def get_single_sample_json(sample, dats_obj_cache):
# print("converting sample to json: " + str(sample))
samp_id = sample['SAMPID']['mapped_value']
subj_id = sample['SUBJID']['mapped_value']
subject = sample['subject']
# Uberon id (or EFO id, contrary to the documentation)
anat_id = sample['SMUBRID']['mapped_value']
if anat_id is None:
print("No Uberon/anatomy ID specified for sample " + samp_id)
sys.exit(1)
anatomy_identifier = None
anatomy_alt_ids = None
# TODO - query anatomy term from UBERON/EFO instead?
anatomy_name = sample['SMTSD']['mapped_value']
# EFO id
if re.match(r'^EFO_\d+', anat_id):
anatomy_identifier = OrderedDict([("identifier", anat_id),
("identifierSource", "EFO")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=" +
str(anat_id)), ("identifierSource", "EFO")
])
]
# Uberon id
else:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
# anatomical part
anat_part_key = ":".join(["AnatomicalPart", anatomy_name])
if anat_part_key in dats_obj_cache:
anatomical_part = dats_obj_cache[anat_part_key]
else:
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
dats_obj_cache[anat_part_key] = anatomical_part
# human experimental subject/patient
subject_sex = DatsObj(
"Dimension", [("name", DatsObj("Annotation", [("value", "Gender")])),
("description", "Gender of the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "SEX"),
("identifierSource", "GTEx")])),
("values", [subject['SEX']['mapped_value']])])
subject_age = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Age range")])),
("description", "Age range of the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "AGE"),
("identifierSource", "GTEx")])),
("values", [subject['AGE']['mapped_value']])])
subject_hardy_scale = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Hardy scale")])),
("description", "Hardy scale death classification for the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "DTHHRDY"),
("identifierSource", "GTEx")])),
("values", [subject['DTHHRDY']['mapped_value']])])
subject_characteristics = [subject_sex, subject_age, subject_hardy_scale]
# human experimental subject/patient
subj_key = ":".join(["Material", subj_id])
if subj_key in dats_obj_cache:
subject_material = dats_obj_cache[subj_key]
else:
subject_material = DatsObj(
"Material", [("name", subj_id),
("identifier", {
"identifier": subj_id
}), ("description", "GTEx subject " + subj_id),
("characteristics", subject_characteristics),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", util.get_donor_roles(dats_obj_cache))])
dats_obj_cache[subj_key] = subject_material
specimen_annot = util.get_annotation("specimen", dats_obj_cache)
rna_extract_annot = util.get_annotation("RNA extract", dats_obj_cache)
# biological/tissue sample
sample_name = samp_id
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("description",
anatomy_name + " specimen collected from subject " + subj_id),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", [specimen_annot]),
("derivesFrom", [subject_material, anatomical_part])])
# RNA extracted from tissue sample
rna_material = DatsObj(
"Material",
[("name", "RNA from " + sample_name),
("description", "total RNA extracted from " + anatomy_name +
" specimen collected from subject " + subj_id),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", [rna_extract_annot]),
("derivesFrom", [biological_sample_material])])
return rna_material
def get_files_dats_datasets(cache, dats_samples_d, sample_manifest, file_guids,
no_circular_links):
file_datasets_l = []
wgs_datatype = DatsObj(
"DataType",
[("information", util.get_annotation("DNA sequencing", cache)),
("method", util.get_annotation("whole genome sequencing assay",
cache)),
("platform", util.get_annotation("Illumina", cache))])
def get_wgs_datatype():
dkey = ".".join(["DataType", "WGS"])
return cache.get_obj_or_ref(dkey, lambda: wgs_datatype)
snp_datatype = DatsObj(
"DataType", [("information", util.get_annotation("SNP", cache)),
("method", util.get_annotation("SNP analysis", cache))])
cnv_datatype = DatsObj(
"DataType", [("information", util.get_annotation("CNV", cache)),
("method", util.get_annotation("CNV analysis", cache))])
def get_snp_datatype():
dkey = ".".join(["DataType", "SNP"])
return cache.get_obj_or_ref(dkey, lambda: snp_datatype)
def get_cnv_datatype():
dkey = ".".join(["DataType", "CNV"])
return cache.get_obj_or_ref(dkey, lambda: cnv_datatype)
nhlbi_key = ":".join(["Organization", "NHLBI"])
