve_project(args.outp, 'w')
logging.debug('Found output project %s' %(output_project.name))
applet_project = resolve_project(args.applets, 'r')
logging.debug('Found applet project %s' %(applet_project.name))
mapping_applet = find_applet_by_name(MAPPING_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' %(mapping_applet.name))
input_shield_applet = find_applet_by_name(INPUT_SHIELD_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' %(input_shield_applet.name))
workflow_output_folder = resolve_folder(output_project, args.outf + '/workflows/' + experiment.get('accession') + '/' + 'rep%d' %(biorep_n))
fastq_output_folder = resolve_folder(output_project, args.outf + '/fastqs/' + experiment.get('accession') + '/' + 'rep%d' %(biorep_n))
mapping_output_folder = resolve_folder(output_project, args.outf + '/raw_bams/' + experiment.get('accession') + '/' + 'rep%d' %(biorep_n))
if args.raw:
workflow_title = 'Map %s rep%d to %s (no filter)' %(experiment.get('accession'), biorep_n, args.assembly)
workflow_name = 'ENCODE raw mapping pipeline'
else:
workflow_title = 'Map %s rep%d to %s and filter' %(experiment.get('accession'), biorep_n, args.assembly)
workflow_name = 'ENCODE mapping pipeline'
if args.tag:
workflow_title += ': %s' %(args.tag)
workflow = dxpy.new_dxworkflow(
title=workflow_title,
name=workflow_name,
project=output_project.get_id(),
folder=workflow_output_folder
)
input_shield_stage_id = workflow.add_stage(
input_shield_applet,
name='Gather inputs %s rep%d' %(experiment.get('accession'), biorep_n),
folder=fastq_output_folder,
stage_input=input_shield_stage_input
)
mapping_stage_id = workflow.add_stage(
mapping_applet,
name='Map %s rep%d' %(experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={'input_JSON': dxpy.dxlink({'stage': input_shield_stage_id, 'outputField': 'output_JSON'})}
)
if not args.raw:
final_output_folder = resolve_folder(output_project, args.outf + '/bams/' + experiment.get('accession') + '/' + 'rep%d' %(biorep_n))
filter_qc_applet = find_applet_by_name(FILTER_QC_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' %(filter_qc_applet.name))
filter_qc_stage_id = workflow.add_stage(
filter_qc_applet,
name='Filter and QC %s rep%d' %(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}),
'paired_end': dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'paired_end'})
}
)
xcor_applet = find_applet_by_name(XCOR_APPLET_NAME, applet_project.get_id())
logging.debug('Found applet %s' %(xcor_applet.name))
xcor_stage_id = workflow.add_stage(
xcor_applet,
name='Calculate cross-correlation %s rep%d' %(experiment.get('accession'), biorep_n),
folder=final_output_folder,
stage_input={
'input_bam': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'filtered_bam'}),
'paired_end': dxpy.dxlink({'stage': filter_qc_stage_id, 'outputField': 'paired_end'})
}
)
''' This should all be done in the shield's postprocess entrypoint
if args.accession_outputs:
derived_from = input_shield_stage_input.get('reads1')
if reads2:
derived_from.append(reads2)
files_json = {dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'}) : {
'notes': 'Biorep%d | Mapped to %s' %(biorep_n, input_shield_stage_input.get('reference_tar')),
'lab': 'j-michael-cherry',
'award': 'U41HG006992',
'submitted_by': '*****@*****.**',
'file_format': 'bam',
'output_type': 'alignments',
'derived_from': derived_from,
'dataset': experiment.get('accession')}
}
output_shield_stage_id = workflow.add_stage(
output_shield_applet,
name='Accession outputs %s rep%d' %(experiment.get('accession'), biorep_n),
folder=mapping_output_folder,
stage_input={'files': [dxpy.dxlink({'stage': mapping_stage_id, 'outputField': 'mapped_reads'})],
'files_json': files_json,
'key': input_shield_stage_input.get('key')}
)
'''
return workflow
def build_workflow():
if parameters["folder_provided"] == "false":
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
