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Summary: We have created PhenomicDB, a multi-species genotype/phenotype database by merging public genotype/phenotype data from a wide range of model organisms and Homo sapiens. Until now these data were available in distinct organism-specific databases (e.g. WormBase, OMIM, FlyBase and MGI). We compiled this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indices (NCBI Gene), and by the use of associated orthology relationships. With its use-case-oriented user interface, PhenomicDB allows scientists to compare and browse known phenotypes for a given gene or a set of genes from different organisms simultaneously. Availability: PhenomicDB has been implemented at Schering AG as described below. A PhenomicDB implementation differing in some technical details has been set up for the public at Metalife AG http://www.phenomicDB.de Contact: bertram.weiss@schering.de Supplementary information: database model, semantic mapping table.

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.

Machine-learning based entity extraction requires a large corpus of annotated training to achieve acceptable results. However, the cost of expert annotation of relevant data, coupled with issues of inter-annotator variability, makes it expensive and time-consuming to create the necessary corpora. We report here on a simple method for the automatic creation of large quantities of imperfect training data for a biological entity (gene or protein) extraction system. We used resources available in the FlyBase model organism database; these resources include a curated lists of genes and the articles from which the entries were drawn, together a synonym lexicon. We applied simple pattern matching to identify gene names in the associated abstracts and filtered these entities using the list of curated entries for the article. This process created a data set that could be used to train a simple Hidden Markov Model (HMM) entity tagger. The results from the HMM tagger were comparable to those reported by other groups (F-measure of 0.75). This method has the advantage of being rapidly transferable to new domains that have similar existing resources.

Cellular signaling is an area of extensive and active research. It is also a complex topic, with information from many different cell types and organisms all contributing to the plethora of data available in this multidisciplinary field. In this issue, "Mapping Cellular Signaling," Science ( ) and STKE bring you a series of roadmaps to some signaling processes important in many cell types, signaling specific to immune cells, signaling in development and neural differentiation, and signaling in plants. The Viewpoints in Science provide both a broad overview of exciting developments in the field and new insights into the biological relevance, whereas the associated Connections Maps provide details about the molecular components involved in these pathways. The Connections Map database is intended to provide a mechanism by which potentially overwhelming amounts of information on signal transduction can be organized by experts in the field in way that is accessible to a broad audience of both experts and novices. [The Connections Maps][1] are dynamic interactive entry points for the information that is contained in the underlying database. This information represents a compendium of expert knowledge about the components involved in receiving, processing, and transmitting cellular signaling events and the relations between these components. The components are organized into pathways, allowing the interactions between components to be illustrated and allowing a convenient visual tool for beginning to understand the complexity of cellular signaling systems. The pathways are more simplified than what actually occurs within any given cell, but they help to place components into a context for understanding cellular functions. Experts in the field, whom we call Pathway Authorities, supply the data about the components, their relations, and the pathways in which they occur. This information is updated as often as is deemed necessary by the authority. New information in the database is immediately available through the Connections Maps because of the dynamic interface between the database and the Connections Maps as viewed at STKE. In order to understand the presentation of the information through the Connections Maps, it is helpful to understand the underlying structure for the information in the database. The information in the Connections Maps is organized into categories we call "canonical" and "specific." Canonical information is based on consensus knowledge from many cell types in many organisms. Specific information represents the details about a specific component as it exists in a particular cell at a particular developmental stage in a particular organism. The basic element in the Connections Map database is a component, which can be either canonical or specific, and which represents the basic attributes of that signaling entity. Components are pathway-independent in that they are found in nature (and in the Connections Maps) incorporated into multiple pathways. Once a component is incorporated into a pathway by an authority, we create a new data record to store information that is relevant to the function of that component in the context of the particular pathway. Thus, the component now has two associated records: (i) the general or pathway-independent information and (ii) pathway-dependent information that pertains to its occurrence in a particular pathway, but may not be generalizable to that component's roles in other pathways. In addition to information about the components, the database contains information about the interactions among components. The information about the components and the relations between components is elucidated with a combination of natural language descriptions, attributes defined by terms in controlled vocabularies, selected references, and links to external databases, such as the Entrez database of gene and protein sequences and FlyBase. The pathways presently in the Connections Maps represent a small fraction of the total we expect to amass. These pathways were started by scientists willing to be pioneers in this novel method for systematizing information and in this new form of authorship. Authorities are at work on many more pathways, including mitogen-activated protein kinase pathways, specific G protein-coupled receptor pathways, intrinsic apoptosis pathways, and many others. The editors and Authorities invite you to explore the pathways. Although each of the pathways highlighted in this week's focus issue has been reviewed by scientists in the field, the community is encouraged to send in feedback that will help validate and authenticate the information in the database, and thus to join us in the process of building what promises to be an incredible resource for scientists, educators, students, or anyone interested in cellular regulation. [1]: http://stke.sciencemag.org/cm/

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.

