Gene summaries are textual paragraphs that describe gene function based on the current scientific knowledge. However, the task of manually writing these summaries by curators at biological databases does not scale to match the number of publications. We developed a method that automatically generates gene summaries based on structured data in a database. Our method is broadly applicable and has resulted in several thousand gene summaries for C. elegans and eight other nematode species, completely automating an important curation task. The presentation of gene data as human-readable text summaries fills a data-gap and may facilitate valuable insights into gene function.

Background In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. Development and Testing of the Ontology Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Results and Significance Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.

WormBase (www.wormbase.org) is a central repository for research data on the biology, genetics and genomics of Caenorhabditis elegans and other nematodes. The project has evolved from its original remit to collect and integrate all data for a single species, and now extends to numerous nematodes, ranging from evolutionary comparators of C. elegans to parasitic species that threaten plant, animal and human health. Research activity using C. elegans as a model system is as vibrant as ever, and we have created new tools for community curation in response to the ever-increasing volume and complexity of data. To better allow users to navigate their way through these data, we have made a number of improvements to our main website, including new tools for browsing genomic features and ontology annotations. Finally, we have developed a new portal for parasitic worm genomes. WormBase ParaSite (parasite.wormbase.org) contains all publicly available nematode and platyhelminth annotated genome sequences, and is designed specifically to support helminth genomic research.

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 F 1 -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/ .

Biological databases are a useful resource for parlaying existing data into new experimental hypotheses. Populating such databases with information from the published literature, however, can be labor intensive and thus is aided greatly by text mining tools capable of retrieving key facts from full text.The Textpresso information retrieval system (http://www.textpresso.org) is used by several Model Organism Database (MOD) communities to assist in curation [Müller HM, Kenny EE, Sternberg PW. 2004. PLoS Biol. Nov;2(11):e309; Van Auken KM, Fey P, Berardini TZ, et al. 2012. Database (Oxford). Nov 17;2012:bas040.] Textpresso searches the full text of literature using keywords and/or categories, groups of semantically related words, to identify sentences within a paper that describe specific aspects of biology.Using the anatomy ontology of oro‐pharyngeal structures developed in the FEED project [Druzinsky R, et al., in prep ], we are constructing a Textpresso site to aid in curation of the oro‐pharyngeal muscle literature. At this time, we have amassed a database of over 2000 searchable references. We will present a demonstration of Textpresso for Oro‐Pharyngeal Anatomy and show how it can be used to construct meaningful queries on the full text of the literature.

WormBase (http://www.wormbase.org/) is a highly curated resource dedicated to supporting research using the model organism Caenorhabditis elegans. With an electronic history predating the World Wide Web, WormBase contains information ranging from the sequence and phenotype of individual alleles to genome-wide studies generated using next-generation sequencing technologies. In recent years, we have expanded the contents to include data on additional nematodes of agricultural and medical significance, bringing the knowledge of C. elegans to bear on these systems and providing support for underserved research communities. Manual curation of the primary literature remains a central focus of the WormBase project, providing users with reliable, up-to-date and highly cross-linked information. In this update, we describe efforts to organize the original atomized and highly contextualized curated data into integrated syntheses of discrete biological topics. Next, we discuss our experiences coping with the vast increase in available genome sequences made possible through next-generation sequencing platforms. Finally, we describe some of the features and tools of the new WormBase Web site that help users better find and explore data of interest.

Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database (MOD) groups. Due to its manual nature, this task is time-consuming and labor-intensive, and thus considered one of the bottlenecks in literature curation. There have been many previous attempts of automatic identification of GO terms and associated information from full text. However, few systems have delivered an accuracy that is comparable to human annotators. One recognized challenge in developing such systems is the lack of marked passagelevel evidence text that provides the basis for making GO annotations. To this end, we aim to create a corpus that includes the GO evidence text along with the three essential elements of GO annotations: 1) a gene or gene product, 2) a GO term and 3) a GO evidence code. To ensure our results are consistent with real-life GO annotation data, we recruited a team of eight professional GO curators from the biocuration community, and asked them to follow their routine GO annotation protocols. With the aid of a web-based annotation tool, our annotators marked up

The rate of release of ammonia from leaf tissues of Glycine max and Triticum aestivum suspended in urea solutions was relatively low for initial periods of up to 40 min, due to retention of ammonia by the tissues. Urease, EC 3.5.1.5, could be assayed by the subsequent rate of ammonia release. The method was applied to leaves of 29 other species.