nhlbi = cache.get_obj_or_ref(nhlbi_key, lambda: NIH_NHLBI)
creators = [nhlbi]
def make_data_standard(format):
return DatsObj("DataStandard",
[("name", format),
("type", util.get_value_annotation("format", cache)),
("description", format + " file format")])
cram_ds_key = ":".join(["DataStandard", "CRAM"])
cram_dstan = cache.get_obj_or_ref(cram_ds_key,
lambda: make_data_standard("CRAM"))
vcf_ds_key = ":".join(["DataStandard", "VCF"])
vcf_dstan = cache.get_obj_or_ref(vcf_ds_key,
lambda: make_data_standard("VCF"))
n_samples = len(dats_samples_d)
n_samples_found = 0
for sample_id in dats_samples_d:
dats_sample = dats_samples_d[sample_id]
# look up corresponding file paths in manifest file
if sample_id not in sample_manifest:
logging.debug("sample not found in manifest - " + sample_id)
continue
n_samples_found += 1
ms = sample_manifest[sample_id]
material_type = 'DNA'
wgs_type = get_wgs_datatype()
snp_type = get_snp_datatype()
cnv_type = get_cnv_datatype()
# ------------------------------------------------
# WGS sequence - CRAM and CRAI files
# ------------------------------------------------
def get_filename(gs_uri):
m = re.match(r'^.*\/([^\/]+)$', gs_uri)
if m is None:
logging.fatal("unable to parse filename from " + gs_uri)
sys.exit(1)
filename = m.group(1)
return filename
gs_cram = ms['gs_cram']['mapped_value']
gs_crai = ms['gs_crai']['mapped_value']
# GUID lookup
cram_file = get_filename(gs_cram)
crai_file = get_filename(gs_crai)
cram_doi = file_guids[cram_file]['Sodium_GUID']['raw_value']
cram_size = file_guids[cram_file]['File size']['raw_value']
cram_md5 = file_guids[cram_file]['md5sum']['raw_value']
crai_doi = file_guids[crai_file]['Sodium_GUID']['raw_value']
crai_md5 = file_guids[crai_file]['md5sum']['raw_value']
# handle file size values with "e" in them
def filesize_to_int(size):
if re.match(r'.*e.*', size):
size = int(float(size))
else:
size = int(size)
# Google Cloud Platform / Google Storage copy
gs_cram_access = DatsObj("Access", [("accessURL", gs_cram)])
gs_cram_distro = DatsObj(
"DatasetDistribution",
[
("access", gs_cram_access),
("identifier", DatsObj("Identifier",
[("identifier", cram_doi)])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier",
[("identifier", crai_doi),
("relationType", "cram_index")])
]),
("size", filesize_to_int(cram_size)),
# TODO - add file size units
("conformsTo", [cram_dstan])
])
# AWS / S3 copy
s3_cram = ms['s3_cram']['mapped_value']
s3_crai = ms['s3_crai']['mapped_value']
s3_cram_access = DatsObj("Access", [("accessURL", s3_cram)])
s3_cram_distro = DatsObj(
"DatasetDistribution",
[
("access", s3_cram_access),
("identifier", DatsObj("Identifier",
[("identifier", cram_doi)])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier",
[("identifier", crai_doi),
("relationType", "cram_index")])
]),
("size", filesize_to_int(cram_size)),
# TODO - add file size units
("conformsTo", [cram_dstan])
])
m = re.match(r'^.*\/([^\/]+)$', gs_cram)
if m is None:
logging.fatal("unable to parse filename from CRAM file URI " +
gs_cram)
sys.exit(1)
filename = m.group(1)
# TODO - replace this with DATS-specific MD5 checksum encoding (TBD)
md5_dimension = DatsObj(
"Dimension", [("name", util.get_value_annotation("MD5", cache)),
("values", [cram_md5])])
cram_dataset = DatsObj("Dataset", [
("distributions", [gs_cram_distro, s3_cram_distro]),
("dimensions", [md5_dimension]),
("title", filename),
("types", [wgs_type]),
("creators", creators),
])
cram_da = DatsObj(
"DataAcquisition",
[("name", filename), ("input", [dats_sample.getIdRef()])
# ("uses", []) # software used
])
cram_dataset.set("producedBy", cram_da)
# circular link back to enclosing Dataset as the output
if not no_circular_links:
cram_da.set("output", [cram_dataset.getIdRef()])
file_datasets_l.append(cram_dataset)
# ------------------------------------------------
# Variant calls - VCF and CSI files
# ------------------------------------------------
gs_vcf = ms['gs_vcf']['mapped_value']
gs_csi = ms['gs_csi']['mapped_value']
if gs_vcf is None:
logging.warn("no VCF file found for " + sample_id)
continue
# GUID lookup
vcf_file = get_filename(gs_vcf)
csi_file = get_filename(gs_csi)
vcf_doi = file_guids[vcf_file]['Sodium_GUID']['raw_value']
vcf_size = file_guids[vcf_file]['File size']['raw_value']
vcf_md5 = file_guids[vcf_file]['md5sum']['raw_value']
csi_doi = file_guids[csi_file]['Sodium_GUID']['raw_value']
csi_md5 = file_guids[csi_file]['md5sum']['raw_value']
