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Emily Dimmer

Publications and source records attributed to Emily Dimmer.

5 recordsLinked to original sources
PubMed2006

GOAnnotator: linking protein GO annotations to evidence text.

BACKGROUND: Annotation of proteins with gene ontology (GO) terms is ongoing work and a complex task. Manual GO annotation is precise and precious, but it is time-consuming. Therefore, instead of curated annotations most of the proteins come with uncurated annotations, which have been generated automatically. Text-mining systems that use literature for automatic annotation have been proposed but they do not satisfy the high quality expectations of curators. RESULTS: In this paper we describe an approach that links uncurated annotations to text extracted from literature. The selection of the text is based on the similarity of the text to the term from the uncurated annotation. Besides substantiating the uncurated annotations, the extracted texts also lead to novel annotations. In addition, the approach uses the GO hierarchy to achieve high precision. Our approach is integrated into GOAnnotator, a tool that assists the curation process for GO annotation of UniProt proteins. CONCLUSION: The GO curators assessed GOAnnotator with a set of 66 distinct UniProt/SwissProt proteins with uncurated annotations. GOAnnotator provided correct evidence text at 93% precision. This high precision results from using the GO hierarchy to only select GO terms similar to GO terms from uncurated annotations in GOA. Our approach is the first one to achieve high precision, which is crucial for the efficient support of GO curators. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/rebil/tools/goa/.

Journal Article↗
PubMed2005

Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools.

The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.

Animals↗
PubMed2004

The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.

Animals↗
PubMed2004

Transgenic analysis of sugar beet xyloglucan endo-transglucosylase/hydrolase Bv-XTH1 and Bv-XTH2 promoters reveals overlapping tissue-specific and wound-inducible expression profiles.

The identification and analysis of tissue-specific gene regulatory elements will improve our knowledge of the molecular mechanisms that control the growth and development of different plant tissues and offer potentially useful tools for the genetic engineering of plants. A polymerase chain reaction (PCR)-based 5'-genome walk from sequences of an isolated sugar beet xyloglucan endo-transglucosylase hydrolase (XTH) gene led to the isolation of two independent upstream fragments. They were 1332 and 2163 base pairs upstream of the XTH ATG start site, respectively. In vivo transgenic assays in sugar beet hairy roots and Arabidopsis thaliana revealed that both fragments had promoter function and, in A. thaliana, directed expression in vascular tissues within the root, leaves and petals. Promoter activity was also observed in the leaf trichomes and within rapidly expanding stem internodes. Expression driven by both promoters was found to be wound inducible. Overall, the spatial and temporal expression pattern of these promoters suggested that the corresponding Bv-XTH genes (designated Bv-XTH1 and Bv-XTH2) may be involved in secondary cell wall formation. This work provides new insights on molecular mechanisms that could be exploited for the genetic engineering of sugar beet crops.

Journal Article↗