Metadata is standardized information about your data and will be used to annotate the data files in Synapse. Four metadata files are required for each study:

• Individual

• Biospecimen

• Assay

• 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.

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

Open

Linking of individual and specment identifiers

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.

The biospecimen and individual animal metadata files are linked by the individualID variable. Verify these values are consistent across these two files.  Each individualID may have more than one associated specimenID.  Not all data will have an associated biospecimen – for instance behavioral or imaging studies may only have records in the individual animal metadata file.

The biospecimen and assay metadata files are linked by the specimenID and it should be consistent across these two files.  If more than one assay was conducted on the same specimen, the same specimenID should be used throughout. 

Template: template_biospecimen.xlsx

Assays

Each assay metadata file contains information about the assay and there are multiple templates since the information collected will vary by assay. 

The assay metadata and biospecimen metadata files are linked by the specimenID variable, which appears in both files and it should be consistent across these two files.

Not all assays have related assay metadata templates, but let the DCC know if you would like to collaborate on the development of new templates.

• Templates:

  • RNAseq

  • single-cellRNAseq

  • nanostring

  • immunosassay

  • metabolomics

  • proteomics

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:

1. 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
  analysis

• File formats: FASTQ, BAM, VCF, TSV

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1287035/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694403/ >

2. 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/ >,
  < >

3. 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: < >,
  < >

4. 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 analysis

• File formats: FASTQ, BAM, VCF, TSV

• Resources: < >,
  < >

5. 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 analysis

• File formats: BAM, VCF, TSV, MATLAB

• Resources: < >,
  < >

6. 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: < >,
  < >

7. Metagenomics: This involves studying genetic material from microbial communities in various
   environments.

• Common assays: Shotgun sequencing

• Analyses: Taxonomic classification, functional annotation, network analysis, phylogenetic
  reconstruction

• File formats: FASTQ, QIIME, MG-RAST, Metagenome Assembly Tool (MAT)

• Resources: < >,
  < >

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.

Template: template_manifest.xlsx

Specify:

• path – the path of the file to upload (local, server, cloud)

• parentID – each file will have a SynapseID for its staging location

• Annotations are key-value pairs that associate metadata with a file and help users find and query data.  (see Synapse Annotation documentation)

• Provenance is a means of describing a relationship between raw and processed data (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). You are now ready to validate the 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.

climbDB

https://www.synapse.org/#!Synapse:syn26137185