From 7bab9517d30a70fe4be1239bce737baa5ed8233d Mon Sep 17 00:00:00 2001 From: Munyoki Kilyungi Date: Tue, 29 Aug 2023 13:22:54 +0300 Subject: Document proposed classification scheme Signed-off-by: Munyoki Kilyungi --- .../gn-classification-scheme.gmi | 45 ++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 topics/next-gen-databases/gn-classification-scheme.gmi (limited to 'topics') diff --git a/topics/next-gen-databases/gn-classification-scheme.gmi b/topics/next-gen-databases/gn-classification-scheme.gmi new file mode 100644 index 0000000..2d66f3c --- /dev/null +++ b/topics/next-gen-databases/gn-classification-scheme.gmi @@ -0,0 +1,45 @@ +# Understanding GN's Classification Scheme + +In GeneNetwork (GN), data is grouped into specific discrete categories. Let's dive into the currently implemented scheme: + +* Species: This category groups data based on different species, like humans, mice, or plants. +* Set/Group (InbredSet): This groups data according to a group of genetically similar organisms. +* DatasetType: This classified data according to it's type. There are three main types: Genotypes (genetic makeup), Molecular Traits (molecular characteristics), and Phenotypes (observable traits). + +The aforementioned classification scheme is inspired by GN's menu structure which forms the skeleton of the proposed classification. You can query metadata about this classification: "gn:ResourceClassificationScheme". This classification scheme has 3 levels: + +* gnc:DatasetType +* gnc:Set +* gn:Species. + +Here's a depper look at each level: + +* gnc:DatasetType: This level encompasses subcategories like "gnc:Probeset," "gnc:Genotype," and "gnc:Phenotype." +* gnc:Set: This level includes all the members listed in the InbredSet table. +* gn:Species: This level consists of all the members from the Species table. + +The beauty of this system is that most of the resources in GN can be accurately categorized using it. Instead of using specific properties like "gnt:belongsToSpecies" or "gnt:belongsToSet," we can utilize the xkos⁰ approach. For instance, to classify a resource, we can use the concept of xkos and apply the relationship "xkos:classifiedUnder." Here's an example of a resource that has been classified: + +``` +gn:Gtexv8_sto_0220 xkos:classifiedUnder gnc:Probeset . +gn:Gtexv8_sto_0220 xkos:classifiedUnder gn:setGtex_v8 +``` + +This means that the resource "gn:Gtexv8_sto_0220" is classified under the category "gnc:Probeset" and also under the set "gn:setGtex_v8." + +To query this classification using SPARQL, we can use the following code snippet: + +``` +PREFIX xkos: +PREFIX gnc: +PREFIX gn: + +SELECT * WHERE { + gn:Gtexv8_sto_0220 xkos:classifiedUnder ?datasetType . + gn:Gtexv8_sto_0220 xkos:classifiedUnder ?set . + gnc:DatasetType skos:member ?datasetType . + gnc:Set skos:member ?set . +} +``` + +=> https://rdf-vocabulary.ddialliance.org/xkos.html ⁰ XKOS: An SKOS extension for representing statistical classifications. -- cgit v1.2.3