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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.

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

In collaboration with the journal GENETICS, we've developed and launched a pipeline by which interactive full-text HTML/PDF journal articles are published with named entities linked to corresponding resource pages in "WormBase":http://www.wormbase.org/ (WB). Our interactive articles allow a reader to click on over ten different data type objects (gene, protein, transgene, etc.) and be directed to the relevant webpage. This seamless connection from the article to summaries of data types promotes a deeper level of understanding for the naïve reader, and incisive evaluation for the sophisticated reader. Further, this collaboration allows us to identify and collect information before the publication of the article. The pipeline uses automated recognition scripts to identify entities that already exist in the database and a self-reporting form we created at WB that is sent to the author by GENETICS for submitting entities that do not already exist in our database. We include a manual quality control step to make sure ambiguous links are corrected, and that all new entities have been reported and linked properly. The automated entity recognition scripts allows us to potentially link any object found in a database as well as to expand this pipeline to other databases. We have already adapted this pipeline for linking Saccharomyces cerevisiae GENETICS articles to the "Saccharomyces Genome Database":http://www.yeastgenome.org/ (SGD) and are currently expanding this pipeline for linking genes in Drosophila articles to "FlyBase":http://flybase.org/. By integrating journals and databases, we are integrating the major modes of communication in the biological sciences, which will undoubtedly increase the pace of discovery.

The Gene Ontology (GO) is a well-established, structured vocabulary that has been successfully used for 10 years in the annotation of proteins. GO terms, created in consultation with the biology community, are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently GO consists of more than 26,500 terms distributed over three ontologies that describe the molecular function, biological process and subcellular location of a protein in a generic cell.The Gene Ontology Annotation (GOA) database ("http://www.ebi.ac.uk/GOA":http://www.ebi.ac.uk/GOA) aims to provide high-quality manual and electronic GO annotations to proteins within the UniProt Knowledgebase (UniProtKB). By annotating all ‘known’ proteins with GO terms and transferring this knowledge to highly similar ‘unknown’ proteins, GOA offers a valuable contribution to the understanding of all proteomes.As well as generating manual annotation, made by extracting experimental evidence from full text peer-reviewed publications, GOA produces electronic annotation by making large-scale assignments of GO terms to proteins using computational methods. To date we have six electronic annotation methods including; InterPro2GO, Swiss-Prot Keyword2GO and the projection of annotations between orthologous species using Ensembl Compara.GOA provides annotated entries for over 180,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. In addition, by integrating GO annotations from model organism groups (e.g. FlyBase, GeneDB, MGI, RGD, SGD and TAIR), GOA ensures the dataset remains a key reference. GOA prioritises the annotation of the human proteome and provides this annotation to the GO Consortium’s Reference Genome project.GOA produces monthly releases of annotations to the human, mouse, rat, zebrafish, cow, chicken and Arabidopsis proteomes as well as a file for the multiple species within UniProtKB. The GOA dataset can be queried through a user-friendly web interface via our QuickGO browser("http://www.ebi.ac.uk/QuickGO":http://www.ebi.ac.uk/QuickGO) or downloaded in a parsable format via the EBI("ftp://ftp.ebi.ac.uk/pub/databases/GO/goa":ftp://ftp.ebi.ac.uk/pub/databases/GO/goa) and GO FTP sites. The GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results.

Complete functional annotation of genomes is a powerful tool for researchers; however, such annotation is a time-consuming task limited by the availability of experimental data. The function of genes for which there is no experimental data can often be predicted via comparison to related, annotated sequences of known function. We describe here the Reference Genome project, an effort from the Gene Ontology (GO) Consortium to fully annotate twelve genomes to rigorous standards: human, plus eleven organisms that are important models in biomedical research, including mouse, fly, zebrafish, yeast and E. coli. To achieve this, we examine existing experimentally based annotations in a phylogenetic context in order to infer the function(s) of ancestral proteins and propagate these annotations to their descendants. This endeavor faces many difficult challenges, such as: the determination and provision of reference protein sets for each genome; the identification of gene families for curation; the application of consistent best practices for annotation; the development of methodologies for evaluating progress towards our goal; and the development of software tools to support this effort. Annotated genomes are greatly valuable to the research community and will provide the basis for using sequence similarity to annotate further genomes. An overview of the project as well as links to all resources described below can be found at "http://geneontology.org/GO.refgenome.shtml":http://geneontology.org/GO.refgenome.shtmlThis work is supported by NHGRI grant HG002273 (Gene Ontology Consortium) and NIGMS GM081084-01A1 (Phylogenetic tree building and annotation software development). Pascale Gaudet for the Reference Genome Group of the Gene Ontology Consortium. The Reference Genome project is overseen by Pascale Gaudet (dictyBase), Rex Chisholm (dictyBase) and includes these representatives from the curatorial staff: Tanya Berardini (TAIR), Emily Dimmer (GOA), Stacia R. Engel (SGD), Petra Fey (dictyBase), David P. Hill (MGI), Doug Howe (ZFIN), Jim Hu (EcoliWiki), Rachael Huntley (GOA), Varsha K. Khodiyar (UCL), Ranjana Kishore (WormBase), Donghui Li (TAIR), Ruth C. Lovering (UCL), Fiona McCarthy (AgBase), Li Ni (MGI), Victoria Petri (RGD), Deborah A. Siegel (EcoliWiki), Susan Tweedie (FlyBase), Kimberly Van Auken (WormBase), and Valerie Wood (GeneDB)—as well as the following computational staff representatives: Siddhartha Basu (dictyBase), Seth Carbon (BBOP), Mary Dolan (MGI), and Christopher J. Mungall (BBOP)—those establishing the protein families to be annotated: Kara Dolinski (PPOD), Michael S. Livstone (PPOD), and Paul Thomas (PANTHER)—and, the four PIs of the GO Consortium: Michael Ashburner (FlyBase), Judith A. Blake (MGI), J. Michael Cherry (SGD), and Suzanna E. Lewis (BBOP).

Background Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear. Results PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article. Conclusion We show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully.

Background Proteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Drosophila melanogaster based on insight into domain interactions. Results Three high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions. All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction. Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the bird's eye view of the network of interest. Fly-DPI can be accessed at http://flydpi.nhri.org.tw. Conclusion This work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation.

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: 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.