Non-standardized metadata files
import os
input_dir = "./isa_container/input_files"
os.listdir(path = input_dir)
['.DS_Store', 'IDs']
IDs_file_path = "./isa_container/input_files/IDs/ACTIONdemonstrator_XOmics_IDs_fake.csv"
import pandas as pd
IDs_df = pd.read_csv(IDs_file_path).iloc[:10,]
print("Data frame dimensions\n", IDs_df.shape)
print("Column names\n", IDs_df.columns)
print("Missing value counts\n", IDs_df.isna().sum())
IDs_df.head
Data frame dimensions
(10, 5)
Column names
Index(['XOmicsPhenoID', 'XOmicsGenoID', 'XOmicsFamID', 'XOmicsMethylID',
'XOmicsmetaboID'],
dtype='object')
Missing value counts
XOmicsPhenoID 0
XOmicsGenoID 0
XOmicsFamID 0
XOmicsMethylID 0
XOmicsmetaboID 0
dtype: int64
<bound method NDFrame.head of XOmicsPhenoID XOmicsGenoID XOmicsFamID XOmicsMethylID XOmicsmetaboID 0 XOP1 XOG1 XOF1 XOE1 XOM1 1 XOP2 XOG2 XOF2 XOE2 XOM2 2 XOP3 XOG3 XOF3 XOE3 XOM3 3 XOP4 XOG4 XOF4 XOE4 XOM4 4 XOP5 XOG5 XOF5 XOE5 XOM5 5 XOP6 XOG6 XOF6 XOE6 XOM6 6 XOP7 XOG7 XOF7 XOE7 XOM7 7 XOP8 XOG8 XOF8 XOE8 XOM8 8 XOP9 XOG9 XOF9 XOE9 XOM9 9 XOP10 XOG10 XOF10 XOE10 XOM10>
# print current directory
import numpy as np
import os
os.getcwd()
'/Users/philippe/Documents/git/isa-api2/isa-api/isa-cookbook/content/notebooks'
Create a new ISA object
# create new investigation with single study
from isatools.model import *
investigation = Investigation()
# define title
investigation.title = "X-omics data analysis, integration and stewardship demonstrator dataset: NTR ACTION omics data"
investigation.title
'X-omics data analysis, integration and stewardship demonstrator dataset: NTR ACTION omics data'
investigation.description = "Predict childhood aggression with multi-omics data and demonstrate the FAIRification process and data analysis of a multi-omics project"
investigation.identifier = "tbd"
# add one study to investigation
investigation.studies.append(Study())
investigation.studies
[isatools.model.Study(filename='', identifier='', title='', description='', submission_date='', public_release_date='', contacts=[], design_descriptors=[], publications=[], factors=[], protocols=[], assays=[], sources=[], samples=[], process_sequence=[], other_material=[], characteristic_categories=[], comments=[], units=[])]
According to MetaboLights help site, the title should ideally be the same as for a corresponding manuscript.
investigation.studies[0].title = "X-omics data analysis, integration and stewardship demonstrator dataset: NTR ACTION omics data"
investigation.studies[0].identifier = "tbd" # TODO: add identifier; update title
investigation.studies[0].filename = "s_study.txt"
investigation.studies[0]
isatools.model.Study(filename='s_study.txt', identifier='tbd', title='X-omics data analysis, integration and stewardship demonstrator dataset: NTR ACTION omics data', description='', submission_date='', public_release_date='', contacts=[], design_descriptors=[], publications=[], factors=[], protocols=[], assays=[], sources=[], samples=[], process_sequence=[], other_material=[], characteristic_categories=[], comments=[], units=[])
Ontologies can be searched e.g. at http://www.ontobee.org/ or https://www.ebi.ac.uk/ols/index.
FAIR genomes lookups: https://github.com/fairgenomes/fairgenomes-semantic-model/tree/main/lookups
# ontologies
ontologies = {
"afo": OntologySource(
name = "AFO",
description = "Allotrope Merged Ontology Suite"),
"chebi": OntologySource(
name = "CHEBI",
description = "Chemical Entities of Biological Interest"),
"chmo": OntologySource(
name = "CHMO",
description = "Chemical Methods Ontology"),
"edam": OntologySource(
name = "EDAM",
description = "Bioinformatics operations, data types, formats, identifiers and topics"),
"efo": OntologySource(
name = "EFO",
description = "Experimental Factor Ontology"),
"ero": OntologySource(
name = "eagle-i resource ontology",
description = "An ontology of research resources such as instruments, protocols, reagents, animal models and biospecimens"),
"maxo": OntologySource(
name = "MAXO",
description = "Medical Action Ontology"),
"msio": OntologySource(
name = "MSIO",
description = "Metabolite Standards Initiative Ontology"),
"ncbitaxon": OntologySource(
name = "NCBITAXON",
description = "NCBI organismal classification"),
"ncit": OntologySource(
name = "NCIT",
description = "NCI Thesaurus OBO Edition"),
"obi": OntologySource(
name = "OBI",
description = "Ontology for Biomedical Investigations"),
"pato": OntologySource(
name = "PATO",
description = "PATO - the Phenotype And Trait Ontology"),
"uberon": OntologySource(
name = "UBERON",
description = "Uber-anatomy ontology")
}
# add ontologies to investigation
for o in ontologies.values():
investigation.ontology_source_references.append(o)
Note that the protocol used in the process to derive sample from source MUST be of type 'sample collection' (see https://isa-specs.readthedocs.io/en/latest/isatab.html#study-table-file).
protocol_params = {
"anatomical entity": ProtocolParameter(
parameter_name = OntologyAnnotation(
term = "anatomical entity",
term_source = ontologies["uberon"],
term_accession = "http://purl.obolibrary.org/obo/UBERON_0001062"))
}
# define sample collection protocol
sample_collection_protocol = Protocol(
name = "sample collection",
# see github.com/ISA-tools/isa-specs/blob/master/source/isatab.rst
# -> MUST be of type 'sample collection'
protocol_type = OntologyAnnotation(term = "sample collection"),
parameters = [protocol_params["anatomical entity"]])
investigation.studies[0].protocols.append(sample_collection_protocol)
See also https://ebi.ac.uk/metabolights/guides/Protocol/Protocol for protocols required for submission in MetaboLights, i.e. Sample collection, Extraction, Chromatography, Mass spectrometry, Data transformation, and Metabolite identification.
