Tools for neuroanatomy and neurogenetics in Drosophila.

We demonstrate the feasibility of generating thousands of transgenic Drosophila melanogaster lines in which the expression of an exogenous gene is reproducibly directed to distinct small subsets of cells in the adult brain. We expect the expression patterns produced by the collection of 5,000 lines that we are currently generating to encompass all neurons in the brain in a variety of intersecting patterns. Overlapping 3-kb DNA fragments from the flanking noncoding and intronic regions of genes thought to have patterned expression in the adult brain were inserted into a defined genomic location by site-specific recombination. These fragments were then assayed for their ability to function as transcriptional enhancers in conjunction with a synthetic core promoter designed to work with a wide variety of enhancer types. An analysis of 44 fragments from four genes found that >80% drive expression patterns in the brain; the observed patterns were, on average, comprised of <100 cells. Our results suggest that the D. melanogaster genome contains >50,000 enhancers and that multiple enhancers drive distinct subsets of expression of a gene in each tissue and developmental stage. We expect that these lines will be valuable tools for neuroanatomy as well as for the elucidation of neuronal circuits and information flow in the fly brain.

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge.

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.

Using Apollo to browse and edit genome annotations.

An annotation is any feature that can be tied to genomic sequence, such as an exon, transcript, promoter, or transposable element. As biological knowledge increases, annotations of different types need to be added and modified, and links to other sources of information need to be incorporated, to allow biologists to easily access all of the available sequence analysis data and design appropriate experiments. The Apollo genome browser and editor offers biologists these capabilities. Apollo can display many different types of computational evidence, such as alignments and similarities based on BLAST searches (UNITS 3.3 & 3.4), and enables biologists to utilize computational evidence to create and edit gene models and other genomic features, e.g., using experimental evidence to refine exon-intron structures predicted by gene prediction algorithms. This protocol describes simple ways to browse genome annotation data, as well as techniques for editing annotations and loading data from different sources.

Identification of putative noncoding polyadenylated transcripts in Drosophila melanogaster.

Analysis of EST and cDNA collections from a number of metazoan species has identified genes encoding long polyadenylated transcripts that do not contain ORFs of lengths typical for protein-encoding mRNAs. Noncoding functions of such polyadenylated transcripts have been elucidated in only a few examples. The corresponding genes neither contain hallmark sequence motifs nor appear to have been conserved across phyla. Thus, it is impossible to systematically identify new members of this class of gene by using sequence homology and traditional gene-finding algorithms that depend on protein-coding potential. Consequently, even their approximate number has not been established for any metazoan genome. We curated polyadenylated transcripts with limited protein-coding capacity from intergenic regions of the Drosophila melanogaster genome. We used RT-PCR assays, hybridization to RNA blots and whole-mount embryos, and computational analyses to characterize candidate transcripts. We verify the structures and expression of 17 distinct, likely non-protein-coding polyadenylated transcripts. We show that the expression of many of these transcripts is conserved in other Drosophila species, indicating that they have important biological functions.

A computational and experimental approach to validating annotations and gene predictions in the Drosophila melanogaster genome.

Five years after the completion of the sequence of the Drosophila melanogaster genome, the number of protein-coding genes it contains remains a matter of debate; the number of computational gene predictions greatly exceeds the number of validated gene annotations. We have assembled a collection of >10,000 gene predictions that do not overlap existing gene annotations and have developed a process for their validation that allows us to efficiently prioritize and experimentally validate predictions from various sources by sequencing RT-PCR products to confirm gene structures. Our data provide experimental evidence for 122 protein-coding genes. Our analyses suggest that the entire collection of predictions contains only approximately 700 additional protein-coding genes. Although we cannot rule out the discovery of genes with unusual features that make them refractory to existing methods, our results suggest that the D. melanogaster genome contains approximately 14,000 protein-coding genes.

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

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