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FlyBase (flybase.org) is an essential online database for researchers using Drosophila melanogaster as a model organism, facilitating access to a diverse array of information that includes genetic, molecular, genomic and reagent resources. Here, we describe the introduction of several new features at FlyBase, including Pathway Reports, paralog information, disease models based on orthology, customizable tables within reports and overview displays (‘ribbons’) of expression and disease data. We also describe a variety of recent important updates, including incorporation of a developmental proteome, upgrades to the GAL4 search tab, additional Experimental Tool Reports, migration to JBrowse for genome browsing and improvements to batch queries/downloads and the Fast-Track Your Paper tool.

UDP-glycosyltransferases (UGTs) are important conjugation enzymes found in all kingdoms of life, catalyzing a sugar conjugation with small lipophilic compounds and playing a crucial role in detoxification and homeostasis. The UGT gene family is defined by a signature motif in the C-terminal domain where the uridine diphosphate (UDP)-sugar donor binds. UGTs have been identified in a number of insect genomes over the last decade and much progress has been achieved in characterizing their expression patterns and molecular functions. Here, we present an update of the complete repertoire of UGT genes in Drosophila melanogaster and provide a brief overview of the latest research in this model insect. A total of 35 UGT genes are found in the D. melanogaster genome, localized to chromosomes 2 and 3 with a high degree of gene duplications on the chromosome arm 3R. All D. melanogaster UGT genes have now been named in FlyBase according to the unified UGT nomenclature guidelines. A phylogenetic analysis of UGT genes shows lineage-specific gene duplications. Analysis of anatomical and induced gene expression patterns demonstrate that some UGT genes are differentially expressed in various tissues or after environmental treatments. Extended searches of UGT orthologs from 18 additional Drosophila species reveal a diversity of UGT gene numbers and composition. The roles of Drosophila UGTs identified to date are briefly reviewed, and include xenobiotic metabolism, nicotine resistance, olfaction, cold tolerance, sclerotization, pigmentation, and immunity. Together, the updated genomic information and research overview provided herein will aid further research in this developing field.

Transposon annotation is a very dynamic field of genomics and various tools assigned to support this bioinformatics endeavor were reported. Genome ARTIST (GA) software was initially developed for mapping artificial transposons mobilized during insertional mutagenesis projects. Now, the new functions of GA_v2 qualify it as an effective companion for mapping and annotation of class II natural transposons in assembled genomes, contigs or sequencing reads.Tabular export of mapping and annotation data for subsequent high-throughput data analysis, the export of a list of flanking sequences around either the coordinates of insertion or around the target site duplications (TSDs) and generation of a consensus sequence for the respective flanking sequences are all key assets of GA_v2.Additionally, we developed two accompanying short scripts that enable the user to annotate transposons existent in assembled genomes and to use various annotation offered by FlyBase for Drosophila melanogaster genome.Herein, we present the applicability of GA_v2 for a preliminary annotation of the class II transposon P-element in the genome of D. melanogaster strain Horezu, Romania, which was sequenced with Nanopore technology in our laboratory. Our results point that GA_v2 is a reliable tool to be integrated in pipelines designed to perform transposon annotation in new sequenced genomes.GA_v2 is open source software compatible with Ubuntu, Mac OS and Windows and is available at https://github.com/genomeartist/genomeartist and at www.genomeartist.ro.

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.

Herein we describe the distribution of P{lacW} and P{EP} artificial transposons in the genome of Drosophila melanogaster. A total number of 5,560 P{lacW} and 3,786 P{EP} insertions available on FlyBase were extracted from this database and ordered according to the chromosomal regions they hit. Comparative bioinformatics analysis revealed that the insertion’s patterns are similar for P{lacW} and P{EP}. The two patterns are significantly correlated for chromosomes X, 2L, 2R and 3L, but not for 3R.Fourier analysis revealed a periodic behaviour in the distribution of insertions, which approximates insertional hotspots. Our results raise questions concerning if and how the local chromosomal landscape affects the insertion patterns of different but related transposons.

With arguably the best finished and expertly annotated genome assembly, Drosophila melanogaster is a formidable genetics model to study all aspects of biology. Nearly a decade ago, the 12 Drosophila genomes project expanded D. melanogaster’s breadth as a comparative model through the community-development of an unprecedented genus- and genome-wide comparative resource. However, since its inception, these datasets for evolutionary inference and biological discovery have become increasingly outdated, outmoded, and inaccessible. Here, we provide an updated and upgradable comparative genomics resource of Drosophila divergence and selection, flyDIVaS, based on the latest genomic assemblies, curated FlyBase annotations, and recent OrthoDB orthology calls. flyDIVaS is an online database containing D. melanogaster-centric orthologous gene sets, CDS and protein alignments, divergence statistics (% gaps, dN, dS, dN/dS), and codon-based tests of positive Darwinian selection. Out of 13,920 protein-coding D. melanogaster genes, ∼80% have one aligned ortholog in the closely related species, D. simulans, and ∼50% have 1–1 12-way alignments in the original 12 sequenced species that span over 80 million yr of divergence. Genes and their orthologs can be chosen from four different taxonomic datasets differing in phylogenetic depth and coverage density, and visualized via interactive alignments and phylogenetic trees. Users can also batch download entire comparative datasets. A functional survey finds conserved mitotic and neural genes, highly diverged immune and reproduction-related genes, more conspicuous signals of divergence across tissue-specific genes, and an enrichment of positive selection among highly diverged genes. flyDIVaS will be regularly updated and can be freely accessed at www.flydivas.info. We encourage researchers to regularly use this resource as a tool for biological inference and discovery, and in their classrooms to help train the next generation of biologists to creatively use such genomic big data resources in an integrative manner.