# gender
studyfactor_gender = StudyFactor(
name = "genotypic sex",
factor_type = OntologyAnnotation( # Ontology source reference
term = "genotypic sex", # also used in FAIR genomes
term_source = ontologies["pato"],
term_accession = "http://purl.obolibrary.org/obo/PATO_0020000"))
# female
factorvalue_female = FactorValue(
factor_name = studyfactor_gender,
value = OntologyAnnotation( # str or OntologyAnnotation
term = "XX Genotype", # also used in FAIR genomes
term_source = ontologies["ncit"],
term_accession = "http://purl.obolibrary.org/obo/NCIT_C45976"))
# male
factorvalue_male = FactorValue(
factor_name = studyfactor_gender,
value = OntologyAnnotation( # str or OntologyAnnotation
term = "XY Genotype", # also used in FAIR genomes
term_source = ontologies["ncit"],
term_accession = "http://purl.obolibrary.org/obo/NCIT_C45977"))
# agressive behaviour assessment - T-scores
studyfactor_aggression = StudyFactor(
name = "aggression score",
factor_type = OntologyAnnotation(
term = "childhood aggressive behaviour measurement",
term_source = ontologies["efo"],
term_accession = "http://www.ebi.ac.uk/efo/EFO_0007663"),
comments = [
Comment(name = "T-score reference",
value = "Age- and sex-specific Aggressive Behaviour T-score as described in Hagenbeek et al. https://doi.org/10.3389/fpyst.2020.00165")])
# T-score value
class FactorValueAggressionScore(FactorValue):
def __init__(self,
factor_name = studyfactor_aggression,
value = None,
unit = OntologyAnnotation(
term = "T-score",
term_source = ontologies["ncit"],
term_accession = "http://purl.obolibrary.org/obo/NCIT_C120401"),
comments = None):
super().__init__(factor_name = factor_name, value = value, unit = unit, comments = comments)
assay_genotype = Assay(filename = "a_assay_genotype.txt",
measurement_type = OntologyAnnotation(term = "", term_source = "", term_accession = ""),
technology_type = OntologyAnnotation(term = "nucleotide sequencing", term_source ="", term_accession = ""),
technology_platform = OntologyAnnotation(term = "", term_source = "", term_accession = ""))
# TODO: What sequencing plaform has been used?
# define extraction and measurement protocols
dna_extraction_protocol = Protocol(
name = "DNA extraction",
protocol_type = OntologyAnnotation(
term = "DNA extraction",
term_source = ontologies["obi"],
term_accession = "http://purl.obolibrary.org/obo/OBI_0000257"))
# TODO: check type; compare to FAIR genomes
investigation.studies[0].protocols.append(dna_extraction_protocol)
genotype_profiling_protocol = Protocol(
name = "genotype profiling",
protocol_type = OntologyAnnotation(
term = "genotyping",
term_source = ontologies["efo"],
term_accession = "http://www.ebi.ac.uk/efo/EFO_0000750")
)
investigation.studies[0].protocols.append(genotype_profiling_protocol)
# TODO: check type; compare to FAIR genomes
assay_methylation = Assay(
filename = "a_assay_methylation.txt",
measurement_type = OntologyAnnotation(
term = "Methylation Beta Value",
term_source = ontologies["ncit"],
term_accession = "http://purl.obolibrary.org/obo/NCIT_C164051"),
technology_type = OntologyAnnotation(
term = "DNA methylation profiling by array assay",
term_source = ontologies["obi"],
term_accession = "http://purl.obolibrary.org/obo/OBI_0001332"),
technology_platform = OntologyAnnotation(
term = "Illumina Infinium MethylationEPIC BeadChip",
term_source = ontologies["obi"],
term_accession = "http://purl.obolibrary.org/obo/OBI_0002131")
)
# define extraction and measurement protocols
dna_extraction_protocol = Protocol(
name = "DNA extraction",
protocol_type = OntologyAnnotation(
term = "DNA extraction",
term_source = ontologies["obi"],
term_accession = "http://purl.obolibrary.org/obo/OBI_0000257"))
# TODO: check type; compare to FAIR genomes
investigation.studies[0].protocols.append(dna_extraction_protocol)
methylation_profiling_protocol = Protocol(
name = "methylation profiling",
protocol_type = OntologyAnnotation(
term = "methylation profiling",
term_source = ontologies["efo"],
term_accession = "http://www.ebi.ac.uk/efo/EFO_0000751"),
# components = [OntologyAnnotation(
# term = "Illumina Infinium MethylationEPIC BeadChip",
# term_source = obi,
# term_accession = "http://purl.obolibrary.org/obo/OBI_0002131")]
)
investigation.studies[0].protocols.append(methylation_profiling_protocol)
methylation_data_processing_protocol = Protocol(
name = "methylation data processing protocol",
protocol_type = OntologyAnnotation(
term = "Protocol",
term_source = ontologies["edam"],
term_accession = "http://edamontology.org/data_2531"),
description = "Sinke, Lucy, van Iterson, Maarten, Cats, Davy, Slieker, Roderick, & Heijmans, Bas. (2019, July 11). DNAmArray: Streamlined workflow for the quality control, normalization, and analysis of Illumina methylation array data (Version 2.1). Zenodo. http://doi.org/10.5281/zenodo.3355292",
uri = "http://doi.org/10.5281/zenodo.3355292")
investigation.studies[0].protocols.append(methylation_data_processing_protocol)
assay_metabolomics_amines = Assay(
filename = "a_assay_metabolomics_amines.txt",
measurement_type = OntologyAnnotation(
term = "targeted metabolite profiling",
term_source = ontologies["msio"],
term_accession = "http://purl.obolibrary.org/obo/MSIO_0000100"),
technology_type = OntologyAnnotation(
term = "liquid chromatography-mass spectrometry",
term_source = ontologies["chmo"],
term_accession = "http://purl.obolibrary.org/obo/CHMO_0000524"))
# sample_type = OntologyAnnotation(
# term = "urine specimen",
# term_source = ['obi'],
# term_accession = "http:/purl.obolibrary.org/obo/OBI_0000651")
#
# technology_platform = OntologyAnnotation(
# term = "",
# term_source = ontologies[""],
# term_accession = "")
# TODO: What exact platform/instrument has been used?
assay_metabolomics_OA = Assay(
filename = "a_assay_metabolomics_OA.txt",
measurement_type = OntologyAnnotation(
term = "targeted metabolite profiling",
term_source = ontologies["msio"],
term_accession = "http://purl.obolibrary.org/obo/MSIO_0000100"),
technology_type = OntologyAnnotation(
term = "gas chromatography-mass spectrometry",
term_source = ontologies["chmo"]))
# sample_type = OntologyAnnotation(
# term = "urine specimen",
# term_source = ['obi'],
# term_accession = "http:/purl.obolibrary.org/obo/OBI_0000651")
# technology_platform = OntologyAnnotation(
# term = "",
# term_source = ontologies[""],
# term_accession = "")
# TODO: What exact platform/instrument has been used?
assay_metabolomics_steroids = Assay(
filename = "a_assay_metabolomics_steroids.txt",
measurement_type = OntologyAnnotation(
term = "targeted metabolite profiling",
term_source = ontologies["msio"],
term_accession = "http://purl.obolibrary.org/obo/MSIO_0000100"),
technology_type = OntologyAnnotation(
term = "high-performance liquid chromatography-mass spectrometry",
term_source = ontologies["chmo"],
term_accession = "http://purl.obolibrary.org/obo/CHMO_0000796"))
# sample_type = OntologyAnnotation(
# term = "urine specimen",
# term_source = ['obi'],
# term_accession = "http:/purl.obolibrary.org/obo/OBI_0000651")
# technology_platform = OntologyAnnotation(
# term = "",
# term_source = ontologies[""],
# term_accession = "")
# TODO: What exact platform/instrument has been used?
urine_sampling_protocol = Protocol(
name = "urine sampling",
protocol_type = OntologyAnnotation(
term = 'urine speciment collection',
# term_source = [''],
term_accession = 'http://snomed.info/id/57617002')
)