# Google Cloud Platform / Google Storage copy
gs_vcf_access = DatsObj("Access", [("accessURL", gs_vcf)])
gs_vcf_distro = DatsObj(
"DatasetDistribution",
[
("access", gs_vcf_access),
("identifier", DatsObj("Identifier",
[("identifier", vcf_doi)])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier",
[("identifier", csi_doi),
("relationType", "vcf_index")])
]),
("size", filesize_to_int(vcf_size)),
# TODO - add file size units
("conformsTo", [vcf_dstan])
])
# AWS / S3 copy
s3_vcf = ms['s3_vcf']['mapped_value']
s3_csi = ms['s3_csi']['mapped_value']
s3_vcf_access = DatsObj("Access", [("accessURL", s3_vcf)])
s3_vcf_distro = DatsObj(
"DatasetDistribution",
[
("access", s3_vcf_access),
("identifier", DatsObj("Identifier",
[("identifier", vcf_doi)])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier",
[("identifier", csi_doi),
("relationType", "vcf_index")])
]),
("size", filesize_to_int(vcf_size)),
# TODO - add file size units
("conformsTo", [vcf_dstan])
])
m = re.match(r'^.*\/([^\/]+)$', gs_vcf)
if m is None:
logging.fatal("unable to parse filename from VCF file URI " +
gs_vcf)
sys.exit(1)
filename = m.group(1)
# TODO - replace this with DATS-specific MD5 checksum encoding (TBD)
md5_dimension = DatsObj(
"Dimension", [("name", util.get_value_annotation("MD5", cache)),
("values", [vcf_md5])])
vcf_dataset = DatsObj("Dataset", [
("distributions", [gs_vcf_distro, s3_vcf_distro]),
("dimensions", [md5_dimension]),
("title", filename),
("types", [snp_type, cnv_type]),
("creators", creators),
])
vcf_da = DatsObj(
"DataAcquisition",
[("name", filename), ("input", [dats_sample.getIdRef()])
# ("uses", []) # software used
])
vcf_dataset.set("producedBy", vcf_da)
# circular link back to enclosing Dataset as the output
if not no_circular_links:
vcf_da.set("output", [vcf_dataset.getIdRef()])
file_datasets_l.append(vcf_dataset)
logging.info("found " + str(n_samples_found) + " / " + str(n_samples) +
" sample(s) in TOPMed file manifest")
return file_datasets_l
def get_single_dna_extract_json(cache, study, study_md, subj_var_values,
samp_var_values):
# Almost all samples in TOPMed WGS phase are blood samples, named "Blood", "Peripheral Blood"...
# Few samples are saliva samples probably due to sample collection issues
name = None
if 'BODY_SITE' in samp_var_values:
name = 'BODY_SITE'
elif 'Body_Site' in samp_var_values:
name = 'Body_Site'
elif 'Body Site' in samp_var_values:
name = 'Body Site'
anat_id = None
anatomy_name = None
if name is not None:
if "blood" in samp_var_values[name]['value'].lower():
anatomy_name = "blood"
anat_id = "0000178"
elif samp_var_values[name]['value'].lower() == "saliva":
anatomy_name = "saliva"
anat_id = "0001836"
else:
logging.fatal(
"encountered BODY_SITE other than 'Blood' and 'Saliva' in TOPMed sample metadata - "
+ samp_var_values['BODY_SITE']['value'])
sys.exit(1)
if anat_id is not None:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
# extract subject attributes
gender = None
age = None
visit_year = None
sys_bp = None
dias_bp = None
disease = {}
disease['hypertension'] = "unknown"
for name in subj_var_values:
name_upper = name.upper()
if name_upper == "GENDER" or name_upper == "SEX":
gender = subj_var_values[name]['value'].lower()
elif name_upper == "VISIT_AGE" or name_upper == "AGE" or name_upper == "AGE_ENROLL": #need to confirm that these allmean the same thing
age = subj_var_values[name]['value']
elif name_upper == "VISIT_YEAR":
visit_year = subj_var_values[name]['value']
elif name_upper == "SYSBP":
sys_bp = subj_var_values[name]['value']
elif name_upper == "DIASBP":
dias_bp = subj_var_values[name]['value']
elif name_upper == "HYPERTENSION" or name_upper == "HIGHBLOODPRES":
if subj_var_values[name]['value'].lower(
) == "yes" or subj_var_values[name]['value'] == '1':
disease['hypertension'] = "yes"
elif re.match(r'\S', subj_var_values[name]['value']):
disease['hypertension'] = "no"
# anatomical part
anatomical_part = None
if anatomy_name is not None:
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
subject_characteristics = []
subject_bearerOfDisease = []
# harmonized/standardized characteristics
if gender is not None:
subject_sex = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Gender")])),
("description", "Gender of the subject"), ("values", [gender])])
subject_characteristics.append(subject_sex)
if age is not None:
subject_age = DatsObj(
"Dimension", [("name", DatsObj("Annotation", [("value", "Age")])),
