Complete Genome Sequence of the Citrobacter freundii Type Strain.

Citrobacter freundii is a species of facultative anaerobic Gram-negative bacteria of the family Enterobacteriaceae The complete genome is composed of a single chromosomal circle of 4,957,773 bp with a G+C content of 52%

Characterization of MtnE, the fifth metallothionein member in Drosophila

Metallothioneins (MTs) constitute a family of cysteine-rich, low molecular weight metal-binding proteins which occur in almost all forms of life. They bind physiological metals, such as zinc and copper, as well as nonessential, toxic heavy metals, such as cadmium, mercury, and silver. MT expression is regulated at the transcriptional level by metal-regulatory transcription factor1 (MTF-1), which binds to the metal-response elements (MREs) in the enhancer/promoter regions of MT genes. Drosophila was thought to have four MT genes, namely, MtnA, MtnB, MtnC, and MtnD. Here we characterize a new fifth member of Drosophila MT gene family, coding for metallothionein E (MtnE). The MtnE transcription unit is located head-to-head with the one of MtnD. The intervening sequence contains four MREs which bind, with different affinities, to MTF-1. Both of the divergently transcribed MT genes are completely dependent on MTF-1, whereby MtnE is consistently more strongly transcribed. MtnE expression is induced in response to heavy metals, notably copper, mercury, and silver, and is upregulated in a genetic background where the other four MTs are missin

Correction: Benchmarking tools for the alignment of functional noncoding DNA

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.AbstractIn follow-up studies to this work [1], we have identified an error in a single line of code responsible for parsing BLASTZ [2] alignments that affects our previously published results for this alignment tool. This error resulted in a reduction in overall alignment coverage, with a concomitant underestimation of alignment sensitivity and overestimation of alignment specificity. As BLASTZ is an important and widely used alignment tool, we present here the revised results of our performance evaluations for BLASTZ together with previously reported results for the other alignment tools studied, which have been subsequently verified (Figures 1-4). The general conclusions presented in [1] remain unchanged, although the following sections concerning BLASTZ performance must be modified in light of our recent findings. The true overall alignment coverage for BLASTZ with and without insertion/deletion evolution and with and without blocks of constraint is shown in Figure 1, and reveals increased overall coverage in the presence of constrained blocks for intermediate to high divergence distances (Figures 1C & 1D) relative to previous results ([1] Figures 3C & 3D). As a consequence, the true overall sensitivity for BLASTZ is increased for intermediate to high divergence distances, especially in the presence of insertion/deletion evolution and constrained blocks (Figure 2D) relative to previous results ([1] Figure 4D). The most important revisions to [1] concern BLASTZ performance in interspersed blocks of constrained sequences (Figures 3, 4). Figure 3 shows that the true constraint coverage, and therefore constraint sensitivity, of BLASTZ is much improved relative to previous results for intermediate to high divergence distances ([1], Figure 5). Thus BLASTZ has increased constraint coverage relative to overall coverage (cp. Figures 1C & 1D with 3A & 3B), indicating that BLASTZ local alignments preferentially occur in constrained sequences for intermediate to high divergence distances, overturning claims on page 6 of [1] to the contrary. Likewise, the claim that BLASTZ has a "dramatic decrease in constraint sensitivity in the presence of indel evolution" on page 10 of [1] is incorrect. The increase in overall coverage, however, decreases the constraint specificity of BLASTZ for intermediate to high divergence distances (Figure 4A & 4B) relative to previous results ([1] Figure 6A & 6B). This decrease in constraint specificity requires reconsideration of the use of BLASTZ local alignments as specific detectors of constrained noncoding sequences discussed page 10 of [1]. Revised performance statistics for BLASTZ are posted along with previous results at [3]. We apologize for any misconception or inconvenience this error may have caused. References: 1. Pollard DA, Bergman CM, Stoye J, Celniker SE, Eisen MB: Benchmarking tools for the alignment of functional noncoding DNA. BMC Bioinformatics 2004, 5:6. 2. Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison RC, Haussler D, Miller W: Human-mouse alignments with BLASTZ. Genome Res 2003, 13:103-7. 3. AlignmentBenchmarking [http://rana.lbl.gov/AlignmentBenchmarking]Peer Reviewe

KAAS: an automatic genome annotation and pathway reconstruction server

The number of complete and draft genomes is rapidly growing in recent years, and it has become increasingly important to automate the identification of functional properties and biological roles of genes in these genomes. In the KEGG database, genes in complete genomes are annotated with the KEGG orthology (KO) identifiers, or the K numbers, based on the best hit information using Smith–Waterman scores as well as by the manual curation. Each K number represents an ortholog group of genes, and it is directly linked to an object in the KEGG pathway map or the BRITE functional hierarchy. Here, we have developed a web-based server called KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) i.e. an implementation of a rapid method to automatically assign K numbers to genes in the genome, enabling reconstruction of KEGG pathways and BRITE hierarchies. The method is based on sequence similarities, bi-directional best hit information and some heuristics, and has achieved a high degree of accuracy when compared with the manually curated KEGG GENES database