# TODO: is this useful a ontology?
investigation.studies[0].protocols.append(urine_sampling_protocol)
extraction_metabolomics = Protocol(
name = "Extraction",
protocol_type = OntologyAnnotation(
term = 'Extraction',
term_source = ontologies["ncit"],
term_accession = 'http://purl.obolibrary.org/obo/NCIT_C61575'),
parameters = [
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Post Extraction")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Derivatization"))
])
chromatography = Protocol(
name = "Chromatography",
protocol_type = OntologyAnnotation(
term = 'Chromatography',
term_source = ontologies["ncit"],
term_accession = 'http://purl.obolibrary.org/obo/NCIT_C16431'),
parameters = [
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Chromatography Instrument")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Column model")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Column type"))
])
labelling_metabolites = Protocol(
name = "Labelling metabolites",
protocol_type = OntologyAnnotation(
term = 'Labelling',
term_source = ontologies["chmo"],
term_accession = 'http://purl.obolibrary.org/obo/CHMO_0001675')
)
mass_spectrometry = Protocol(
name = "Mass spectrometry",
protocol_type = OntologyAnnotation(
term = 'Mass spectrometry',
term_source = ontologies["ncit"],
term_accession = 'http://purl.obolibrary.org/obo/NCIT_C17156'),
parameters = [
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Scan polarity")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Scan m/z range")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Instrument")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Ion source")),
ProtocolParameter(parameter_name=OntologyAnnotation(term = "Mass analyzer"))
])
data_transformation = Protocol(
name = "Data transformation",
protocol_type = OntologyAnnotation(
term = 'Data Transformation',
term_source = ontologies["ncit"],
term_accession = 'http://purl.obolibrary.org/obo/NCIT_C43582')
)
metabolite_identification = Protocol(
name = "Metabolite identification",
protocol_type = OntologyAnnotation(
term = 'peak identification',
term_source = ontologies["afo"],
term_accession = 'http://purl.allotrope.erg/ontologies/process#AFP_0003618')
)
# TODO: Is this the correct ontlogy (source)?
Add samples to study and link to previously defined protocols and assays.
For MetaboLights, sample information should include unique sample name, organism, organism part, sample type (control, QC, experimental sample), other descriptors as factors (age, gender).
for idx, row in IDs_df.iterrows():
# add subjects (sources)
# TODO: issue - source should represent a source material such as urine,
# and sample a respective extract or similar
# check if source was already added already (rows can contain duplicate entries)
#is_new_source = True
source_name = row["XOmicsPhenoID"]
source = next((src for src in investigation.studies[0].sources
if src.name == source_name), None)
if not source:
#is_new_source = False
# create new source for subject
source = Source(name = row["XOmicsPhenoID"])
# Characteristics - Organism - should be included for Metabolights
# here, organism is defined per source, i.e. individual
source.characteristics.append(
Characteristic(
category = OntologyAnnotation(
term = "organism"),
# TODO: add term source and accession for category? would such information be lost in ISA-Tab?
value = OntologyAnnotation(
term = "Homo sapiens",
term_source = ontologies["ncbitaxon"],
term_accession = "http://purl.bioontology.org/ontology/NCBITAXON/9606")))
# TODO: check if family ID should/should not be added / is required for analysis
source.characteristics.append(
Characteristic(category = "family ID",
value = row["XOmicsFamID"]))
source.characteristics.append(
Characteristic(
category = OntologyAnnotation(
term = "childhood aggressive behaviour measurement",
term_source = ontologies["efo"],
term_accession = "http://www.ebi.ac.uk/efo/EFO_0007663"),
value = "", #tscore,
unit = OntologyAnnotation(
term = "T-score",
term_source = ontologies["ncit"],
term_accession = "http://purl.obolibrary.org/obo/NCIT_C120401")))
#source.factor_values.append(FactorValueAggressionScore(value = tscore))
# add subject to study
investigation.studies[0].sources.append(source)
# add samples - sample names need to be unique
# urine sample for metabolomics
if not pd.isna(row["XOmicsmetaboID"]):
# check if urine sample was already added to study
urine_sample_name = "urine_{0}".format(source_name)
urine_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == urine_sample_name), None)
if not urine_sample:
# create a new sample with unique name
urine_sample = Sample(
name = urine_sample_name,
derives_from = [source]) # the individual
# Characteristics - Organism part - should be included for Metabolights
# here, organism part is defined per sample
urine_sample.characteristics.append(
Characteristic(
category = OntologyAnnotation(term = "organism part"),
value = OntologyAnnotation(
term = "urine",
term_source = ontologies["uberon"],
term_accession = "http://purl.obolibrary.org/obo/UBERON_0001088")))
# Characteristics - sample type - should be included for Metabolights
# i.e. control, QC, experimental sample
urine_sample.characteristics.append(
Characteristic(
category = "sample type", # TODO: could not find a term yet; sample type is not an ontological term, but required by MetaboLights
value = OntologyAnnotation(
term = "experimental sample",
term_source = ontologies["chmo"],
term_accession = "http://purl.obolibrary.org/obo/CHMO_0002746")))
# add urine sample to study
investigation.studies[0].samples.append(urine_sample)
# check if urine sampling process exists for source
urine_p_name = "urine_specimen_collection_process_{0}".format(source.name)
urine_collection_process = next(
(prcs for prcs in investigation.studies[0].process_sequence
if prcs.name == urine_p_name), None)
if not urine_collection_process:
# define urine sampling process for this subject
urine_collection_process = Process(
name = urine_p_name,
executes_protocol = sample_collection_protocol,
parameter_values = [
ParameterValue(
category = protocol_params["anatomical entity"], #ProtocolParameter
value = "urine")],
inputs = [source],
outputs = [urine_sample])
investigation.studies[0].process_sequence.append(urine_collection_process)
else:
# urine sampling process already exists for the source
# add urine sample to outputs of existing process
urine_collection_process.outputs.append(urine_sample)
# add samples
# buccal swab sample for genotyping and DNA methylation arrays
if not pd.isna(row["XOmicsGenoID"]) or not pd.isna(row["XOmicsMethylID"]):
buccal_sample_name = "buccal_mucosa_{0}".format(source_name)
buccal_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == buccal_sample_name), None)
if not buccal_sample:
# create sample of buccal mucosa
buccal_sample = Sample(
name = buccal_sample_name,
derives_from = [source]) # same source as urine sample
buccal_sample.characteristics.append(
Characteristic(
category = OntologyAnnotation(term = "organism part"),
value = ""))
# TODO: add more characteristics - compare to urime sample
# add sample to study
investigation.studies[0].samples.append(buccal_sample)
# check if buccal swab sampling process exists for source
# needs to be checked, because multiple samples can be derived from one source
buccal_p_name = "buccal_specimen_collection_process_{0}".format(source.name)
buccal_collection_process = next(
(prcs for prcs in investigation.studies[0].process_sequence