For more than 25 years, FlyBase ( flybase.org ) has served as an online database of biological information on the genus Drosophila, concentrating on the model organism D. melanogaster. Traditionally, FlyBase data have been organized and presented at a gene-by-gene level, which remains a useful perspective when the object of interest is a specific gene or gene product. However, in the modern era of a fully sequenced genome and an increasingly characterized proteome, it is often desirable to compile and analyze lists of genes related by a common function. This may be achieved in FlyBase by searching for genes annotated with relevant Gene Ontology (GO) terms and/or protein domain data. In addition, FlyBase provides preassembled lists of functionally related D. melanogaster genes within "Gene Group" reports. These are compiled manually from the published literature or expert databases and greatly facilitate access to, and analysis of, established gene sets. This chapter describes protocols to produce lists of functionally related genes in FlyBase using GO annotations, protein domain data and the Gene Groups resource, and provides guidance and advice for their further analysis and processing.

The Hymenoptera Genome Database (HGD; http://hymenopteragenome.org ) is a genome informatics resource for insects of the order Hymenoptera, which includes bees, ants and wasps. HGD provides genome browsers with manual annotation tools (JBrowse/Apollo), BLAST, bulk data download, and a data mining warehouse (HymenopteraMine). This chapter focuses on the use of HymenopteraMine to create annotation data sets that can be exported for use in downstream analyses. HymenopteraMine leverages the InterMine platform to combine genome assemblies and official gene sets with data from OrthoDB, RefSeq, FlyBase, Gene Ontology, UniProt, InterPro, KEGG, Reactome, dbSNP, PubMed, and BioGrid, as well as precomputed gene expression information based on publicly available RNAseq. Built-in template queries provide starting points for data exploration, while the QueryBuilder tool supports construction of complex custom queries. The List Analysis and Genomic Regions search tools execute queries based on uploaded lists of identifiers and genome coordinates, respectively. HymenopteraMine facilitates cross-species data mining based on orthology and supports meta-analyses by tracking identifiers across gene sets and genome assemblies.

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

The function of the nxf1 (Nuclear Export Factor 1) gene is the nuclear-cytoplasmic transport of most mRNAs. A characteristic feature of the nxf1 genes in animals belonging to different taxonomic groups is existence of an alternative transcript with a homologous intron referred to as a cassette intron. The following databases were used: GenBank (http://www.ncbi.nlm.nih.gov/); FlyBase (http://flybase.org/); and UCSC Genome (http://genome.ucsc.edu). To build secondary structures of nucleotide sequences we used a UNAFold v3.8 suite (http://mfold.rna.albany.edu/). The nxf1 cassette introns form characteristic secondary structures. The paper discusses the possible functional significance of the intron-retaining transcripts of the nxf1 genes.

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.

