9.4.1. Metadata profile for transcriptomics




Recipe Overview
Reading Time
30 minutes
Executable Code
No
Difficulty
Metadata profile for transcriptomics
FAIRPlus logo
Recipe Type
Guidance
Audience
Principal Investigator, Data Manager, Terminology Manager, Data Scientist, Ontologist

9.4.1.1. Main Objectives:

The main purpose of this recipe is:

To provide guidance on the minimum set of metadata and semantics required to describe any transcriptomics experiments, from standard case-control to dosage-response designs, and from microarrays to single cell RNA sequencing.

9.4.1.1.1. Who is this recipe aimed at?

This document is aimed at anyone interested in the metadata, and specifically the ontology annotations, required to capture variables and experimental factors related to a transcriptomics experiment. General knowledge of transcriptomics experiments would be beneficial. No specific technical knowledge is required.


9.4.1.2. Capability & Maturity Table

Capability

Initial Maturity Level

Final Maturity Level

Interoperability

minimal

repeatable


9.4.1.3. Transcriptomics Data model

Large sections of any transcriptomics data model are not unique to transcriptomics experiments but are common to a wide range of experiments in the biomedical domain. This includes general project- and sample-level information, which will be briefly covered in this recipe but will also be covered in more depth elsewhere.

Where possible, metadata should be mapped to ontologies to maximise the FAIRness of any dataset. This recipe will suggest possible ontologies for metadata fields where these are available. These lists may however not be exhaustive as new ontologies emerge regularly.

9.4.1.3.1. Existing standards and checklists

A set of well-established standards and minimum metadata checklists exist for various aspects of transcriptomics. They include:

Minimum Information About a Microarray Experiment (MIAME) - MIAME is intended to specify all the information necessary for an unambiguous interpretation of a microarray experiment, and potentially to reproduce it. MIAME defines the content but not the format for this information. The MIAME standard has been in existence for over 20 years and has been widely adopted across the scientific community. The data models of the major transcriptomics repositories such as ArrayExpress, the Expression Atlas and the Gene Expression Omnibus (GEO) are MIAME-compliant.

Minimum Information about a high-throughput nucleotide SEQuencing Experiment (MINSEQE) - MINSEQE describes the minimum metadata that is needed to enable the unambiguous interpretation and facilitate reproduction of the results of the experiment. By analogy to the MIAME guidelines for microarray experiments, adherence to the MINSEQE guidelines will improve integration of multiple experiments across different modalities, thereby maximising the value of high-throughput research. MINSEQE has been integrated into a number of transcriptomics and sequencing archives.

Functional Annotation of Animal Genomes (FAANG) - FAANG provides a set of orthogonal standards for the capture of well-structured metadata for experiments, samples and analyses in the animal genomics domain. The FAANG standards support the MIAME and MINSEQE guidelines, and aim to convert them to a concrete specification.

Human Cell Atlas Metadata (HCA-Metadata) - the HCA metadata model provides a concrete specification for capturing the metadata for single cell sequencing experiments. It is based on the three standards above, while focusing on the specific requirements of the single cell space.

9.4.1.3.2. Minimum metadata vs desirable metadata

A minimum metadata set constitutes metadata items that always need to be supplied with any experimental data. For validation purposes, these should not be omittable. Desirable or recommended metadata items on the other hand should be supplied if available but will not cause a validation failure if absent.

9.4.1.3.3. Common metadata

Common metadata include any information that is not specific to transcriptomics experiments but applies to most experiments in the biomedical domain. They include:

  • Project level metadata

  • Common sample level metadata such as species, tissue, cell type etc.

9.4.1.3.3.1. Suggested metadata fields

The following table contains a non-exhaustive list of suggested minimum metadata fields for biological samples. The collection is based on a range of existing metadata standards, including MIAME, MINSEQE, FAANG and HCA. Fields were included if they occurred in at least two of the standards.

Metadata field

Required?

Definition

Comment

unique ID

required

Identifier for a sample that is at least unique within the project

sample type

required

The type of the collected specimen, eg tissue biopsy, blood draw or throat swab

ontology field - e.g. OBI or EFO

species

required

The primary species of the specimen, preferably the taxonomic identifier

This may not be the same as the “host” organism, eg in the case of a PDX tissue sample, the host may be a mouse but the tissue may be human. Ontology field - NCBITaxonomy

tissue/organism part

required

The tissue from which the sample was taken

ontology field - e.g. Uberon

disease

required

Any diseases that may affect the sample

This may not necessarily be the same as the host’s disease, eg healthy brain tissue might be collected from a host with type II diabetes while cirrhotic liver tissue might be collected from an otherwise healthy individual. Ontology field - e.g. MONDO or DO