if prcs.name == buccal_p_name), None)
if not buccal_collection_process:
# define buccal sampling process for this subject
buccal_collection_process = Process(
name = buccal_p_name,
executes_protocol = sample_collection_protocol,
parameter_values = [
ParameterValue(
category = protocol_params["anatomical entity"], #ProtocolParameter
value = "buccal mucosa")],
inputs = [source],
outputs = [buccal_sample]
)
investigation.studies[0].process_sequence.append(buccal_collection_process)
else:
# buccal sampling process already exists for the source
# add buccal sample to outputs of existing process
buccal_collection_process.outputs.append(buccal_sample)
# NOTE: adding extraction process at study level doesn seem to work
# causes sample to disappear from study file
#if not pd.isna(row["XOmicsMethylID"]):
# dna_extraction_process = next(())
# assay_methylation.samples.append(buccal_sample) # first check if already added
# methylation_profiling_process = Process(
# name = "methylation_profiling_{0}".format(source.name),
# executes_protocol = methylation_profiling_protocol,
# inputs = [buccal_sample],
## outputs = [buccal_dna])
Genotype assay
# add samples to genotype assay
for idx, row in IDs_df.iterrows():
source_name = row["XOmicsPhenoID"]
if not pd.isna(row["XOmicsGenoID"]):
buccal_sample_name = "buccal_mucosa_{0}".format(source_name)
buccal_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == buccal_sample_name), None)
genotype_sample = next(
(smpl for smpl in assay_genotype.samples
if smpl.name == buccal_sample_name), None)
if not genotype_sample:
assay_genotype.samples.append(buccal_sample) # first check if already added
# define DNA as material extracted from buccal mucosa sample
# on study level, because the same DNA is used for genotyping AND DNA methylation profiling
# TODO: check if this works; could be that extraction has to be on assay level
# but Study object has process_sequence, i.e. this is technically possible
# define DNA material for this sample
# now trying on assay level
buccal_dna = Material(
name = "buccal_DNA_{0}".format(row["XOmicsGenoID"]),
type_ = "Extract Name")
# define extraction process for buccal DNA
dna_extraction_process = Process(
name = "DNA_extraction_{0}".format(row["XOmicsGenoID"]),
executes_protocol = dna_extraction_protocol,
inputs = [buccal_sample],
outputs = [buccal_dna])
#if not pd.isna(row["XOmicsMethylID"]):
# dna_extraction_process = next(())
genotype_profiling_process = Process(
name = "genotype_profiling_{0}".format(row["XOmicsGenoID"]),
executes_protocol = genotype_profiling_protocol,
inputs = [buccal_dna])
plink(dna_extraction_process, genotype_profiling_process)
assay_genotype.process_sequence.append(dna_extraction_process)
assay_genotype.process_sequence.append(genotype_profiling_process)
# add samples to methylation assay
for idx, row in IDs_df.iterrows():
source_name = row["XOmicsPhenoID"]
if not pd.isna(row["XOmicsMethylID"]):
buccal_sample_name = "buccal_mucosa_{0}".format(source_name)
buccal_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == buccal_sample_name), None)
methylation_sample = next(
(smpl for smpl in assay_methylation.samples
if smpl.name == buccal_sample_name), None)
if not methylation_sample:
assay_methylation.samples.append(buccal_sample) # first check if already added
# define DNA as material extracted from buccal mucosa sample
# on study level, because the same DNA is used for genotyping AND DNA methylation profiling
# TODO: check if this works; could be that extraction has to be on assay level
# but Study object has process_sequence, i.e. this is technically possible
# define DNA material for this sample
# now trying on assay level
buccal_dna = Material(
name = "buccal_DNA_{0}".format(row["XOmicsMethylID"]),
type_ = "Extract Name")
# define extraction process for buccal DNA
dna_extraction_process = Process(
name = "DNA_extraction_{0}".format(row["XOmicsMethylID"]),
executes_protocol = dna_extraction_protocol,
inputs = [buccal_sample],
outputs = [buccal_dna])
#if not pd.isna(row["XOmicsMethylID"]):
# dna_extraction_process = next(())
methylation_profiling_process = Process(
name = "methylation_profiling_{0}".format(row["XOmicsMethylID"]),
executes_protocol = methylation_profiling_protocol,
inputs = [buccal_dna])
plink(dna_extraction_process, methylation_profiling_process)
assay_methylation.process_sequence.append(dna_extraction_process)
assay_methylation.process_sequence.append(methylation_profiling_process)
Metabolomics assays
# add samples, processes and datafiles to metabolomics Amines assay
Assay = assay_metabolomics_amines
# Define datafiles (not all may be relevant)
raw_datafile = DataFile(filename="link/to/raw/data", label="Raw Spectral Data File")
normalized_datafile = DataFile(filename="link/to/normalized_data", label="Normalization Name")
derived_spectral_data_file = DataFile(filename="link/to/spectral_file", label="Derived Spectral Data File")
Data_Transformation_Name = DataFile(filename="link/to/data_transformation_name", label="Data Transformation Name")
MAF = DataFile(filename="link/to/MAF", label="Metabolite Assignment File")
# Loop over samples and add process to samples
for idx, row in IDs_df.iterrows():
source_name = row["XOmicsPhenoID"]
# print(source_name)
if not pd.isna(row["XOmicsmetaboID"]):
# print(row['XOmicsmetaboID'])
urine_sample_name = "urine_{0}".format(source_name)
# print(urine_sample_name)
urine_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == urine_sample_name), None)
metabolomics_sample = next(
(smpl for smpl in Assay.samples
if smpl.name == urine_sample_name), None)
if not metabolomics_sample:
## Extraction
Post_extraction = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Post Extraction")), value = OntologyAnnotation(term="1 uL borate buffer (pH 8.8) with AQC reagent"))
Derivatization = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Derivatization")), value = "AQC")
material_extract = Material(
name = "extract_{0}".format(row["XOmicsmetaboID"]),
type_ = "Extract Name")
extraction_process = Process(
executes_protocol=extraction_metabolomics,
parameter_values=[Post_extraction, Derivatization],
inputs = [urine_sample],
outputs = [material_extract])
## Labelling
material_label = Material(
name ="labeled_{0}".format(row["XOmicsmetaboID"]),
type_ ="Labeled Extract Name")
labelling_process = Process(
executes_protocol=labelling_metabolites,
inputs = [extraction_process.outputs[0]],
outputs = [material_label])
## Chromatography
# separated_molecules = Material(
# name = "separated_molecules_{0}".format(row["XOmicsmetaboID"],
# type_ ="Labeled Extract Name")
# )
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Chromatography Instrument")), value = "Agilent 1290 Infinity II")
column_model = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column model")), value = "Accq-Tag Ultra column (waters + FURHTER SPECS?)")
column_type = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column type")), value = "reverse phase")
chromatography_process = Process(
name = "chromatography_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = chromatography,
parameter_values = [instrument, column_model, column_type],
inputs = [labelling_process.outputs[0]],
outputs = []
# outputs = [separated_molecules]
)
## Mass spectrometry
scan_polarity = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan polarity")), value = "positive")
scan_range = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan m/z range")), value = "5-2000?")