The 51st Annual Drosophila Research Conference took place in Washington, DC, from April 7–11, 2010. Washington, DC, the U.S. capital, is known for its national buildings and historically significant monuments. The Cherry Blossom festival, which was one of the major tourist attractions of the week, was at its peak – as can be seen on the program cover (Fig. 1). This year, the Genetics Society of America (GSA) received a record-breaking number of registrants to the conference. Despite low attendance at other scientific meetings, according to GSA Meetings Manager, Suzy Brown, this year's conference had the “largest number of registrants than any other previous years.” There were 170 talks, more than 850 posters and 13 workshops; so there was a range of information that people could pick according to their interests. Two members of the Drosophila community received GSA honors. Sherry Marts, GSA executive director, awarded Utpal Banerjee (University of California, Los Angeles) the Elizabeth W. Jones Award for Excellence in Education and William Gelbart (Harvard University, Cambridge) received the George W. Beadle Award for outstanding contributions to the community of genetics researchers. The winner of the Larry Sandler Memorial Lecture Award was Leonardo Barbosa Koerich, a student of Antonio Carvalho (Universidade Federal do Rio de Janeiro, Brazil). The topic of his dissertation was the low conservation of gene content of the Y chromosome across 12 species of Drosophila. He worked with 11 genes from the Drosophila melanogaster Y chromosome and located them on the other sequenced Drosophila species. His work was a very nice evolutionary genetic analysis revealing that the Y chromosome of Drosophila melanogaster is actually gaining more genes than losing. This goes against the canonical theory that the Y chromosome is a degenerated X-chromosome that will eventually become fully degenerated. Fig. 1 Cover page of the abstract book of the 51st Annual Drosophila Research Conference held at Washington DC from April 7–11, 2010. Courtesy: Genetics Society of America (GSA). Leonardo's seminar was followed by a panel presentation with a theme discussing the major achievements of renowned Drosophila scientists and their impact on the direction of Drosophila research. The panel was introduced by Hugo J. Bellen (Baylor College of Medicine, Houston) and was started by Thomas Kaufman (Indiana University, Bloomington), who talked about the discovery of the white (w) gene and how it led to future Drosophila genetics. Gerald M. Rubin (Janelia Farm, HHMI, Ashburn) discussed the cloning of w and the sequencing of the genome. Allan Spradling (Carnegie Institution for Science, Baltimore) talked about P-elements and their use in transformation, insertional mutagenesis, and the Genome Disruption Project. Susan Celniker (University of California, Berkley) gave a history of the genome project after the initial sequencing was completed and the development of more genomic tools, including the very high-throughput next generation sequencers. Norbert Perrimon (Harvard Medical School, Boston) discussed how the sequencing of the genome has impacted new tool development for Drosophila melanogaster. Finally, William Gelbart talked about annotation of the Drosophila genome then and now, and delivered a brief history of FlyBase. The First Plenary Session began with the award ceremony of the Annual Drosophila Image Award. Among the 50 images submitted for this award, only 10 were selected as finalists. Guy Blanchard (University of Cambridge, UK) won the award for an outstanding image demonstrating quantitative cell shape changes during gastrulation. Images from F. Coumailleau et al. (University of Geneva, Switzerland) showing directional transport of endocytosed Delta during asymmetric cell division and S. MacArthur et al. (Lawrence Berkeley National Laboratory, Berkeley) demonstrating quantitative differences in DNA occupancy by transcription factors that determine very different output expression patterns were runners-up for the image award. Duojia (DJ) Pan (Johns Hopkins University School of Medicine, Baltimore, MD) started off the first plenary session talks discussing his work in the control of organ size and tumorigenesis by the Hippo (Hpo) pathway. He showed how the Hpo kinase cascade regulates the gene cascade causing apoptosis and proliferation. Previous work from DJ's laboratory has demonstrated that the mammalian pathway is highly conserved and functions in mice as it does in Drosophila melanogaster. Over-expression of a downstream component of the Hpo pathway, YAP [the mammalian homolog of yorkie (yki)], in the mouse liver causes a reversible increase in liver size; but chronic over-expression leads to hepatocellular carcinoma. Kenneth D. Irvine (Rutgers, The State University of New Jersey, Piscataway) talked about his work on Fat (Ft) as a tumor suppressor and a regulator of planar cell polarity. Ft is a cadherin molecule and the Ft pathway has distinct mechanisms that regulate growth versus planar cell polarity. He showed that Ft is part of the Dachs (D), Dachsous (Ds), and Four-jointed (Fj) pathway. Fj and Ds are expressed in complementary gradients. Ds is a ligand that activates Ft, but also polarizes the cells. Clones of ds and fj have opposite effects on planar cell polarity, but have the same transcriptional effects. Chiara Cirelli (University of Wisconsin, Madison) discussed her work on sleep and synaptic plasticity. It is thought that sleep does one of two things in the CNS: enables synaptic plasticity or glycogen replenishment. The glycogen theory is not supported in all animals. Interestingly, fly sleep is very similar to mammalian sleep. In sleep-deprived flies, the expression of synaptic proteins is increased in whole brains and single neurons, giving support to the synaptic plasticity theory. Interestingly, similar changes were seen in period (per) mutants that have circadian rhythm problems. Ting Xie (Stowers Institute for Medical Research, Kansas City, MO) started off the next part of the first plenary session by talking about his contribution to the understanding of germline stem cells (GSC) in the Drosophila ovary. He highlighted the concept that GSCs have a collaborative and rival relationship with one another. For example, he identified some mutant GSCs that have a more competitive advantage over normal stem cells and actually push the wild-type stem cell out of its niche. These mutant cells express more E-cadherin than the wild-type GSC, so it is retained in the niche better. He also discussed his findings of how apparently routine “housekeeping” genes can also be specifically involved in the maintenance of GSCs. These general proteins, including eIF4A and CSN4, have GSC phenotypes, while other family members of these proteins do not have GSC phenotypes. Eric H. Baehrecke (University of Massachusetts Medical School, Worcester) discussed his work on macro-autophagy, which is considered a bulk, non-selective method of destruction. Autophagy is involved in the death of at least four different cell types in Drosophila. The model he uses is salivary gland cells, since at 12 hr after puparium formation they undergo autophagy. He demonstrated that caspases or autophagy genes are sufficient to kill salivary gland cells and they act in an additive manner. He found that some of the engulfment genes that were turned on in his microarray analysis were phagocytosis receptors, including draper (drpr). By expressing drpr RNAi in the salivary gland, autophagy is inhibited. However, drpr appears to have no role in autophagy in the blood or fat body (other sites of autophagy in Drosophila). Lyn Cooley (Yale School of Medicine, New Haven, CT) discussed her work on oogenesis. She found a reversible response to nutritional stress in the post-germarium to pre-vitellogenic stages of oogenesis. She performed an oocyte-enriched protein trap screen and found many RNA-binding proteins that localize to the oocyte through a microtubule-dependent mechanism. In the protein-poor condition (i.e., nutritional stress), many of these RNA-binding proteins start forming aggregates in the nurse cells, which might be due to microtubular condensation. This process is reversible; by giving the females food again, the aggregates disappear. She found that this nutrition response works through insulin signaling: reduced egg chamber production in poor food conditions operates in an insulin-dependent manner. Apart from the excellent first plenary session, the conference was composed of multiple platform and poster sessions on many topics. Since we are not able to cover the broad spectrum of topics presented in this meeting, we present some highlights in this report.

FlyTF (http://www.flytf.org) is a database of computationally predicted and/or experimentally verified site-specific transcription factors (TFs) in the fruit fly Drosophila melanogaster. The manual classification of TFs in the initial version of FlyTF that concentrated primarily on the DNA-binding characteristics of the proteins has now been extended to a more fine-grained annotation of both DNA binding and regulatory properties in the new release. Furthermore, experimental evidence from the literature was classified into a defined vocabulary, and in collaboration with FlyBase, translated into Gene Ontology (GO) annotation. While our GO annotations will also be available through FlyBase as they will be incorporated into the genes' official GO annotation in the future, the entire evidence used for classification including computational predictions and quotes from the literature can be accessed through FlyTF. The FlyTF website now builds upon the InterMine framework, which provides experimental and computational biologists with powerful search and filter functionality, list management tools and access to genomic information associated with the TFs.

Methods: A new method was developed for identifying novel transcription factor regulatory targets based on calculating Local Affinity Density. Techniques from the signal-processing field were used, in particular the Hann digital filter, to calculate the relative binding affinity of different regions based on previously published in vitro binding data. To illustrate this approach, the complete genomes of Drosophila melanogaster and D.pseudoobscura were analyzed for binding sites of the homeodomain proteinc Tinman, an essential heart development gene in both Drosophila and Mouse. The significant binding regions were identified relative to genomic background and assigned to putative target genes. Valid candidates common to both species of Drosophila were selected as a test of conservation. Results: The new method was more sensitive than cluster searches for conserved binding motifs with respect to positive identification of known Tinman targets. Our Local Affinity Density method also identified a significantly greater proportion of Tinman-coexpressed genes than equivalent, optimized cluster searching. In addition, this new method predicted a significantly greater than expected number of genes with previously published RNAi phenotypes in the heart. Availability: Algorithms were implemented in Python, LISP, R and maxima, using MySQL to access locally mirrored sequence data from Ensembl (D.melanogaster release 4.3) and flybase (D.pseudoobscura). All code is licensed under GPL and freely available at http://www.ohsu.edu/cellbio/dev_biol_prog/affinitydensity/. Contact: hazelett@ohsu.edu

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.

