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The evolution of tRNA multigene families remains poorly understood, exhibiting unusual phenomena such as functional conversions of tRNA genes through anticodon shift substitutions. We improved FlyBase tRNA gene annotations from twelve Drosophila species, incorporating previously identified ortholog sets to compare substitution rates across tRNA bodies at single-site and base-pair resolution. All rapidly evolving sites fell within the same metal ion-binding pocket that lies at the interface of the two major stacked helical domains. We applied our tRNA Structure–Function Mapper (tSFM) method independently to each Drosophila species and one outgroup species Musca domestica and found that, although predicted tRNA structure–function maps are generally highly conserved in flies, one tRNA Class-Informative Feature (CIF) within the rapidly evolving ion-binding pocket—Cytosine 17 (C17), ancestrally informative for lysylation identity—independently gained asparaginylation identity and substituted in parallel across tRNAAsn paralogs at least once, possibly multiple times, during evolution of the genus. In D. melanogaster, most tRNALys and tRNAAsn genes are co-arrayed in one large heterologous gene cluster, suggesting that heterologous gene conversion as well as structural similarities of tRNA-binding interfaces in the closely related asparaginyl-tRNA synthetase (AsnRS) and lysyl-tRNA synthetase (LysRS) proteins may have played a role in these changes. A previously identified Asn-to-Lys anticodon shift substitution in D. ananassae may have arisen to compensate for the convergent and parallel gains of C17 in tRNAAsn paralogs in that lineage. Our results underscore the functional and evolutionary relevance of our tRNA structure–function map predictions and illuminate multiple genomic and structural factors contributing to rapid, parallel and compensatory evolution of tRNA multigene families.

The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.

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.

Uncovering how new members of multigene families acquire new functions is an important topic in evolutionary and developmental genetics. CORL proteins (SKOR in mice, Fussel in humans and fussel in Flybase) are a family of CNS specific proteins related to mammalian Sno/Ski oncogenes. Drosophila CORL (dCORL) participates in TGF-β and insulin signaling during development and in adult homeostasis but roles for the two mouse CORL proteins (mCORL) are essentially unknown. A series of studies were conducted to test the hypothesis based on previous results that mCORL1 is more similar to dCORL than mCORL2. Neither an updated alignment nor ectopic expression in adult wings were able to distinguish mCORL1 or mCORL2 from dCORL. Transgene experiments employing a dCORL endogenous function in mushroom body neurons showed that mCORL1 is distinct from mCORL2 and dCORL. mCORL1 and mCORL2 are also distinct in biochemical assays of Smad-binding and BMP signaling. Taken together, the data suggests testable new hypotheses for mCORL2 function in mammalian TGF-β and insulin signaling based on known roles for dCORL. Overall, the study reiterates the value of transgenic methods in Drosophila to provide new information on multigene family evolution and the function of family members in other species.

Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.

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.

Interrupted exons in the pre-mRNA transcripts are ligated together through RNA splicing, which plays a critical role in the regulation of gene expression. Exons with a length ≤ 30 nt are defined as microexons that are unique in identification. However, microexons, especially those shorter than 8 nt, have not been well studied in many organisms due to difficulties in mapping short segments from sequencing reads. Here, we analyzed mRNA-seq data from a variety of Drosophila samples with a newly developed bioinformatic tool, ce-TopHat. In addition to the Flybase annotated, 465 new microexons were identified. Differentially alternatively spliced (AS) microexons were investigated between the Drosophila tissues (head, body, and gonad) and genders. Most of the AS microexons were found in the head and two AS microexons were identified in the sex-determination pathway gene fruitless.

This chapter covers some interesting areas of both Flybase and Twilio. We're going to build a "click-to-call'' call center, where visitors can click a product on a page and begin a Twilio Client call with agents on another web page. To do this, we are going to use Flybase's custom events.

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.

