bkbit.models.genome_annotation module

class bkbit.models.genome_annotation.Activity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Activity', 'biolink:Activity']] = ['biolink:Activity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None)

Bases: ActivityAndBehavior, NamedThing

An activity is something that occurs over a period of time and acts upon or with entities; it may include consuming, processing, transforming, modifying, relocating, using, or generating entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Activity', 'biolink:Activity']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Activity', 'definition_uri': 'https://w3id.org/biolink/vocab/Activity', 'exact_mappings': ['prov:Activity', 'NCIT:C43431', 'STY:T052'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['activity and behavior'], 'narrow_mappings': ['STY:T056', 'STY:T057', 'STY:T064', 'STY:T066', 'STY:T062', 'STY:T065', 'STY:T058']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.ActivityAndBehavior

Bases: Occurrent

Activity or behavior of any independent integral living, organization or mechanical actor in the world

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ActivityAndBehavior', 'definition_uri': 'https://w3id.org/biolink/vocab/ActivityAndBehavior', 'exact_mappings': ['UMLSSG:ACTI'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.Annotation

Bases: ConfiguredBaseModel

Biolink Model root class for entity annotations.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'class_uri': 'biolink:Annotation', 'definition_uri': 'https://w3id.org/biolink/vocab/Annotation', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.AnnotationCollection(*, annotations: list[GeneAnnotation] | None = None, genome_annotations: list[GenomeAnnotation] | None = None, genome_assemblies: list[GenomeAssembly] | None = None)

Bases: ConfiguredBaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

annotations: list[GeneAnnotation] | None

genome_annotations: list[GenomeAnnotation] | None

genome_assemblies: list[GenomeAssembly] | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'https://identifiers.org/brain-bican/genome-annotation-schema', 'tree_root': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.Attribute(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Attribute', 'biolink:Attribute']] = ['biolink:Attribute'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, has_attribute_type: str, has_quantitative_value: list[QuantityValue] | None = None, has_qualitative_value: str | None = None)

Bases: NamedThing, OntologyClass

A property or characteristic of an entity. For example, an apple may have properties such as color, shape, age, crispiness. An environmental sample may have attributes such as depth, lat, long, material.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Attribute', 'biolink:Attribute']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

has_attribute_type: str

has_qualitative_value: str | None

has_quantitative_value: list[QuantityValue] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Attribute', 'definition_uri': 'https://w3id.org/biolink/vocab/Attribute', 'exact_mappings': ['SIO:000614'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['EDAM-DATA', 'EDAM-FORMAT', 'EDAM-OPERATION', 'EDAM-TOPIC'], 'in_subset': ['samples'], 'mixins': ['ontology class'], 'slot_usage': {'name': {'description': "The human-readable 'attribute name' can be set to a string which reflects its context of interpretation, e.g. SEPIO evidence/provenance/confidence annotation or it can default to the name associated with the 'has attribute type' slot ontology term.", 'name': 'name'}}})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.AuthorityType(value)

Bases: str, Enum

An enumeration.

ENSEMBL = 'ENSEMBL'

NCBI = 'NCBI'

class bkbit.models.genome_annotation.BioType(value)

Bases: str, Enum

An enumeration.

noncoding = 'noncoding'

protein_coding = 'protein_coding'

class bkbit.models.genome_annotation.BiologicalEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/BiologicalEntity', 'biolink:BiologicalEntity']] = ['biolink:BiologicalEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None)

Bases: ThingWithTaxon, NamedThing

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/BiologicalEntity', 'biolink:BiologicalEntity']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'aliases': ['bioentity'], 'class_uri': 'biolink:BiologicalEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/BiologicalEntity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['thing with taxon'], 'narrow_mappings': ['WIKIDATA:Q28845870', 'STY:T050', 'SIO:010046', 'STY:T129']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.Checksum(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/Checksum', 'bican:Checksum']] = ['bican:Checksum'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, checksum_algorithm: DigestType | None = None, value: str | None = None)