For nearly 25 years, FlyBase (flybase.org) has provided a freely available online database of biological information about Drosophila species, focusing on the model organism D. melanogaster. The need for a centralized, integrated view of Drosophila research has never been greater as advances in genomic, proteomic, and high-throughput technologies add to the quantity and diversity of available data and resources.FlyBase has taken several approaches to respond to these changes in the research landscape. Novel report pages have been generated for new reagent types and physical interaction data; Drosophila models of human disease are now represented and showcased in dedicated Human Disease Model Reports; other integrated reports have been established that bring together related genes, datasets, or reagents; Gene Reports have been revised to improve access to new data types and to highlight functional data; links to external sites have been organized and expanded; and new tools have been developed to display and interrogate all these data, including improved batch processing and bulk file availability. In addition, several new community initiatives have served to enhance interactions between researchers and FlyBase, resulting in direct user contributions and improved feedback.This chapter provides an overview of the data content, organization, and available tools within FlyBase, focusing on recent improvements. We hope it serves as a guide for our diverse user base, enabling efficient and effective exploration of the database and thereby accelerating research discoveries.

The Biological General Repository for Interaction Datasets (BioGRID) database (http://www.thebiogrid.org) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction.

Background A critical topic of insertional mutagenesis experiments performed on model organisms is mapping the hits of artificial transposons (ATs) at nucleotide level accuracy. Mapping errors may occur when sequencing artifacts or mutations as single nucleotide polymorphisms (SNPs) and small indels are present very close to the junction between a genomic sequence and a transposon inverted repeat (TIR). Another particular item of insertional mutagenesis is mapping of the transposon self-insertions and, to our best knowledge, there is no publicly available mapping tool designed to analyze such molecular events. Results We developed Genome ARTIST, a pairwise gapped aligner tool which works out both issues by means of an original, robust mapping strategy. Genome ARTIST is not designed to use next-generation sequencing (NGS) data but to analyze ATs insertions obtained in small to medium-scale mutagenesis experiments. Genome ARTIST employs a heuristic approach to find DNA sequence similarities and harnesses a multi-step implementation of a Smith-Waterman adapted algorithm to compute the mapping alignments. The experience is enhanced by easily customizable parameters and a user-friendly interface that describes the genomic landscape surrounding the insertion. Genome ARTIST is functional with many genomes of bacteria and eukaryotes available in Ensembl and GenBank repositories. Our tool specifically harnesses the sequence annotation data provided by FlyBase for Drosophila melanogaster (the fruit fly), which enables mapping of insertions relative to various genomic features such as natural transposons. Genome ARTIST was tested against other alignment tools using relevant query sequences derived from the D. melanogaster and Mus musculus (mouse) genomes. Real and simulated query sequences were also comparatively inquired, revealing that Genome ARTIST is a very robust solution for mapping transposon insertions. Conclusions Genome ARTIST is a stand-alone user-friendly application, designed for high-accuracy mapping of transposon insertions and self-insertions. The tool is also useful for routine aligning assessments like detection of SNPs or checking the specificity of primers and probes. Genome ARTIST is an open source software and is available for download at www.genomeartist.ro and at GitHub (https://github.com/genomeartist/genomeartist ). Electronic supplementary material The online version of this article (doi:10.1186/s13100-016-0061-0) contains supplementary material, which is available to authorized users.

FlyBase (http://flybase.org/) is a database of genetic and genomic data on the model organism Drosophila melanogaster and the entire insect family Drosophilidae. The FlyBase Consortium curates, annotates, integrates and maintains a wide variety of data within this domain. Access to the data is provided through graphical and textual user interfaces tailored to particular types of data. FlyBase data types include maps at the cytological, genetic and sequence levels, genes and alleles including their products, functions, expression patterns, mutant phenotypes and genetic interactions as well as aberrant chromosomes, annotated genomes, genetic stock collections, transposons, transgene constructs and insertions, anatomy and images, bibliographic data, and community contact information.

FlyBase (http://flybase.org) is the primary repository of genetic and molecular data of the insect family Drosophilidae. For the most extensively studied species, Drosophila melanogaster, a wide range of data are presented in integrated formats. Data types include mutant phenotypes, molecular characterization of mutant alleles and aberrations, cytological maps, wild-type expression patterns, anatomical images, transgenic constructs and insertions, sequence-level gene models and molecular classification of gene product functions. There is a growing body of data for other Drosophila species; this is expected to increase dramatically over the next year, with the completion of draft-quality genomic sequences of an additional 11 Drosophila species.

The functional classification of genes on a genome-wide scale is now in its infancy, and we make a first attempt to assess existing methods and identify sources of error. To this end, we compared two independent efforts for associating proteins with functions, one implemented by FlyBase and the other by PANTHER at Celera Genomics. Both methods make inferences based on sequence similarity and the available experimental evidence. However, they differ considerably in methodology and process. Overall, assuming that the systematic error across the two methods is relatively small, we find the protein-to-function association error rate of both the FlyBase and PANTHER methods to be

The Gene Ontology Consortium (Gene Ontology Consortium 2000) provides a standardized vocabulary for the annotation of gene attributes, which fall into the three general categories of molecular function, biological process, and cellular component. Organism-specific databases such as FlyBase (FlyBase Consortium 2002), Saccharomyces Genome Database (SGD; Cherry et al. 1998), Mouse Genome Database (MGD; Blake et al. 2002), and WormBase (Stein et al. 2001), have codeveloped this vocabulary, and have used it to annotate genes with the attributes that the biomedical literature asserts that they hold. These databases are incomplete because there are genes whose attributes are not yet all known, and because there is literature that has not yet been digested by the database curators. In such cases it is useful to have a prediction of whether a gene has a certain attribute. Such predictions can help to make the databases more complete (and consequently more useful to researchers) by directing curators toward literature that they may have overlooked. Also, predictions that are not presently supported by the literature provide new hypotheses that may be tested experimentally. A variety of approaches for predicting Gene Ontology (GO) attributes have been attempted. Natural language processing was used in Raychaudhuri et al. (2002) to automate the curator's task of extracting gene–attribute associations from literature abstracts. Others have assigned attributes to genes on the basis of microarray data (Hvidsten et al. 2001) or protein folds (Schug et al. 2002). These approaches are especially valuable for assigning attributes to genes with otherwise unknown function. But once some attributes of a gene are known, statistical patterns among the annotations themselves can be useful for predicting additional attributes. In this paper, we model the probabilistic relationships between the GO annotations using two approaches, one based on decision trees and the other based on Bayesian networks. We assess the models using cross-validation on the SGD and FlyBase databases. We also manually assess 100 of those gene–attribute associations that the models indicate are likely to hold but that have not been annotated in the databases.

