import sys, os
from bioblend.galaxy.objects import GalaxyInstance
from common import get_one
URL = 'http://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# Select "W2 - Bacterial re-sequencing | Paired-end" from published workflows
workflow_name = 'W2 - Bacterial re-sequencing | Paired-end'
previews = gi.workflows.get_previews(name=workflow_name, published=True)
p = get_one(_ for _ in previews if _.published)
# Import the workflow to user space
iw = gi.workflows.import_shared(p.id)
# Create a new history
history_name = '%s output' % workflow_name
h = gi.histories.create(history_name)
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
# Select the datasets
def convert_experiment_to_page_tab(self):
files_to_copy = []
copied_files = []
for file_name in self.file_names:
# print("Working on:" + file_name)
if file_name.endswith('.zip'):
# print('ZIP FILE: ' + file_name)
files_to_copy.append(os.path.join(self.dir_path, file_name))
elif file_name.endswith("idf.txt"):
# if oct(os.stat(self.dir_path)[ST_MODE])[-1:] == '0': # translating only public AE experiments
# continue
# idf_file_path = os.path.join(self.dir_path, file_name)
# files_to_copy.append(idf_file_path)
# self.idf_dict = self.parse_idf(idf_file_path)
# accession = file_name[:file_name.find('.')]
# if "Comment[ArrayExpressAccession]" in self.idf_dict.keys():
# # the case when filename does not correspond to the accession
#
# accession = self.idf_dict["Comment[ArrayExpressAccession]"][0]
# self.out_dir = BASE_DIRECTORY # RootPath is not working for submission. Will change back when it is fixed in BS
if not os.path.exists(os.path.dirname(self.page_tab_filename)):
os.makedirs(os.path.dirname(self.page_tab_filename))
pt_file = open(self.page_tab_filename, 'w')
print("", file=pt_file)
print("Submission\t" + self.accession + "\tPublic",
file=pt_file) # start writing PageTab - only for public experiments for now!!
print_one("Title", "Investigation Title", self.idf_dict, 0, pt_file)
print_one("ReleaseDate", "Public Release Date", self.idf_dict, 0, pt_file)
print("RootPath\t" + "%s/%s" % (self.folder, self.accession), file=pt_file)
print("AttachTo\tArrayExpress", file=pt_file)
print("", file=pt_file)
print("Study\ts-" + self.accession, file=pt_file)
print_one("Title", "Investigation Title", self.idf_dict, 0, pt_file)
print_one("Description", "Experiment Description", self.idf_dict, 0, pt_file)
print_one("Study type", "Comment [AEExperimentType]", self.idf_dict, 0, pt_file)
if self.organisms:
if len(self.organisms) > 1:
print("Organisms\t%s-org\ts-%s" % (self.accession, self.accession), file=pt_file)
for o in self.organisms:
print("Organisms\t%s" % o, file=pt_file)
else:
print("Organism\t%s" % self.organisms[0], file=pt_file)
# print("RootPath\t" + '/E-MTAB/E-MTAB-13/', file=f)
# print("", file=pt_file)
if "Experimental Design" in self.idf_dict.keys():
ed_num = len(self.idf_dict["Experimental Design"])
if ed_num:
# print("Experimental Designs\texp-des-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
for i in range(0, ed_num):
print("Experimental Designs\t%s" % (self.idf_dict["Experimental Design"][i]),
file=pt_file)
print_one("[Ontology]", "Experimental Design Term Source REF", self.idf_dict, i, pt_file)
print_one("[TermId]", "Experimental Design Term Accession Number", self.idf_dict, i,
pt_file)
# print("", file=pt_file)
if "Experimental Factor Name" in self.idf_dict.keys():
ef_num = len(self.idf_dict["Experimental Factor Name"])
if ef_num:
# print("Experimental Factors\texp-fac-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
for i in range(0, ef_num):
print(
"Experimental Factors\t%s" % (self.idf_dict["Experimental Factor Name"][i]),
file=pt_file)
print_one("[Ontology]", "Experimental Factor Term Source REF", self.idf_dict, i, pt_file)
print_one("[TermId]", "Experimental Factor Term Accession Number", self.idf_dict, i,
pt_file)
print_one("[TermName]", "Experimental Factor Type", self.idf_dict, i, pt_file)
# print("", file=pt_file)
if "Quality Control Type" in self.idf_dict.keys():
qc_num = len(self.idf_dict["Quality Control Type"])
if qc_num:
