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SwissBioPics (www.swissbiopics.org) is a freely available resource of interactive, high-resolution cell images designed for the visualization of subcellular location data. SwissBioPics provides images describing cell types from all kingdoms of life—from the specialized muscle, neuronal and epithelial cells of animals, to the rods, cocci, clubs and spirals of prokaryotes. All cell images in SwissBioPics are drawn in Scalable Vector Graphics (SVG), with each subcellular location tagged with a unique identifier from the controlled vocabulary of subcellular locations and organelles of UniProt (https://www.uniprot.org/locations/). Users can search and explore SwissBioPics cell images through our website, which provides a platform for users to learn more about how cells are organized. A web component allows developers to embed SwissBioPics images in their own websites, using the associated JavaScript and a styling template, and to highlight subcellular locations and organelles by simply providing the web component with the appropriate identifier(s) from the UniProt-controlled vocabulary or the 'Cellular Component' branch of the Gene Ontology (www.geneontology.org), as well as an organism identifier from the National Center for Biotechnology Information taxonomy (https://www.ncbi.nlm.nih.gov/taxonomy). The UniProt website now uses SwissBioPics to visualize the subcellular locations and organelles where proteins function. SwissBioPics is freely available for anyone to use under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Database URL www.swissbiopics.org [ABSTRACT FROM AUTHOR]

UniProt Knowledgebase (UniProtKB) is a publicly available database with access to a vast amount of protein sequence and functional information. To widen the scope of the publications associated with a protein entry, UniProt has introduced the computationally mapped additional bibliography section, which includes literature collected from external sources. In this article, we describe a text mining system, eGenPub, which selects articles that are ‘about’ specific proteins and allows automatic identification of additional bibliography for given UniProt protein entries. Focusing on plant proteins initially, eGenPub utilizes a gene normalization tool called pGenN, and a trained support vector machine model, which achieves a precision of 95.3%, to predict whether an article, based on its abstract, should be linked to a given UniProt entry. We have conducted a full-scale PubMed processing using eGenPub for eight common plant species. Altogether, 9025 articles are identified as relevant bibliography for 4752 UniProt entries, among which 5252 are additional papers not in the existing publication section. These newly computationally mapped additional bibliography via eGenPub is being integrated in the UniProt production pipeline, and can be accessed via the UniProtKB protein entry publication view. [ABSTRACT FROM AUTHOR]