Release 6, the latest reference genome assembly of the fruit fly Drosophila melanogaster, was released by the Berkeley Drosophila Genome Project in 2014; it replaces their previous Release 5 genome assembly, which had been the reference genome assembly for over 7 years. With the enormous amount of information now attached to the D. melanogaster genome in public repositories and individual laboratories, the replacement of the previous assembly by the new one is a major event requiring careful migration of annotations and genome-anchored data to the new, improved assembly. In this report, we describe the attributes of the new Release 6 reference genome assembly, the migration of FlyBase genome annotations to this new assembly, how genome features on this new assembly can be viewed in FlyBase (http://flybase.org) and how users can convert coordinates for their own data to the corresponding Release 6 coordinates.

With the advancement of new high throughput sequencing technologies, there has been an increase in the number of genome sequencing projects worldwide, which has yielded complete genome sequences of human, animals and plants. Subsequently, several labs have focused on genome annotation, consisting of assigning functions to gene products, mostly using Gene Ontology (GO) terms. As a consequence, there is an increased heterogeneity in annotations across genomes due to different approaches used by different pipelines to infer these annotations and also due to the nature of the GO structure itself. This makes a curator's task difficult, even if they adhere to the established guidelines for assessing these protein annotations. Here we develop a genome-scale approach for integrating GO annotations from different pipelines using semantic similarity measures. We used this approach to identify inconsistencies and similarities in functional annotations between orthologs of human and Drosophila melanogaster, to assess the quality of GO annotations derived from InterPro2GO mappings compared to manually annotated GO annotations for the Drosophila melanogaster proteome from a FlyBase dataset and human, and to filter GO annotation data for these proteomes. Results obtained indicate that an efficient integration of GO annotations eliminates redundancy up to 27.08 and 22.32% in the Drosophila melanogaster and human GO annotation datasets, respectively. Furthermore, we identified lack of and missing annotations for some orthologs, and annotation mismatches between InterPro2GO and manual pipelines in these two proteomes, thus requiring further curation. This simplifies and facilitates tasks of curators in assessing protein annotations, reduces redundancy and eliminates inconsistencies in large annotation datasets for ease of comparative functional genomics.

Phenotypic analysis combined with molecular genetics is a powerful tool for mapping gene function onto the genome. Phenotypic data are, by their nature, descriptive, and as varied as the range of mutant phenotypes that can be presented by the organism under study. This paper discusses the mechanisms FlyBase has implemented to systematise published phenotypic data about Drosophila, and provides an introduction to the query tools available for the mining of the data. Though FlyBase is specific to Drosophila, the issues faced in devising protocols for capturing, storing and reporting data are the same issues faced by any database with an interest in using phenotypic data to maximise the potential of genomic analysis.

Flytrap is a web-enabled relational database of transposable element insertions in Drosophila melanogaster. A green fluorescent protein (GFP) artificial exon carried by a transposable P-element is mobilized and inserted into a host gene intron creating a GFP fusion protein. The sequence of the tagged gene is determined by sequencing inverse-PCR products derived from genomic DNA. Flytrap contains two principle data types: micrographs of protein localization and a cellular component ontology, based on rules derived from the Gene Ontology consortium (http://www.geneontology.org), describing protein localization. Flytrap also has links to gene information contained in Flybase (http:// flybase.bio.indiana.edu). The system is designed to accept submissions of micrographs and descriptions from any type of tissue (e.g. wing imaginal disk, ovary) and at any stage of development. Insertion lines can be searched using a number of queries, including Berkeley Drosophila Genome Project (BDGP) numbers and protein localization. In addition, Flytrap provides online order forms linked to each insertion line so that users may request any line generated from this project. Flytrap may be accessed from the homepage at http://flytrap.med. yale.edu.

Mitochondria are organelles present in the cytoplasm of most eukaryotic cells; although they have their own DNA, the majority of the proteins necessary for a functional mitochondrion are coded by the nuclear DNA and only after transcription and translation they are imported in the mitochondrion as proteins. The primary role of the mitochondrion is electron transport and oxidative phosphorylation. Although it has been studied for a long time, the interest of researchers in mitochondria is still alive thanks to the discovery of mitochondrial role in apoptosis, aging and cancer. Aim of the MitoDrome database is to annotate the Drosophila melanogaster nuclear genes coding for mitochondrial proteins in order to contribute to the functional characterization of nuclear genes coding for mitochondrial proteins and to knowledge of gene diseases related to mitochondrial dysfunctions. Indeed D. melanogaster is one of the most studied organisms and a model for the Human genome. Data are derived from the comparison of Human mitochondrial proteins versus the Drosophila genome, ESTs and cDNA sequence data available in the FlyBase database. Links from the MitoDrome entries to the related homologous entries available in MitoNuC will be soon imple-mented. The MitoDrome database is available at http://bighost.area.ba.cnr.it/BIG/MitoDrome. Data are organised in a flat-file format and can be retrieved using the SRS system.

