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Availability of renal literature in six bibliographic databases.

ABSTRACT: BACKGROUND: When searching for renal literature, nephrologists must choose between several different bibliographic databases. We compared the availability of renal clinical studies in six major bibliographic databases. METHODS: We gathered 151 renal systematic reviews, which collectively contained 2195 unique citations referencing primary studies in the form of journal articles, meeting articles or meeting abstracts published between 1963 and 2008. We searched for each citation in three subscription-free bibliographic databases (PubMed, Google Scholar and Scirus) and three subscription-based databases (EMBASE, Ovid-MEDLINE and ISI Web of Knowledge). For the subscription-free databases, we determined which full-text journal articles were available free of charge via links to the article source. RESULTS: The proportion of journal articles contained within each of the six databases ranged from 96 to 97%; results were similar for meeting articles. Availability of meeting abstracts was poor, ranging from 0 to 37% (P < 0.01) with ISI Web of Knowledge containing the largest proportion [37%, 95% confidence interval (95% CI) 32-43%]. Among the subscription-free databases, free access to full-text articles was highest in Google Scholar (38% free, 95% CI 36-41%), and was only marginally higher (39%) when all subscription-free databases were searched. After 2000, free access to full-text articles increased to 49%. CONCLUSIONS: Over 99% of renal clinical journal articles are available in at least one major bibliographic database. Subscription-free databases provide free full-text access to almost half of the articles published after the year 2000, which may be of particular interest to clinicians in settings with limited access to subscription-based resources.

SUBMITTER: Shariff SZ

PROVIDER: S-EPMC3506156 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Availability of renal literature in six bibliographic databases.

Shariff Salimah Z SZ Sontrop Jessica M JM Iansavichus Arthur V AV Haynes R Brian RB Weir Matthew A MA Gandhi Sonja S Cuerden Meaghan S MS Garg Amit X AX

Clinical kidney journal 20121111 6

BACKGROUND: When searching for renal literature, nephrologists must choose between several different bibliographic databases. We compared the availability of renal clinical studies in six major bibliographic databases. METHODS: We gathered 151 renal systematic reviews, which collectively contained 2195 unique citations referencing primary studies in the form of journal articles, meeting articles or meeting abstracts published between 1963 and 2008. We searched for each citation in three subscri ...[more]

PMID: 23185693

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Project description:BackgroundThere is a considerable interest in characterizing the biological role of specific protein residue substitutions through mutagenesis experiments. Additionally, recent efforts related to the detection of disease-associated SNPs motivated both the manual annotation, as well as the automatic extraction, of naturally occurring sequence variations from the literature, especially for protein families that play a significant role in signaling processes such as kinases. Systematic integration and comparison of kinase mutation information from multiple sources, covering literature, manual annotation databases and large-scale experiments can result in a more comprehensive view of functional, structural and disease associated aspects of protein sequence variants. Previously published mutation extraction approaches did not sufficiently distinguish between two fundamentally different variation origin categories, namely natural occurring and induced mutations generated through in vitro experiments.ResultsWe present a literature mining pipeline for the automatic extraction and disambiguation of single-point mutation mentions from both abstracts as well as full text articles, followed by a sequence validation check to link mutations to their corresponding kinase protein sequences. Each mutation is scored according to whether it corresponds to an induced mutation or a natural sequence variant. We were able to provide direct literature links for a considerable fraction of previously annotated kinase mutations, enabling thus more efficient interpretation of their biological characterization and experimental context. In order to test the capabilities of the presented pipeline, the mutations in the protein kinase domain of the kinase family were analyzed. Using our literature extraction system, we were able to recover a total of 643 mutations-protein associations from PubMed abstracts and 6,970 from a large collection of full text articles. When compared to state-of-the-art annotation databases and high throughput genotyping studies, the mutation mentions extracted from the literature overlap to a good extent with the existing knowledgebases, whereas the remaining mentions suggest new mutation records that were not previously annotated in the databases.ConclusionUsing the proposed residue disambiguation and classification approach, we were able to differentiate between natural variant and mutagenesis types of mutations with an accuracy of 93.88. The resulting system is useful for constructing a Gold Standard set of mutations extracted from the literature by human experts with minimal manual curation effort, providing direct pointers to relevant evidence sentences. Our system is able to recover mutations from the literature that are not present in state-of-the-art databases. Human expert manual validation of a subset of the literature extracted mutations conducted on 100 mutations from PubMed abstracts highlights that almost three quarters (72%) of the extracted mutations turned out to be correct, and more than half of these had not been previously annotated in databases.

Dataset Information

Availability of renal literature in six bibliographic databases.

Publications

Availability of renal literature in six bibliographic databases.

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