("description", "Age of the subject"),
("values", [age])])
subject_characteristics.append(subject_age)
if visit_year is not None:
subject_visitYear = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Visit year")])),
("description", "Year of visit, to use for longitudinal analysis"),
("values", [visit_year])
])
subject_characteristics.append(subject_visitYear)
if sys_bp is not None:
subject_sysBP = DatsObj(
"Dimension",
[("name",
DatsObj("Annotation", [("value", "Systolic blood pressure")])),
("description",
"Systolic blood pressure of subject, measured in mmHg"),
("values", [sys_bp])])
subject_characteristics.append(subject_sysBP)
if dias_bp is not None:
subject_diasBP = DatsObj(
"Dimension",
[("name",
DatsObj("Annotation", [("value", "Diastolic blood pressure")])),
("description",
"Diastolic blood pressure of subject, measured in mmHg"),
("values", [dias_bp])])
subject_characteristics.append(subject_diasBP)
if disease['hypertension'] != "unknown":
disease_name = "hypertension"
disease_id = "10763"
disease_identifier = OrderedDict([
("identifier", "DOID:" + str(disease_id)),
("identifierSource", "Disease Ontology")
])
disease_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/DOID_" + str(disease_id)),
("identifierSource", "Disease Ontology")
])
]
subject_hypertension = DatsObj("Disease", [
("name", "Hypertension"),
("identifier", disease_identifier),
("alternateIdentifiers", disease_alt_ids),
("diseaseStatus",
DatsObj("Annotation", [("value", disease['hypertension']),
("valueIRI", "")])),
])
subject_bearerOfDisease.append(subject_hypertension)
# create a DATS Dimension from a dbGaP variable value
def make_var_dimension(name, var_value):
value = var_value["value"]
dim = DatsObj("Dimension",
[("name", DatsObj("Annotation", [("value", name)])),
("values", [value])])
# find existing DATS identifier for the corresponding Dataset Dimension
if "var" in var_value:
id = var_value["var"]["id"]
dbgap_var_dim = study_md['dbgap_vars'][id]
dim.setProperty("identifier",
dbgap_var_dim.get("identifier").getIdRef())
return dim
# create DATS Dimensions for dbGaP subject metadata
subject_dimensions = [
make_var_dimension(vname, subj_var_values[vname])
for vname in sorted(subj_var_values)
]
# create DATS Dimensions for dbGaP sample metadata
sample_dimensions = [
make_var_dimension(vname, samp_var_values[vname])
for vname in sorted(samp_var_values)
]
# "raw" characteristics from dbGaP metadata
subject_characteristics.extend(subject_dimensions)
sample_characteristics = sample_dimensions
human_t = util.get_taxon_human(cache)
subj_id = subj_var_values['SUBJECT_ID']['value']
dbgap_subj_id = subj_var_values['dbGaP_Subject_ID']['value']
samp_id = samp_var_values['SAMPLE_ID']['value']
dbgap_samp_id = samp_var_values['dbGaP_Sample_ID']['value']
study_title = study.get("title")
# human experimental subject/patient
subject_material = DatsObj(
"Material",
[("name", subj_id), ("identifier", {
"identifier": subj_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_subj_id, "dbGaP")]),
("description", study_title + " subject " + subj_id),
("characteristics", subject_characteristics),
("bearerOfDisease", subject_bearerOfDisease), ("taxonomy", [human_t]),
("roles", util.get_donor_roles(cache))])
# TODO - use DatsObjCache
specimen_annot = util.get_annotation("specimen")
dna_extract_annot = util.get_annotation("DNA extract")
# biological/tissue sample
sample_name = samp_id
sample_derives_from = [subject_material]
sample_descr = "specimen collected from subject " + subj_id
if anatomical_part is not None:
sample_derives_from.append(anatomical_part)
sample_descr = anatomy_name + " " + sample_descr
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_samp_id, "dbGaP")]),
("description", sample_descr),
("characteristics", sample_characteristics), ("taxonomy", [human_t]),
("roles", [specimen_annot]), ("derivesFrom", sample_derives_from)])
# DNA extracted from tissue sample
dna_descr = "DNA extracted from specimen collected from subject " + subj_id
if anatomical_part is not None:
dna_descr = "DNA extracted from " + anatomy_name + " specimen collected from subject " + subj_id
dna_material = DatsObj("Material",
[("name", "DNA from " + sample_name),
("description", dna_descr),
("taxonomy", [human_t]),
("roles", [dna_extract_annot]),
("derivesFrom", [biological_sample_material])])
return dna_material
def get_sample_dats_material(cache, dats_subject, study, study_md,
samp_var_values):