output_folder=parameters["Output"],
)
else:
wf = dxpy.new_dxworkflow(
name='WARDEN_workflow',
description='RNA-SEQ Workflow',
)
wf_outputs = []
htseq_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["htseq"],
state="closed",
return_handler=True)
genome_cov_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["genome_coverage"],
state="closed",
return_handler=True)
bigwig_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["bigwig"],
state="closed",
return_handler=True)
combine_counts_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["combine_counts"],
state="closed",
return_handler=True)
limma_applet = dxpy.search.find_one_data_object(classname="applet",
name=app_names["limma"],
state="closed",
return_handler=True)
simple_DE_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["simple_DE"],
state="closed",
return_handler=True)
bw_viewer_applet = dxpy.search.find_one_data_object(
classname="applet",
name=app_names["bw_viewer"],
state="closed",
return_handler=True)
sample_num = 0
htseq_results = []
bigwig_files = []
index_project, index_id = parameters["index_file"].split(":")
gtf_project, gtf_id = parameters["gtf_file"].split(":")
genome_length_project, genome_length_id = parameters[
"genome_sizes_file"].split(":")
gene_length_project, gene_length_id = parameters["gene_length_file"].split(
":")
fpkm_results = []
fpkm_log2_results = []
for sample_name in samples:
bam_id = samples[sample_name]
bam_link = dxpy.dxlink(bam_id)
htseq_input = {"input_bam": bam_link}
if parameters["sort_order"] == "position":
htseq_input["order"] = "pos"
else:
htseq_input["order"] = "name"
htseq_input["annotation_file"] = dxpy.dxlink({
"project": gtf_project,
"id": gtf_id
})
htseq_input["gene_length_file"] = dxpy.dxlink({
"project": gene_length_project,
"id": gene_length_id
})
htseq_input["prefix"] = sample_name
htseq_input["strand"] = parameters["strandedness"]
htseq_input["feature_type"] = parameters["feature_type"]
htseq_input["id_attribute"] = parameters["id_attribute"]
htseq_input["mode"] = parameters["mode"]
htseq_input["nonunique"] = parameters["nonunique"]
htseq_input["secondary_alignments"] = parameters[
"secondary_alignments"]
htseq_input["supplementary_alignments"] = parameters[
"supplementary_alignments"]
htseq_stage_id = wf.add_stage(
htseq_applet,
stage_input=htseq_input,
instance_type=parameters["htseq_instance"],
name=sample_name + ":HTSEQ COUNT",
folder="HTSEQ")
htseq_results.append(
dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "htseq_counts"
}))
wf_outputs += [
{
"name": sample_name + "_htseqcounts",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "htseq_counts"
}
}
},
]
if parameters["id_attribute"] == "gene_name":
fpkm_results.append((dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "fpkm"
})))
fpkm_log2_results.append((dxpy.dxlink({
"stage": htseq_stage_id,
"outputField": "fpkm_log2"
})))
wf_outputs += [
{
"name": sample_name + "_fpkm",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "fpkm"
}
}
},
{
"name": sample_name + "_fpkm_log2",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": htseq_stage_id,
"outputField": "fpkm_log2"
}
}
},
]
if parameters["run_coverage"] == 'true':
gcb_input = {}
gcb_input["input_bam"] = bam_link
if parameters["sort_order"] == "name":
gcb_input["sorted"] = False
else:
gcb_input["sorted"] = True
gcb_input["genome_sizes_file"] = dxpy.dxlink({
"project": genome_length_project,
"id": genome_length_id
})
gcb_input["strandedness"] = parameters["strandedness"]
gcb_input["output_prefix"] = sample_name
gcb_stage_id = wf.add_stage(genome_cov_applet,
stage_input=gcb_input,
instance_type="azure:mem3_ssd1_x8",
name=sample_name + ":COVERAGE",
folder="COVERAGE")
bg2bw_all_input = {}
bg2bw_all_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"all_coverage_file"
})
bg2bw_all_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_all_input["output_prefix"] = sample_name