Bases: Entity

Checksum values associated with digital entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://identifiers.org/brain-bican/vocab/Checksum', 'bican:Checksum']]

checksum_algorithm: DigestType | None

deprecated: bool | None

description: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'https://identifiers.org/brain-bican/bican-core-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

type: list[str] | None

value: str | None

class bkbit.models.genome_annotation.ChemicalEntityOrGeneOrGeneProduct

Bases: ConfiguredBaseModel

A union of chemical entities and children, and gene or gene product. This mixin is helpful to use when searching across chemical entities that must include genes and their children as chemical entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'definition_uri': 'https://w3id.org/biolink/vocab/ChemicalEntityOrGeneOrGeneProduct', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.ConfiguredBaseModel

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.Dataset(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Dataset', 'biolink:Dataset']] = ['biolink:Dataset'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: InformationContentEntity

an item that refers to a collection of data from a data source.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Dataset', 'biolink:Dataset']]

creation_date: date | None

deprecated: bool | None

description: str | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Dataset', 'definition_uri': 'https://w3id.org/biolink/vocab/Dataset', 'exact_mappings': ['IAO:0000100', 'dctypes:Dataset', 'schema:dataset', 'dcid:Dataset'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.DigestType(value)

Bases: str, Enum

An enumeration.

MD5 = 'spdx:checksumAlgorithm_md5'

SHA1 = 'spdx:checksumAlgorithm_sha1'

SHA256 = 'spdx:checksumAlgorithm_sha256'

class bkbit.models.genome_annotation.Entity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Entity', 'biolink:Entity']] = ['biolink:Entity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None)

Bases: ConfiguredBaseModel

Root Biolink Model class for all things and informational relationships, real or imagined.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Entity', 'biolink:Entity']]

deprecated: bool | None

description: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'class_uri': 'biolink:Entity', 'definition_uri': 'https://w3id.org/biolink/vocab/Entity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

type: list[str] | None

class bkbit.models.genome_annotation.Gene(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Gene', 'biolink:Gene']] = ['biolink:Gene'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None, symbol: str | None = None)

Bases: GeneOrGeneProduct, ChemicalEntityOrGeneOrGeneProduct, GenomicEntity, BiologicalEntity, PhysicalEssence, OntologyClass

A region (or regions) that includes all of the sequence elements necessary to encode a functional transcript. A gene locus may include regulatory regions, transcribed regions and/or other functional sequence regions.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Gene', 'biolink:Gene']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'broad_mappings': ['NCIT:C45822'], 'class_uri': 'biolink:Gene', 'definition_uri': 'https://w3id.org/biolink/vocab/Gene', 'exact_mappings': ['SO:0000704', 'SIO:010035', 'WIKIDATA:Q7187', 'dcid:Gene'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['NCBIGene', 'ENSEMBL', 'HGNC', 'MGI', 'ZFIN', 'dictyBase', 'WB', 'WormBase', 'FB', 'RGD', 'SGD', 'PomBase', 'OMIM', 'KEGG.GENES', 'UMLS', 'Xenbase', 'AspGD', 'PHARMGKB.GENE'], 'in_subset': ['translator_minimal', 'model_organism_database'], 'mixins': ['gene or gene product', 'genomic entity', 'chemical entity or gene or gene product', 'physical essence', 'ontology class'], 'narrow_mappings': ['bioschemas:gene']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

symbol: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.GeneAnnotation(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/GeneAnnotation', 'bican:GeneAnnotation']] = ['bican:GeneAnnotation'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None, symbol: str | None = None, molecular_type: BioType | str | None = None, source_id: str | None = None, referenced_in: GenomeAnnotation | str)

Bases: Gene

Represents a single gene. Includes metadata about the gene, such as its molecular type and the genome annotation it was referenced from.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://identifiers.org/brain-bican/vocab/GeneAnnotation', 'bican:GeneAnnotation']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'https://identifiers.org/brain-bican/genome-annotation-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

molecular_type: BioType | str | None

name: str | None

provided_by: list[str] | None

referenced_in: GenomeAnnotation | str

source_id: str | None

symbol: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.GeneOrGeneProduct(*, name: str | None = None)