# print("Quality Control\texp-qual-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
for i in range(0, qc_num):
print("Quality Controls\t" + self.idf_dict["Quality Control Type"][i], file=pt_file)
print_one("[Ontology]", "Quality Control Term Source REF", self.idf_dict, i, pt_file)
print_one("[TermId]", "Quality Control Term Accession Number", self.idf_dict, i, pt_file)
# print("", file=pt_file)
if "Replicate Type" in self.idf_dict.keys():
rep_num = len(self.idf_dict["Replicate Type"])
if rep_num:
# print("Replicate\texp-rep-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
for i in range(0, rep_num):
print("Replicates\t%s" % (self.idf_dict["Replicate Type"][i]), file=pt_file)
print_one("[Ontology]", "Replicate Term Source REF", self.idf_dict, i, pt_file)
print_one("[TermId]", "Replicate Term Accession Number", self.idf_dict, i, pt_file)
# print("", file=pt_file)
if "Normalizations" in self.idf_dict.keys():
nor_num = len(self.idf_dict["Normalization Type"])
if nor_num:
# print("Normalizations\texp-nrom-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
for i in range(0, nor_num):
print("Normalization Type\t%s" % (self.idf_dict["Normalization Type"][i]),
file=pt_file)
print_one("[Ontology]", "Normalization Term Source REF", self.idf_dict, i, pt_file)
print_one("[TermId]", "Normalization Term Accession Number", self.idf_dict, i, pt_file)
# print("", file=pt_file)
pub_num1 = 0
pub_num2 = 0
pub_num3 = 0 # multiple ways in MAGE-TAB to describe publications
if "PubMed ID" in self.idf_dict.keys():
pub_num1 = len(self.idf_dict["PubMed ID"])
if "Publication DOI" in self.idf_dict.keys():
pub_num2 = len(self.idf_dict["Publication DOI"])
if "Publication Title" in self.idf_dict.keys():
pub_num3 = len(self.idf_dict["Publication Title"])
for i in range(0, max(pub_num1, pub_num2, pub_num3)):
print("", file=pt_file)
if "PubMed ID" in self.idf_dict.keys() and i < len(self.idf_dict["PubMed ID"]):
print("Publication\t" + self.idf_dict["PubMed ID"][i],
file=pt_file) # in PageTab identifier of the Publication section is the PubMed id, if present
else:
print("Publication", file=pt_file)
print_one("Title", "Publication Title", self.idf_dict, i, pt_file)
print_one("Authors", "Publication Author List", self.idf_dict, i, pt_file)
print_one("DOI", "Publication DOI", self.idf_dict, i, pt_file)
print_one("Status", "Publication Status", self.idf_dict, i, pt_file)
affiliation_list = []
address_list = []
if "Person Last Name" in self.idf_dict.keys():
person_num = len(
self.idf_dict["Person Last Name"]) # we assume that all contacts will have at least Last Name
for i in range(0, person_num):
print("", file=pt_file)
print("Author", file=pt_file)
name = self.idf_dict["Person Last Name"][i]
if "Person Mid Initials" in self.idf_dict.keys() and i < len(
self.idf_dict["Person Mid Initials"]):
name = self.idf_dict["Person Mid Initials"][i] + " " + name
if "Person First Name" in self.idf_dict.keys() and i < len(self.idf_dict["Person First Name"]):
name = self.idf_dict["Person First Name"][i] + " " + name
print("Name\t" + name, file=pt_file)
print_one("Email", "Person Email", self.idf_dict, i, pt_file)
print_one("Role", "Person Role", self.idf_dict, i, pt_file)
print_one("Phone", "Person Phone", self.idf_dict, i, pt_file)
print_one("Fax", "Person Fax", self.idf_dict, i, pt_file)
affiliation = ""
if "Person Affiliation" in self.idf_dict.keys() and i < len(
self.idf_dict["Person Affiliation"]):
affiliation = self.idf_dict["Person Affiliation"][i]
address = ""
if "Person Address" in self.idf_dict.keys() and i < len(self.idf_dict["Person Address"]):
address = self.idf_dict["Person Address"][i]
index = -1
if affiliation in affiliation_list: # we have already seen this affiliation
index = affiliation_list.index(affiliation)
if address_list[index] != address:
# turns out the address is different -
# let's treat this as a different organization after all
index = -1
if index != -1:
print("<affiliation>\to" + str(index + 1), file=pt_file)
else:
affiliation_list.append(affiliation)
address_list.append(address)
print("<affiliation>\to" + str(len(affiliation_list)), file=pt_file)