Computational identification of developmental enhancers: conservation and function of transcription factor binding-site clusters in Drosophila melanogaster and Drosophila pseudoobscura

BACKGROUND: The identification of sequences that control transcription in metazoans is a major goal of genome analysis. In a previous study, we demonstrated that searching for clusters of predicted transcription factor binding sites could discover active regulatory sequences, and identified 37 regions of the Drosophila melanogaster genome with high densities of predicted binding sites for five transcription factors involved in anterior-posterior embryonic patterning. Nine of these clusters overlapped known enhancers. Here, we report the results of in vivo functional analysis of 27 remaining clusters. RESULTS: We generated transgenic flies carrying each cluster attached to a basal promoter and reporter gene, and assayed embryos for reporter gene expression. Six clusters are enhancers of adjacent genes: giant, fushi tarazu, odd-skipped, nubbin, squeeze and pdm2; three drive expression in patterns unrelated to those of neighboring genes; the remaining 18 do not appear to have enhancer activity. We used the Drosophila pseudoobscura genome to compare patterns of evolution in and around the 15 positive and 18 false-positive predictions. Although conservation of primary sequence cannot distinguish true from false positives, conservation of binding-site clustering accurately discriminates functional binding-site clusters from those with no function. We incorporated conservation of binding-site clustering into a new genome-wide enhancer screen, and predict several hundred new regulatory sequences, including 85 adjacent to genes with embryonic patterns. CONCLUSIONS: Measuring conservation of sequence features closely linked to function - such as binding-site clustering - makes better use of comparative sequence data than commonly used methods that examine only sequence identity

An Extracellular Interactome of Immunoglobulin and LRR Proteins Reveals Receptor-Ligand Networks

Extracellular domains of cell surface receptors and ligands mediate cell-cell communication, adhesion, and initiation of signaling events, but most existing protein-protein “interactome” data sets lack information for extracellular interactions. We probed interactions between receptor extracellular domains, focusing on a set of 202 proteins composed of the Drosophila melanogaster immunoglobulin superfamily (IgSF), fibronectin type III (FnIII), and leucine-rich repeat (LRR) families, which are known to be important in neuronal and developmental functions. Out of 20,503 candidate protein pairs tested, we observed 106 interactions, 83 of which were previously unknown. We “deorphanized” the 20 member subfamily of defective-in-proboscis-response IgSF proteins, showing that they selectively interact with an 11 member subfamily of previously uncharacterized IgSF proteins. Both subfamilies interact with a single common “orphan” LRR protein. We also observed interactions between Hedgehog and EGFR pathway components. Several of these interactions could be visualized in live-dissected embryos, demonstrating that this approach can identify physiologically relevant receptor-ligand pairs

Systematic image-driven analysis of the spatial Drosophila embryonic expression landscape

We created innovative virtual representation for our large scale Drosophila insitu expression dataset. We aligned an elliptically shaped mesh comprised of small triangular regions to the outline of each embryo. Each triangle defines a unique location in the embryo and comparing corresponding triangles allows easy identification of similar expression patterns.The virtual representation was used to organize the expression landscape at stage 4-6. We identified regions with similar expression in the embryo and clustered genes with similar expression patterns.We created algorithms to mine the dataset for adjacent non-overlapping patterns and anti-correlated patterns. We were able to mine the dataset to identify co-expressed and putative interacting genes.Using co-expression we were able to assign putative functions to unknown genes

The transposable elements of the Drosophila melanogaster euchromatin: a genomics perspective.

BACKGROUND: Transposable elements are found in the genomes of nearly all eukaryotes. The recent completion of the Release 3 euchromatic genomic sequence of Drosophila melanogaster by the Berkeley Drosophila Genome Project has provided precise sequence for the repetitive elements in the Drosophila euchromatin. We have used this genomic sequence to describe the euchromatic transposable elements in the sequenced strain of this species. RESULTS: We identified 85 known and eight novel families of transposable element varying in copy number from one to 146. A total of 1,572 full and partial transposable elements were identified, comprising 3.86% of the sequence. More than two-thirds of the transposable elements are partial. The density of transposable elements increases an average of 4.7 times in the centromere-proximal regions of each of the major chromosome arms. We found that transposable elements are preferentially found outside genes; only 436 of 1,572 transposable elements are contained within the 61.4 Mb of sequence that is annotated as being transcribed. A large proportion of transposable elements is found nested within other elements of the same or different classes. Lastly, an analysis of structural variation from different families reveals distinct patterns of deletion for elements belonging to different classes. CONCLUSIONS: This analysis represents an initial characterization of the transposable elements in the Release 3 euchromatic genomic sequence of D. melanogaster for which comparison to the transposable elements of other organisms can begin to be made. These data have been made available on the Berkeley Drosophila Genome Project website for future analyses.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are