Bases: MacromolecularMachineMixin

A union of gene loci or gene products. Frequently an identifier for one will be used as proxy for another

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:GeneOrGeneProduct', 'definition_uri': 'https://w3id.org/biolink/vocab/GeneOrGeneProduct', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['CHEMBL.TARGET', 'IUPHAR.FAMILY'], 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class bkbit.models.genome_annotation.Genome(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Genome', 'biolink:Genome']] = ['biolink:Genome'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None)

Bases: GenomicEntity, BiologicalEntity, PhysicalEssence, OntologyClass

A genome is the sum of genetic material within a cell or virion.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Genome', 'biolink:Genome']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Genome', 'close_mappings': ['dcid:GenomeAssemblyUnit'], 'definition_uri': 'https://w3id.org/biolink/vocab/Genome', 'exact_mappings': ['SO:0001026', 'SIO:000984', 'WIKIDATA:Q7020'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'in_subset': ['model_organism_database'], 'mixins': ['genomic entity', 'physical essence', 'ontology class']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.GenomeAnnotation(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/GenomeAnnotation', 'bican:GenomeAnnotation']] = ['bican:GenomeAnnotation'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None, version: str | None = None, digest: list[Checksum | str] | None = None, content_url: list[str] | None = None, authority: AuthorityType | None = None, reference_assembly: GenomeAssembly | str)

Bases: Genome

Represents a genome annotation. Includes metadata about the genome, such as its version and reference assembly.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

authority: AuthorityType | None

category: list[Literal['https://identifiers.org/brain-bican/vocab/GenomeAnnotation', 'bican:GenomeAnnotation']]

content_url: list[str] | None

deprecated: bool | None

description: str | None

digest: list[Checksum | str] | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'https://identifiers.org/brain-bican/genome-annotation-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

reference_assembly: GenomeAssembly | str

synonym: list[str] | None

type: list[str] | None

version: str | None

xref: list[str] | None

class bkbit.models.genome_annotation.GenomeAssembly(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/GenomeAssembly', 'bican:GenomeAssembly']] = ['bican:GenomeAssembly'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, version: str | None = None, strain: str | None = None)

Bases: ThingWithTaxon, NamedThing

Represents a genome assembly. A genome assembly is a computational representation of a genome sequence.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://identifiers.org/brain-bican/vocab/GenomeAssembly', 'bican:GenomeAssembly']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'https://identifiers.org/brain-bican/genome-annotation-schema', 'mixins': ['thing with taxon']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

strain: str | None

synonym: list[str] | None

type: list[str] | None

version: str | None

xref: list[str] | None

class bkbit.models.genome_annotation.GenomicEntity(*, has_biological_sequence: str | None = None)

Bases: ConfiguredBaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

has_biological_sequence: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:GenomicEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/GenomicEntity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'in_subset': ['translator_minimal'], 'mixin': True, 'narrow_mappings': ['STY:T028', 'GENO:0000897']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.InformationContentEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/InformationContentEntity', 'biolink:InformationContentEntity']] = ['biolink:InformationContentEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: NamedThing

a piece of information that typically describes some topic of discourse or is used as support.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/InformationContentEntity', 'biolink:InformationContentEntity']]

creation_date: date | None

deprecated: bool | None

description: str | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'aliases': ['information', 'information artefact', 'information entity'], 'class_uri': 'biolink:InformationContentEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/InformationContentEntity', 'exact_mappings': ['IAO:0000030'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['doi'], 'narrow_mappings': ['UMLSSG:CONC', 'STY:T077', 'STY:T078', 'STY:T079', 'STY:T080', 'STY:T081', 'STY:T082', 'STY:T089', 'STY:T102', 'STY:T169', 'STY:T171', 'STY:T185']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.LinkMLMeta(root: RootModelRootType = PydanticUndefined)

Bases: RootModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

root: dict[str, Any]

class bkbit.models.genome_annotation.MacromolecularMachineMixin(*, name: str | None = None)

