FlyBase 2.0: the next generation.

FlyBase (flybase.org) is a knowledge base that supports the community of researchers that use the fruit fly, Drosophila melanogaster, as a model organism. The FlyBase team curates and organizes a diverse array of genetic, molecular, genomic, and developmental information about Drosophila. At the beginning of 2018, 'FlyBase 2.0' was released with a significantly improved user interface and new tools. Among these important changes are a new organization of search results into interactive lists or tables (hitlists), enhanced reference lists, and new protein domain graphics. An important new data class called 'experimental tools' consolidates information on useful fly strains and other resources related to a specific gene, which significantly enhances the ability of the Drosophila researcher to design and carry out experiments. With the release of FlyBase 2.0, there has also been a restructuring of backend architecture and a continued development of application programming interfaces (APIs) for programmatic access to FlyBase data. In this review, we describe these major new features and functionalities of the FlyBase 2.0 site and how they support the use of Drosophila as a model organism for biological discovery and translational research.

FlyBase at 25: looking to the future.

Since 1992, FlyBase (flybase.org) has been an essential online resource for the Drosophila research community. Concentrating on the most extensively studied species, Drosophila melanogaster, FlyBase includes information on genes (molecular and genetic), transgenic constructs, phenotypes, genetic and physical interactions, and reagents such as stocks and cDNAs. Access to data is provided through a number of tools, reports, and bulk-data downloads. Looking to the future, FlyBase is expanding its focus to serve a broader scientific community. In this update, we describe new features, datasets, reagent collections, and data presentations that address this goal, including enhanced orthology data, Human Disease Model Reports, protein domain search and visualization, concise gene summaries, a portal for external resources, video tutorials and the FlyBase Community Advisory Group.

End-of-life care for people with chronic kidney disease: cause of death, place of death and hospital costs.

BACKGROUND: End-of-life care for people with chronic kidney disease (CKD) has been identified as an area of great clinical need internationally. We estimate causes and place of death and cost of hospital care for people with CKD in England in the final 3 years of life. METHODS: Hospital Episode Statistics data were linked to Office for National Statistics mortality data to identify all patients in England aged ≥18 years who died 1 April 2006-31 March 2010, and had a record of hospital care after 1 April 2003 (the study group). The underlying cause and place of death were examined in Office for National Statistics data, for patients without and with CKD (identified by International Classification of Diseases version 10 codes N18, I12 and I13). Costs of hospital admissions and outpatient attendances were estimated using National Health Service Reference Cost data. Associations between CKD and hospital costs, and between place of death and hospital costs in those with CKD, were examined using multivariate regressions. RESULTS: There were 1 602 105 people in the study group. Of these, 13.2% were recorded as having CKD. The proportion of deaths at home was 10.7% in people with CKD and 17.2% in the age- and gender-matched non-CKD group. Regression analysis suggests that CKD was associated with an increase in hospital costs of £3380 in the last 12 months of life, holding constant place of death, comorbidities and other variables. For the CKD group, home death was associated with a reduction in hospital costs of £2811 in the 12 months before death. The most commonly recorded cause of death in people with CKD was heart disease. CKD was not mentioned on the death certificate in two-thirds of deaths in people with the condition. CONCLUSIONS: People with CKD are less likely to die at home than those without CKD. The condition is associated with increased hospital costs at the end of life regardless of place of death. Home death in CKD is associated with a substantial reduction in hospital costs at the end of life.

Thinking ahead--the need for early Advance Care Planning for people on haemodialysis: A qualitative interview study.

BACKGROUND: There is a need to improve end-of-life care for people with end-stage kidney disease, particularly due to the increasingly elderly, frail and co-morbid end-stage kidney disease population. Timely, sensitive and individualised Advance Care Planning discussions are acceptable and beneficial for people with end-stage kidney disease and can help foster realistic hopes and goals. AIM: To explore the experiences of people with end-stage kidney disease regarding starting haemodialysis, its impact on quality of life and their preferences for future care and to explore the Advance Care Planning needs of this population and the timing of this support. STUDY DESIGN: Semi-structured qualitative interview study of people receiving haemodialysis. Interviews were analysed using thematic analysis. Recruitment ceased once data saturation was achieved. SETTING/PARTICIPANTS: A total of 20 patients at two UK National Health Service hospitals, purposively sampled by age, time on haemodialysis and symptom burden. RESULTS: Themes emerged around: Looking Back, emotions of commencing haemodialysis; Current Experiences, illness and treatment burdens; and Looking Ahead, facing the realities. Challenges throughout the trajectory included getting information, communicating with staff and the 'conveyor belt' culture of haemodialysis units. Participants reported a lack of opportunity to discuss their future, particularly if their health deteriorated, and variable involvement in treatment decisions. However, discussion of these sensitive issues was more acceptable to some than others. CONCLUSION: Renal patients have considerable unmet Advance Care Planning needs. There is a need to normalise discussions about preferences and priorities in renal and haemodialysis units earlier in the disease trajectory. However, an individualised approach is essential - one size does not fit all.