bg2bw_all_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_all_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-ALL",
folder="BIGWIG")
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_all_stage_id,
"outputField": "bigwig"
}))
wf_outputs += [
{
"name": sample_name + "_all_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_all_stage_id,
"outputField": "bigwig"
}
}
},
]
if parameters["strandedness"] != "no":
bg2bw_pos_input = {}
bg2bw_pos_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"pos_coverage_file"
})
bg2bw_pos_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_pos_input["output_prefix"] = sample_name
bg2bw_pos_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_pos_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-POS",
folder="BIGWIG")
wf_outputs += [
{
"name": sample_name + "_pos_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_pos_stage_id,
"outputField": "bigwig"
}
}
},
]
bg2bw_neg_input = {}
bg2bw_neg_input["bedgraph_file"] = dxpy.dxlink({
"stage":
gcb_stage_id,
"outputField":
"neg_coverage_file"
})
bg2bw_neg_input["genome_sizes_file"] = dxpy.dxlink({
"project":
genome_length_project,
"id":
genome_length_id
})
bg2bw_neg_input["output_prefix"] = sample_name
bg2bw_neg_stage_id = wf.add_stage(
bigwig_applet,
stage_input=bg2bw_neg_input,
instance_type="azure:mem2_ssd1_x4",
name=sample_name + ":BED To BW-NEG",
folder="BIGWIG")
wf_outputs += [
{
"name": sample_name + "_neg_bigwig",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": bg2bw_neg_stage_id,
"outputField": "bigwig"
}
}
},
]
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_pos_stage_id,
"outputField": "bigwig"
}))
bigwig_files.append(
dxpy.dxlink({
"stage": bg2bw_neg_stage_id,
"outputField": "bigwig"
}))
sample_num += 1
combine_input = {
"count_files": htseq_results,
"name_value": "htseq",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_counts_stage_id = wf.add_stage(combine_counts_applet,
stage_input=combine_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE HTSEQ")
wf_outputs += [
{
"name": "combined_counts",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_counts_stage_id,
"outputField": "count_file"
}
}
},
]
if parameters["id_attribute"] == "gene_name":
combine_fpkm_input = {
"count_files": fpkm_results,
"name_value": "fpkm",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_fpkm_stage_id = wf.add_stage(
combine_counts_applet,
stage_input=combine_fpkm_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE FPKM")
combine_fpkm_log2_input = {
"count_files": fpkm_log2_results,
"name_value": "fpkm.log2",
"sample_files": [dxpy.dxlink(final_sample_list_id)]
}
combine_fpkm_log2_stage_id = wf.add_stage(
combine_counts_applet,
stage_input=combine_fpkm_log2_input,
instance_type="azure:mem2_ssd1_x1",
name="COMBINE FPKMlog2")
wf_outputs += [
{
"name": "combined_fpkm",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_fpkm_stage_id,
"outputField": "count_file"
}
}
},
{
"name": "combined_fpkm_log2",
"class": "file",
"outputSource": {
"$dnanexus_link": {
"stage": combine_fpkm_log2_stage_id,
"outputField": "count_file"
}
}
},
]
if parameters["BW_VIEWER"] != "None" and parameters[
"run_coverage"] == 'true':
bw_project, bw_file = parameters["BW_VIEWER"].split(":")
viewer_link = dxpy.dxlink({"project": bw_project, "id": bw_file})
bw_viewer_input = {"viewer": viewer_link, "bigwig_files": bigwig_files}
bw_viewer_stage_id = wf.add_stage(bw_viewer_applet,
stage_input=bw_viewer_input,
instance_type="azure:mem2_ssd1_x1",
name="BIGWIG_VIEWER",
folder="BIGWIG")
wf_outputs += [
{
"name": "bw_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": bw_viewer_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
if parameters["limma_DE_viewer"] != "None":
limma_viewer_project, limma_viewer_file = parameters[
"limma_DE_viewer"].split(":")
