FlyBase: enhancing Drosophila Gene Ontology annotations

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

BC4GO: a full-text corpus for the BioCreative IV GO task

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

Drosophila Neurotrophins Reveal a Common Mechanism for Nervous System Formation

Neurotrophic interactions occur in Drosophila, but to date, no neurotrophic factor had been found. Neurotrophins are the main vertebrate secreted signalling molecules that link nervous system structure and function: they regulate neuronal survival, targeting, synaptic plasticity, memory and cognition. We have identified a neurotrophic factor in flies, Drosophila Neurotrophin (DNT1), structurally related to all known neurotrophins and highly conserved in insects.By investigating with genetics the consequences of removing DNT1 or adding it in excess, we show that DNT1 maintains neuronal survival, as more neurons die in DNT1 mutants and expression of DNT1 rescues naturally occurring cell death, and it enables targeting by motor neurons. We show that Spa¨ tzle and a further fly neurotrophin superfamily member, DNT2, also have neurotrophic functions in flies. Our findings imply that most likely a neurotrophin was present in the common ancestor of all bilateral organisms, giving rise to invertebrate and vertebrate neurotrophins through gene or whole-genome duplications. This work provides a missing link between aspects of neuronal function in flies and vertebrates, and it opens the opportunity to use Drosophila to investigate further aspects of neurotrophin function and to model related diseases

A FAIR guide for data providers to maximise sharing of human genomic data

It is generally acknowledged that, for reproducibility and progress of human genomic research, data sharing is critical. For every sharing transaction, a successful data exchange is produced between a data consumer and a data provider. Providers of human genomic data (e.g., publicly or privately funded repositories and data archives) fulfil their social contract with data donors when their shareable data conforms to FAIR (findable, accessible, interoperable, reusable) principles. Based on our experiences via Repositive (https://repositive.io), a leading discovery platform cataloguing all shared human genomic datasets, we propose guidelines for data providers wishing to maximise their shared data’s FAIRness. Citation: Corpas M, Kovalevskaya NV, McMurray A, Niel

Ten simple rules for making training materials FAIR

Author summary: Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it’s sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They’re often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all

Expansion of the Gene Ontology knowledgebase and resources

The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.National Institutes of Health/National Human Genome Research Institute [HG002273] awarded to the PI group formed by (alphabetically) Judith A. Blake, J. Michael Cherry, Suzanna E. Lewis, Paul W. Sternberg and Paul D. Thomas, as well as additional funding awarded to each participating institution. For more details please visit: http://geneontology.org/page/go-consortium-contributors-list. Funding for open access charge: National Institutes of Health/National Human Genome Research Institute [HG002273]

Genome-wide fine-scale recombination rate variation in Drosophila melanogaster

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity

Ten simple rules for making training materials FAIR.

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all

The Australasian COVID-19 Trial (ASCOT) to assess clinical outcomes in hospitalised patients with SARS-CoV-2 infection (COVID-19) treated with lopinavir/ritonavir and/or hydroxychloroquine compared to standard of care: A structured summary of a study protocol for a randomised controlled trial

Objectives: To determine if lopinavir/ritonavir +/- hydroxychloroquine will reduce the proportion of participants who survive without requiring ventilatory support, 15 days after enrolment, in adult participants with non-critically ill SARS-CoV-2 infection. Trial design: ASCOT is an investigator-initiated, multi-centre, open-label, randomised controlled trial. Participants will have been hospitalised with confirmed COVID-19, and will be randomised 1:1:1:1 to receive lopinavir /ritonavir, hydroxychloroquine, both or neither drug in addition to standard of care management. Participants: Participants will be recruited from >80 hospitals across Australia and New Zealand, representing metropolitan and regional centres in both public and private sectors. Admitted patients will be eligible if aged ≥ 18 years, have confirmed SARS-CoV-2 by nucleic acid testing in the past 12 days and are expected to remain an inpatient for at least 48 hours from the time of randomisation. Potentially eligible participants will be excluded if admitted to intensive care or requiring high level respiratory support, are currently receiving study drugs or their use is contraindicated due to allergy, drug interaction or comorbidities (including baseline QTc prolongation of 470ms for women or 480ms for men), or death is anticipated imminently