# Almost all samples in TOPMed WGS phase are blood samples, named "Blood", "Peripheral Blood"...
# Few samples are saliva samples probably due to sample collection issues
name = None
if 'BODY_SITE' in samp_var_values:
name = 'BODY_SITE'
elif 'Body_Site' in samp_var_values:
name = 'Body_Site'
elif 'Body Site' in samp_var_values:
name = 'Body Site'
anat_id = None
anatomy_name = None
if name is not None:
if "blood" in samp_var_values[name]['value'].lower():
anatomy_name = "blood"
anat_id = "0000178"
elif samp_var_values[name]['value'].lower() == "saliva":
anatomy_name = "saliva"
anat_id = "0001836"
else:
logging.fatal(
"encountered BODY_SITE other than 'Blood' and 'Saliva' in TOPMed sample metadata - "
+ samp_var_values['BODY_SITE']['value'])
sys.exit(1)
def make_anat_part(anat_id, anatomy_name):
# anatomical part
anatomical_part = DatsObj("AnatomicalPart", [("name", anatomy_name)])
if anat_id is not None:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
anatomical_part.set("identifier", anatomy_identifier)
anatomical_part.set("alternateIdentifiers", anatomy_alt_ids)
return anatomical_part
if anatomy_name is not None:
# use cached value for AnatomicalPart if possible
anat_part_key = ":".join(["AnatomicalPart", anatomy_name])
anatomical_part = cache.get_obj_or_ref(
anat_part_key, lambda: make_anat_part(anat_id, anatomy_name))
else:
anatomical_part = None
# create a DATS Dimension from a dbGaP variable value
def make_var_dimension(name, var_value):
value = var_value["value"]
dim = DatsObj("Dimension",
[("name", DatsObj("Annotation", [("value", name)])),
("values", [value])])
# find existing DATS identifier for the corresponding Dataset Dimension
if "var" in var_value:
dbgap_var_dim = var_value["var"]["dim"]
dim.setProperty("identifier",
dbgap_var_dim.get("identifier").getIdRef())
return dim
# create DATS Dimensions for dbGaP sample metadata
sample_dimensions = [
make_var_dimension(vname, samp_var_values[vname])
for vname in sorted(samp_var_values)
]
sample_characteristics = sample_dimensions
samp_id = samp_var_values['SAMPLE_ID']['value']
dbgap_samp_id = samp_var_values['dbGaP_Sample_ID']['value']
study_title = study.get("title")
specimen_annot = util.get_annotation("specimen", cache)
# corresponding DATS subject Material
subj_key = ":".join(["Material", dats_subject.get("name")])
dats_subj = cache.get_obj_or_ref(subj_key, lambda: dats_subject)
dats_subj_name = dats_subject.get("name")
# biological/tissue sample
sample_name = samp_id
sample_derives_from = [dats_subj]
sample_descr = "specimen collected from subject " + dats_subj_name
if anatomical_part is not None:
sample_derives_from.append(anatomical_part)
sample_descr = anatomy_name + " " + sample_descr
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_samp_id, "dbGaP")]),
("description", sample_descr),
("characteristics", sample_characteristics),
("taxonomy", [util.get_taxon_human(cache)]),
("roles", [specimen_annot]), ("derivesFrom", sample_derives_from)])
# RNA or DNA extracted from tissue sample
stype = "DNA"
# TODO - check if RNA, not DNA
dna_or_rna_descr = stype + " extracted from specimen collected from subject " + dats_subj_name
if anatomical_part is not None:
dna_or_rna_descr = stype + " extracted from " + anatomy_name + " specimen collected from subject " + dats_subj_name
dna_or_rna_material = DatsObj(
"Material",
[("name", stype + " from " + sample_name),
("description", dna_or_rna_descr),
("taxonomy", [util.get_taxon_human(cache)]),
("roles", [util.get_annotation(stype + " extract", cache)]),
("derivesFrom", [biological_sample_material])])
return dna_or_rna_material
def get_dbgap_studies(qterm):
studies = []
study = None
lnum = 0
# Add newline before each occurrence of "Versions" if not already present
lines = []
for line in GTEX_STUDIES_STR.split('\n'):
m = re.match(r'^(\S+.*)(Versions?.*)$', line)
if m is None:
lines.append(line)
else:
lines.append(m.group(1))
lines.append(m.group(2))
for line in lines:
lnum += 1
# blank line
if re.match(r'^\s*$', line):
continue
# study id
m = re.match('^(phs\S+)$', line)
if m is not None:
study = {'id': m.group(1)}
studies.append(study)
continue
# study description
m = re.match(r'^Genotype-Tissue Expression(.*)$', line)
if m is not None:
study['descr'] = m.group(1)
continue
# embargo release(s)
m = re.match(r'^(Version.*)$', line)
if m is not None:
if 'versions' not in study:
study['versions'] = []
study['versions'].append(m.group(1))
continue
# details/participants/type of study
m = re.match('^VDAS(\d+)(\D.*)Links$', line)
if m is not None:
study['n_participants'] = int(m.group(1))
study['study_type'] = m.group(2)
continue
# platform
m = re.match(r'^(HiSeq.*)$', line)
if m is not None:
study['platform'] = m.group(1)
continue
# parse error
logging.fatal("unexpected content at line " + str(lnum) +
" of dbGaP studies: " + line)
sys.exit(1)
n_studies = len(studies)
logging.info("found " + str(n_studies) + " GTEx study in dbGaP")
# convert studies to DATS Datasets
datasets = []
for s in studies:
m = re.match(r'^phs\d+\.(v\d+)\.p\d+$', s['id'])
if m is None:
logging.fatal(
"unable to parse dataset/study version from study id " +
s['id'])
sys.exit(1)
version = m.group(1)
dimensions = [
DatsObj("Dimension", [
("name", {
"value": "Actual Subject Count"
}),
("description",
"The actual number of subjects entered into a clinical trial."
