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Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database groups. Owing to its manual nature, this task is considered one of the bottlenecks in literature curation. There have been many previous attempts at automatic identification of GO terms and supporting information from full text. However, few systems have delivered an accuracy that is comparable with humans. One recognized challenge in developing such systems is the lack of marked sentence-level evidence text that provides the basis for making GO annotations. We aim to create a corpus that includes the GO evidence text along with the three core elements of GO annotations: (i) a gene or gene product, (ii) a GO term and (iii) a GO evidence code. To ensure our results are consistent with real-life GO data, we recruited eight professional GO curators and asked them to follow their routine GO annotation protocols. Our annotators marked up more than 5000 text passages in 200 articles for 1356 distinct GO terms. For evidence sentence selection, the inter-annotator agreement (IAA) results are 9.3% (strict) and 42.7% (relaxed) in F1-measures. For GO term selection, the IAAs are 47% (strict) and 62.9% (hierarchical). Our corpus analysis further shows that abstracts contain ∼ 10% of relevant evidence sentences and 30% distinct GO terms, while the Results/Experiment section has nearly 60% relevant sentences and >70% GO terms. Further, of those evidence sentences found in abstracts, less than one-third contain enough experimental detail to fulfill the three core criteria of a GO annotation. This result demonstrates the need of using full-text articles for text mining GO annotations. Through its use at the BioCreative IV GO (BC4GO) task, we expect our corpus to become a valuable resource for the BioNLP research community. Database URL: http://www.biocreative.org/resources/corpora/bc-iv-go-task-corpus/., (Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.)

FlyBase (http://flybase.org) is the leading website and database of Drosophila genes and genomes. Whether you are using the fruit fly Drosophila melanogaster as an experimental system or wish to understand Drosophila biological knowledge in relation to human disease or to other model systems, FlyBase can help you successfully find the information you are looking for. Here, we demonstrate some of our more advanced searching systems and highlight some of our new tools for searching the wealth of data on FlyBase. The first section explores gene function in FlyBase, using our TermLink tool to search with Controlled Vocabulary terms and our new RNA-Seq Search tool to search gene expression. The second section of this article describes a few ways to search genomic data in FlyBase, using our BLAST server and the new implementation of GBrowse 2, as well as our new FeatureMapper tool. Finally, we move on to discuss our most powerful search tool, QueryBuilder, before describing pre-computed cuts of the data and how to query the database programmatically.

FlyBase is the model organism database for Drosophila genetic and genomic information. Over the last 20 years, FlyBase has had to adapt and change to keep abreast of advances in biology and database design. We are continually looking for ways to improve curation efficiency and efficacy. Genetic literature curation focuses on the extraction of genetic entities (e.g. genes, mutant alleles, transgenic constructs) and their associated phenotypes and Gene Ontology terms from the published literature. Over 2000 Drosophila research articles are now published every year. These articles are becoming ever more data-rich and there is a growing need for text mining to shoulder some of the burden of paper triage and data extraction. In this article, we describe our curation workflow, along with some of the problems and bottlenecks therein, and highlight the opportunities for text mining. We do so in the hope of encouraging the BioCreative community to help us to develop effective methods to mine this torrent of information. DATABASE URL: http://flybase.org

FlyBase (http://flybase.org) is the leading database and web portal for genetic and genomic information on the fruit fly Drosophila melanogaster and related fly species. Whether you use the fruit fly as an experimental system or want to apply Drosophila biological knowledge to another field of study, FlyBase can help you successfully navigate the wealth of available Drosophila data. Here, we review the FlyBase web site with novice and less-experienced users of FlyBase in mind and point out recent developments stemming from the availability of genome-wide data from the modENCODE project. The first section of this paper explains the organization of the web site and describes the report pages available on FlyBase, focusing on the most popular, the Gene Report. The next section introduces some of the search tools available on FlyBase, in particular, our heavily used and recently redesigned search tool QuickSearch, found on the FlyBase homepage. The final section concerns genomic data, including recent modENCODE (http://www.modencode.org) data, available through our Genome Browser, GBrowse.

FlyBase (http://flybase.org) is a database of Drosophila genetic and genomic information. Gene Ontology (GO) terms are used to describe three attributes of wild-type gene products: their molecular function, the biological processes in which they play a role, and their subcellular location. This article describes recent changes to the FlyBase GO annotation strategy that are improving the quality of the GO annotation data. Many of these changes stem from our participation in the GO Reference Genome Annotation Project--a multi-database collaboration producing comprehensive GO annotation sets for 12 diverse species.