Seminar 26 Sept: Modelling rate of decline (slope) of a biomarker as a quantitative genetic trait using mixed effects models: application to loss of renal function (GFR) in type 1 diabetes
Gene Ontology (GO) is a major public annotation effort, which provides descriptions of the molecular functions, biological processes and sub-cellular locations attributed to gene products from all organisms. GO links primary biological knowledge to information provided in highly-controlled, structured vocabularies (or ontologies), and is designed to improve the accessibility of scientific knowledge to search engines and algorithmic processing. Consequently, GO has proved to be highly beneficial to investigators who need to understand and analyse large amounts of data produced from a range of high-throughput investigative techniques. In addition, GO annotations are incorporated into numerous popular biological knowledgebases such as EntrezGene, UniProKB, Ensembl and GeneCards.
Unfortunately, the accumulated knowledge of human biological systems is currently under-represented by GO and this is reflected by the interpretation of human-focused high-throughput datasets, which often identify quite general processes associated with a particular disease or condition of interest. Increased annotation of human genes, using more specific GO terms, will enable more specific interpretation of the vast number of human-focused high-throughput datasets. However, the detailed annotation required can only be achieved through the curation of published experimental data by trained GO curators.
The UCL-based Gene Ontology team focuses on the annotation of human genes and we are keen to represent the expert knowledge available at UCL in the GO Consortium dataset. The UCL-based team will prioritise the annotation of any genes or papers submitted to us. Furthermore, we are keen to hear from any scientist interested in ensuring the full annotation of their favorite gene.
Please visit our website for more information.
Any queries or submission of papers or genes to annotate should be directed to Ruth Lovering at email@example.com
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