This education article describes a modular laboratory exercise in which undergraduates use fruit flies to generate novel experimental data while learning to perform advanced molecular techniques., We have developed and tested two linked but separable structured inquiry exercises using a set of Drosophila melanogaster GAL4 enhancer trap strains for an upper-level undergraduate laboratory methods course at Bucknell University. In the first, students learn to perform inverse PCR to identify the genomic location of the GAL4 insertion, using FlyBase to identify flanking sequences and the primary literature to synthesize current knowledge regarding the nearest gene. In the second, we cross each GAL4 strain to a UAS-CD8-GFP reporter strain, and students perform whole mount CNS dissection, immunohistochemistry, confocal imaging, and analysis of developmental expression patterns. We have found these exercises to be very effective in teaching the uses and limitations of PCR and antibody-based techniques as well as critical reading of the primary literature and scientific writing. Students appreciate the opportunity to apply what they learn by generating novel data of use to the wider research community.

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.

Erratum to: Mol. Cells 2008; 26(4), 350–355 This paper was initially submitted to Journal ‘Molecules and Cells’ by Suk-Young Park & Young-Han Song. In the Materials and Methods section, “Large form” should be changed to “lok-PB isoform reported in FlyBase”. The revised full sentence is as follows

Objective To review the data on the origins, phenotype, and function of embryonic phagocytes that has accumulated over past decade. Data Sources Most of the relevant articles were selected based on the PubMed database entries. In additional, the Interactive Fly database ( http://sdb.bio.purdue.edu/fly/aimain/1aahome.htm ), FlyBase ( http://flybase.bio.indiana.edu:82/ ), and TBase ( http://tbase.jax.org/ ) were used to search for relevant information and articles. Data Synthesis Phagocytes in a vertebrate embryo develop in two sites (yolk sac and liver) and contribute to organogenesis in part through their ability to recognize and clear apoptotic cells. Yolk sac-derived phagocytes differ in differentiation pathway and marker gene expression from macrophages produced via classic hematopoietic progenitors in the liver. Conclusion We argue that yolk sac-derived phagocytes constitute a separate cell lineage. This conclusion raises the question of whether primitive phagocytes persist into the adulthood.

International audience; GIF‐DB and FlyNets are two WWW databases describing molecular (protein‐DNA, protein‐RNA and protein‐protein) interactions occuring in the fly Drosophila melanogaster (http://gifts.univ‐mrs.fr/GIFTS\ₕome\ₚage.html ). GIF‐DB is a specialised database which focuses on molecular interactions involved in the process of embryonic pattern formation, whereas FlyNets is a new and more general database, the long‐term goal of which is to report on any published molecular interaction occuring in the fly. The information content of both databases is distributed in specific lines arranged into an EMBL‐ (or GenBank‐) like format. These databases achieve a high level of integration with other databases such as FlyBase, EMBL, GenBank and SWISS‐PROT through numerous hyperlinks. In addition, we also describe SOS‐DGDB, a new collection of annotated Drosophila gene sequences, in which binding sites for regulatory proteins are directly visible on the DNA primary sequence and hyperlinked both to GIF‐DB and TRANSFAC database entries.

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

The laboratory mouse serves as a pivotal animal model in gene expression and function studies, because it is closely related to the human and because tissues from many different mouse strains and mutants are readily available for detailed expression analysis. Array-based methods will provide a tremendous amount of expression information. In order to take full advantage of these data, we must be able to analyse them in a broader biological context.To address these issues and to cope with the complexity of gene expression information for the mouse in general, we developed the Gene Expression Database (GXD). GXD is designed as an open-ended system that can integrate many different types of expression data, such as RNA in situ hybridisation and immunohistochemistry data, Northern and Western blot data, RT-PCR data, cDNA data and microarray data. Thus, as data accumulate, GXD can provide increasingly complete information about what transcripts and proteins are produced by what genes; where, when and in what amounts these gene products are expressed; and how their expression is affected in different mouse strains and mutants. Through collaborative efforts with the MRC Human Genetics Unit and the University in Edinburgh, UK1, we have established a comprehensive hierarchically structured dictionary of anatomical terms for the mouse. The dictionary is used to annotate expression patterns in standardised ways and will facilitate an integrated description of expression, anatomy and phenotype/disease data. GXD is available at http://www.informatics.jax.org/. It is integrated with the Mouse Genome Database (MGD) to enable a combined analysis of genotype, expression and phenotype data. In collaboration with Flybase, the Saccharomyces Genome Database and MGD we are building shared controlled vocabularies to describe biological processes, and cellular function and location of gene products. These classification schemes and the associated data curation will provide important new search parameters for expression data.