RNA-Seq is a powerful tool for the annotation of genomes, in particular for the identification of isoforms and UTRs. Nevertheless, several software tools exist and no standard strategy to obtain a reliable annotation is yet established. We tested different combinations of the most commonly used reference-based alignment tools (TopHat, GSNAP) in combination with two frequently used reference-based assemblers (Cufflinks, Scripture) and evaluated the potential of RNA-Seq to improve the annotation of Drosophila pseudoobscura. While GSNAP maps a higher proportion of reads, TopHat resulted in a more accurate annotation when used in combination with Cufflinks. Scripture had the lowest sensitivity. Interestingly, after subsampling to the same coverage for GSNAP and TopHat, we find that both mappers have similar performance, implying that the advantage of TopHat is mainly an artifact of the lower coverage. Overall, we observed a low concordance among the different approaches tested both at junction and isoform levels. Using data from both sexes of two adult strains of D. pseudoobscura we detected alternative splicing for about 30% of the FlyBase multiple-exon genes. Moreover, we extended the boundaries for 6523 genes (about 40%). We annotated 669 new genes, 45% of them with splicing evidence. Most of the new genes are located on unassembled contigs, reflecting their incomplete annotation. Finally, we identified 99 additional new genes that are not represented in the current genome contigs of D. pseudoobscura, probably due to location in genomic regions that are difficult to assemble (e.g. heterochromatic regions).

Background The enormous potential of natural variation for the functional characterization of genes has been neglected for a long time. Only since recently, functional geneticists are starting to account for natural variation in their analyses. With the new sequencing technologies it has become feasible to collect sequence information for multiple individuals on a genomic scale. In particular sequencing pooled DNA samples has been shown to provide a cost-effective approach for characterizing variation in natural populations. While a range of software tools have been developed for mapping these reads onto a reference genome and extracting SNPs, linking this information to population genetic estimators and functional information still poses a major challenge to many researchers. Results We developed PoPoolation DB a user-friendly integrated database. Popoolation DB links variation in natural populations with functional information, allowing a wide range of researchers to take advantage of population genetic data. PoPoolation DB provides the user with population genetic parameters (Watterson's θ or Tajima's π), Tajima's D, SNPs, allele frequencies and indels in regions of interest. The database can be queried by gene name, chromosomal position, or a user-provided query sequence or GTF file. We anticipate that PoPoolation DB will be a highly versatile tool for functional geneticists as well as evolutionary biologists. Conclusions PoPoolation DB, available at http://www.popoolation.at/pgt, provides an integrated platform for researchers to investigate natural polymorphism and associated functional annotations from UCSC and Flybase genome browsers, population genetic estimators and RNA-seq information.

Genomic annotations describing structural and functional features of genes and gene products through controlled terminologies and ontologies are extremely valuable, especially for computational analyses aimed at inferring new biomedical knowledge, which rely on available annotations. Yet, they are incomplete, especially for recently studied genomes, and only some of available annotations represent highly reliable human curated information. In order to help and speedup the time-consuming curation process and improve available annotations, computational methods able to provide prioritized lists of predicted annotations are paramount. Starting from a previous work on automatic prediction of Gene Ontology annotations based on singular value decomposition (SVD) of gene-to-term annotation matrix, here we propose a novel prediction algorithm that incorporates gene clustering based on gene functional similarity computed on Gene Ontology annotations. We tested both prediction methods performing k-fold cross-validation on two organism genomes, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase). Results demonstrate effectiveness of our approach.

We propose an algorithm that predicts potentially missing Gene Ontology annotations, in order to speed up the time-consuming annotation curation process. The proposed method extends a previous work based on the singular value decomposition of the gene-term annotation matrix and incorporates gene clustering, based on gene functional similarity computed by means of the Gene Ontology annotations. We tested the prediction method by performing K-fold cross-validation on the genomes of two organisms, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase).

Background The availability of 12 fully sequenced Drosophila species genomes provides an excellent opportunity to explore the evolutionary mechanism, structure and function of gene families in Drosophila. Currently, several important resources, such as FlyBase, FlyMine and DroSpeGe, have been devoted to integrating genetic, genomic, and functional data of Drosophila into a well-organized form. However, all of these resources are gene-centric and lack the information of the gene families in Drosophila. Description FlyPhy is a comprehensive phylogenomic analysis platform devoted to analyzing the genes and gene families in Drosophila. Genes were classified into families using a graph-based Markov Clustering algorithm and extensively annotated by a number of bioinformatic tools, such as basic sequence features, functional category, gene ontology terms, domain organization and sequence homolog to other databases. FlyPhy provides a simple and user-friendly web interface to allow users to browse and retrieve the information at multiple levels. An outstanding feature of the FlyPhy is that all the retrieved results can be added to a workset for further data manipulation. For the data stored in the workset, multiple sequence alignment, phylogenetic tree construction and visualization can be easily performed to investigate the sequence variation of each given family and to explore its evolutionary mechanism. Conclusion With the above functionalities, FlyPhy will be a useful resource and convenient platform for the Drosophila research community. The FlyPhy is available at http://bioinformatics.zj.cn/fly/.

FlyBase ( http://flybase.org ) is the primary database of integrated genetic and genomic data about the Drosophilidae, of which Drosophila melanogaster is the most extensively studied species. Information in FlyBase originates from a variety of sources ranging from large-scale genome projects to the primary research literature. Data-types include sequence-level gene models, molecular classification of gene product functions, mutant phenotypes, mutant lesions and chromosome aberrations, gene expression patterns, transgene insertions, and anatomical images. Query tools allow interrogation of FlyBase through DNA or protein sequence, by gene or mutant name, or through terms from the several ontologies used to capture functional, phenotypic, and anatomical data. Links between FlyBase and external databases provide extensive opportunity for extending exploration into other model organism databases and resources of biological and molecular information. This review will introduce the FlyBase web server and query tools.