Many of the effects on fitness in population genetics are due not to single locations in the genome, but to the interaction of genetic variants at multiple locations in the genome. Of particular interest are ‘completely epistatic’ interactions, where a combination of genetic variants is required to produce an effect, and the effect cannot occur with any other combination. In diploids, epistasis is strongly connected to meiotic recombination, a process which can both assemble and destroy beneficial combinations of genetic variants. Additionally, epistatic interactions can be hard to detect in empirical studies, and mathematical models of epistasis and recombination are challenging to analyse, so despite their ubiquity epistatic interactions are regularly not considered. As a result, there is little consensus on when high levels of recombination might be expected, or how strongly recombination affects beneficial or deleterious fitness effects controlled by epistatic interactions. We address this question by conducting a meta-analysis and simulations. The meta-analysis used data drawn and curated fromDrosophila melanogasterstudies in Flybase. We extracted studies relating genetic combinations and phenotypically detectable effects on fitness, then analysed the relationship between the rate of recombination and effect on fitness with a statistical model. We also ran simulations under a two-locus Wright-Fisher model with recombination and epistatic selection. The results of both approaches indicated a tendency for genetic combinations with an epistatic effect on fitness to occur in an environment of reduced meiotic recombination. Two possible explanations for this are that the variants controlling such interactions are selected for in regions where there is little recombination, or that such interactions lead to selection for lower rates of recombination in the regions where those variants appear.

The 2019 Nucleic Acids Research (NAR) Database Issue contains 168 papers spanning molecular biology. Among them, 64 are new and another 92 are updates describing resources that appeared in the Issue previously. The remaining 12 are updates on databases most recently published elsewhere. This Issue contains two Breakthrough articles, on the Virtual Metabolic Human (VMH) database which links human and gut microbiota metabolism with diet and disease, and Vibrism DB, a database of mouse brain anatomy and gene (co-)expression with sophisticated visualization and session sharing. Major returning nucleic acid databases include RNAcentral, miRBase and LncRNA2Target. Protein sequence databases include UniProtKB, InterPro and Pfam, while wwPDB and RCSB cover protein structure. STRING and KEGG update in the section on metabolism and pathways. Microbial genomes are covered by IMG/M and resources for human and model organism genomics include Ensembl, UCSC Genome Browser, GENCODE and Flybase. Genomic variation and disease are well-covered by GWAS Catalog, PopHumanScan, OMIM and COSMIC, CADD being another major newcomer. Major new proteomics resources reporting here include iProX and jPOSTdb. The entire database issue is freely available online on the NAR website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 506 entries, adding 66 new resources and eliminating 147 discontinued URLs, bringing the current total to 1613 databases. It is available at http://www.oxfordjournals.org/nar/database/c.

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.

DNA synthesis during replication or repair is a fundamental cellular process that is catalyzed by a set of evolutionary conserved polymerases. Despite a large body of research, the DNA polymerases of Drosophila melanogaster have not yet been systematically reviewed, leading to inconsistencies in their nomenclature, shortcomings in their functional (Gene Ontology, GO) annotations and an under-appreciation of the extent of their characterization. Here, we describe the complete set of DNA polymerases in D. melanogaster, applying nomenclature already in widespread use in other species, and improving their functional annotation. A total of 19 genes encode the proteins comprising three replicative polymerases (alpha-primase, delta, epsilon), five translesion/repair polymerases (zeta, eta, iota, Rev1, theta) and the mitochondrial polymerase (gamma). We also provide an overview of the biochemical and genetic characterization of these factors in D. melanogaster. This work, together with the incorporation of the improved nomenclature and GO annotation into key biological databases, including FlyBase and UniProtKB, will greatly facilitate access to information about these important proteins.

