Genotation: Actionable Knowledge for the Scientific Reader

​Scientific data gathered at an unprecedented rate due to technological advancements in the last few decades have resulted in a variety of genetics-related biomedical databases, where information in each database is grouped by genetic terms such as genes, mutations, and proteins (e.g. NCBI Gene contains descriptive information by gene, and NCBI Clinvar contains clinically relevant variants by gene). To overcome challenges of knowledge management introduced with the disparate biomedical databases and the growth of journal articles published online, modern scientific readers have adopted habits such as search, filter, scan, link, annotate and analyze.

Search, filter and link practices are mostly conducted as queries on generic search engines (Google, Yahoo etc.) fraught with inefficiencies such as: 1) switching mental context away from reading and comprehension; 2) wasting time sifting through results that may or may not yield desired information; and 3) possibility of locating inaccurate information on non-authoritative websites. Using genetic terms mentioned within a peer-reviewed journal article as the key, content of the article could be linked to deeper knowledge stored in these external databases. In a June 2016 issue of Experimental Biology and Medicine Nagahawatte et al, from The University of Tennessee Health Science Center, present Genotation as an informatics solution for scientists to consume fragments of knowledge from scientific manuscripts annotated with supplementary information. Genotation is an informatics solution that will automatically link information from biomedical databases relevant to the content of an article in a format that a user could visually interact with by “point and click” mouse moves (visual interactions), enhancing communication and prompting hypotheses generation for future studies.

Genotation allows scientists to search the PubMed Central repository, or upload a manuscript of preference. Manuscripts in Hyper Text Markup Language (HTML) or Portable Document Format are accepted as input. A novel rule based named entity recognition algorithm, Genomine, is used to identify genomics-related terms from the input. Genomine’s current accuracy rating is shown by an F-score of .65. The identified genomic terms are augmented with content from the knowledgebase complied of several freely available gold-standard databases, such as ClinVar, PharmGKB, dbSNP, MedGen and Uniprot. Supplements are presented on the web interface alongside the article for visual interaction as actionable knowledge. In addition, the reader can download an interactive graphical summary of the supplements to be shared over social media. Genotation is freely available to the scientific community on The lead author further reflects, “Currently we are sharing publicly available datasets with a considerable amount of inherent noise. We envision Genotation to be the platform where scientists share manually curated datasets, with very little noise to supplement the content. At that point will we will realize the true potential of Genotation.”

Dr. Steven R. Goodman, Editor-in-Chief of Experimental Biology and Medicine, said “Genotation is a powerful bioinformatics tool that biomedical researchers will find to be of great value. We are pleased to have published this important contribution to our readers’ tool chest”

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Source: Experimental Biology and Medicine