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Instrument")), value = "AB SCIEX Qtrap 6500")
ion_source = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Ion source")), value = "ESI")
mass_analyzer = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Mass Analyzer")), value = "triple quadrupole linear ion trap")
mass_spectrometry_process = Process(
name = "mass_spectrometry_{0}".format(row["XOmicsmetaboID"]),
executes_protocol= mass_spectrometry,
parameter_values = [scan_polarity, scan_range, instrument, ion_source, mass_analyzer],
# inputs = [separated_molecules],
inputs = [],
outputs = [raw_datafile]
)
## Data transformation
data_transformation_process = Process(
name = "data_transformation_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = data_transformation,
inputs = [raw_datafile],
outputs = [normalized_datafile, derived_spectral_data_file]
)
## Metabolite identification
metabolite_identification_process = Process(
name = "metabolite_identification_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = metabolite_identification,
inputs = [normalized_datafile],
outputs= [Data_Transformation_Name, MAF]
)
# Link processes
plink(extraction_process, labelling_process)
plink(labelling_process, chromatography_process)
plink(chromatography_process, mass_spectrometry_process)
plink(mass_spectrometry_process, data_transformation_process)
plink(data_transformation_process, metabolite_identification_process)
# Add samples, materials and data files to the amines assay
Assay.samples.append(urine_sample)
Assay.other_material.append(material_extract)
Assay.other_material.append(material_label)
# Assay.other_material.append(separated_molecules)
Assay.data_files.append(raw_datafile)
Assay.data_files.append(normalized_datafile)
Assay.data_files.append(derived_spectral_data_file)
Assay.data_files.append(Data_Transformation_Name)
Assay.data_files.append(MAF)
## Add processes to the amines assay
Assay.process_sequence.append(extraction_process)
Assay.process_sequence.append(labelling_process)
Assay.process_sequence.append(chromatography_process)
Assay.process_sequence.append(mass_spectrometry_process)
Assay.process_sequence.append(data_transformation_process)
Assay.process_sequence.append(metabolite_identification_process)
# add samples, processes and datafiles to metabolomics OA assay
Assay = assay_metabolomics_OA
# Define datafiles (not all may be relevant)
raw_datafile = DataFile(filename="link/to/raw/data", label="Raw Spectral Data File")
normalized_datafile = DataFile(filename="link/to/normalized_data", label="Normalization Name")
derived_spectral_data_file = DataFile(filename="link/to/spectral_file", label="Derived Spectral Data File")
Data_Transformation_Name = DataFile(filename="link/to/data_transformation_name", label="Data Transformation Name")
MAF = DataFile(filename="link/to/MAF", label="Metabolite Assignment File")
# Loop over samples and add process to samples
for idx, row in IDs_df.iterrows():
source_name = row["XOmicsPhenoID"]
# print(source_name)
if not pd.isna(row["XOmicsmetaboID"]):
# print(row['XOmicsmetaboID'])
urine_sample_name = "urine_{0}".format(source_name)
# print(urine_sample_name)
urine_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == urine_sample_name), None)
metabolomics_sample = next(
(smpl for smpl in Assay.samples
if smpl.name == urine_sample_name), None)
if not metabolomics_sample:
Assay.samples.append(urine_sample)
## Extraction
Post_extraction = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Post Extraction")), value = "1 uL pyridine")
Derivatization = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Derivatization")), value = "oximation followed by silylation")
material_extract = Material(
name = "extract_{0}".format(row["XOmicsmetaboID"]),
type_ = "Extract Name")
extraction_process = Process(
executes_protocol=extraction_metabolomics,
parameter_values=[Post_extraction, Derivatization],
inputs = [urine_sample],
outputs = [material_extract])
## Labelling
material_label = Material(
name ="labeled_{0}".format(row["XOmicsmetaboID"]),
type_ ="Labeled Extract Name")
labelling_process = Process(
executes_protocol=labelling_metabolites,
inputs = [extraction_process.outputs[0]],
outputs = [material_label])
# ## Chromatography
# separated_molecules = Material(
# name = "separated_molecules_{0}".format(row["XOmicsmetaboID"],
# type_ ="Labeled Extract Name")
# )
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Chromatography Instrument")), value = "Agilent Technologies 7890A")
column_model = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column model")), value = "HP-5MS UI (5% Phenyl Methyl Silox), 30 m x 0.25 m ID column with a film thickness of 25 um")
column_type = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column type")), value = "low polarity")
chromatography_process = Process(
name = "chromatography_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = chromatography,
parameter_values = [instrument, column_model, column_type],
inputs = [labelling_process.outputs[0]],
outputs = [],
# outputs = [separated_molecules]
)
## Mass spectrometry
scan_polarity = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan polarity")), value = "positive")
scan_range = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan m/z range")), value = "50-500")
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Instrument")), value = "Agilent Technologies mass selective detector (MSD 5975C) and MultiPurpose Sampler (MPS, MXY016-02A, GERSTEL)")
ion_source = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Ion source")), value = "EI (70 eV)")
mass_analyzer = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Mass Analyzer")), value = "single-quadrupole")
mass_spectrometry_process = Process(
name = "mass_spectrometry_{0}".format(row["XOmicsmetaboID"]),
executes_protocol= mass_spectrometry,
parameter_values = [scan_polarity, scan_range, instrument, ion_source, mass_analyzer],
inputs = [],
# inputs = [separated_molecules],
outputs = [raw_datafile]
)
## Data transformation
data_transformation_process = Process(
name = "data_transformation_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = data_transformation,
inputs = [raw_datafile],
outputs = [normalized_datafile, derived_spectral_data_file]
)
## Metabolite identification
metabolite_identification_process = Process(
name = "metabolite_identification_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = metabolite_identification,
inputs = [normalized_datafile],
outputs= [Data_Transformation_Name, MAF]
)
# ## Link processes
plink(extraction_process, labelling_process)
plink(labelling_process, chromatography_process)
plink(chromatography_process, mass_spectrometry_process)
plink(mass_spectrometry_process, data_transformation_process)
plink(data_transformation_process, metabolite_identification_process)
# ## Add samples, materials and data files to the OA assay
Assay.other_material.append(material_extract)
Assay.other_material.append(material_label)
# Assay.other_material.append(separated_molecules)
Assay.data_files.append(raw_datafile)
Assay.data_files.append(normalized_datafile)
Assay.data_files.append(derived_spectral_data_file)
Assay.data_files.append(Data_Transformation_Name)
Assay.data_files.append(MAF)
# ## Add processes to the OA assay
Assay.process_sequence.append(extraction_process)
Assay.process_sequence.append(labelling_process)
Assay.process_sequence.append(chromatography_process)
Assay.process_sequence.append(mass_spectrometry_process)
Assay.process_sequence.append(data_transformation_process)
Assay.process_sequence.append(metabolite_identification_process)
# add samples, processes and datafiles to metabolomics steroids assay
Assay = assay_metabolomics_steroids
# Define datafiles (not all may be relevant)
raw_datafile = DataFile(filename="link/to/raw/data", label="Raw Spectral Data File")
normalized_datafile = DataFile(filename="link/to/normalized_data", label="Normalization Name")
derived_spectral_data_file = DataFile(filename="link/to/spectral_file", label="Derived Spectral Data File")
Data_Transformation_Name = DataFile(filename="link/to/data_transformation_name", label="Data Transformation Name")
MAF = DataFile(filename="link/to/MAF", label="Metabolite Assignment File")
# Loop over samples and add process to samples
for idx, row in IDs_df.iterrows():
source_name = row["XOmicsPhenoID"]
# print(source_name)
if not pd.isna(row["XOmicsmetaboID"]):
# print(row['XOmicsmetaboID'])
urine_sample_name = "urine_{0}".format(source_name)
# print(urine_sample_name)
urine_sample = next(
(smpl for smpl in investigation.studies[0].samples
if smpl.name == urine_sample_name), None)
metabolomics_sample = next(
(smpl for smpl in Assay.samples
if smpl.name == urine_sample_name), None)
if not metabolomics_sample:
Assay.samples.append(urine_sample)
## Extraction
Post_extraction = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Post Extraction")), value = "1 uL filtered urine")
Derivatization = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Derivatization")), value = "NA")
material_extract = Material(
name = "extract_{0}".format(row["XOmicsmetaboID"]),
type_ = "Extract Name")
extraction_process = Process(
executes_protocol=extraction_metabolomics,
parameter_values=[Post_extraction, Derivatization],
inputs = [urine_sample],
outputs = [material_extract])
## Labelling
material_label = Material(
name ="labeled_{0}".format(row["XOmicsmetaboID"]),
type_ ="Labeled Extract Name")
labelling_process = Process(
executes_protocol=labelling_metabolites,
inputs = [extraction_process.outputs[0]],
outputs = [material_label])
## Chromatography
# separated_molecules = Material(
# name = "new_separated_molecules_{0}".format(row["XOmicsmetaboID"],
# type_ ="Labeled Extract Name")
# )
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Chromatography Instrument")), value = "Agilent 1290")
column_model = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column model")), value = "Acquity UPLC CSH C18 column (Waters)")
column_type = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Column type")), value = "reverse phase")
chromatography_process = Process(
name = "chromatography_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = chromatography,
parameter_values = [instrument, column_model, column_type],
inputs = [labelling_process.outputs[0]],
outputs = []
#outputs = [separated_molecules]
)
## Mass spectrometry
scan_polarity = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan polarity")), value = "switching positive and negative ion mode !! MAYBE SERPARATE INTO NEGATIVE AND POSITIVE ASSAY?")