Background Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. Description DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link. Conclusion DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at http://www.droidb.org.

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms., Author Summary The task of deciphering the complex transcriptional regulatory networks controlling development is one of the major current challenges for molecular biology. The problem is difficult, if not impossible, to solve without a detailed knowledge of the spatiotemporal dynamics of gene expression. Thus, to understand development, we need to identify and functionally characterize all players in regulatory networks. Data on gene expression dynamics obtained from whole transcriptome microarray experiments, combined with in situ hybridization mRNA localisation patterns for a subset of genes, may provide a route for predicting the localisation of gene expression for those genes for which in situ data has not been generated, as well as suggesting functional information for uncharacterised genes. Here, we report the development of one of the first methods for predicting the localisation of gene expression during Drosophila embryogenesis from microarray data. Pooling the subset of genes in the fly genome with in situ data to form functional units, localised in space and time for relevant developmental processes, facilitates the statement of a classification problem, which we address with machine-learning methods. Our approach promotes a richer annotation of biological function for genes in the absence of costly and time-consuming experimental analysis.

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.

From 18-21 June 2007, the Genetics Society of Canada (GSC) held its 50th anniversary celebration in the New Residence Hall of McGill University in Montreal in conjunction with the 9th Canadian Drosophila Research Conference (CanFly), which is held in odd-numbered years. The meeting was a huge success with nearly 200 participants, and outstanding research presentations from all parts of Canada and beyond. The Canadian Drosophila research community is very vibrant: there are approximately 50 research labs in the country that work entirely or mostly in the Drosophila system, and a search of FlyBase for Canadian addresses yields a total of 248.

The identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster. This proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart’s data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available. DAPPER offers an easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.

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.

The iBeetle-Base provides access to sequence and phenotype information for genes of the beetle Tribolium castaneum. It has been updated including more and updated data and new functions. RNAi phenotypes are now available for >50% of the genes, which represents an expansion of 60% compared to the previous version. Gene sequence information has been updated based on the new official gene set OGS3 and covers all genes. Interoperability with FlyBase has been enhanced: First, gene information pages of homologous genes are interlinked between both databases. Second, some steps of a new query pipeline allow transforming gene lists from either species into lists with related gene IDs, names or GO terms. This facilitates the comparative analysis of gene functions between fly and beetle. The backend of the pipeline is implemented as endpoints of a RESTful interface, such that it can be reused by other projects or tools. A novel online interface allows the community to propose GO terms for their gene of interest expanding the range of animals where GO terms are defined. iBeetle-Base is available at http://ibeetle-base.uni-goettingen.de/.

Background The incorporation of annotated sequence information from multiple related species in commonly used databases (Ensembl, Flybase, Saccharomyces Genome Database, Wormbase, etc.) has increased dramatically over the last few years. This influx of information has provided a considerable amount of raw material for evaluation of evolutionary relationships. To aid in the process, we have developed JCoDA (Java Codon Delimited Alignment) as a simple-to-use visualization tool for the detection of site specific and regional positive/negative evolutionary selection amongst homologous coding sequences. Results JCoDA accepts user-inputted unaligned or pre-aligned coding sequences, performs a codon-delimited alignment using ClustalW, and determines the dN/dS calculations using PAML (Phylogenetic Analysis Using Maximum Likelihood, yn00 and codeml) in order to identify regions and sites under evolutionary selection. The JCoDA package includes a graphical interface for Phylip (Phylogeny Inference Package) to generate phylogenetic trees, manages formatting of all required file types, and streamlines passage of information between underlying programs. The raw data are output to user configurable graphs with sliding window options for straightforward visualization of pairwise or gene family comparisons. Additionally, codon-delimited alignments are output in a variety of common formats and all dN/dS calculations can be output in comma-separated value (CSV) format for downstream analysis. To illustrate the types of analyses that are facilitated by JCoDA, we have taken advantage of the well studied sex determination pathway in nematodes as well as the extensive sequence information available to identify genes under positive selection, examples of regional positive selection, and differences in selection based on the role of genes in the sex determination pathway. Conclusions JCoDA is a configurable, open source, user-friendly visualization tool for performing evolutionary analysis on homologous coding sequences. JCoDA can be used to rapidly screen for genes and regions of genes under selection using PAML. It can be freely downloaded at http://www.tcnj.edu/~nayaklab/jcoda.