Brief summaries describing the function of each gene’s product(s) are of great value to the research community, especially when interpreting genome-wide studies that reveal changes to hundreds of genes. However, manually writing such summaries, even for a single species, is a daunting task; for example, the Drosophila melanogaster genome contains almost 14 000 protein-coding genes. One solution is to use computational methods to generate summaries, but this often fails to capture the key functions or express them eloquently. Here, we describe how we solicited help from the research community to generate manually written summaries of D. melanogaster gene function. Based on the data within the FlyBase database, we developed a computational pipeline to identify researchers who have worked extensively on each gene. We e-mailed these researchers to ask them to draft a brief summary of the main function(s) of the gene’s product, which we edited for consistency to produce a ‘gene snapshot’. This approach yielded 1800 gene snapshot submissions within a 3-month period. We discuss the general utility of this strategy for other databases that capture data from the research literature. Database URL: https://flybase.org/

The extracellular matrix (ECM) is an assembly of hundreds of proteins that structurally supports the cells it surrounds and biochemically regulates their functions. Drosophila melanogaster has emerged as a powerful model organism to study fundamental mechanisms underlying ECM protein secretion, ECM assembly, and ECM roles in pathophysiological processes. However, as of today, we do not possess a well-defined list of the components forming the ECM of this organism. We previously reported the development of computational pipelines to define the matrisome - the ensemble of genes encoding ECM and ECM-associated proteins - of humans, mice, zebrafish and C. elegans. Using a similar approach, we report here that our pipeline has identified 641 genes constituting the Drosophila matrisome. We further classify these genes into different structural and functional categories, including an expanded way to classify genes encoding proteins forming apical ECMs. We illustrate how having a comprehensive list of Drosophila matrisome proteins can be used to annotate large proteomic datasets and identify unsuspected roles for the ECM in pathophysiological processes. Last, to aid the dissemination and usage of the proposed definition and categorization of the Drosophila matrisome by the scientific community, our list has been made available through three public portals: The Matrisome Project (http://matrisome.org), The FlyBase (https://flybase.org/), and GLAD (https://www.flyrnai.org/tools/glad/web/)., Highlights • We developed a bioinformatics pipeline combining gene- and protein-sequence analysis to predict the Drosophila matrisome. • The in-silico Drosophila matrisome comprises 641 genes. • We propose a comprehensive classification of genes encoding proteins forming apical ECMs. • The Drosophila matrisome list is now available through three public portals: The Matrisome Project, FlyBase, and GLAD.

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.

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.

Synthesis of polypeptides from mRNA (translation) is a fundamental cellular process that is coordinated and catalyzed by a set of canonical ‘translation factors’. Surprisingly, the translation factors of Drosophila melanogaster have not yet been systematically identified, leading to inconsistencies in their nomenclature and shortcomings in functional (Gene Ontology, GO) annotations. Here, we describe the complete set of translation factors in D. melanogaster, applying nomenclature already in widespread use in other species, and revising their functional annotation. The collection comprises 43 initiation factors, 12 elongation factors, 3 release factors and 6 recycling factors, totaling 64 of which 55 are cytoplasmic and 9 are mitochondrial. We also provide an overview of notable findings and particular insights derived from Drosophila about these factors. This catalog, together with the incorporation of the improved nomenclature and GO annotation into FlyBase, will greatly facilitate access to information about the functional roles of these important proteins.

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.

The use of Drosophila melanogaster as a model for studying human disease is well established, reflected by the steady increase in both the number and proportion of fly papers describing human disease models in recent years. In this article, we highlight recent efforts to improve the availability and accessibility of the disease model information in FlyBase (http://flybase.org), the model organism database for Drosophila. FlyBase has recently introduced Human Disease Model Reports, each of which presents background information on a specific disease, a tabulation of related disease subtypes, and summaries of experimental data and results using fruit flies. Integrated presentations of relevant data and reagents described in other sections of FlyBase are incorporated into these reports, which are specifically designed to be accessible to non-fly researchers in order to promote collaboration across model organism communities working in translational science. Another key component of disease model information in FlyBase is that data are collected in a consistent format ­­– using the evolving Disease Ontology (an open-source standardized ontology for human-disease-associated biomedical data) – to allow robust and intuitive searches. To facilitate this, FlyBase has developed a dedicated tool for querying and navigating relevant data, which include mutations that model a disease and any associated interacting modifiers. In this article, we describe how data related to fly models of human disease are presented in individual Gene Reports and in the Human Disease Model Reports. Finally, we discuss search strategies and new query tools that are available to access the disease model data in FlyBase., Drosophila Collection: Drosophila melanogaster is well established as a model for studying human disease. Here, we highlight recent efforts to enhance the availability and accessibility of disease model data in FlyBase, the model organism database for Drosophila.