), ("types", [util.get_annotation("Actual Subject Number")]),
("values", [s['n_participants']])
])
]
types = [
OrderedDict([
("information", util.get_annotation("DNA sequencing")),
("method",
util.get_annotation("whole genome sequencing assay")),
("platform", HISEQ_TYPES[s['platform']])
])
]
# TODO - Specify creators and release date(s) of this particular dataset.
# This may require parsing some of the metadata files and/or documents.
# TODO - required field - using NIH NHLBI as placeholder, but need to revisit and assign specific study-level creator
creators = [NIH_NHGRI]
# TODO - find better location for study_type?
extra_props = [
DatsObj("CategoryValuesPair", [("category", "study_type"),
("values", [s['study_type']])])
]
# Dataset
dataset = DatsObj(
"Dataset",
[
("identifier", DatsObj("Identifier",
[("identifier", s['id'])])),
("version", version),
# ("dates", []),
#("title", s['descr']),
("title",
"Genotype-Tissue Expression Project (GTEx) WGS and RNA-Seq data"
),
("storedIn", DB_GAP),
("types", types),
("creators", creators),
("dimensions", dimensions),
("extraProperties", extra_props)
# ("producedBy", data_analysis),
# ("distributions", [DatsObj("DatasetDistribution", [
# ("access", DatsObj("Access", [
# ("landingPage", GTEX_DATASETS_URL)
# ]))
# ])]),
])
datasets.append(dataset)
return datasets
#To do: incorporate
#HumanOmni5-Quad
#HumanOmni2.5
#Infinium HumanExome BeadChip
#HiSeq 2000
#HiSeq 2000
#GeneChip Human Gene 1.0 ST Array
#HiSeq 2000
#HiSeq X Ten
DBGAP_QUERY_URL_PREFIX = 'https://www.ncbi.nlm.nih.gov/gap/?term='
DBGAP_GTEX_QUERY_URL = DBGAP_QUERY_URL_PREFIX + 'phs000424'
HISEQ_TYPES = {
"HiSeq 2000": util.get_annotation("Illumina HiSeq 2000"),
"HiSeq X Ten": util.get_annotation("Illumina HiSeq X Ten"),
}
# TODO - duplicated from rnaseq_datasets.py
DB_GAP = DatsObj("DataRepository", [("name", "dbGaP")])
NIH_NHGRI = DatsObj("Organization",
[("name", "National Human Genome Research Institute"),
("abbreviation", "NHGRI")])
# TODO - use DatsObjCache
cache = None
GTEX_V7_RNASEQ_TYPE = DatsObj(
"DataType",
[("information", util.get_annotation("transcription profiling", cache)),
def get_files_dats_datasets(cache, dats_samples_d, p_samples, gh_samples,
protected_cram_files, no_circular_links):
file_datasets = []
rnaseq_datatype = DatsObj("DataType", [
("information", util.get_annotation("transcription profiling", cache)),
("method", util.get_annotation("RNA-seq assay", cache)),
("platform", util.get_annotation("Illumina", cache))
])
def get_rnaseq_datatype():
dkey = ".".join(["DataType", "RNA-seq"])
return cache.get_obj_or_ref(dkey, lambda: rnaseq_datatype)
wgs_datatype = DatsObj(
"DataType",
[("information", util.get_annotation("DNA sequencing", cache)),
("method", util.get_annotation("whole genome sequencing assay",
cache)),
("platform", util.get_annotation("Illumina", cache))])
def get_wgs_datatype():
dkey = ".".join(["DataType", "WGS"])
return cache.get_obj_or_ref(dkey, lambda: wgs_datatype)
broad_key = ":".join(["Organization", "Broad Institute"])
broad = cache.get_obj_or_ref(
broad_key,
lambda: DatsObj("Organization", [("name", "Broad Institute")]))
creators = [broad]
def make_data_standard(format):
return DatsObj("DataStandard",
[("name", format),
("type", util.get_value_annotation("format", cache)),
("description", format + " file format")])
cram_ds_key = ":".join(["DataStandard", "CRAM"])
cram_ds = cache.get_obj_or_ref(cram_ds_key,
lambda: make_data_standard("CRAM"))
crai_ds_key = ":".join(["DataStandard", "CRAI"])
crai_ds = cache.get_obj_or_ref(crai_ds_key,
lambda: make_data_standard("CRAI"))
for sample_id in protected_cram_files:
file = protected_cram_files[sample_id]
material_type = None
ds_types = None
# determine file type
if re.search(r'wgs\/', file['cram_file_aws']['raw_value']):
material_type = 'DNA'
ds_types = get_wgs_datatype()
gcp_suffix = '_gcp'
elif re.search(r'rnaseq\/', file['cram_file_aws']['raw_value']):
material_type = 'RNA'
ds_types = get_rnaseq_datatype()
gcp_suffix = ''
else:
logging.fatal(