sex

required

The biological/genetic sex of the sample

ontology field - e.g. PATO

development stage

required

The developmental stage of the sample

ontology field - e.g. Uberon or Hsadpdv; species dependent

collection date

required

The date on which the sample was collected, in a standardised format

Collection date in combination with other fields such as location and disease may be sufficient to de-anonymise a sample

external accessions

recommended

Accession numbers from any external resources to which the sample was submitted

eg Biosamples, Biostudies

strain

recommended

Strain of the species from which the sample was collected, if applicable

ontology field - e.g. NCBITaxonomy

ancestry/ethnicity

recommended

Ancestry or ethnic group of the individual from which the sample was collected

ontology field - e.g. HANCESTRO

age

recommended

Age of the organism from which the sample was collected

age unit

recommended

Unit of the value of the age field

ontology field - e.g. UO

BMI

recommended

Body mass index of the individual from which the sample was collected

Only applies to human samples

treatment category

recommended

Treatments that the sample might have undergone after collection

ontology field - e.g. OBI, NCIt or OGMS

cell type

recommended

The cell type(s) known or selected to be present in the sample

ontology field - e.g. CL

growth conditions

recommended

Features relating to the growth and/or maintenance of the sample

genetic variation

recommended

Any relevant genetic differences from the specimen or sample to the expected genomic information for this species, eg abnormal chromosome counts, major translocations or indels

sample collection technique

recommended

The technique used to collect the specimen, eg blood draw or surgical resection

ontology field - e.g. EFO or OBI

phenotype

recommended

Any relevant (usually abnormal) phenotypes of the specimen or sample

ontology field - e.g. HP or MP; species dependent

cell cycle

recommended

The cell cycle phase of the sample (for synchronized growing cells or a single-cell sample), if known

ontology field - e.g. GO

cell location

recommended

The cell location from which genetic material was collected (usually either nucleus or mitochondria)

ontology field - e.g. GO

9.4.1.3.4. Assay metadata

Assay-level metadata covers any metadata directly related to the preparation of the biomaterial undergoing the assay and the process of performing the assay. Most, though not all, of this metadata is specific to transcriptomics. Examples include:

  • Library information, including what sample the library was originally derived from and whether any replicates are biological or technical

  • Process (wet experiment) metadata

  • Technology type (e.g. bulk RNA-Seq, scRNA-Seq, CITE-Seq)

  • Instrument metadata

  • Other assay-specific metadata

  • QC information

  • Workflow metadata

9.4.1.3.4.1. Suggested metadata fields

The following table contains a non-exhaustive list of suggested minimum metadata fields for assays. The collection is based on a range of existing metadata standards, including MIAME, MINSEQE and HCA. This list can and should be further broken down based on specific technologies used, such as microarrays or whole genome sequencing.

Metadata field

Required?

Definition

Comment

unique ID

required

Identifier for the assay that is at least unique within the project

experiment type

required

The type of experiment performed, eg ATAC-seq or seqFISH

ontology field - e.g. EFO or OBI

extracted nucleic acid/material type

required

The type of material that was extracted from the sample, eg polyA RNA

ontology field - e.g. ChEBI or EFO

platform

required

The type of instrument used to perform the assay, eg Illumina HiSeq 4000 or Fluidigm C1 microfluidics platform

ontology field - e.g. EFO or OBI

nucleic acid extraction method

required

Technique used to extract the nucleic acid from the cell

ontology field - e.g. EFO or OBI

cDNA library amplication method

required

Technique used to amplify a cDNA library

ontology field - e.g. EFO or OBI

array or sequencing method

required

The array or sequencing technology used - may be the same as experiment type or can be a more specific term

ontology field - e.g. EFO or OBI

biological or technical replicate

required

Information whether the sample on which the assay was performed was biological or technical replicate.

boolean or CV

end bias

required

The type of tag or end bias the library has, eg 3 prime tag or 5 prime end bias

standardised field or ontology

external accessions

recommended

Accession numbers from external resources to which assay or protocol information was submitted

eg protocols.io, AE

instrument model

required

The specific instrument on which the assay was performed. Essential for QC purposes.