Background Phenotype ontologies are queryable classifications of phenotypes. They provide a widely-used means for annotating phenotypes in a form that is human-readable, programatically accessible and that can be used to group annotations in biologically meaningful ways. Accurate manual annotation requires clear textual definitions for terms. Accurate grouping and fruitful programatic usage require high-quality formal definitions that can be used to automate classification. The Drosophila phenotype ontology (DPO) has been used to annotate over 159,000 phenotypes in FlyBase to date, but until recently lacked textual or formal definitions. Results We have composed textual definitions for all DPO terms and formal definitions for 77% of them. Formal definitions reference terms from a range of widely-used ontologies including the Phenotype and Trait Ontology (PATO), the Gene Ontology (GO) and the Cell Ontology (CL). We also describe a generally applicable system, devised for the DPO, for recording and reasoning about the timing of death in populations. As a result of the new formalisations, 85% of classifications in the DPO are now inferred rather than asserted, with much of this classification leveraging the structure of the GO. This work has significantly improved the accuracy and completeness of classification and made further development of the DPO more sustainable. Conclusions The DPO provides a set of well-defined terms for annotating Drosophila phenotypes and for grouping and querying the resulting annotation sets in biologically meaningful ways. Such queries have already resulted in successful function predictions from phenotype annotation. Moreover, such formalisations make extended queries possible, including cross-species queries via the external ontologies used in formal definitions. The DPO is openly available under an open source license in both OBO and OWL formats. There is good potential for it to be used more broadly by the Drosophila community, which may ultimately result in its extension to cover a broader range of phenotypes.

Background: RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial. Results: Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files. Conclusion: Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.

Background During alternative splicing, the inclusion of an exon in the final mRNA molecule is determined by nuclear proteins that bind cis-regulatory sequences in a target pre-mRNA molecule. A recent study suggested that the regulatory codes of individual RNA-binding proteins may be nearly immutable between very diverse species such as mammals and insects. The model system Drosophila melanogaster therefore presents an excellent opportunity for the study of alternative splicing due to the availability of quality EST annotations in FlyBase. Methods In this paper, we describe an in silico analysis pipeline to extract putative exonic splicing regulatory sequences from a multiple alignment of 15 species of insects. Our method, ESTs-to-ESRs (E2E), uses graph analysis of EST splicing graphs to identify mutually exclusive (ME) exons and combines phylogenetic measures, a sliding window approach along the multiple alignment and the Welch's t statistic to extract conserved ESR motifs. Results The most frequent 100% conserved word of length 5 bp in different insect exons was "ATGGA". We identified 799 statistically significant "spike" hexamers, 218 motifs with either a left or right FDR corrected spike magnitude p-value < 0.05 and 83 with both left and right uncorrected p < 0.01. 11 genes were identified with highly significant motifs in one ME exon but not in the other, suggesting regulation of ME exon splicing through these highly conserved hexamers. The majority of these genes have been shown to have regulated spatiotemporal expression. 10 elements were found to match three mammalian splicing regulator databases. A putative ESR motif, GATGCAG, was identified in the ME-13b but not in the ME-13a of Drosophila N-Cadherin, a gene that has been shown to have a distinct spatiotemporal expression pattern of spliced isoforms in a recent study. Conclusions Analysis of phylogenetic relationships and variability of sequence conservation as implemented in the E2E spikes method may lead to improved identification of ESRs. We found that approximately half of the putative ESRs in common between insects and mammals have a high statistical support (p < 0.01). Several Drosophila genes with spatiotemporal expression patterns were identified to contain putative ESRs located in one exon of the ME exon pairs but not in the other.

Background Anatomy ontologies are query-able classifications of anatomical structures. They provide a widely-used means for standardising the annotation of phenotypes and expression in both human-readable and programmatically accessible forms. They are also frequently used to group annotations in biologically meaningful ways. Accurate annotation requires clear textual definitions for terms, ideally accompanied by images. Accurate grouping and fruitful programmatic usage requires high-quality formal definitions that can be used to automate classification and check for errors. The Drosophila anatomy ontology (DAO) consists of over 8000 classes with broad coverage of Drosophila anatomy. It has been used extensively for annotation by a range of resources, but until recently it was poorly formalised and had few textual definitions. Results We have transformed the DAO into an ontology rich in formal and textual definitions in which the majority of classifications are automated and extensive error checking ensures quality. Here we present an overview of the content of the DAO, the patterns used in its formalisation, and the various uses it has been put to. Conclusions As a result of the work described here, the DAO provides a high-quality, queryable reference for the wild-type anatomy of Drosophila melanogaster and a set of terms to annotate data related to that anatomy. Extensive, well referenced textual definitions make it both a reliable and useful reference and ensure accurate use in annotation. Wide use of formal axioms allows a large proportion of classification to be automated and the use of consistency checking to eliminate errors. This increased formalisation has resulted in significant improvements to the completeness and accuracy of classification. The broad use of both formal and informal definitions make further development of the ontology sustainable and scalable. The patterns of formalisation used in the DAO are likely to be useful to developers of other anatomy ontologies.