# test_annotation¶

Tests performed on the annotations of an instance of cobra.Model.

## Module Contents¶

test_annotation.test_metabolite_annotation_presence(model)[source]

Expect all metabolites to have a non-empty annotation attribute.

This test checks if any annotations at all are present in the SBML annotations field for each metabolite, irrespective of the type of annotation i.e. specific database cross-references, ontology terms, additional information. For this test to pass the model is expected to have metabolites and each of them should have some form of annotation.

Implementation: Check if the annotation attribute of each cobra.Metabolite object of the model is unset or empty.

test_annotation.test_reaction_annotation_presence(model)[source]

Expect all reactions to have a non-empty annotation attribute.

This test checks if any annotations at all are present in the SBML annotations field for each reaction, irrespective of the type of annotation i.e. specific database cross-references, ontology terms, additional information. For this test to pass the model is expected to have reactions and each of them should have some form of annotation.

Implementation: Check if the annotation attribute of each cobra.Reaction object of the model is unset or empty.

test_annotation.test_gene_product_annotation_presence(model)[source]

Expect all genes to have a non-empty annotation attribute.

This test checks if any annotations at all are present in the SBML annotations field (extended by FBC package) for each gene product, irrespective of the type of annotation i.e. specific database, cross-references, ontology terms, additional information. For this test to pass the model is expected to have genes and each of them should have some form of annotation.

Implementation: Check if the annotation attribute of each cobra.Gene object of the model is unset or empty.

test_annotation.test_metabolite_annotation_overview(model, db)[source]

Expect all metabolites to have annotations from common databases.

Specific database cross-references are paramount to mapping information. To provide references to as many databases as possible helps to make the metabolic model more accessible to other researchers. This does not only facilitate the use of a model in a broad array of computational pipelines, it also promotes the metabolic model itself to become an organism-specific knowledge base.

For this test to pass, each metabolite annotation should contain cross-references to a number of databases. The currently selection is listed in annotation.py, but an ongoing discussion can be found at https://github.com/opencobra/memote/issues/332. For each database this test checks for the presence of its corresponding namespace ID to comply with the MIRIAM guidelines i.e. they have to match those defined on https://identifiers.org/.

Since each database is quite different and some potentially incomplete, it may not be feasible to achieve 100% coverage for each of them. Generally it should be possible, however, to obtain cross-references to at least one of the databases for all metabolites consistently.

Implementation: Check if the keys of the annotation attribute of each cobra.Metabolite of the model match with a selection of common biochemical databases. The annotation attribute of cobrapy components is a dictionary of key:value pairs.

test_annotation.test_reaction_annotation_overview(model, db)[source]

Expect all reactions to have annotations from common databases.

Specific database cross-references are paramount to mapping information. To provide references to as many databases as possible helps to make the metabolic model more accessible to other researchers. This does not only facilitate the use of a model in a broad array of computational pipelines, it also promotes the metabolic model itself to become an organism-specific knowledge base.

For this test to pass, each reaction annotation should contain cross-references to a number of databases. The currently selection is listed in annotation.py, but an ongoing discussion can be found at https://github.com/opencobra/memote/issues/332. For each database this test checks for the presence of its corresponding namespace ID to comply with the MIRIAM guidelines i.e. they have to match those defined on https://identifiers.org/.

Since each database is quite different and some potentially incomplete, it may not be feasible to achieve 100% coverage for each of them. Generally it should be possible, however, to obtain cross-references to at least one of the databases for all reactions consistently.

Implementation: Check if the keys of the annotation attribute of each cobra.Reaction of the model match with a selection of common biochemical databases. The annotation attribute of cobrapy components is a dictionary of key:value pairs.

test_annotation.test_gene_product_annotation_overview(model, db)[source]

Expect all genes to have annotations from common databases.

Specific database cross-references are paramount to mapping information. To provide references to as many databases as possible helps to make the metabolic model more accessible to other researchers. This does not only facilitate the use of a model in a broad array of computational pipelines, it also promotes the metabolic model itself to become an organism-specific knowledge base.

For this test to pass, each gene annotation should contain cross-references to a number of databases. The currently selection is listed in annotation.py, but an ongoing discussion can be found at https://github.com/opencobra/memote/issues/332. For each database this test checks for the presence of its corresponding namespace ID to comply with the MIRIAM guidelines i.e. they have to match those defined on https://identifiers.org/.