We proposed a new measure (SimPLD) for calculating the semantic similarity of terms in gene ontology (GO) based on the depth of least common ancestor (LCA) of two terms and the path length between them in GO hierarchy. The similarity between genes is computed based on this measure when it is applied to the GO-terms related to those genes. The method is based on the average of SimPLD between the GO terms annotated for both genes in a given gene pair. We evaluated the proposed method with a series of experiments on large groups of genes and proteins from two genomes: Saccharomyces database (SGD) and Drosophila Melanogaster (FlyBase); and one dataset of Human-Yeast protein pairs. The experimental results proved that the method has fairly impressive agreement with Blast sequence similarity. Therefore SimPLD can be used as an automated tool for determining the similarity between genes and proteins.

AphidBase aims to (i) store recently acquired genomic resources on aphids and (ii) compare them to other insect resources as functional annotation tools. For that, the Drosophila melanogaster genome has been loaded in the database using the GMOD open source software for a comparison with the 17 069 pea aphid unique transcripts (contigs) and the 13 639 gene transcripts of the Anopheles gambiae. Links to FlyBase and A.gambiae Entrez databases allow a rapid characterization of the putative functions of the aphid sequences. Text mining of the D.melanogaster literature was performed to construct a network of co-cited gene or protein names, which should facilitate functional annotation of aphid homolog sequences. AphidBase represents one of the first genomic databases for a hemipteran insect. Availability: http://w3.rennes.inra.fr/AphidBase Contact: denis.tagu@rennes.inra.fr, jean-pierre.gauthier@rennes.infra.fr

FlyBase (http://flybase.org/) is the primary database of genetic and genomic data for the insect family Drosophilidae. Historically, Drosophila melanogaster has been the most extensively studied species in this family, but recent determination of the genomic sequences of an additional 11 Drosophila species opens up new avenues of research for other Drosophila species. This extensive sequence resource, encompassing species with well-defined phylogenetic relationships, provides a model system for comparative genomic analyses. FlyBase has developed tools to facilitate access to and navigation through this invaluable new data collection.

Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature. We present a two-stage summarization method, which involves first retrieving relevant articles and then extracting the most informative sentences from the retrieved articles to generate a structured gene summary. The generated summary explicitly covers multiple aspects of a gene, such as the sequence information, mutant phenotypes, and molecular interaction with other genes. We propose several heuristic approaches to improve the accuracy in both stages. The proposed methods are evaluated using 10 randomly chosen genes from FlyBase and a subset of Medline abstracts about Drosophila. The results show that the precision of the top selected sentences in the 6 aspects is typically about 50-70%, and the generated summaries are quite informative, indicating that our approaches are effective in automatically summarizing literature information about genes. The generated summaries not only are directly useful to biologists but also serve as useful entry points to enable them to quickly digest the retrieved literature articles.

In this small and charming book, Won for All, Michael Ashburner gives us a glittering account of the sequencing of the Drosophila genome by a public–private partnership between government-funded laboratories and Celera Genomics. He portrays both the working life and the good life of science, with neat character sketches set off by Lewis Miller's excellent portraits. Michael's flair for detail and inveterate name-dropping, albeit of restaurants rather than people, lends itself nicely to re-creating the time and place of key events in this collaboration. The original fast-paced manuscript, which I liked so well when I first saw a draft in 2001, has been updated and provided with extensive footnotes that inform without interrupting the narrative. Technical background is given in two excellent postscripts: a fly primer from Scott Hawley, and an overview of fly functional genomics from Ethan Bier. Michael and I have been orbiting around Cambridge (the original one, as he puts it) for many years. Our paths intersect occasionally and pleasantly, but never so forcibly as over genomics—in keeping with Maurice Wilkins' remark to Horace Judson that: “DNA, you know, is Midas' gold. Everyone who touches it goes mad” [ 1]. It has indeed proved so in the age of sequencing genomes. One of the emergent themes of the book is competition between animal model organisms, which was intense from the 1960s to the 1990s. Flies, worms, molluscs, mice, and men: each had its own set of dedicated followers, and each had strengths and weaknesses in the common goal of understanding the biology of animals and humans in particular. At a slightly greater distance, there was competition with micro-organisms and plants for understanding life in general. Compared with the nematode worm, on which I worked, the fly was the doyen, with half a century of intensive work behind it, 10-fold more researchers, a rich morphology of wings and legs and bristles, and a huge range of sophisticated genetic techniques. The worm was a shy newcomer, kicked into excitement by Sydney Brenner, who chose it for its small size and rapid growth. Never mind the fly's sophistication; for those of us who came early the worm was a sandpit to play in: anything we did was novel because so little had been done before. With the advent of molecular cloning in the 1970s, developmental biology entered a new and thrilling phase. No longer were experiments scored only by visible outcomes; now the aim was to isolate and study the genes controlling these tangible phenomena. There were triumphs: for example, homeobox genes controlling development in the fly, and cell-death genes found in the worm. And now the fly displayed anew one of its long-known attributes: the polytene chromosomes in the salivary glands. Composed of multiple aligned copies of the fly's DNA, they provide an extraordinary pocket map of its genome, gratis. The polytenes were so advantageous to gene hunters that attempts were made to find a genetic modification of the worm that would produce something comparable, to no avail. However, this initial disadvantage soon turned to the advantage of the worm researchers, because we were forced to construct our own physical map from cloned fragments of worm DNA. Clumsy and incomplete at first, the map grew into a full coverage of the genome. We arrayed our clones on membranes and proudly announced our artificial polytenes. And the arrays were more than polytenes: not only a map, but actual fragments that could be shipped around the world and used directly in people's experiments. More was to come. The physical map allowed the worm an early start on genome sequencing, long before automation was sufficiently advanced to tackle the whole thing. Our sequence was publicly released as we went along, and soon, as researchers isolated individual genes from human and other organisms, they more often found a match to worm than anything else. People were drawn to the worm, and it prospered, increasing pressure on the fly community to start their own genome project. I had nothing to do with the fly scientifically, but its genome made a big impact on me through the Human Genome Project. Michael disarmingly suggests that it's a mystery why Celera would sequence the fly for no financial return. The reason, of course, was that it provided a doable and very public advertisement for the human genome sequence, which was Celera's real business. It meant that Gerry Rubin, as leader of the fly sequencing project, could be persuaded to speak in support of the company at US congressional hearings that discussed appropriations for the public sequencing effort. Celera's fly sequence was indeed “won for all,” but for the human it would be a different story, as those who searched for public–private partnership discovered in December 1999 [ 2]. And where human sequence went, there went everything. Had the human genome sequencing project gone private, the public databases, which are one of the most important foundations of modern biology, would have withered and been replaced by a corporate entity under the control not of its users but of its shareholders. Gerry was the fly hero, doing what he had to do to get his organism sequenced. But despite Scott Hawley's opening words, Michael is a hero for us all. Celera initially posted the fly sequence with copyright restrictions, against all previous agreements, and it was Michael who ensured that the restrictions were removed. Later, he was prominent in supporting free release of the human sequence. Today, in his development of FlyBase and Gene Ontology, and in his championing of open-access publication, he continues as a giant of public-domain science. The marvellous outcome is that the worm, the fly, the human, and hundreds of other genomes have all been won for all. The rivalries have been transformed into a new wholeness of biology, united through its common history written in the language of the genomes.