Model organisms have been a key prerequisite in uncovering the mechanisms governing various aspects of development and disease. In the era of deep sequencing, multi-omics data integration, high-throughput screening, and personalized medicines, researchers are constantly exploring new avenues to address the biological problems in living organisms. Ease of handling, availability of genetic toolset to carry out functional studies, and relevance to human health make a model organism the default choice to perform experiments. Drosophila has been an instrumental model organism to study the mechanisms of development for several decades. The striking similarity between the fly and human disease genes also makes it an appropriate system to study the disease etiology and screen for therapeutic targets. Here, we describe the use of the Drosophila model in understanding the organism development and design principles based on these studies that provide significant insights into mechanisms of human disease. We discuss the choice of Drosophila as a model system, various genetic toolkits available in the fly, and attempts to use Drosophila in developing human disease model and drug discovery. Finally, we discuss the importance of Drosophila in stem cell studies and catalog the resources available to the Drosophila research community. We conclude the chapter with the discussion of new approaches to utilize the power of Drosophila as a model organism.

The catalytic activities of enzymes can be described using Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. These annotations are available from numerous biological databases and are routinely accessed by researchers and bioinformaticians to direct their work. However, enzyme data may not be congruent between different resources, while the origin, quality and genomic coverage of these data within any one resource are often unclear. GO/EC annotations are assigned either manually by expert curators or inferred computationally, and there is potential for errors in both types of annotation. If such errors remain unchecked, false positive annotations may be propagated across multiple resources, significantly degrading the quality and usefulness of these data. Similarly, the absence of annotations (false negatives) from any one resource can lead to incorrect inferences or conclusions. We are systematically reviewing and enhancing the functional annotation of the enzymes of Drosophila melanogaster, focusing on improvements within the FlyBase (www.flybase.org) database. We have reviewed four major enzyme groups to date: oxidoreductases, lyases, isomerases and ligases. Herein, we describe our review workflow, the improvement in the quality and coverage of enzyme annotations within FlyBase and the wider impact of our work on other related databases.

Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI ( http://evoppi.i3s.up.pt ) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

MicroRNAs (miRNAs) are a class of small RNAs, evolutionarily conserved endogenous non-coding RNAs that regulate their target mRNA expression by either inactivating or degrading mRNA genes; thus playing an important role in the growth and development of an organism. Maruca vitrata is an insect pest of leguminous plants like pigeon pea, cowpea and mung bean and is pantropical. In this study, we perform BLAST on all known miRNAs against the transcriptome data of M. vitrata and thirteen miRNAs were identified. These miRNAs were characterised and their target genes were identified using TargetScan and were functionally annotated using FlyBase. The importance of the structure of pre-miRNA in the Drosha activity led to study the backbone torsion angles of predicted pre-miRNAs (mvi-miR-9751, mvi-miR-649-3p, mvi-miR-4057 and mvi-miR-1271) to identify various nucleotide triplets that contribute to the variation of torsion angle values at various structural motifs of a pre-miRNA.

