bioNerDS: exploring bioinformatics' database and software use through literature mining - PubMed (original) (raw)

bioNerDS: exploring bioinformatics' database and software use through literature mining

Geraint Duck et al. BMC Bioinformatics. 2013.

Abstract

Background: Biology-focused databases and software define bioinformatics and their use is central to computational biology. In such a complex and dynamic field, it is of interest to understand what resources are available, which are used, how much they are used, and for what they are used. While scholarly literature surveys can provide some insights, large-scale computer-based approaches to identify mentions of bioinformatics databases and software from primary literature would automate systematic cataloguing, facilitate the monitoring of usage, and provide the foundations for the recovery of computational methods for analysing biological data, with the long-term aim of identifying best/common practice in different areas of biology.

Results: We have developed bioNerDS, a named entity recogniser for the recovery of bioinformatics databases and software from primary literature. We identify such entities with an F-measure ranging from 63% to 91% at the mention level and 63-78% at the document level, depending on corpus. Not attaining a higher F-measure is mostly due to high ambiguity in resource naming, which is compounded by the on-going introduction of new resources. To demonstrate the software, we applied bioNerDS to full-text articles from BMC Bioinformatics and Genome Biology. General mention patterns reflect the remit of these journals, highlighting BMC Bioinformatics's emphasis on new tools and Genome Biology's greater emphasis on data analysis. The data also illustrates some shifts in resource usage: for example, the past decade has seen R and the Gene Ontology join BLAST and GenBank as the main components in bioinformatics processing.

Abstract: Conclusions We demonstrate the feasibility of automatically identifying resource names on a large-scale from the scientific literature and show that the generated data can be used for exploration of bioinformatics database and software usage. For example, our results help to investigate the rate of change in resource usage and corroborate the suspicion that a vast majority of resources are created, but rarely (if ever) used thereafter. bioNerDS is available at http://bionerds.sourceforge.net/.

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Figures

Figure 1

Figure 1

Flowchart of bioNerDS’ name recognition strategy.

Figure 2

Figure 2

Relative usage of top resources in Genome Biology over time. Highlights the relative usage of some well known bioinformatics resources within the top 50 resources used at document level within Genome Biology.

Figure 3

Figure 3

Relative usage of top resources in BMC Bioinformatics over time. Highlights the relative usage of some well known bioinformatics resources within the top 50 resources used at document level within BMC Bioinformatics.

Figure 4

Figure 4

Genome Biology’s upper and lower 95% bounds. Comparison of a resource’s change in relative use, compared to the expected change based on a random walk using a Gaussian distribution fitted to the normalised resource usage changes from a baseline in Year 0 for Genome Biology. The upper and lower 95% bounds are calculated as two standard deviations from the mean.

Figure 5

Figure 5

BMC Bioinformatics’s upper and lower 95% bounds. Comparison of a resource’s change in relative use, compared to the expected change based on a random walk using a Gaussian distribution fitted to the normalised resource usage changes from a baseline in Year 0 for BMC Bioinformatics. The upper and lower 95% bounds are calculated as two standard deviations from the mean.

Figure 6

Figure 6

Genome Biology’s variation in top 50 resource usage. The sum of normalised frequencies against the sum of absolute differences for Genome Biology’s top 50 resource mentions with interesting outliers labelled. The y axis highlights the relative level of use of a resource, whereas the x axis shows the level of variation of tool use across the years 2000 to 2011.

Figure 7

Figure 7

BMC Bioinformatics’s variation in top 50 resource usage. The sum of normalised frequencies against the sum of absolute differences for BMC Bioinformatics’s top 50 resource mentions with interesting outliers labelled. The y axis highlights the relative level of use of a resource, whereas the x axis shows the level of variation of tool use across the years 2000 to 2011.

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References

    1. Cannata N, Merelli E, Altman RB. Time to organize the bioinformatics resourceome. PLoS Comput Biol. 2005;1(7):e76. doi: 10.1371/journal.pcbi.0010076. [ http://www.ncbi.nlm.nih.gov/pubmed/16738704] - DOI - PMC - PubMed
    1. Wren JD, Bateman A. Databases, data tombs and dust in the wind. Bioinformatics. 2008;24(19):2127–2128. doi: 10.1093/bioinformatics/btn464. [ http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/19/2127] - DOI - PubMed
    1. Altschul SF, Gish W, Miller W, Myers EW, Lipman D J etal. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–410. - PubMed
    1. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG. Clustal W and Clustal X version 2.0. Bioinformatics (Oxford, England) 2007;23(21):2947–2948. doi: 10.1093/bioinformatics/btm404. [ http://www.ncbi.nlm.nih.gov/pubmed/17846036] - DOI - PubMed
    1. Eales JM, Pinney JW, Stevens RD, Robertson DL. Methodology capture discriminating between the “best” and the rest of community practice. BMC Bioinformatics. 2008;9:359. doi: 10.1186/1471-2105-9-359. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2553348. - DOI - PMC - PubMed

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