scan_range = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Scan m/z range")), value = "5-3000?")
instrument = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Instrument")), value = "Agilent 6460")
ion_source = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Ion source")), value = "ESI")
mass_analyzer = ParameterValue(category = ProtocolParameter(parameter_name=OntologyAnnotation(term="Mass Analyzer")), value = "triple quadrupole")
mass_spectrometry_process = Process(
name = "mass_spectrometry_{0}".format(row["XOmicsmetaboID"]),
executes_protocol= mass_spectrometry,
parameter_values = [scan_polarity, scan_range, instrument, ion_source, mass_analyzer],
# inputs = [separated_molecules],
inputs = [],
outputs = [raw_datafile]
)
## Data transformation
data_transformation_process = Process(
name = "data_transformation_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = data_transformation,
inputs = [raw_datafile],
outputs = [normalized_datafile, derived_spectral_data_file]
)
## Metabolite identification
metabolite_identification_process = Process(
name = "metabolite_identification_{0}".format(row["XOmicsmetaboID"]),
executes_protocol = metabolite_identification,
inputs = [normalized_datafile],
outputs= [Data_Transformation_Name, MAF]
)
## Link processes
plink(extraction_process, labelling_process)
plink(labelling_process, chromatography_process)
plink(chromatography_process, mass_spectrometry_process)
plink(mass_spectrometry_process, data_transformation_process)
plink(data_transformation_process, metabolite_identification_process)
## Add samples, materials and data files to the steroids assay
Assay.other_material.append(material_extract)
Assay.other_material.append(material_label)
# Assay.other_material.append(separated_molecules)
Assay.data_files.append(raw_datafile)
Assay.data_files.append(normalized_datafile)
Assay.data_files.append(derived_spectral_data_file)
Assay.data_files.append(Data_Transformation_Name)
Assay.data_files.append(MAF)
## Add processes to the steroids assay
Assay.process_sequence.append(extraction_process)
Assay.process_sequence.append(labelling_process)
Assay.process_sequence.append(chromatography_process)
Assay.process_sequence.append(mass_spectrometry_process)
Assay.process_sequence.append(data_transformation_process)
Assay.process_sequence.append(metabolite_identification_process)
# add assays to study
investigation.studies[0].assays.append(assay_genotype)
investigation.studies[0].assays.append(assay_methylation)
investigation.studies[0].assays.append(assay_metabolomics_amines)
investigation.studies[0].assays.append(assay_metabolomics_OA)
investigation.studies[0].assays.append(assay_metabolomics_steroids)
# create ISA files directory
out_dir = "isa_template"
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# write to ISA-Tab
from isatools import isatab
isatab.dump(investigation, out_dir)
print()
2021-12-01 22:51:43,707 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2995, 3000, 3005, 3010, 3015, 3020, 3025, 3030, 3035, 3040] 2021-12-01 22:51:43,709 [WARNING]: isatab.py(write_study_table_files:1194) >> [2997, 2996, 2995, 2999, 2998, 3002, 3001, 3000, 3004, 3003, 3007, 3006, 3005, 3009, 3008, 3012, 3011, 3010, 3014, 3013, 3017, 3016, 3015, 3019, 3018, 3022, 3021, 3020, 3024, 3023, 3027, 3026, 3025, 3029, 3028, 3032, 3031, 3030, 3034, 3033, 3037, 3036, 3035, 3039, 3038, 3042, 3041, 3040, 3044, 3043] 2021-12-01 22:51:43,709 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2995, 2997, 2996], [2995, 2999, 2998], [3000, 3002, 3001], [3000, 3004, 3003], [3005, 3009, 3008], [3005, 3007, 3006], [3010, 3012, 3011], [3010, 3014, 3013], [3015, 3017, 3016], [3015, 3019, 3018], [3020, 3022, 3021], [3020, 3024, 3023], [3025, 3027, 3026], [3025, 3029, 3028], [3030, 3034, 3033], [3030, 3032, 3031], [3035, 3037, 3036], [3035, 3039, 3038], [3040, 3042, 3041], [3040, 3044, 3043]] 2021-12-01 22:51:43,753 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2998, 3003, 3008, 3013, 3018, 3023, 3028, 3033, 3038, 3043] 2021-12-01 22:51:43,755 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2998, 3046, 3045, 3047], [3003, 3049, 3048, 3050], [3008, 3052, 3051, 3053], [3013, 3055, 3054, 3056], [3018, 3058, 3057, 3059], [3023, 3061, 3060, 3062], [3028, 3064, 3063, 3065], [3033, 3067, 3066, 3068], [3038, 3070, 3069, 3071], [3043, 3073, 3072, 3074]] 2021-12-01 22:51:43,757 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2998, 3046, 3045, 3047], [3003, 3049, 3048, 3050], [3008, 3052, 3051, 3053], [3013, 3055, 3054, 3056], [3018, 3058, 3057, 3059], [3023, 3061, 3060, 3062], [3028, 3064, 3063, 3065], [3033, 3067, 3066, 3068], [3038, 3070, 3069, 3071], [3043, 3073, 3072, 3074]] 2021-12-01 22:51:43,778 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2998, 3003, 3008, 3013, 3018, 3023, 3028, 3033, 3038, 3043] 2021-12-01 22:51:43,781 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2998, 3076, 3075, 3077], [3003, 3079, 3078, 3080], [3008, 3082, 3081, 3083], [3013, 3085, 3084, 3086], [3018, 3088, 3087, 3089], [3023, 3091, 3090, 3092], [3028, 3094, 3093, 3095], [3033, 3097, 3096, 3098], [3038, 3100, 3099, 3101], [3043, 3103, 3102, 3104]] 2021-12-01 22:51:43,782 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2998, 3076, 3075, 3077], [3003, 3079, 3078, 3080], [3008, 3082, 3081, 3083], [3013, 3085, 3084, 3086], [3018, 3088, 3087, 3089], [3023, 3091, 3090, 3092], [3028, 3094, 3093, 3095], [3033, 3097, 3096, 3098], [3038, 3100, 3099, 3101], [3043, 3103, 3102, 3104]] 2021-12-01 22:51:43,848 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2996, 3001, 3006, 3011, 3016, 3021, 3026, 3031, 3036, 3041] 2021-12-01 22:51:43,849 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3111, 3110, 3113, 3112, 3114, 3115, 3116, 3117], [3001, 3119, 3118, 3121, 3120, 3122, 3123, 3124, 3125], [3006, 3127, 3126, 3129, 3128, 3130, 3131, 3132, 3133], [3011, 3135, 3134, 3137, 3136, 3138, 3139, 3140, 3141], [3016, 3143, 3142, 3145, 3144, 3146, 3147, 3148, 3149], [3021, 3151, 3150, 3153, 3152, 3154, 3155, 3156, 3157], [3026, 3159, 3158, 3161, 3160, 3162, 3163, 3164, 3165], [3031, 3167, 3166, 3169, 3168, 3170, 3171, 3172, 3173], [3036, 3175, 3174, 3177, 3176, 3178, 3179, 3180, 3181], [3041, 3183, 3182, 3185, 3184, 3186, 3187, 3188, 3189]] 2021-12-01 22:51:43,851 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3111, 3110, 3113, 3112, 3114, 3115, 3116, 3117], [3001, 3119, 3118, 3121, 3120, 3122, 3123, 3124, 3125], [3006, 3127, 3126, 3129, 3128, 3130, 3131, 3132, 3133], [3011, 3135, 3134, 3137, 3136, 3138, 3139, 3140, 3141], [3016, 3143, 3142, 3145, 3144, 3146, 3147, 3148, 3149], [3021, 3151, 3150, 3153, 3152, 3154, 3155, 3156, 3157], [3026, 3159, 3158, 3161, 3160, 3162, 3163, 3164, 3165], [3031, 3167, 3166, 3169, 3168, 3170, 3171, 3172, 3173], [3036, 