Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,367 gene families through orthologous and paralogous gene classification, whole-genome alignments, and synteny. Additional gene annotations include ontology-based protein structure and function; genetic, epigenetic, and phenotypic diversity; and pathway associations. Gramene's Plant Reactome provides a knowledgebase of cellular-level plant pathway networks. Specifically, it uses curated rice reference pathways to derive pathway projections for an additional 66 species based on gene orthology, and facilitates display of gene expression, gene–gene interactions, and user-defined omics data in the context of these pathways. As a community portal, Gramene integrates best-of-class software and infrastructure components including the Ensembl genome browser, Reactome pathway browser, and Expression Atlas widgets, and undergoes periodic data and software upgrades. Via powerful, intuitive search interfaces, users can easily query across various portals and interactively analyze search results by clicking on diverse features such as genomic context, highly augmented gene trees, gene expression anatomograms, associated pathways, and external informatics resources. All data in Gramene are accessible through both visual and programmatic interfaces.

In 2009, we started a project to support the teaching and learning of university-level plant sciences, called Teaching Tools in Plant Biology. Articles in this series are published by the plant science journal, The Plant Cell (published by the American Society of Plant Biologists). Five years on, we investigated how the published materials are being used through an analysis of the Google Analytics pageviews distribution and through a user survey. Our results suggest that this project has had a broad, global impact in supporting higher education, and also that the materials are used differently by individuals in terms of their role (instructor, independent learner, student) and geographical location. We also report on our ongoing efforts to develop a global learning community that encourages discussion and resource sharing.

In 2009, we started a project to support the teaching and learning of university-level plant sciences, called Teaching Tools in Plant Biology. Articles in this series are published by the plant science journal, The Plant Cell (published by the American Society of Plant Biologists). Five years on, we investigated how the published materials are being used through an analysis of the Google Analytics pageviews distribution and through a user survey. Our results suggest that this project has had a broad, global impact in supporting higher education, and also that the materials are used differently by individuals in terms of their role (instructor, independent learner, student) and geographical location. We also report on our ongoing efforts to develop a global learning community that encourages discussion and resource sharing.

With the increasing amount of digital journal submissions, there is a need to deploy new scalable computational methods to improve information accessibilities. One common task is to identify useful information and named entity from text documents such as journal article submission. However, there are many technical challenges to limit applicability of the general methods and lack of general tools. In this paper, we present domain informational vocabulary extraction (DIVE) project, which aims to enrich digital publications through detection of entity and key informational words and by adding additional annotations. In a first of its kind to our knowledge, our system engages authors of the peer-reviewed articles and the journal publishers by integrating DIVE implementation in the manuscript proofing and publication process. The system implements multiple strategies for biological entity detection, including using regular expression rules, ontology, and a keyword dictionary. These extracted entities are then stored in a database and made accessible through an interactive web application for curation and evaluation by authors. Through the web interface, the authors can make additional annotations and corrections to the current results. The updates can then be used to improve the entity detection in subsequent processed articles in the future. We describe our framework and deployment in details. In a pilot program, we have deployed the first phase of development as a service integrated with the journals Plant Physiology and The Plant cell published by the American Society of Plant Biologists (ASPB). We present usage statistics to date since its production on April 2018. We compare automated recognition results from DIVE with results from author curation and show the service achieved on average 80% recall and 70% precision per article. In contrast, an existing biological entity extraction tool, a biomedical named entity recognizer (ABNER), can only achieve 47% recall and return a much larger candidate set.