Many publications describe sets of genes or gene products that share a common biology. For example, genome-wide studies and phylogenetic analyses identify genes related in sequence; high-throughput genetic and molecular screens reveal functionally related gene products; and advanced proteomic methods can determine the subunit composition of multi-protein complexes. It is useful for such gene collections to be presented as discrete lists within the appropriate Model Organism Database (MOD) so that researchers can readily access these data alongside other relevant information. To this end, FlyBase (flybase.org), the MOD for Drosophila melanogaster, has established a 'Gene Group' resource: high-quality sets of genes derived from the published literature and organized into individual report pages. To facilitate further analyses, Gene Group Reports also include convenient download and analysis options, together with links to equivalent gene groups at other databases. This new resource will enable researchers with diverse backgrounds and interests to easily view and analyse acknowledged D. melanogaster gene sets and compare them with those of other species.

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.

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.

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.

Model organism databases (MODs) facilitate the connections between published research papers with genes and other biological information. MODs aim to make research data easier to access to the research community, especially for researchers relying on genetic data and other information about a specific species. This paper follows previous research [1] that attempted to use quantitative data to determine if and how literature curated by a MOD makes a difference to the access and reuse of the curated data. The research addresses whether articles that have been through the detailed curation process of a MOD are more likely to be cited when compared to 'similar' articles that are not curated. For this research, citations for articles curated by FlyBase, a MOD for genetic and molecular data for the Drosophilidae insect family, were compared with articles identified as having similar genetic and molecular data, but not yet given a detailed curation by FlyBase. In addition, citation counts from a larger set of articles retrieved through a title and keyword search for Drosophilidae are also compared.

The concept of orthology provides a foundation for formulating hypotheses on gene and genome evolution, and thus forms the cornerstone of comparative genomics, phylogenomics and metagenomics. We present the update of OrthoDB-the hierarchical catalog of orthologs (http://www.orthodb.org). From its conception, OrthoDB promoted delineation of orthologs at varying resolution by explicitly referring to the hierarchy of species radiations, now also adopted by other resources. The current release provides comprehensive coverage of animals and fungi representing 252 eukaryotic species, and is now extended to prokaryotes with the inclusion of 1115 bacteria. Functional annotations of orthologous groups are provided through mapping to InterPro, GO, OMIM and model organism phenotypes, with cross-references to major resources including UniProt, NCBI and FlyBase. Uniquely, OrthoDB provides computed evolutionary traits of orthologs, such as gene duplicability and loss profiles, divergence rates, sibling groups, and now extended with exon-intron architectures, syntenic orthologs and parent-child trees. The interactive web interface allows navigation along the species phylogenies, complex queries with various identifiers, annotation keywords and phrases, as well as with gene copy-number profiles and sequence homology searches. With the explosive growth of available data, OrthoDB also provides mapping of newly sequenced genomes and transcriptomes to the current orthologous groups.

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.

Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, “meta” information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.

ITIS, WoRMS, EOL, iBOL, GBIF, ANTABIF, OBIS. FlyBase, FishBase, FosFARbase, Fauna Europaea. World Modern Foraminifera Database, EDNA Fossil Insect Database, European Diatom Database, The Reptile Database, Mammalian Species of the World. Today’s biologists are blessed with databases, and there is no doubt about their relevance for scientific work. (Palaeo-)Biologists put a lot of time and efforts into establishing and maintaining a multitude of databases, not to mention their endeavours to raise funds for this work. Many databases are produced and edited by a multitude of authors and editors, providing a great amount of data being supplied to the project within a short period of time — all too often at the expense of quality. The recent success of databases is intrinsically tied to the increasing access to, and use of, the World Wide Web, and it comes as no surprise that only few such biological databases have been initiated prior to the late 1990s. A rare example of an evidentially long-standing, relational database, authored and managed by an individual expert of a crustacean class, is the Kempf Database Ostracoda. Prof. Eugen Karl Kempf, now celebrating his eightieth anniversary, started data compilation in the 1960s as a card index and innovatively switched to a machine punch card system in the 1970s. Ever since the first publication from his taxonomic-bibliographic database in 1980, ostracod researchers worldwide benefitted from an ineffably high amount of, currently, more than 285 000 datasets of highest integrity. It is time to honour the person behind the Kempf Database Ostracoda.

