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The experimental data, as described in Su et al. 2020 is captured in the Investigation/Study/Assay (ISA) metadata framework (Sansone et al. 2012). A Jupyter Notebook that was used to create these ISA files can be found in tools/Su_2020_prepare_ISA_metadata. More information on the ISA framework can be found on the ISAtools website. The ISA files generated in tubular format(.tsv) are then used to generate rdf triples. Since our sparql endpoint is the FairDataPoint, in which the triplestore is GraphDB, we converted the ISA-tab files into rdf-turtle format(.ttl). The script for this conversion is tools/Su_2020_prepare_ISA_metadata/isa-to-rdfs.py. The transformation instruction is done by following the guide from isatool author(https://github.com/ISA-tools/isa-api/blob/master/isa-cookbook/content/notebooks/querying-isa-with-graphql-and-sparql.ipynb).
Metabolomics data has been extracted from Supplementary Table S1 from Su et al., 2020. Metabolite names were mapped to ChEBI identifiers and omics data and features metadata files in .csv format were created.
The R notebook used to execute the different preparation steps can be found in tools/Su_2020_prepare_metabolomics. It contains descriptions of each preparation step. A rendered .html is also available. A detailed description of the generated output files is available in the Metabolomics section of Data and metadata. Additional information on setting up the R environment is available in the README file.
The FAIRification of the phenotypic data were obtained from Supplementary Table S1 from Su et al., 2020. With the phenopackets schema (https://phenopacket-schema.readthedocs.io/en/latest/index.html), we could categorize the data entries accordingly. The data (in csv format) will then need to be converted into RDFs to import into Fair data point. This process is done by the Fair Data Cube infrastructure (https://github.com/Xomics/FAIRDataCube/tree/FDCube-in-box), which was inspired by the cde-in-box(https://github.com/ejp-rd-vp/cde-in-box) method. This process requires the csv data formatting and yarrrml template generation. These two steps can be done by using the python scripts written in tools/Su_2020_prepare_phenotype_data.