Toward an interactive article: integrating journals and biological databases
1 Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA
2 Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA
3 Dartmouth Journal Services, Pilgrim Five, Suite 5, 5 Pilgrim Park Road, Waterbury, VT 05676, USA
4 Genetics Society of America, 9650 Rockville Pike, Bethesda, MD 20814-3998, USA
5 Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
6 Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815, USA
7 Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
Citation and License
BMC Bioinformatics 2011, 12:175 doi:10.1186/1471-2105-12-175Published: 19 May 2011
Journal articles and databases are two major modes of communication in the biological sciences, and thus integrating these critical resources is of urgent importance to increase the pace of discovery. Projects focused on bridging the gap between journals and databases have been on the rise over the last five years and have resulted in the development of automated tools that can recognize entities within a document and link those entities to a relevant database. Unfortunately, automated tools cannot resolve ambiguities that arise from one term being used to signify entities that are quite distinct from one another. Instead, resolving these ambiguities requires some manual oversight. Finding the right balance between the speed and portability of automation and the accuracy and flexibility of manual effort is a crucial goal to making text markup a successful venture.
We have established a journal article mark-up pipeline that links GENETICS journal articles and the model organism database (MOD) WormBase. This pipeline uses a lexicon built with entities from the database as a first step. The entity markup pipeline results in links from over nine classes of objects including genes, proteins, alleles, phenotypes and anatomical terms. New entities and ambiguities are discovered and resolved by a database curator through a manual quality control (QC) step, along with help from authors via a web form that is provided to them by the journal. New entities discovered through this pipeline are immediately sent to an appropriate curator at the database. Ambiguous entities that do not automatically resolve to one link are resolved by hand ensuring an accurate link. This pipeline has been extended to other databases, namely Saccharomyces Genome Database (SGD) and FlyBase, and has been implemented in marking up a paper with links to multiple databases.
Our semi-automated pipeline hyperlinks articles published in GENETICS to model organism databases such as WormBase. Our pipeline results in interactive articles that are data rich with high accuracy. The use of a manual quality control step sets this pipeline apart from other hyperlinking tools and results in benefits to authors, journals, readers and databases.