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

FlyBase (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.

Insertional mutagenesis experiments performed onDrosophila melanogastermodel often relies on induced mobilization of artificial transposons derived from P mobile element. In an attempt to detect transposition preferences, we accomplished a pilot study concerning the insertional patterns ofP{lacW}andP{EP}constructs in the third chromosome ofD. melanogaster.Our inventory inquiry considered 2177 insertions ofP{lacW}and 1646 insertions ofP{EP}available in FB2016_02 release of FlyBase and revealed insertional hotspots and coldspots in 3L and 3R, but also a preference of both artificial transposons to insert in 3R. The general distribution ofP{lacW}andP{EP}insertions appears to be similar but not identical, probably due to differences in size and molecular architecture of these transposons. Our results may have predictive value for experimental design of insertional mutagenesis screenings, but are also expected to contribute to a better understanding ofPtransposon biology.

The breadth and depth of biomedical literature are increasing year upon year. To keep abreast of these increases, FlyBase, a database for Drosophila genomic and genetic information, is constantly exploring new ways to mine the published literature to increase the efficiency and accuracy of manual curation and to automate some aspects, such as triaging and entity extraction. Toward this end, we present the ‘tagtog’ system, a web-based annotation framework that can be used to mark up biological entities (such as genes) and concepts (such as Gene Ontology terms) in full-text articles. tagtog leverages manual user annotation in combination with automatic machine-learned annotation to provide accurate identification of gene symbols and gene names. As part of the BioCreative IV Interactive Annotation Task, FlyBase has used tagtog to identify and extract mentions of Drosophila melanogaster gene symbols and names in full-text biomedical articles from the PLOS stable of journals. We show here the results of three experiments with different sized corpora and assess gene recognition performance and curation speed. We conclude that tagtog-named entity recognition improves with a larger corpus and that tagtog-assisted curation is quicker than manual curation. Database URL: www.tagtog.net, www.flybase.org

An accurate, comprehensive, non-redundant and up-to-date bibliography is a crucial component of any Model Organism Database (MOD). Principally, the bibliography provides a set of references that are specific to the field served by the MOD. Moreover, it serves as a backbone to which all curated biological data can be attributed. Here, we describe the organization and main features of the bibliography in FlyBase (flybase.org), the MOD for Drosophila melanogaster. We present an overview of the current content of the bibliography, the pipeline for identifying and adding new references, the presentation of data within Reference Reports and effective methods for searching and retrieving bibliographic data. We highlight recent improvements in these areas and describe the advantages of using the FlyBase bibliography over alternative literature resources. Although this article is focused on bibliographic data, many of the features and tools described are applicable to browsing and querying other datasets in FlyBase.

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species.

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

Drosophila melanogaster cell lines are important resources for cell biologists. Here, we catalog the expression of exons, genes, and unannotated transcriptional signals for 25 lines. Unannotated transcription is substantial (typically 19% of euchro- matic signal). Conservatively, we identify 1405 novel transcribed regions; 684 of these appear to be new exons of neighboring, oftendistant, genes. Sixty-four percent of genes areexpressed detectably in atleastone line, but only 21%are detected in all lines. Each cell line expresses, on average, 5885 genes, including a common set of 3109. Expression levels vary over several orders of magnitude. Major signaling pathways are well represented: most differentiation pathways are ''off'' andsurvival/growth pathways''on.''Roughly 50%ofthe genesexpressed by eachlinearenot part ofthe commonset, and these show considerable individuality. Thirty-one percent are expressed at a higher level in at least one cell line than in any single developmental stage, suggesting that each line is enriched for genes characteristic of small sets of cells. Most re- markable is that imaginal disc-derived lines can generally be assigned, on the basis of expression, to small territories within developing discs. These mappings reveal unexpected stability of even fine-grained spatial determination. No two cell lines show identical transcription factor expression. We conclude that each line has retained features of an individual founder cell superimposed on a common ''cell line'' gene expression pattern. (Supplemental material is available for this article. The data from this study have been submitted to the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession nos. GSE15596, GSE16269-GSE16290, GSE16321- GSE16322, GSE16325, and GSE18040. All of the microarray data, RNA-seq data, and expression scores for genes and exons are available from the Data Coordination Center of modENCODE (http://modencode.org), and much of the data are also available from the Drosophila Genomic Resources Center (https://dgrc.cgb.indiana.edu/) and FlyBase (http:// flybase.org/).)