limma_viewer_link = dxpy.dxlink({
"project": limma_viewer_project,
"id": limma_viewer_file
})
if parameters["run_limma"] == 'true' and parameters[
"limma_runnable"] == "true":
limma_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"calcNormFactors_method":
parameters["calcNormFactors_method"],
"filter_count_type":
parameters["filter_count_type"],
"filter_count":
int(parameters["filter_count"]),
"p_value_adjust":
parameters["p_value_adjust"],
"contrasts_file":
dxpy.dxlink(comparisons_limma_id)
}
if parameters["limma_DE_viewer"] != "None":
limma_input["difex_viewer"] = limma_viewer_link
limma_stage_id = wf.add_stage(limma_applet,
stage_input=limma_input,
instance_type="azure:mem1_ssd1_x4",
name="LIMMA")
wf_outputs += [
{
"name": "limma_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "limma_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": limma_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
if parameters["run_simple_dif_ex"] == 'true':
simple_DE_input = {
"input_count_file":
dxpy.dxlink({
"stage": combine_counts_stage_id,
"outputField": "count_file"
}),
"sample_list_file":
dxpy.dxlink(final_sample_list_id),
"contrasts_file":
dxpy.dxlink(comparisons_all_id),
"difex_viewer":
limma_viewer_link
}
if parameters["limma_DE_viewer"] != "None":
simple_DE_input["difex_viewer"] = limma_viewer_link
simple_DE_stage_id = wf.add_stage(
simple_DE_applet,
stage_input=simple_DE_input,
instance_type="azure:mem1_ssd1_x4",
name="SIMPLE DIFFERENTIAL_EXPRESSION")
wf_outputs += [
{
"name": "simple_DE_outfiles",
"class": "array:file",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "out_files"
}
}
},
{
"name": "simple_DE_viewer",
"class": "record",
"outputSource": {
"$dnanexus_link": {
"stage": simple_DE_stage_id,
"outputField": "viewer_bookmark"
}
}
},
]
wf.update(workflow_outputs=wf_outputs)
wf.close()
return wf.get_id()
def test_workflow_completion(self):
dxworkflow = dxpy.new_dxworkflow(name="my workflow")
self.assert_completion("dx run my", "my workflow ")
dxworkflow.hide()
self.assert_no_completions("dx run my")
def createWorkflow(stepsToDo,
priors,
extras,
resultsFolder,
projectId,
appProjectId=None):
'''This function will populate a workflow for the stepsToDo.'''
if len(stepsToDo) < 1:
return None
if appProjectId == None:
appProjectId = projectId
# create a workflow object
wf = dxpy.new_dxworkflow(title=extras['name'],
name=extras['name'],
folder=resultsFolder,
project=projectId,
description=extras['description'])
# NOTE: prevStepResults dict contains links to result files to be generated by previous steps
prevStepResults = {}
for step in stepsToDo:
appName = STEPS[step]['app']
app = dxencode.find_applet_by_name(appName, appProjectId)
appInputs = {}
# file inputs
for fileToken in STEPS[step]['inputs'].keys():
appInp = STEPS[step]['inputs'][fileToken]
if fileToken in prevStepResults:
appInputs[appInp] = prevStepResults[fileToken]
elif fileToken in priors:
if isinstance(priors[fileToken], list):
appInputs[appInp] = []
for fid in priors[fileToken]:
appInputs[appInp] += [dxencode.get_file_link(fid)]
else:
appInputs[appInp] = dxencode.get_file_link(
priors[fileToken])
else:
print "ERROR: step '" + step + "' can't find input '" + fileToken + "'!"
sys.exit(1)
# Non-file app inputs
if 'params' in STEPS[step]:
for param in STEPS[step]['params'].keys():
appParam = STEPS[step]['params'][param]
if param in extras:
appInputs[appParam] = extras[param]
else:
print "ERROR: unable to locate '" + param + "' in extras."
sys.exit(1)
# Add wf stage
stageId = wf.add_stage(app,
stage_input=appInputs,
folder=resultsFolder)
# outputs, which we will need to link to
for fileToken in STEPS[step]['results'].keys():
#appOut = STEPS[step]['results'][fileToken]
appOut = fileToken ## not the value
prevStepResults[fileToken] = dxpy.dxlink({
'stage': stageId,
'outputField': appOut
})
wfRun = wf.run({})
return wfRun.describe()