# print out Organizations - a separate PageTab section for each
org_num = len(affiliation_list)
for i in range(0, org_num):
print("", file=pt_file)
print("Organization\to" + str(i + 1), file=pt_file)
if not affiliation_list[i]:
print("Name\t" + address_list[i], file=pt_file)
else:
print("Name\t" + affiliation_list[i], file=pt_file)
print("Address\t" + address_list[i], file=pt_file)
# end of IDF transformation (except Protocols - will come after Files extracted from SDRF(s) )
if "Protocol Name" in self.idf_dict.keys():
protocol_num = len(self.idf_dict["Protocol Name"])
protocols_id = 'protocols-' + self.accession
if protocol_num > 0:
print("", file=pt_file)
print("Experiment Protocols\t%s\ts-%s" % (protocols_id, self.accession), file=pt_file)
print("", file=pt_file)
print("Protocols[%s]\tName\tType\t[Ontology]\t[TermId]\t"
"Description\tParameters\tHardware\tSoftware\tContact" % protocols_id, file=pt_file)
for i in range(0, protocol_num):
p_row = [self.idf_dict["Protocol Name"][i] + "\t" + self.idf_dict["Protocol Name"][i]]
p_type = get_one("Protocol Type", self.idf_dict, i)
p_row.append(p_type)
p_ontology = get_one("Protocol Term Source REF", self.idf_dict, i)
p_row.append(p_ontology)
p_id = get_one("Protocol Term Accession Number", self.idf_dict, i)
p_row.append(p_id)
p_description = get_one("Protocol Description", self.idf_dict, i)
p_row.append(p_description)
p_params = get_one("Protocol Parameters", self.idf_dict, i)
p_row.append(p_params)
p_Hardware = get_one("Protocol Hardware", self.idf_dict, i)
p_row.append(p_Hardware)
p_software = get_one("Protocol Software", self.idf_dict, i)
p_row.append(p_software)
p_contact = get_one("Protocol Contact", self.idf_dict, i)
p_row.append(p_contact)
print('\t'.join(p_row), file=pt_file)
print("", file=pt_file)
# print_one("Type", "Protocol Type", self.idf_dict, i, pt_file)
# print_one("[Ontology]", "Protocol Term Source REF", self.idf_dict, i, pt_file)
# print_one("[TermId]", "Protocol Term Accession Number", self.idf_dict, i, pt_file)
# print_one("Description", "Protocol Description", self.idf_dict, i, pt_file)
# print_one("Protocol Parameters", "Protocol Parameters", self.idf_dict, i, pt_file)
# print_one("Protocol Hardware", "Protocol Hardware", self.idf_dict, i, pt_file)
# print_one("Protocol Software", "Protocol Software", self.idf_dict, i, pt_file)
# print_one("Protocol Contact", "Protocol Contact", self.idf_dict, i, pt_file)
if "SDRF File" in self.idf_dict.keys():
sdrf_num = len(self.idf_dict["SDRF File"])
for i in range(0, sdrf_num):
# if i > 0: print(file_name)
sdrf_file_path = os.path.join(self.out_dir, self.idf_dict["SDRF File"][i])
# files_to_copy.append(sdrf_file_path)
with open(sdrf_file_path, "r") as sdrf_file:
sdrf_content = sdrf_file.readlines()
for k in range(len(sdrf_content)):
sdrf_content[k] = sdrf_content[k].replace('"', '')
header = sdrf_content[0].rstrip('\n').split('\t')
mask = self.sdrf_mask(header, sdrf_content)
# organisms = self.get_organism(sdrf_content, header)
print("", file=pt_file)
char_text = self.manage_chars(sdrf_content, parent_id='s-' + self.accession,
accession=self.accession)
if char_text:
# print("Characteristics\tchars-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
print(char_text, file=pt_file)
_factors = [n for n in header if 'factor' in n.lower()]
factors_pt_txt = self.get_char_txt(header, sdrf_content, _factors,
ch_id="exp_factor-" + self.accession,
title="Experimental Factors\texp_factor-%s\ts-%s" % (
self.accession, self.accession),
att_name="Factors", id_base='factors')
if factors_pt_txt:
print("\n" + factors_pt_txt, file=pt_file)
new_header = [t for t, flag in zip(header, mask) if flag] # apply mask to the header
new_contents = []
for j in range(1, len(sdrf_content)):
current_line = sdrf_content[j].rstrip('\n').split('\t')
new_contents.append([t for t, flag in zip(current_line, mask) if flag])
# as well as to the contents - now we have a SDRF (in newContents) with less columns
file_array = [] # list of File column indices
for idx, headerStr in enumerate(new_header):
if headerStr.endswith(" File"):