3175, 3174, 3177, 3176, 3178, 3179, 3180, 3181], [3041, 3183, 3182, 3185, 3184, 3186, 3187, 3188, 3189]] 2021-12-01 22:51:43,937 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2996, 3001, 3006, 3011, 3016, 3021, 3026, 3031, 3036, 3041] 2021-12-01 22:51:43,939 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3196, 3195, 3198, 3197, 3199, 3200, 3201, 3202], [3001, 3204, 3203, 3206, 3205, 3207, 3208, 3209, 3210], [3006, 3212, 3211, 3214, 3213, 3215, 3216, 3217, 3218], [3011, 3220, 3219, 3222, 3221, 3223, 3224, 3225, 3226], [3016, 3228, 3227, 3230, 3229, 3231, 3232, 3233, 3234], [3021, 3236, 3235, 3238, 3237, 3239, 3240, 3241, 3242], [3026, 3244, 3243, 3246, 3245, 3247, 3248, 3249, 3250], [3031, 3252, 3251, 3254, 3253, 3255, 3256, 3257, 3258], [3036, 3260, 3259, 3262, 3261, 3263, 3264, 3265, 3266], [3041, 3268, 3267, 3270, 3269, 3271, 3272, 3273, 3274]] 2021-12-01 22:51:43,941 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3196, 3195, 3198, 3197, 3199, 3200, 3201, 3202], [3001, 3204, 3203, 3206, 3205, 3207, 3208, 3209, 3210], [3006, 3212, 3211, 3214, 3213, 3215, 3216, 3217, 3218], [3011, 3220, 3219, 3222, 3221, 3223, 3224, 3225, 3226], [3016, 3228, 3227, 3230, 3229, 3231, 3232, 3233, 3234], [3021, 3236, 3235, 3238, 3237, 3239, 3240, 3241, 3242], [3026, 3244, 3243, 3246, 3245, 3247, 3248, 3249, 3250], [3031, 3252, 3251, 3254, 3253, 3255, 3256, 3257, 3258], [3036, 3260, 3259, 3262, 3261, 3263, 3264, 3265, 3266], [3041, 3268, 3267, 3270, 3269, 3271, 3272, 3273, 3274]] 2021-12-01 22:51:44,024 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [2996, 3001, 3006, 3011, 3016, 3021, 3026, 3031, 3036, 3041] 2021-12-01 22:51:44,025 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3281, 3280, 3283, 3282, 3284, 3285, 3286, 3287], [3001, 3289, 3288, 3291, 3290, 3292, 3293, 3294, 3295], [3006, 3297, 3296, 3299, 3298, 3300, 3301, 3302, 3303], [3011, 3305, 3304, 3307, 3306, 3308, 3309, 3310, 3311], [3016, 3313, 3312, 3315, 3314, 3316, 3317, 3318, 3319], [3021, 3321, 3320, 3323, 3322, 3324, 3325, 3326, 3327], [3026, 3329, 3328, 3331, 3330, 3332, 3333, 3334, 3335], [3031, 3337, 3336, 3339, 3338, 3340, 3341, 3342, 3343], [3036, 3345, 3344, 3347, 3346, 3348, 3349, 3350, 3351], [3041, 3353, 3352, 3355, 3354, 3356, 3357, 3358, 3359]] 2021-12-01 22:51:44,027 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[2996, 3281, 3280, 3283, 3282, 3284, 3285, 3286, 3287], [3001, 3289, 3288, 3291, 3290, 3292, 3293, 3294, 3295], [3006, 3297, 3296, 3299, 3298, 3300, 3301, 3302, 3303], [3011, 3305, 3304, 3307, 3306, 3308, 3309, 3310, 3311], [3016, 3313, 3312, 3315, 3314, 3316, 3317, 3318, 3319], [3021, 3321, 3320, 3323, 3322, 3324, 3325, 3326, 3327], [3026, 3329, 3328, 3331, 3330, 3332, 3333, 3334, 3335], [3031, 3337, 3336, 3339, 3338, 3340, 3341, 3342, 3343], [3036, 3345, 3344, 3347, 3346, 3348, 3349, 3350, 3351], [3041, 3353, 3352, 3355, 3354, 3356, 3357, 3358, 3359]]
import json
from isatools.isajson import ISAJSONEncoder
print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
{
"@id": "#investigation/5131975408",
"comments": [],
"description": "Predict childhood aggression with multi-omics data and demonstrate the FAIRification process and data analysis of a multi-omics project",
"identifier": "tbd",
"ontologySourceReferences": [
{
"@id": "#ontology/5133002736",
"comments": [],
"description": "Allotrope Merged Ontology Suite",
"file": "",
"name": "AFO",
"version": ""
},
{
"@id": "#ontology/5133003936",
"comments": [],
"description": "Chemical Entities of Biological Interest",
"file": "",
"name": "CHEBI",
"version": ""
},
{
"@id": "#ontology/5133003552",
"comments": [],
"description": "Chemical Methods Ontology",
"file": "",
"name": "CHMO",
"version": ""
},
{
"@id": "#ontology/5134609760",
"comments": [],
"description": "Bioinformatics operations, data types, formats, identifiers and topics",
"file": "",
"name": "EDAM",
"version": ""
},
{
"@id": "#ontology/5134607360",
"comments": [],
"description": "Experimental Factor Ontology",
"file": "",
"name": "EFO",
"version": ""
},
{
"@id": "#ontology/5134130768",
"comments": [],
"description": "An ontology of research resources such as instruments, protocols, reagents, animal models and biospecimens",
"file": "",
"name": "eagle-i resource ontology",
"version": ""
},
{
"@id": "#ontology/5134130912",
"comments": [],
"description": "Medical Action Ontology",
"file": "",
"name": "MAXO",
"version": ""
},
{
"@id": "#ontology/5134130864",
"comments": [],
"description": "Metabolite Standards Initiative Ontology",
"file": "",
"name": "MSIO",
"version": ""
},
{
"@id": "#ontology/5134130624",
"comments": [],
"description": "NCBI organismal classification",
"file": "",
"name": "NCBITAXON",
"version": ""
},
{
"@id": "#ontology/5134131152",
"comments": [],
"description": "NCI Thesaurus OBO Edition",
"file": "",
"name": "NCIT",
"version": ""
},
{
"@id": "#ontology/5134131104",
"comments": [],
"description": "Ontology for Biomedical Investigations",
"file": "",
"name": "OBI",
"version": ""
},
{
"@id": "#ontology/5134130480",
"comments": [],
"description": "PATO - the Phenotype And Trait Ontology",
"file": "",
"name": "PATO",
"version": ""
},
{
"@id": "#ontology/5133078096",
"comments": [],
"description": "Uber-anatomy ontology",
"file": "",
"name": "UBERON",
"version": ""
}
],
"people": [],
"publicReleaseDate": "",
"publications": [],
"studies": [
{
"@id": "#study/5133159920",
"assays": [
{
"@id": "#5132283808",
"characteristicCategories": [],
"comments": [],
"dataFiles": [],
"filename": "a_assay_genotype.txt",
"materials": {
"otherMaterials": [],
"samples": [
{
"@id": "#sample/5133932000",
"characteristics": [
{
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP1"
},
{
"@id": "#sample/5134467568",
"characteristics": [
{
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP2"
},
{
"@id": "#sample/5133764544",
"characteristics": [
{
"category": {
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP3"
},
{
"@id": "#sample/5133746080",
"characteristics": [
{
"category": {
"@id": "#annotation_value/0557443d-8c59-4497-867f-d28b85c8be43"
},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP4"
},
{
"@id": "#sample/5133887952",
"characteristics": [
{
"category": {
"@id": "#annotation_value/9763ae08-29c0-434a-b9cc-31d663983f8f"
},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP5"
},
{
"@id": "#sample/5133852832",
"characteristics": [
{
"category": {
"@id": "#annotation_value/d42ebbc9-6e6a-4176-a3c5-8780546f6b15"
},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP6"
},
{
"@id": "#sample/5133855376",
"characteristics": [
{
"category": {
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP7"
},
{
"@id": "#sample/5133771440",
"characteristics": [
{
"category": {
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP8"
},
{