"unable to determine material/sequence type from cram_file_aws="
+ file['cram_file_aws']['raw_value'])
sys.exit(1)
# RNA-Seq keys = sample_id cram_file cram_file_md5 cram_file_size cram_index cram_file_aws cram_index_aws
# WGS keys = same as above + firecloud_id
cram_file = file['cram_file' + gcp_suffix]['raw_value']
cram_file_md5 = file['cram_file_md5']['raw_value']
# TODO - review the following encoding decisions:
# - storing .crai URI as relatedIdentifier of the DatasetDistribution for the .cram file
# - storing MD5 checksum of the .cram file as an extraProperty of the DatasetDistribution
# - storing firecloud_id as a relatedIdentifier of the Dataset (not the DatasetDistribution)
# Google Cloud Platform / Google Storage copy
gs_access = DatsObj(
"Access",
[("landingPage", file['cram_file' + gcp_suffix]['raw_value'])])
gs_distro = DatsObj(
"DatasetDistribution",
[
("access", gs_access),
("identifier",
DatsObj("Identifier",
[("identifier",
file['cram_file' + gcp_suffix]['raw_value'])])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier",
[("identifier",
file['cram_index' + gcp_suffix]['raw_value']),
("relationType", "cram_index")])
]),
("size", int(file['cram_file_size']['raw_value'])),
# TODO - add unit for bytes, include IRI?
# ("unit", util.get_value_annotation("bytes", cache))
("conformsTo", [
cache.get_obj_or_ref(cram_ds_key,
lambda: make_data_standard("CRAM"))
])
])
# AWS / S3 copy
s3_access = DatsObj(
"Access", [("landingPage", file['cram_file_aws']['raw_value'])])
s3_distro = DatsObj(
"DatasetDistribution",
[
("access", s3_access),
("identifier",
DatsObj("Identifier", [
("identifier", file['cram_file_aws']['raw_value'])
])),
("relatedIdentifiers", [
DatsObj("RelatedIdentifier", [
("identifier", file['cram_index_aws']['raw_value']),
("relationType", "cram_index")
])
]),
("size", int(file['cram_file_size']['raw_value'])),
# TODO - add unit for bytes, include IRI?
# ("unit", util.get_value_annotation("bytes", cache))
("conformsTo", [
cache.get_obj_or_ref(cram_ds_key,
lambda: make_data_standard("CRAM"))
])
])
m = re.match(r'^.*\/([^\/]+)$', cram_file)
if m is None:
logging.fatal("unable to parse filename from CRAM file URI " +
cram_file)
sys.exit(1)
filename = m.group(1)
# TODO - replace this with DATS-specific MD5 checksum encoding (TBD)
md5_dimension = DatsObj(
"Dimension", [("name", util.get_value_annotation("MD5", cache)),
("values", [file['cram_file_md5']['raw_value']])])
ds = DatsObj("Dataset", [
("distributions", [gs_distro, s3_distro]),
("dimensions", [md5_dimension]),
("title", filename),
("types", [ds_types]),
("creators", creators),
])
# add firecloud_id for WGS
if 'firecloud_id' in file:
f_id = DatsObj("RelatedIdentifier",
[("identifier", file['firecloud_id']['raw_value']),
("identifierSource", "FireCloud")])
ds.set("relatedIdentifiers", [f_id])
# input RNA/DNA extract that was sequenced
if sample_id not in dats_samples_d:
logging.fatal("no sample exists for " + sample_id +
" found in file " +
file['cram_file_aws']['raw_value'])
sys.exit(1)
dats_sample = dats_samples_d[sample_id]
dats_samp_key = ":".join(["Material", dats_sample.get("name")])
dats_samp = cache.get_obj_or_ref(dats_samp_key, lambda: dats_sample)
da = DatsObj(
"DataAcquisition",
[("name", filename), ("input", [dats_samp])
# ("uses", []) # software used
])
if not no_circular_links:
# circular link back to enclosing Dataset as the output
da.set("output", [ds.getIdRef()])
ds.set("producedBy", da)
file_datasets.append(ds)
return file_datasets
def get_sample_dats_material(cache, dats_subject, p_sample, gh_sample,
var_lookup):
samp_id = p_sample['SAMPID']['mapped_value']
subj_id = p_sample['SUBJID']['mapped_value']
# retrieve id reference for the Identifier of the DATS Dimension for the "all subjects" consent group version of the variable
def get_var_id(name):
return var_lookup[name]['dim'].get("identifier").getIdRef()
# Uberon id (or EFO id, contrary to the documentation)
anat_id = p_sample['SMUBRID']['mapped_value']
if anat_id is None:
print("No Uberon/anatomy ID specified for sample " + samp_id)
sys.exit(1)