file_array.append(idx)
for j in range(0, len(file_array)): # we treat each of the File columns separately
file_name_prop_vector = collections.OrderedDict()
# map from file name to property vector, i.e., a set of all the different props
for k in range(0, len(new_contents)):
prop_array = new_contents[k]
file_name = prop_array[file_array[j]]
if len(prop_array[0:file_array[j]]) > 0:
# props will be, for a specific File, all other values in SDRF
props = '\t'.join(prop_array[0:file_array[j]]) + '\t' + '\t'.join(
prop_array[file_array[len(file_array) - 1] + 1:len(new_header)])
else:
props = '\t'.join(prop_array[file_array[len(file_array) - 1] + 1:len(new_header)])
prop_vector = collections.OrderedDict()
if file_name in file_name_prop_vector.keys():
prop_vector = file_name_prop_vector[file_name]
prop_vector[props] = 1
file_name_prop_vector[file_name] = prop_vector
if len(file_name_prop_vector) == 0:
continue
minimum = min([len(v) for v in file_name_prop_vector.values()])
# for files in this column, what is the minimum number of different props that they have?
maximum = max([len(v) for v in file_name_prop_vector.values()])
if minimum < 3:
# we think this column contains e.g. .cel files (where each file has a single props vector),
# or perhaps, for 2-colour experiments, two different lines per file
print("", file=pt_file)
print(new_header[file_array[j]], "Section\ts" + str(i) + "-" + str(j), file=pt_file)
print("", file=pt_file)
if len(prop_array[0:file_array[j]]) > 0: # Files table header
print("Files\t" + '\t'.join(new_header[0:file_array[j]]) + '\t' + '\t'.join(
new_header[file_array[len(file_array) - 1] + 1:len(new_header)]), file=pt_file)
else:
print(
"Files\t" + '\t'.join(
new_header[file_array[len(file_array) - 1] + 1:len(new_header)]),
file=pt_file)
for f_name in file_name_prop_vector.keys(): # the actual Files table
f_path = os.path.join(self.dir_path, f_name)
if os.path.exists(f_path):
files_to_copy.append(f_path)
else:
f_path = os.path.join(self.dir_path, 'unpacked', f_name)
if os.path.exists(f_path):
files_to_copy.append(f_path)
# else:
# raise Exception("%s NOT FOUND.")
for props in file_name_prop_vector[f_name].keys():
if f_name.strip():
print(f_name, file=pt_file, end="")
print("\t", file=pt_file, end="")
print(props, file=pt_file)
else:
print("", file=pt_file)
print(new_header[file_array[j]], "Section\ts" + str(i) + "-" + str(j), file=pt_file)
for idx, f_name in enumerate(file_name_prop_vector.keys()):
# for this case we will create a separate section for each File
if f_name.strip() == '':
continue
section_id = "s" + str(i) + "-" + str(j) + "-" + str(idx)
print("", file=pt_file)
print("Data File: " + f_name + "\t" + section_id + "\ts" + str(i) + "-" + str(j),
file=pt_file) # section header
print("", file=pt_file)
print("File\t" + f_name, file=pt_file) # single file
print("", file=pt_file)
if len(prop_array[0:file_array[j]]) > 0:
print("List of associated samples [" + section_id + "]\t" + '\t'.join(
new_header[0:file_array[j]]) + '\t' + '\t'.join(
new_header[file_array[len(file_array) - 1] + 1:len(new_header)]),
file=pt_file) # "sample table" header
else:
print("List of associated samples [" + section_id + "]\t" + '\t'.join(
new_header[file_array[len(file_array) - 1] + 1:len(new_header)]),
file=pt_file)
for props in file_name_prop_vector[f_name].keys(): # the actual "sample table"
print("\t", file=pt_file, end="")
print(props, file=pt_file)
print("", file=pt_file)
print("MAGE-TAB Files\tmt-" + self.accession, file=pt_file)
print("", file=pt_file)
print("File\t" + self.idf_file_path.split('/')[-1] + '\tmt-' + self.accession, file=pt_file)
print("Type\tIDF File", file=pt_file)
print("Format\ttab-delimited text", file=pt_file)
print("Description\tInvestigation Design Format (IDF)", file=pt_file)
print("", file=pt_file)
for i in range(0, sdrf_num):
print("File\t" + self.idf_dict["SDRF File"][i] + '\tmt-' + self.accession, file=pt_file)
print("Type\tSDRF File", file=pt_file)
print("Format\ttab-delimited text", file=pt_file)
print("Description\tSample and Data Relationship Format (SDRF)", file=pt_file)