RNA-seq was used to generate an extensive map of the Drosophila melanogaster transcriptome by broad sampling of 10 developmental stages. In total, 142.2 million uniquely mapped 64–100-bp paired-end reads were generated on the Illumina GA II yielding 356× sequencing coverage. More than 95% of FlyBase genes and 90% of splicing junctions were observed. Modifications to 30% of FlyBase gene models were made by extension of untranslated regions, inclusion of novel exons, and identification of novel splicing events. A total of 319 novel transcripts were identified, representing a 2% increase over the current annotation. Alternate splicing was observed in 31% of D. melanogaster genes, a 38% increase over previous estimations, but significantly less than that observed in higher organisms. Much of this splicing is subtle such as tandem alternate splice sites.

DroID (http://droidb.org/), the Drosophila Interactions Database, is a comprehensive public resource for Drosophila gene and protein interactions. DroID contains genetic interactions and experimentally detected protein-protein interactions curated from the literature and from external databases, and predicted protein interactions based on experiments in other species. Protein interactions are annotated with experimental details and periodically updated confidence scores. Data in DroID is accessible through user-friendly, intuitive interfaces that allow simple or advanced searches and graphical visualization of interaction networks. DroID has been expanded to include interaction types that enable more complete analyses of the genetic networks that underlie biological processes. In addition to protein-protein and genetic interactions, the database now includes transcription factor-gene and regulatory RNA-gene interactions. In addition, DroID now has more gene expression data that can be used to search and filter interaction networks. Orthologous gene mappings of Drosophila genes to other organisms are also available to facilitate finding interactions based on gene names and identifiers for a number of common model organisms and humans. Improvements have been made to the web and graphical interfaces to help biologists gain a comprehensive view of the interaction networks relevant to the genes and systems that they study.

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.

The cytoskeleton is a dynamic network of filamentous protein polymers required for virtually all cellular processes. It consists of three major classes, filamentous actin (F-actin), intermediate filaments and microtubules, all displaying characteristic structural properties, functions, cellular distributions and sets of interacting regulatory proteins. One unique class of proteins, the spectraplakins, bind, regulate, and integrate the functions of all three classes of cytoskeleton proteins. Spectraplakins are giant, evolutionary conserved multidomain proteins (spanning up to 9000 aa) that are true members of the plakin, spectrin and Gas2-like protein families. They have OMIM-listed disease links to epidermolysis bullosa and hereditary sensory and autonomic neuropathy (HSAN). Their role in disease is likely underrepresented since studies in model animal systems have revealed critical roles in polarity, morphogenesis, differentiation and maintenance, migration, signalling and intracellular trafficking in a variety of tissues. This enormous diversity of spectraplakin function is consistent with the numerous isoforms produced from single genomic loci that combine different sets of functional domains in distinct cellular contexts. To study the broad range of functions and complexity of these proteins, Drosophila is a powerful model. Thus, the fly spectraplakin Short stop (Shot) acts as an actin-microtubule linker and plays important roles in many developmental processes, which provide experimentally amenable and relevant contexts in which to study spectraplakin functions. For these studies, a versatile range of relevant experimental resources that facilitate genetics and transgenic approaches, highly refined genomics tools, and an impressive set of spectraplakin-specific genetic and molecular tools is readily available. Here we use the example of Shot to illustrate how the various tools and strategies available for Drosophila can be employed to decipher and dissect cellular roles and molecular mechanisms of spectraplakins.