Bases: ConfiguredBaseModel

A union of gene locus, gene product, and macromolecular complex. These are the basic units of function in a cell. They either carry out individual biological activities, or they encode molecules which do this.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:MacromolecularMachineMixin', 'definition_uri': 'https://w3id.org/biolink/vocab/MacromolecularMachineMixin', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class bkbit.models.genome_annotation.MaterialSample(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/MaterialSample', 'biolink:MaterialSample']] = ['biolink:MaterialSample'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None)

Bases: SubjectOfInvestigation, PhysicalEntity

A sample is a limited quantity of something (e.g. an individual or set of individuals from a population, or a portion of a substance) to be used for testing, analysis, inspection, investigation, demonstration, or trial use. [SIO]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/MaterialSample', 'biolink:MaterialSample']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'aliases': ['biospecimen', 'sample', 'biosample', 'physical sample'], 'class_uri': 'biolink:MaterialSample', 'definition_uri': 'https://w3id.org/biolink/vocab/MaterialSample', 'exact_mappings': ['OBI:0000747', 'SIO:001050'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['BIOSAMPLE', 'GOLD.META'], 'mixins': ['subject of investigation']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.NamedThing(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/NamedThing', 'biolink:NamedThing']] = ['biolink:NamedThing'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None)

Bases: Entity

a databased entity or concept/class

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/NamedThing', 'biolink:NamedThing']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:NamedThing', 'definition_uri': 'https://w3id.org/biolink/vocab/NamedThing', 'exact_mappings': ['BFO:0000001', 'WIKIDATA:Q35120', 'UMLSSG:OBJC', 'STY:T071', 'dcid:Thing'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.Occurrent

Bases: PhysicalEssenceOrOccurrent

A processual entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Occurrent', 'definition_uri': 'https://w3id.org/biolink/vocab/Occurrent', 'exact_mappings': ['BFO:0000003'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.OntologyClass(*, id: str)

Bases: ConfiguredBaseModel

a concept or class in an ontology, vocabulary or thesaurus. Note that nodes in a biolink compatible KG can be considered both instances of biolink classes, and OWL classes in their own right. In general you should not need to use this class directly. Instead, use the appropriate biolink class. For example, for the GO concept of endocytosis (GO:0006897), use bl:BiologicalProcess as the type.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

id: str

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:OntologyClass', 'comments': ["This is modeled as a mixin. 'ontology class' should not be the primary type of a node in the KG. Instead you should use an informative bioloink category, such as AnatomicalEntity (for Uberon classes), ChemicalSubstance (for CHEBI or CHEMBL), etc", "Note that formally this is a metaclass. Instances of this class are instances in the graph, but can be the object of 'type' edges. For example, if we had a node in the graph representing a specific brain of a specific patient (e.g brain001), this could have a category of bl:Sample, and by typed more specifically with an ontology class UBERON:nnn, which has as category bl:AnatomicalEntity"], 'definition_uri': 'https://w3id.org/biolink/vocab/OntologyClass', 'exact_mappings': ['owl:Class', 'schema:Class'], 'examples': [{'description': "the class 'brain' from the Uberon anatomy ontology", 'value': 'UBERON:0000955'}], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['MESH', 'UMLS', 'KEGG.BRITE'], 'mixin': True, 'see_also': ['https://github.com/biolink/biolink-model/issues/486']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.OrganismTaxon(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/OrganismTaxon', 'biolink:OrganismTaxon']] = ['biolink:OrganismTaxon'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, has_taxonomic_rank: str | None = None)