print("", file=pt_file)
data_links = []
if 'Comment [SecondaryAccession]' in self.idf_dict.keys():
data_links += self.idf_dict['Comment [SecondaryAccession]']
elif 'Comment[SecondaryAccession]' in self.idf_dict.keys():
data_links += self.idf_dict['Comment[SecondaryAccession]']
arrays = []
arrays_id = []
res = retrieve_arrays_by_exp_acc(self.accession)
if res:
for a in res:
array = a.array
if array:
arrays.append("%s\tBiostudies\tArray" % array)
arrays_id.append(array)
if arrays_id:
for i in arrays_id:
# print("Array Design\t%s\tarrays-%s" % (i, self.accession), file=pt_file)
print ("", file=pt_file)
print("Link\t%s\ts-%s"%(i, self.accession), file=pt_file)
print("Type\tBiostudies", file=pt_file)
print("Description\tArray Design", file=pt_file)
related_studies = []
for acc in data_links:
if acc.startswith('E-GEOD-'):
related_studies.append(
"r-%s\t%s\tGEO (ArrayExpress)\tRelated GEO Study" % (acc, acc))
elif acc.startswith('E-MTAB-'):
related_studies.append(
"r-%s\t%s\tBiostudies (ArrayExpress)\tRelated ArrayExpress Study" % (acc, acc))
if related_studies:
print("", file=pt_file)
print("Related Studies\trelated-studies-%s\ts-%s" % (self.accession, self.accession), file=pt_file)
print("Related Studies[related-studies-%s]\tAccession\tType\tDescription", file=pt_file)
for r in related_studies:
print(r, file=pt_file)
if data_links:
print("", file=pt_file)
print("Study Links\tslinks-" + self.accession, file=pt_file)
print("", file=pt_file)
print("Links[slinks-%s]\tType\tDescription" % self.accession, file=pt_file)
for i in range(len(data_links)):
acc = data_links[i]
if acc.startswith('ERP'):
print("%s\tENA\tENA Project" % acc, file=pt_file)
elif acc.startswith('E-GEOD-'):
print(
"%s\tGEO\tRelated ArrayExpress Study" % acc,
file=pt_file)
elif acc.startswith('E-MTAB-'):
print(
"%s\tBiostudies\tRelated ArrayExpress Study" % acc,
file=pt_file)
seq_links = []
if 'Comment [SequenceDataURI]' in self.idf_dict.keys():
seq_links += self.idf_dict['Comment [SequenceDataURI]']
elif 'Comment[SequenceDataURI]' in self.idf_dict.keys():
seq_links += self.idf_dict['Comment[SequenceDataURI]']
if seq_links:
for i in range(len(seq_links)):
print("%s\tENA\tENA Runs" % seq_links[i].split('/')[-1], file=pt_file)
# print("E-MTAB-1\tBiostudy\tRelated Experiment", file=pt_file)
# for a in arrays:
# print(a, file=pt_file)
pt_file.close()
# print(files_to_copy)
for f in files_to_copy:
if os.path.isdir(f):
continue
f_name = os.path.split(f)[-1]
des_file = os.path.join(self.out_dir, f_name)
# print(des_file)
if os.path.exists(des_file):
if not filecmp.cmp(f, des_file):
# print("overwriting ", f_name)
# print(f)
shutil.copy2(f, self.out_dir)
else:
# print("copying ", f_name)
shutil.copy2(f, self.out_dir)
copied_files.append(des_file)
if f_name.endswith('.zip'):
# print("extracting:: " + f_name)
zip_file = zipfile.ZipFile(f, 'r')
zip_file = zipfile.ZipFile(f, 'r')
files = zip_file.namelist()
copied_files += [os.path.join(self.out_dir, a) for a in files]
zip_file.extractall(self.out_dir)
zip_file.close()
return copied_files
import sys
from bioblend.galaxy.objects import GalaxyInstance
from common import get_one # noqa:I100,I201
URL = 'https://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# Select "W2 - Bacterial re-sequencing | Paired-end" from published workflows
workflow_name = 'W2 - Bacterial re-sequencing | Paired-end'
previews = gi.workflows.get_previews(name=workflow_name, published=True)
p = get_one(_ for _ in previews if _.published)
# Import the workflow to user space
iw = gi.workflows.import_shared(p.id)
# Create a new history
history_name = f"{workflow_name} output"
h = gi.histories.create(history_name)
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
URL = 'http://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# import the workflow from the JSON dump
with open("small.ga") as f:
wf = gi.workflows.import_new(f.read())
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
# Select the input dataset
ds_name = '/RNA-Seq - Listeria monocytogenes/Listeria_monocytogenes_EGD_e_uid61583/NC_003210.rnt'
ld = get_one(l.get_datasets(name=ds_name))