The economic impact of acute kidney injury in England.

BACKGROUND: Acute kidney injury (AKI) is one of the most common complications affecting hospital inpatients around the world. It is associated with high mortality and adverse long-term outcomes, but there is uncertainty regarding its prevalence and cost. We estimate the prevalence of AKI in hospital inpatients in a universal health-care system, and the immediate and long-term impacts on survival, quality of life and health-care costs. METHODS: We examined prevalence of AKI in inpatients using both routine national data for the National Health Service (NHS) in England, and laboratory data from East Kent Hospitals. We used regression analyses to estimate the impact of AKI on mortality and length of hospital stay, and a Markov model to estimate the impact on quality-adjusted life years and NHS costs. RESULTS: AKI was recorded in 2.43% of hospital admissions in Hospital Episode Statistics (HES), but age- and gender-standardized estimates derived from laboratory data suggest the true prevalence may be more than five times as high (14.15%). We estimate that the annual number of excess inpatient deaths associated with AKI in England may be above 40,000. The annual cost of AKI-related inpatient care in England is estimated at £1.02 billion, just over 1% of the NHS budget. The lifetime cost of post-discharge care for people who had AKI during hospital admission in 2010-11 is estimated at £179 million. CONCLUSIONS: AKI prevalence in inpatients may be considerably higher than previously thought, and up to four fifths of cases may not be captured in routine hospital data. AKI is associated with large numbers of in-hospital deaths and with high NHS costs. Comparison of HES and East Kent data suggests that most of the cases recorded in HES may be relatively severe AKI (AKIN 2-3).

The development and piloting of the REnal specific Advanced Communication Training (REACT) programme to improve Advance Care Planning for renal patients.

BACKGROUND: In recent years, the End-Stage Kidney Disease population has increased and is ever more frail, elderly and co-morbid. A care-focused approach needs to be incorporated alongside the disease focus, to identify those who are deteriorating and improve communication about preferences and future care. Yet many renal professionals feel unprepared for such discussions. AIM: To develop and pilot a REnal specific Advanced Communication Training (REACT) programme to address the needs of End-Stage Kidney Disease patients and renal professionals. DESIGN: Two-part study: (1) development of the REnal specific Advanced Communication Training programme informed by multi-professional focus group and patient survey and (2) piloting of the programme. SETTING/PARTICIPANTS: The REnal specific Advanced Communication Training programme was piloted with 16 participants (9 renal nurses/health-care assistants and 7 renal consultants) in two UK teaching hospitals. RESULTS: The focus group identified the need for better information about end-of-life phase, improved awareness of patient perspectives, skills to manage challenging discussions, 'hands on' practice in a safe environment and follow-up to discuss experiences. The patient survey demonstrated a need to improve communication about concerns, treatment plans and decisions. The developed REnal specific Advanced Communication Training programme was acceptable and feasible and was associated with a non-significant increase in confidence in communicating about end-of-life issues (pre-training: 6.6/10, 95% confidence interval: 5.7-7.4; post-training: 6.9/10, 95% confidence interval: 6.1-7.7, unpaired t-test - p = 0.56), maintained at 3 months. CONCLUSION: There is a need to improve end-of-life care for End-Stage Kidney Disease patients, to enable them to make informed decisions about future care. Challenges include prioritising communication training among service providers.

FlyBase: updates to the Drosophila genes and genomes database.

FlyBase (flybase.org) is a model organism database and knowledge base about Drosophila melanogaster, commonly known as the fruit fly. Researchers from around the world rely on the genetic, genomic, and functional information available in FlyBase, as well as its tools to view and interrogate these data. In this article, we describe the latest developments and updates to FlyBase. These include the introduction of single-cell RNA sequencing data, improved content and display of functional information, updated orthology pipelines, new chemical reports, and enhancements to our outreach resources.

Automatic categorization of diverse experimental information in the bioscience literature.

BACKGROUND: Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. RESULTS: We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. CONCLUSIONS: Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.

Estimating the financial cost of chronic kidney disease to the NHS in England.

BACKGROUND: Chronic kidney disease (CKD) is a major challenge for health care systems around the world, and the prevalence rates appear to be increasing. We estimate the costs of CKD in a universal health care system. METHODS: Economic modelling was used to estimate the annual cost of Stages 3-5 CKD to the National Health Service (NHS) in England, including CKD-related prescribing and care, renal replacement therapy (RRT), and excess strokes, myocardial infarctions (MIs) and Methicillin-Resistant Staphylococcus Aureus (MRSA) infections in people with CKD. RESULTS: The cost of CKD to the English NHS in 2009-10 is estimated at £ 1.44 to £ 1.45 billion, which is ≈ 1.3% of all NHS spending in that year. More than half this sum was spent on RRT, which was provided for 2% of the CKD population. The economic model estimates that ≈ 7000 excess strokes and 12 000 excess MIs occurred in the CKD population in 2009-10, relative to an age- and gender-matched population without CKD. The cost of excess strokes and MIs is estimated at £ 174-£ 178 million. CONCLUSIONS: The financial impact of CKD is large, with particularly high costs relating to RRT and cardiovascular complications. It is hoped that these detailed cost estimates will be useful in analysing the cost-effectiveness of treatments for CKD.