Organisms of vastly differing morphologies, ecologies, and behaviors—such as fruit flies and humans—are now known to share a multitude of molecular, cellular, and developmental processes. Not only is there extensive similarity in the sequences of fly and human genes, but in addition, almost all of the proteins and major signal transduction pathways that control cell division and differentiation in mammals are also found in the fruitfly Drosophila melanogaster (Rubin et al. 2000; http://flybase.bio.indiana.edu/). Components in these pathways perform the same biochemical functions and act in the same order in both fruitfly and mammalian cells.

The sequence and genome annotations of Drosophila melanogaster were initially published in late 1999 and early 2000. Since then, the Berkeley Drosophila Genome Project (BDGP) and FlyBase have improved the quality of the sequence and reviewed the annotations by hand, respectively, to produce an account of the fruit fly genome that is of the highest quality. This review discusses the main features of this process, both from the point of view of the biology revealed in the end result and in the development of software that has been central to this genome sequencing and annotation project.

To build a foundation for the complete genome analysis of Bombyx mori , we have constructed an EST database. Because gene expression patterns deeply depend on tissues as well as developmental stages, we analyzed many cDNA libraries prepared from various tissues and different developmental stages to cover the entire set of Bombyx genes. So far, the Bombyx EST database contains 35,000 ESTs from 36 cDNA libraries, which are grouped into ≈11,000 nonredundant ESTs with the average length of 1.25 kb. The comparison with FlyBase suggests that the present EST database, SilkBase, covers >55% of all genes of Bombyx . The fraction of library-specific ESTs in each cDNA library indicates that we have not yet reached saturation, showing the validity of our strategy for constructing an EST database to cover all genes. To tackle the coming saturation problem, we have checked two methods, subtraction and normalization, to increase coverage and decrease the number of housekeeping genes, resulting in a 5–11% increase of library-specific ESTs. The identification of a number of genes and comprehensive cloning of gene families have already emerged from the SilkBase search. Direct links of SilkBase with FlyBase and WormBase provide ready identification of candidate Lepidoptera-specific genes.

Summary: DroPhEA is a core module of a web application that facilitates research in insect functional genomics through enrichment analysis on mutant phenotypes of fruit fly (Drosophila melanogaster). The phenotypes investigated in the analyses can be predefined by FlyBase or customized by users. DroPhEA allows users to specify mutation or ortholog types, displays enriched term results in a hierarchical structure and supports analyses on gene sets of all insect species with a fully sequenced genome. Availability: http://evol.nhri.org.tw/phenome/DroPhEA/ Contact: liaoby@nhri.org.tw Supplementary Information: Supplementary data are available at Bioinformatics online.

Glutathione transferases (GST) are an ancient superfamily comprising a large number of paralogous proteins in a single organism. This multiplicity of GSTs has allowed the copies to diverge for neofunctionalization with proposed roles ranging from detoxication and oxidative stress response to involvement in signal transduction cascades. We performed a comparative genomic analysis using FlyBase annotations and Drosophila melanogaster GST sequences as templates to further annotate the GST orthologs in the 12 Drosophila sequenced genomes. We found that GST genes in the Drosophila subgenera have undergone repeated local duplications followed by transposition, inversion, and micro-rearrangements of these copies. The colinearity and orientations of the orthologous GST genes appear to be unique in many of the species which suggests that genomic rearrangement events have occurred multiple times during speciation. The high micro-plasticity of the genomes appears to have a functional contribution utilized for evolution of this gene family.

The following topics are discussed: Human Genome Project information, including the Human Genome News; lists of genome databases worldwide; lists of genome centers worldwide; lists of molecular biology-oriented WWW servers; example of WWW servers that provide links with multiple databases; molecular biology software collections; short descriptions of example molecular biology databases (abstracted from the respective sources); EMBL nucleotide sequence database; dbEST; SWISS-PROT protein sequence database; Protein Data Bank; Genome Data Base; online Mendelian inheritance in man; Flybase; ACEDB-based genome databases. >

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.

The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects.

The first recorded scientific publication on Drosophila was 310 years ago (Mentzel, 1684). By 1980 about 35,000 papers on Drosophila had been published and at the time of writing this total had risen to over 60,000. By the year 2000, there will be over 80,000 Drosophila publications — and the on-going publication rate will be more than 4,000 a year. There is nothing unique in this rate of growth — it is typical for any “active” subject to double its output every fifteen years (see de Solla Price, 1986). Sooner or later, of course, the curve must plateau but, until it does, the individual scientist faces an obvious problem. Not all of the papers published will be of the standard of those in Development. Nevertheless, just sifting those that are worthy of reading from those that are not will be (indeed is) a daunting task. What is to be done? The answer is obvious, we must exploit the power of computers to point us to papers that we need to read. We must also exploit the power of computers to provide us with basic data about our organism. Luckily, there is every prospect that the power of engines to process and access these data will increase, and their relative cost decrease, with the growth in scientific information.