PhenoMiner: quantitative phenotype curation at the rat genome database by Stanley J. F. Laulederkind, et.al. (Database (2013) 2013 : bat015 doi: 10.1093/database/bat015)
Abstract:
The Rat Genome Database (RGD) is the premier repository of rat genomic and genetic data and currently houses >40 000 rat gene records as well as human and mouse orthologs, >2000 rat and 1900 human quantitative trait loci (QTLs) records and >2900 rat strain records. Biological information curated for these data objects includes disease associations, phenotypes, pathways, molecular functions, biological processes and cellular components. Recently, a project was initiated at RGD to incorporate quantitative phenotype data for rat strains, in addition to the currently existing qualitative phenotype data for rat strains, QTLs and genes. A specialized curation tool was designed to generate manual annotations with up to six different ontologies/vocabularies used simultaneously to describe a single experimental value from the literature. Concurrently, three of those ontologies needed extensive addition of new terms to move the curation forward. The curation interface development, as well as ontology development, was an ongoing process during the early stages of the PhenoMiner curation project.
Database URL: http://rgd.mcw.edu
The line:
A specialized curation tool was designed to generate manual annotations with up to six different ontologies/vocabularies used simultaneously to describe a single experimental value from the literature.
sounded relevant to topic maps.
Turns out to be five ontologies and the article reports:
The ‘Create Record’ page (Figure 4) is where the rest of the data for a single record is entered. It consists of a series of autocomplete text boxes, drop-down text boxes and editable plain text boxes. All of the data entered are associated with terms from five ontologies/vocabularies: RS, CMO, MMO, XCO and the optional MA (Mouse Adult Gross Anatomy Dictionary) (13)
Important to note that authoring does not require the user to make explicit the properties underlying any of the terms from the different ontologies.
Some users probably know that level of detail but what is important is the capturing of their knowledge of subject sameness.
A topic map extension/add-on to such a system could flesh out those bare terms to provide a basis for treating terms from different ontologies as terms for the same subjects.
That merging/mapping detail need not bother an author or casual user.
But it increases the odds that future data sets can be reliably integrated with this one.
And issues with the correctness of a mapping can be meaningfully investigated.
If it helps, think of correctness of mappping as accountability, for someone else.