anatomy_identifier = None
anatomy_alt_ids = None
# TODO - query anatomy term from UBERON/EFO instead?
anatomy_name = p_sample['SMTSD']['mapped_value']
def make_anat_part(anat_id, anatomy_name):
# EFO id
if re.match(r'^EFO_\d+', anat_id):
anatomy_identifier = OrderedDict([("identifier", anat_id),
("identifierSource", "EFO")])
anatomy_alt_ids = [
OrderedDict([(
"identifier",
"https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form="
+ str(anat_id)), ("identifierSource", "EFO")])
]
# Uberon id
else:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
# anatomical part
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
return anatomical_part
# use cached value for AnatomicalPart if possible
anat_part_key = ":".join(["AnatomicalPart", anatomy_name])
anatomical_part = cache.get_obj_or_ref(
anat_part_key, lambda: make_anat_part(anat_id, anatomy_name))
# use URI from GitHub GTEx id dump if available
identifier = samp_id
if gh_sample is not None:
identifier_id = gh_sample['Destination URL']['raw_value']
subj_key = ":".join(["Material", dats_subject.get("name")])
dats_subj = cache.get_obj_or_ref(subj_key, lambda: dats_subject)
# add sample characteristics from p_sample metadata
sample_chars = []
for key in p_sample:
if re.match(r'^(subject|id)$', key):
continue
# TODO - currently including only a small subset of the available values for demonstration purposes
if not re.match(r'^(SMATSSCR|SMRIN|SMMAPRT|SMGNSDTC)$', key):
continue
var = p_sample[key]
# print("got key=" + key + " var=" + str(var))
mapped_val = var['mapped_value']
char = DatsObj("Dimension",
[("name", util.get_value_annotation(key, cache)),
("identifier", get_var_id(key)),
("values", [mapped_val])])
sample_chars.append(char)
# biological/tissue sample
biological_sample_material = DatsObj(
"Material",
[("name", samp_id), ("identifier", {
"identifier": identifier
}),
("description",
anatomy_name + " specimen collected from subject " + subj_id),
("characteristics", sample_chars),
("taxonomy", [util.get_taxon_human(cache)]),
("roles", [util.get_annotation("specimen", cache)]),
("derivesFrom", [dats_subj, anatomical_part])])
# analysis freeze classification
smafrze = p_sample['SMAFRZE']['mapped_value']
# expected sequence type depending on data freeze classification
expected_stype = None
stype = None
if smafrze == "RNASEQ":
expected_stype = "RNA"
elif smafrze == "WGS":
expected_stype = "DNA"
elif smafrze == "WES":
expected_stype = "DNA"
# Illumina OMNI SNP Array
elif smafrze == "OMNI":
expected_stype = "DNA"
elif smafrze == "EXCLUDE":
pass
else:
logging.fatal("unknown SMAFRZE " + smafrze)
sys.exit(1)
# sample type - DNA or RNA
stype = None
smnabtcht = p_sample['SMNABTCHT']['mapped_value']
if re.match(r'^DNA ([iI]solation|[eE]xtraction).*', smnabtcht):
stype = 'DNA'
elif re.match(r'^RNA ([iI]solation|[eE]xtraction).*', smnabtcht):
stype = 'RNA'
elif re.match(
r'DNA or RNA Extraction from Paxgene-derived Lysate Plate Based',
smnabtcht):
stype = 'RNA'
elif re.match(r'Transfer To Matrix \(Manual\)', smnabtcht):
stype = 'DNA'
if stype is None:
if expected_stype is not None:
stype = expected_stype
else:
print("couldn't determine sequence type for smafrze=" + smafrze +
" smnabtcht=" + smnabtcht)
return None
else:
if (expected_stype is not None) and (stype != expected_stype):
logging.fatal("seq type " + stype +
" doesn't match expected stype " + expected_stype)
sys.exit(1)
# DNA or RNA extract
dna_or_rna_material = DatsObj(
"Material",
[("name", stype + " from " + samp_id),
("description", "total " + stype + " extracted from " + anatomy_name +
" specimen collected from subject " + subj_id),
("taxonomy", [util.get_taxon_human(cache)]),
("roles", [util.get_annotation(stype + " extract", cache)]),
("derivesFrom", [biological_sample_material])])
return dna_or_rna_material