Metadata is standardized information about your data and will be used to annotate the data files in Synapse. These Four metadata files are required for needed for each study:
Individual
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Biospecimen
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Assay
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Manifest
UPDATE - May 2023
The following approach will be deprecated by Q3 2023. Stay tuned for updates to the workflow.
The climb database variables that are exported for Synapse are mapped here https://www.synapse.org/#!Synapse:syn26137185
Metadata Templates
Metadata templates provide guidance for allowed variable keys and values on the dictionary and values worksheets. The AD Metadata Dictionary includes the latest information about allowed values. The latest templates are linked below.
In order for your metadata to validate successfully, download the latest Metadata Templates, populate with the relevant information and validate. Once you have populated the metadata fields, export the template worksheet from each file as a plain-text comma-separated file (CSV). and follow the validation instructions below.
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Individual Animal
This file contains metadata about each individual animal that is part of a study. Each animal will be described in one row with information that is true of the animal as a whole (eg, individualID, genotype).
Template: template_individual_animal_model-ad.xlsx
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Biospecimen
A biospecimen is a sample of cells, tissue, RNA, DNA, etc. This metadata file contains information about each biospecimen that is part of a study, including details like what organ and tissue the specimen is from.
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Multiomics refers to the study of multiple biological systems or processes simultaneously using various techniques that generate different types of data. Here is a summary of some common omics data types, their assays, analyses, and file formats:
Genomics: This involves studying DNA sequencing and analysis, as well as gene expression
profiling through RNA sequencing.
Common assays: Sanger sequencing, whole-genome sequencing (WGS), RNA sequencing (RNA-seq)
Analyses: Variant calling, assembly, alignment to reference genome, differential expression
analysisFile formats: FASTQ, BAM, VCF, TSV
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1287035/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694403/ >
Proteomics: This refers to the study of proteins and their interactions in biological systems.
Common assays: Mass spectrometry (MS), microarray analysis, immunoprecipitation (IP)
Analyses: Peptide identification, protein quantification, network analysis, pathway enrichment
File formats: MGF, MSG, TAB, PDB
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2564307/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1893633/ >
Metabolomics: This involves studying metabolic pathways and biochemical processes in living
organisms.
Common assays: GC-MS, LC-MS, NMR spectroscopy, mass spectrometry (MS) ionization
Analyses: Metabolite identification, quantification, pathway analysis, network analysis
File formats: CSV, MATLAB, SIMCA, JUPYTER notebooks
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1592706/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604083/ >
Transcriptomics: This refers to the study of RNA expression patterns in biological systems.
Common assays: RNA sequencing (RNA-seq)
Analyses: Differential expression analysis, gene set enrichment analysis, pathway analysis,
network analysisFile formats: FASTQ, BAM, VCF, TSV
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2571094/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2600283/ >
Epigenomics: This involves studying changes in DNA methylation patterns or other epigenetic
marks that regulate gene expression without changing the underlying DNA sequence.
Common assays: Whole-genome bisulfite sequencing (WGBS), ChIP-seq, ATAC-seq
Analyses: Methylation profiling, ChIP-binding site analysis, histone modification analysis,
network analysisFile formats: BAM, VCF, TSV, MATLAB
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2918536/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078701/ >
Immunomics: This refers to the study of the immune system and its interactions with other
biological systems.
Common assays: Flow cytometry, mass cytometry, single-cell RNA sequencing (scRNA-seq)
Analyses: Cellular composition analysis, functional annotation, network analysis, clustering
File formats: FCS, MATLAB, R, Seurat
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072154/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5803139/ >
Metagenomics: This involves studying genetic material from microbial communities in various
environments.
Common assays: Shotgun sequencing
Analyses: Taxonomic classification, functional annotation, network analysis, phylogenetic
reconstructionFile formats: FASTQ, QIIME, MG-RAST, Metagenome Assembly Tool (MAT)
Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189520/ >,
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4790965/ >
Each of these omics data types has its own specific requirements for analysis and interpretation,
which may involve specialized software tools or expertise in specific analytical techniques.
However, integrating multiomics data can provide a more comprehensive understanding of biological
systems and mechanisms, leading to new insights into disease and potential therapeutic targets.
Manifest
A tab-delimited manifest file allows you to upload and download many data files, and set annotations, at once a client (Python, R, command line). Each row in the manifest species the file to be uploaded and the annotations to be applied.
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path– the path of the file to upload (local, server, cloud)parentID– the Curator each file will provide a Synapse ID of the Staging/ folder where the file will be uploaded. have a SynapseID for its staging locationAnnotations are key-value pairs that associate metadata with a file and help users find and query data. For more info, (see the Synapse Annotation documentation for annotations.)
Provenance is a means of describing a relationship between raw and processed data . For more info, see the Synapse documentation on provenance(see Synapse Provenance documentation). If you are uploading the results of an analysis, you may add a Used column to a manifest to give the Synapse ID(s) of the raw files that went into the analysis. If multiple Synapse IDs should be associated with a processed file, separate them with a semicolon
Once you have populated the manifest fields, export the template worksheet as a plain-text tab-separated file (TSV). With the three metadata CSVs and manifest TSV complete, you are You are now ready to validate the study four metadata files.
Metadata Validation
To standardize data submissions and quality control, we’ve built a metadata validation tool, dccvalidator, that will perform several data quality checks on metadata templates and manifest files.
Validated metadata can be uploaded to the staging location provided by the DCC Curator. See more information about uploading data.