ontology field - e.g. EFO or OBI

assay start time

recommended

The exact time at which the assay was started

assay end time

recommended

The exact time at which the assay was completed

assay duration

recommended

The duration, in a relevant time unit (eg minutes or hours), of the assay from start to finish

array quality

recommended

The overall quality of the array

chemical compound

recommended

Any relevant chemical compounds used in the assay

ontology field - e.g. ChEBI

labeling molecule used

recommended

The type of labeling molecule used in an array-based experiment

ontology field - e.g. ChEBI

spike-in kit used

recommended

Information about the spike-in kit used during sequencing library preparation

cDNA primer

recommended

Type of primer used for cDNA synthesis from RNA, eg polyA or random

standardised field or ontology

library strandedness

recommended

The strandedness of the cDNA library

standardised field or ontology

cell quality

recommended

Information about the quality of a single cell such as morphology or percent viability

standardised field or ontology

cell barcode

recommended

Information about the cell identifier barcode used to tag individual cells in single cell sequencing

UMI barcode

recommended

Information about the Unique Molecular Identifier barcodes used to tag DNA fragments

9.4.1.3.5. Analysis metadata

Analysis-level metadata includes any metadata related to the files that come out of the experiment, from the sequencing or imaging files generated directly by the machine to files generated during the various stages of processing and analysis, as well as details of any analyses performed. It is very important to always capture versions of software and reference genomes used in order to allow accurate replication of results. Analysis metadata includes

  • Type of analysis

  • File formats, e.g. BAM, fastq or gene count

  • File location e.g. URL

  • Summarisation of data, e.g. enrichment analysis

9.4.1.3.5.1. Suggested metadata fields

The following table contains a non-exhaustive list of suggested minimum metadata fields for analyses. The collection is based on a range of existing metadata standards, including MIAME, MINSEQE and HCA. This list can and should be further broken down based on the specific analysis type (primary, secondary or teriatry analysis, meta-analysis etc)

Metadata field

Required?

Definition

Comment

analysis type

required

The type of analysis performed, eg genome assembly or variant calling

ontology field - e.g. EFO, OBI or EDAM

computational method

required

The specific computational method or algorithm used as part of the analysis

ontology field - e.g. EFO or EDAM

normalisation strategy

required

The approach used to normalise the data

ontology field - e.g. EFO or EDAM

file format

required

The file format in which the analysis is provided

ontology field - e.g. EDAM

file storage location

required

The location in which the data files are stored

software package

recommended

The software package used for data analysis

software version

recommended

The exact version number of the software package

analysis date

recommended

The date on which the analysis was performed

read index

recommended

The sequencing read a specific file represents, eg read1 or index1

read length

recommended

The length of a sequenced read in this file, in nucleotides.

assembly type

recommended

The assembly type of the genome reference file, eg primary, complete or patch assembly.

standardised field or ontology

reference genome version

recommended

The genome version of the reference file.

9.4.1.4. Ontologies for transcriptomics data

While it is essential that any transcriptomics metadata be annotated with ontology terms wherever possible, there is no absolute set of ontologies that must be used above all others. There is however a consensus set of ontologies and other standardised resources that are commonly used in transcriptomics metadata, including in the main data archives. The most commonly used ontologies and fields they apply to are listed below. This table represents an absolute minimum of ontology annotations that should be included in a transcriptomics metadata set for it to be considered as FAIR. Not all fields suggested for ontology annotation in the previous section are repeated here for this reason.

Data type

Ontology/Entity sources

Type

Notes

Species

NCBI taxonomy Scientific name + ID

Ontology

Tissue

Uberon term and Id

Ontology

Cell type

CL term and Id

Ontology

Disease

MONDO, DO or MeSH

Ontology

no single solution - options vary depending on resource and individual requirements

Phenotype/Trait

HPO (human), MP(other mammals), various others for model organisms (yeast, zebrafish, Xenopus, C. elegans)

Ontology

Experiment Type

EFO, OBI

Ontology

e.g. RNASeq, CITESeq etc. -

Cell location/cycle

GO

Ontology

Developmental stage

HSAPDV/Uberon

Ontology

Chemical compound

ChEBI

Ontology

Chemical compound

UniChem, SMILE, InChi

Entity

Gene/protein

ENSEMBL, ENTREZ_GENE, UNIPROT, HGNC ID, INSDC

Entity

Metabolites

MetaboLights compound accession, ChEBI

Entity

Nucleotide reference sequence

ReqSeq

Entity

9.4.1.5. Summary

Using common transcriptomics metadata standards, in particular the fields listed above as guidance, it is possible to easily define a comprehensive metadata template to capture all the experimental variables to describe any transcriptomics experiment in a FAIR-compliant way. A generic step-by-step guide for designing a metadata template is provided here


9.4.1.6. Authors

Name

ORCID

Affiliation

Type

ELIXIR Node

Contribution

University of Luxembourg

Writing - Review & Editing

Karsten Quast

Boehringer-Ingelheim AG

GSK


9.4.1.7. License

This page is released under the Creative Commons 4.0 BY license.