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Entities are special labels for dataset fields that contain specific content, such as genes, variants, vault objects, samples, and more. Entities allow for cross-dataset data harmonization, easy filtering, Beacons, and other entity-specific functions.

Supported Entites

The following entities are supported:

Entity Description Example
sample Sample ID (may also refer to a patient, aliquot, or replicate). This is the basic bio-unit that a set of variants may belong to. TCGA-02-0001-01
vault_objects SolveBio vault object ID (can be a file, dataset, or folder) 510110131292845817
literature Pubmed ID of a scientific paper 19684571
genomic_region Chromosome and start/stop position of a genomic interval GRCH38-7-117559590-117559593
vault SolveBio vault ID 2956
gene Gene symbol (using standard HUGO nomenclature) BRCA2
variant SolveBio variant ID for a unique variant GRCH38-7-140753336-140753336-T
dataset SolveBio dataset ID 1126936965182430633
copy_number_variant SolveBio copy number variant ID BRCA2 amplification
subject Subject ID SUBJ001

Setting Entities

Entities can be set on import or later via the web UI or API.

Automated import

SolveBio automatically extracts and labels the right fields as entities for common genomics filetypes such as VCF and GFF3/GTFs. For all other files, SolveBio's entity detection automatically detects if fields contain certain entities such as genes or variants.

Manually on import

Entities can be manually set on data import. This can be done via a template on data import for new datasets or data migration for existing datasets. Please see importing data for an example.


Entities can be added, removed, or switched to any field on SolveBio on any dataset where the user has write access. On the dataset view, any field with a orange label next to the field type is an entity field. Entities can be changed by clicking on the pencil icon.

Editing a dataset field

This opens a modal where the entity can be removed, reset, or added.

Editing a dataset field

Using Entities on SolveBio


Variants, genes, and literature have web explorers for individual entities, which brings together the wealth of public information about the entity, tailored for each type of entity. These explorers also display beacons for each entity, or which public or private datasets where this entity has been found. See examples such as BRCA2, EGFR T790M, and 19684571 for gene, variant, and literature respectively.

Filtering datasets directly

Datasets can be queried by entities instead of specifying the exact field name that the entity labels. Expression that query datasets can also use entity filters directly.

Cross-dataset comparisons

Many common representations of each entity can be harmonized for easy comparison with the entity_ids expression.

Finally, variant datasets have samples in common, can be used in the Variant Comparison App workflow.