BACKGROUND: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants. RESULTS: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa = 0.88). GO2PUB returned 69% of the relevant articles, GoPubMed: 40% and PubMed: 29%. GO2PUB and GoPubMed have 17% of their results in common, corresponding to 24% of the total number of relevant results. 70% of the articles returned by more than one tool were relevant. 36% of the relevant articles were returned only by GO2PUB, 17% only by GoPubMed and 14% only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77% and 40% for the first queries, and of 70% and 38% for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries. CONCLUSIONS: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.
BACKGROUND: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants. RESULTS: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa = 0.88). GO2PUB returned 69% of the relevant articles, GoPubMed: 40% and PubMed: 29%. GO2PUB and GoPubMed have 17% of their results in common, corresponding to 24% of the total number of relevant results. 70% of the articles returned by more than one tool were relevant. 36% of the relevant articles were returned only by GO2PUB, 17% only by GoPubMed and 14% only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77% and 40% for the first queries, and of 70% and 38% for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries. CONCLUSIONS: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.
Authors: M A Harris; J Clark; A Ireland; J Lomax; M Ashburner; R Foulger; K Eilbeck; S Lewis; B Marshall; C Mungall; J Richter; G M Rubin; J A Blake; C Bult; M Dolan; H Drabkin; J T Eppig; D P Hill; L Ni; M Ringwald; R Balakrishnan; J M Cherry; K R Christie; M C Costanzo; S S Dwight; S Engel; D G Fisk; J E Hirschman; E L Hong; R S Nash; A Sethuraman; C L Theesfeld; D Botstein; K Dolinski; B Feierbach; T Berardini; S Mundodi; S Y Rhee; R Apweiler; D Barrell; E Camon; E Dimmer; V Lee; R Chisholm; P Gaudet; W Kibbe; R Kishore; E M Schwarz; P Sternberg; M Gwinn; L Hannick; J Wortman; M Berriman; V Wood; N de la Cruz; P Tonellato; P Jaiswal; T Seigfried; R White Journal: Nucleic Acids Res Date: 2004-01-01 Impact factor: 16.971
Authors: Dietrich Rebholz-Schuhmann; Harald Kirsch; Miguel Arregui; Sylvain Gaudan; Mark Riethoven; Peter Stoehr Journal: Bioinformatics Date: 2007-01-15 Impact factor: 6.937
Authors: Uma S Mudunuri; Mohamad Khouja; Stephen Repetski; Girish Venkataraman; Anney Che; Brian T Luke; F Pascal Girard; Robert M Stephens Journal: PLoS One Date: 2013-12-02 Impact factor: 3.240
Authors: Maria Taboada; Hadriana Rodríguez; Diego Martínez; María Pardo; María Jesús Sobrido Journal: Database (Oxford) Date: 2014-06-04 Impact factor: 3.451