Bases: NamedThing

A classification of a set of organisms. Example instances: NCBITaxon:9606 (Homo sapiens), NCBITaxon:2 (Bacteria). Can also be used to represent strains or subspecies.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/OrganismTaxon', 'biolink:OrganismTaxon']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

has_taxonomic_rank: str | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'aliases': ['taxon', 'taxonomic classification'], 'class_uri': 'biolink:OrganismTaxon', 'definition_uri': 'https://w3id.org/biolink/vocab/OrganismTaxon', 'exact_mappings': ['WIKIDATA:Q16521', 'STY:T001', 'bioschemas:Taxon'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['NCBITaxon', 'MESH', 'UMLS'], 'in_subset': ['model_organism_database'], 'narrow_mappings': ['dcid:BiologicalSpecies'], 'values_from': ['NCBITaxon']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.PhysicalEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/PhysicalEntity', 'biolink:PhysicalEntity']] = ['biolink:PhysicalEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None)

Bases: PhysicalEssence, NamedThing

An entity that has material reality (a.k.a. physical essence).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/PhysicalEntity', 'biolink:PhysicalEntity']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEntity', 'exact_mappings': ['STY:T072'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['physical essence'], 'narrow_mappings': ['STY:T073']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.PhysicalEssence

Bases: PhysicalEssenceOrOccurrent

Semantic mixin concept. Pertains to entities that have physical properties such as mass, volume, or charge.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEssence', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEssence', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.PhysicalEssenceOrOccurrent

Bases: ConfiguredBaseModel

Either a physical or processual entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEssenceOrOccurrent', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEssenceOrOccurrent', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.Procedure(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Procedure', 'biolink:Procedure']] = ['biolink:Procedure'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None)

Bases: ActivityAndBehavior, NamedThing

A series of actions conducted in a certain order or manner

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Procedure', 'biolink:Procedure']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Procedure', 'definition_uri': 'https://w3id.org/biolink/vocab/Procedure', 'exact_mappings': ['UMLSSG:PROC', 'dcid:MedicalProcedure'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['CPT'], 'mixins': ['activity and behavior'], 'narrow_mappings': ['STY:T059', 'STY:T060', 'STY:T061', 'STY:T063']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.genome_annotation.QuantityValue(*, has_unit: str | None = None, has_numeric_value: float | None = None)

Bases: Annotation

A value of an attribute that is quantitative and measurable, expressed as a combination of a unit and a numeric value

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

has_numeric_value: float | None

has_unit: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:QuantityValue', 'definition_uri': 'https://w3id.org/biolink/vocab/QuantityValue', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.SubjectOfInvestigation

Bases: ConfiguredBaseModel

An entity that has the role of being studied in an investigation, study, or experiment

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:SubjectOfInvestigation', 'definition_uri': 'https://w3id.org/biolink/vocab/SubjectOfInvestigation', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.TaxonomicRank(*, id: str)

Bases: OntologyClass

A descriptor for the rank within a taxonomic classification. Example instance: TAXRANK:0000017 (kingdom)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

id: str

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:TaxonomicRank', 'definition_uri': 'https://w3id.org/biolink/vocab/TaxonomicRank', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['TAXRANK'], 'mappings': ['WIKIDATA:Q427626']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.ThingWithTaxon(*, in_taxon: list[str] | None = None, in_taxon_label: str | None = None)

Bases: ConfiguredBaseModel

A mixin that can be used on any entity that can be taxonomically classified. This includes individual organisms; genes, their products and other molecular entities; body parts; biological processes

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

in_taxon: list[str] | None

in_taxon_label: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ThingWithTaxon', 'definition_uri': 'https://w3id.org/biolink/vocab/ThingWithTaxon', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.genome_annotation.VersionedNamedThing(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/VersionedNamedThing', 'bican:VersionedNamedThing']] = ['bican:VersionedNamedThing'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, version: str, revision_of: str | None = None)

Bases: NamedThing

An iteration of the biolink:NamedThing class that stores metadata about the object’s version.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://identifiers.org/brain-bican/vocab/VersionedNamedThing', 'bican:VersionedNamedThing']]

deprecated: bool | None

description: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'from_schema': 'https://identifiers.org/brain-bican/bican-core-schema', 'slot_usage': {'version': {'name': 'version', 'required': True}}})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

revision_of: str | None

synonym: list[str] | None

type: list[str] | None

version: str

xref: list[str] | None