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. 2006 Dec 20:1:19.
doi: 10.1186/1747-5333-1-19.

GOAnnotator: linking protein GO annotations to evidence text

Francisco M Couto  1 , Mário J Silva, Vivian Lee, Emily Dimmer, Evelyn Camon, Rolf Apweiler, Harald Kirsch, Dietrich Rebholz-Schuhmann
Affiliations

GOAnnotator: linking protein GO annotations to evidence text

Francisco M Couto et al. J Biomed Discov Collab. .

Abstract

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/.

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Figures

Figure 1
Figure 1
List of documents related with a given protein. The list is sorted by the most similar term extracted from each document. The curator can use the Extract option to see the extracted terms together with the evidence text. By default GOAnnotator uses only the abstract, but the curator can use the AddText option to replace or insert text.
Figure 2
Figure 2
GO terms extracted. For each uncurated annotation, GOAnnotator shows the similar GO terms extracted from a sentence of the selected document. If any of the sentences provides correct evidence for the uncurated annotation, or if the evidence supports a GO term similar to that present in the uncurated annotation, the curator can use the Add option to store the annotation together with the document reference, the evidence codes and any comments.
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References

    1. Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R. The Gene Ontology Annotations (GOA) Database: sharing knowledge in UniProt with Gene Ontology. Nucleic Acids Research. 2004;32:262–266. doi: 10.1093/nar/gkh021. - DOI - PMC - PubMed
    1. GO-Consortium The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research. 2004:D258–D261. doi: 10.1093/nar/gkh036. - DOI - PMC - PubMed
    1. Apweiler R, Bairoch A, Wu C, Barker W, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin M, Natale D, O'Donovan C, Redaschi N, Yeh L. UniProt: the Universal Protein Knowledgebase. Nucleic Acids Research. 2004:D115–D119. doi: 10.1093/nar/gkh131. - DOI - PMC - PubMed
    1. Andrade M, Valencia A. Automatic Extraction of Keywords from Scientific Text: Application to the Knowledge Domain of Protein Families. Bioinformatics. 1998;14:600–607. doi: 10.1093/bioinformatics/14.7.600. - DOI - PubMed
    1. Chiang J, Yu H. MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. Bioinformatics. 2003;19:1417–1422. doi: 10.1093/bioinformatics/btg160. - DOI - PubMed

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