input_map = {'input_tsv': ld}
# Run the workflow on a new history with the selected dataset as
# input, overriding the index of the column to remove; wait until the
# computation is complete.
history_name = 'get_col output'
params = {'Cut1': {'columnList': 'c2'}}
print 'Running workflow: %s [%s]' % (wf.name, wf.id)
outputs, out_hist = wf.run(input_map, history_name, params=params, wait=True)
URL = 'https://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# import the workflow from the JSON dump
with open("small.ga") as f:
wf = gi.workflows.import_new(f.read())
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
# Select the input dataset
ds_name = '/RNA-Seq - Listeria monocytogenes/Listeria_monocytogenes_EGD_e_uid61583/NC_003210.rnt'
ld = get_one(l.get_datasets(name=ds_name))
input_map = {'input_tsv': ld}
# Run the workflow on a new history with the selected dataset as
# input, overriding the index of the column to remove; wait until the
# computation is complete.
history_name = 'get_col output'
params = {'Cut1': {'columnList': 'c2'}}
print('Running workflow: %s [%s]' % (wf.name, wf.id))
outputs, out_hist = wf.run(input_map, history_name, params=params, wait=True)
import sys, os
from bioblend.galaxy.objects import GalaxyInstance
from common import get_one
URL = 'http://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# Select "W5 - Metagenomics" from published workflows
workflow_name = 'W5 - Metagenomics'
previews = gi.workflows.get_previews(name=workflow_name, published=True)
p = get_one(_ for _ in previews if _.published)
# Import the workflow to user space
iw = gi.workflows.import_shared(p.id)
# Create a new history
history_name = '%s output' % workflow_name
h = gi.histories.create(history_name)
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
# Select the "/Metagenomics/MetagenomicsDataset.fq" dataset
import sys
from bioblend.galaxy.objects import GalaxyInstance
from common import get_one
URL = 'https://orione.crs4.it'
API_KEY = os.getenv('GALAXY_API_KEY', 'YOUR_API_KEY')
if API_KEY == 'YOUR_API_KEY':
sys.exit('API_KEY not set, see the README.txt file')
gi = GalaxyInstance(URL, API_KEY)
# Select "W5 - Metagenomics" from published workflows
workflow_name = 'W5 - Metagenomics'
previews = gi.workflows.get_previews(name=workflow_name, published=True)
p = get_one(_ for _ in previews if _.published)
# Import the workflow to user space
iw = gi.workflows.import_shared(p.id)
# Create a new history
history_name = '%s output' % workflow_name
h = gi.histories.create(history_name)
# Select the "Orione SupMat" library
library_name = 'Orione SupMat'
l = get_one(gi.libraries.list(name=library_name))
import sys
from bioblend.galaxy.objects import GalaxyInstance
from common import get_one
URL = "https://orione.crs4.it"
API_KEY = os.getenv("GALAXY_API_KEY", "YOUR_API_KEY")
if API_KEY == "YOUR_API_KEY":
sys.exit("API_KEY not set, see the README.txt file")
gi = GalaxyInstance(URL, API_KEY)
# Select "W3 - Bacterial de novo assembly | Paired-end" from published workflows
workflow_name = "W3 - Bacterial de novo assembly | Paired-end"
previews = gi.workflows.get_previews(name=workflow_name, published=True)
p = get_one(_ for _ in previews if _.published)
# Import the workflow to user space
iw = gi.workflows.import_shared(p.id)
# Create a new history
history_name = "%s output" % workflow_name
h = gi.histories.create(history_name)
# Select the "Orione SupMat" library
library_name = "Orione SupMat"
l = get_one(gi.libraries.list(name=library_name))
