This month, Europe PMC released a new version of SciLite, a powerful tool for highlighting annotations in life sciences publications. SciLite is powered by the Europe PMC annotation platform via the open annotation API, which provides access to over 1.3 billion annotations. Highlighting annotations in the text enables users to easily scan the article and locate key biological entities, such as genes/proteins, accession numbers, protein interactions, diseases, gene-disease relationship and more.
SciLite annotations has been redesigned to improve the speed of information retrieval and assist scientists and database curators to scan articles, extract facts and evidence from the biomedical literature, as well as locate the primary data cited in a given publication. Imagine yourself being able to locate and quickly visualise a protein structure of your interest on a single page! This is how SciLite helps with speeding scientific discovery.
Annotations can be accessed via the ‘Annotations’ option on the right-hand side on the article page. Clicking on ‘Annotations’ opens a new panel and selected terms will be highlighted in the text of the article. Notice that annotations can only be highlighted on articles with an open access license.
The new SciLite version includes new annotation types, a new annotation panel interface and new features. Annotation types now include cell, cell lines, clinical drugs, molecular processes, organ tissues, pathway, anatomy and phenotype. The new panel offers easy navigation through annotations and displays a popup window with a quick link to relevant data resources.
Additionally, the new version offers a chemical structure viewer. Readers are able to visualise protein and chemical structures in the annotation panel as well as in the highlighted text.
Highlighted annotations display links to relevant database records enabling users to locate the primary data in the text by linking text mined and curated bioentities to public life sciences databases. Additionally, the new improvements include options to endorse, report errors or share the annotation via a linkback URL.
Identifying a plethora of complex biological terms and concepts in publications was made possible due to a successful partnership with a variety of text mining groups that use text mining algorithms to identify different types of biological entities, and enable text miners to showcase their work to a wider public via SciLite annotations.
Europe PMC acknowledges all the annotation providers for cooperating towards submitting their annotations and welcomes new contributions from text-mining and curation communities to share annotations via the annotations submission service. Europe PMC would also like to thank all the participants who took part in usability sessions, to test and feedback on the improvements to the Scilite Annotations tool.
Want to know more about SciLite, annotation APIs or submitting annotations? Get in touch with firstname.lastname@example.org.