| Title |
The Impact of Focused Gene Ontology Curation of Specific Mammalian Systems
|
|---|---|
| Published in |
PLOS ONE, December 2011
|
| DOI | 10.1371/journal.pone.0027541 |
| Pubmed ID | |
| Authors |
Yasmin Alam-Faruque, Rachael P. Huntley, Varsha K. Khodiyar, Evelyn B. Camon, Emily C. Dimmer, Tony Sawford, Maria J. Martin, Claire O'Donovan, Philippa J. Talmud, Peter Scambler, Rolf Apweiler, Ruth C. Lovering |
| Abstract |
The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 8% |
| United Kingdom | 1 | 3% |
| Spain | 1 | 3% |
| Portugal | 1 | 3% |
| Unknown | 33 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 41% |
| Student > Ph. D. Student | 6 | 15% |
| Other | 5 | 13% |
| Lecturer | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 46% |
| Medicine and Dentistry | 3 | 8% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Computer Science | 2 | 5% |
| Social Sciences | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 6 | 15% |