Since each database is quite different and some potentially incomplete, it may not be feasible to achieve 100% coverage for each of them. Generally it should be possible, however, to obtain cross-references to at least one of the databases for all gene products consistently.

Implementation: Check if the keys of the annotation attribute of each cobra.Gene of the model match with a selection of common genome databases. The annotation attribute of cobrapy components is a dictionary of key:value pairs.

test_annotation.test_metabolite_annotation_wrong_ids(model, db)[source]

Expect all annotations of metabolites to be in the correct format.

To identify databases and the identifiers belonging to them, computational tools rely on the presence of specific patterns. Only when these patterns can be identified consistently is an ID truly machine-readable. This test checks if the database cross-references in metabolite annotations conform to patterns defined according to the MIRIAM guidelines, i.e. matching those that are defined at https://identifiers.org/.

The required formats, i.e., regex patterns are further outlined in annotation.py. This test does not carry out a web query for the composed URI, it merely controls that the regex patterns match the identifiers.

Implementation: For those metabolites whose annotation keys match any of the tested databases, check if the corresponding values match the identifier pattern of each database.

test_annotation.test_reaction_annotation_wrong_ids(model, db)[source]

Expect all annotations of reactions to be in the correct format.

To identify databases and the identifiers belonging to them, computational tools rely on the presence of specific patterns. Only when these patterns can be identified consistently is an ID truly machine-readable. This test checks if the database cross-references in reaction annotations conform to patterns defined according to the MIRIAM guidelines, i.e. matching those that are defined at https://identifiers.org/.

The required formats, i.e., regex patterns are further outlined in annotation.py. This test does not carry out a web query for the composed URI, it merely controls that the regex patterns match the identifiers.

Implementation: For those reaction whose annotation keys match any of the tested databases, check if the corresponding values match the identifier pattern of each database.

test_annotation.test_gene_product_annotation_wrong_ids(model, db)[source]

Expect all annotations of genes/gene-products to be in the correct format.

To identify databases and the identifiers belonging to them, computational tools rely on the presence of specific patterns. Only when these patterns can be identified consistently is an ID truly machine-readable. This test checks if the database cross-references in reaction annotations conform to patterns defined according to the MIRIAM guidelines, i.e. matching those that are defined at https://identifiers.org/.

The required formats, i.e., regex patterns are further outlined in annotation.py. This test does not carry out a web query for the composed URI, it merely controls that the regex patterns match the identifiers.

Implementation: For those genes whose annotation keys match any of the tested databases, check if the corresponding values match the identifier pattern of each database.

test_annotation.test_metabolite_id_namespace_consistency(model)[source]

Expect metabolite identifiers to be from the same namespace.

In well-annotated models it is no problem if the pool of main identifiers for metabolites consists of identifiers from several databases. However, in models that lack appropriate annotations, it may hamper the ability of other researchers to use it. Running the model through a computational pipeline may be difficult without first consolidating the namespace.

Hence, this test checks if the main metabolite identifiers can be attributed to one single namespace based on the regex patterns defined at https://identifiers.org/

Implementation: Generate a table with each column corresponding to one database from the selection and each row to a metabolite identifier. A Boolean entry indicates whether the identifier matches the regular expression of the corresponding database. Since the Biocyc pattern matches broadly, we assume that any instance of an identifier matching to Biocyc AND any other database pattern is a false positive match for Biocyc and thus set it to false. Sum the positive matches for each database and assume that the largest set is the ‘main’ identifier namespace.

test_annotation.test_reaction_id_namespace_consistency(model)[source]

Expect reaction identifiers to be from the same namespace.

In well-annotated models it is no problem if the pool of main identifiers for reactions consists of identifiers from several databases. However, in models that lack appropriate annotations, it may hamper the ability of other researchers to use it. Running the model through a computational pipeline may be difficult without first consolidating the namespace.

Hence, this test checks if the main reaction identifiers can be attributed to one single namespace based on the regex patterns defined at https://identifiers.org/

Implementation: Generate a pandas.DataFrame with each column corresponding to one database from the selection and each row to the reaction ID. A boolean entry indicates whether the metabolite ID matches the regex pattern of the corresponding database. Since the Biocyc pattern matches quite, assume that any instance of an identifier matching to Biocyc AND any other DB pattern is a false positive match for Biocyc and then set the boolean to false. Sum the positive matches for each database and assume that the largest set is the ‘main’ identifier namespace.