"@id": "#sample/5133773648",
"characteristics": [
{
"category": {
"@id": "#annotation_value/67ae10a5-0a1b-4e41-97ef-fee3f3ade35a"
},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP9"
},
{
"@id": "#sample/5133898608",
"characteristics": [
{
"category": {
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},
"comments": [],
"value": ""
}
],
"comments": [],
"factorValues": [],
"name": "buccal_mucosa_XOP10"
}
]
},
"measurementType": {
"@id": "#annotation_value/7f1d91c5-79b8-4984-9530-d733ce686194",
"annotationValue": "",
"comments": [],
"termAccession": "",
"termSource": ""
},
"processSequence": [
{
"@id": "#process/5133774128",
"comments": [],
"date": "",
"executesProtocol": {
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},
"inputs": [
{
"@id": "#sample/5133932000"
}
],
"name": "DNA_extraction_XOG1",
"nextProcess": {
"@id": "#process/5133774176"
},
"outputs": [
{
"@id": "#material/extract-5133774032"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5133774176",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
"@id": "#material/extract-5133774032"
}
],
"name": "genotype_profiling_XOG1",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5133774128"
}
},
{
"@id": "#process/5134137808",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
"@id": "#sample/5134467568"
}
],
"name": "DNA_extraction_XOG2",
"nextProcess": {
"@id": "#process/5134138240"
},
"outputs": [
{
"@id": "#material/extract-5134139008"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134138240",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
"@id": "#material/extract-5134139008"
}
],
"name": "genotype_profiling_XOG2",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134137808"
}
},
{
"@id": "#process/5134005920",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
"@id": "#sample/5133764544"
}
],
"name": "DNA_extraction_XOG3",
"nextProcess": {
"@id": "#process/5134004336"
},
"outputs": [
{
"@id": "#material/extract-5134137856"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134004336",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
"@id": "#material/extract-5134137856"
}
],
"name": "genotype_profiling_XOG3",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134005920"
}
},
{
"@id": "#process/5134005824",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG4",
"nextProcess": {
"@id": "#process/5134005248"
},
"outputs": [
{
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}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134005248",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
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}
],
"name": "genotype_profiling_XOG4",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134005824"
}
},
{
"@id": "#process/5134005200",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG5",
"nextProcess": {
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},
"outputs": [
{
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}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134006112",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
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}
],
"name": "genotype_profiling_XOG5",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134005200"
}
},
{
"@id": "#process/5134006160",
"comments": [],
"date": "",
"executesProtocol": {
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},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG6",
"nextProcess": {
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},
"outputs": [
{
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}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134006784",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
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}
],
"name": "genotype_profiling_XOG6",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
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}
},
{
"@id": "#process/5134006400",
"comments": [],
"date": "",
"executesProtocol": {
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},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG7",
"nextProcess": {
"@id": "#process/5134006544"
},
"outputs": [
{
"@id": "#material/extract-5134005632"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134006544",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
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}
],
"name": "genotype_profiling_XOG7",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
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}
},
{
"@id": "#process/5134006736",
"comments": [],
"date": "",
"executesProtocol": {
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},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG8",
"nextProcess": {
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},
"outputs": [
{
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}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134006688",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
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}
],
"name": "genotype_profiling_XOG8",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134006736"
}
},
{
"@id": "#process/5134006448",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
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}
],
"name": "DNA_extraction_XOG9",
"nextProcess": {
"@id": "#process/5134006256"
},
"outputs": [
{
"@id": "#material/extract-5134006592"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134006256",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5134607888"
},
"inputs": [
{
"@id": "#material/extract-5134006592"
}
],
"name": "genotype_profiling_XOG9",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134006448"
}
},
{
"@id": "#process/5134006832",
"comments": [],
"date": "",
"executesProtocol": {
"@id": "#protocol/5132734720"
},
"inputs": [
{
"@id": "#sample/5133898608"
}
],
"name": "DNA_extraction_XOG10",
"nextProcess": {
"@id": "#process/5134005776"
},
"outputs": [
{
"@id": "#material/extract-5134006064"
}
],
"parameterValues": [],
"performer": ""
},
{
"@id": "#process/5134005776",
"comments": [],
"date": "",
"executesProtocol": {
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},
"inputs": [
{
"@id": "#material/extract-5134006064"
}
],
"name": "genotype_profiling_XOG10",
"outputs": [],
"parameterValues": [],
"performer": "",
"previousProcess": {
"@id": "#process/5134006832"
}
}
],
"technologyPlatform": {
"@id": "#annotation_value/11aef089-9db2-4bce-abe2-cf12542d5407",
"annotationValue": "",
"comments": [],
"termAccession": "",
"termSource": ""
},
"technologyType": {
"@id": "#annotation_value/5c32e86d-6298-494c-a8b3-c68cc4eb9ecc",
"annotationValue": "nucleotide sequencing",
"comments": [],
"termAccession": "",
"termSource": ""
},
"unitCategories": []
},
{
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