FlyBase: a guided tour of highlighted features.

FlyBase provides a centralized resource for the genetic and genomic data of Drosophila melanogaster. As FlyBase enters our fourth decade of service to the research community, we reflect on our unique aspects and look forward to our continued collaboration with the larger research and model organism communities. In this study, we emphasize the dedicated reports and tools we have constructed to meet the specialized needs of fly researchers but also to facilitate use by other research communities. We also highlight ways that we support the fly community, including an external resources page, help resources, and multiple avenues by which researchers can interact with FlyBase.

Exploring FlyBase Data Using QuickSearch.

FlyBase (flybase.org) is the primary online database of genetic, genomic, and functional information about Drosophila species, with a major focus on the model organism Drosophila melanogaster. The long and rich history of Drosophila research, combined with recent surges in genomic-scale and high-throughput technologies, mean that FlyBase now houses a huge quantity of data. Researchers need to be able to rapidly and intuitively query these data, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This unit describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2016 by John Wiley & Sons, Inc.

Gene Model Annotations for Drosophila melanogaster: The Rule-Benders.

In the context of the FlyBase annotated gene models in Drosophila melanogaster, we describe the many exceptional cases we have curated from the literature or identified in the course of FlyBase analysis. These range from atypical but common examples such as dicistronic and polycistronic transcripts, noncanonical splices, trans-spliced transcripts, noncanonical translation starts, and stop-codon readthroughs, to single exceptional cases such as ribosomal frameshifting and HAC1-type intron processing. In FlyBase, exceptional genes and transcripts are flagged with Sequence Ontology terms and/or standardized comments. Because some of the rule-benders create problems for handlers of high-throughput data, we discuss plans for flagging these cases in bulk data downloads.

Gene Model Annotations for Drosophila melanogaster: Impact of High-Throughput Data.

We report the current status of the FlyBase annotated gene set for Drosophila melanogaster and highlight improvements based on high-throughput data. The FlyBase annotated gene set consists entirely of manually annotated gene models, with the exception of some classes of small non-coding RNAs. All gene models have been reviewed using evidence from high-throughput datasets, primarily from the modENCODE project. These datasets include RNA-Seq coverage data, RNA-Seq junction data, transcription start site profiles, and translation stop-codon read-through predictions. New annotation guidelines were developed to take into account the use of the high-throughput data. We describe how this flood of new data was incorporated into thousands of new and revised annotations. FlyBase has adopted a philosophy of excluding low-confidence and low-frequency data from gene model annotations; we also do not attempt to represent all possible permutations for complex and modularly organized genes. This has allowed us to produce a high-confidence, manageable gene annotation dataset that is available at FlyBase (http://flybase.org). Interesting aspects of new annotations include new genes (coding, non-coding, and antisense), many genes with alternative transcripts with very long 3' UTRs (up to 15-18 kb), and a stunning mismatch in the number of male-specific genes (approximately 13% of all annotated gene models) vs. female-specific genes (less than 1%). The number of identified pseudogenes and mutations in the sequenced strain also increased significantly. We discuss remaining challenges, for instance, identification of functional small polypeptides and detection of alternative translation starts.

Revisiting the protein-coding gene catalog of Drosophila melanogaster using 12 fly genomes.

The availability of sequenced genomes from 12 Drosophila species has enabled the use of comparative genomics for the systematic discovery of functional elements conserved within this genus. We have developed quantitative metrics for the evolutionary signatures specific to protein-coding regions and applied them genome-wide, resulting in 1193 candidate new protein-coding exons in the D. melanogaster genome. We have reviewed these predictions by manual curation and validated a subset by directed cDNA screening and sequencing, revealing both new genes and new alternative splice forms of known genes. We also used these evolutionary signatures to evaluate existing gene annotations, resulting in the validation of 87% of genes lacking descriptive names and identifying 414 poorly conserved genes that are likely to be spurious predictions, noncoding, or species-specific genes. Furthermore, our methods suggest a variety of refinements to hundreds of existing gene models, such as modifications to translation start codons and exon splice boundaries. Finally, we performed directed genome-wide searches for unusual protein-coding structures, discovering 149 possible examples of stop codon readthrough, 125 new candidate ORFs of polycistronic mRNAs, and several candidate translational frameshifts. These results affect >10% of annotated fly genes and demonstrate the power of comparative genomics to enhance our understanding of genome organization, even in a model organism as intensively studied as Drosophila melanogaster.

Annotation of the Drosophila melanogaster euchromatic genome: a systematic review.

BACKGROUND: The recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences. RESULTS: Although the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes. CONCLUSIONS: Identification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.