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Reef-building corals live in a mutualistic relationship with photosynthetic algae (family Symbiodiniaceae) that usually provide most of the energy required by the coral host. This relationship is sensitive to temperature stress; as little as a 1°C increase often leads to the collapse of the association. This sensitivity has led to an interest in the potential of more stress-tolerant algae to supplement or substitute for the normal Symbiodiniaceae mutualists. In this respect, the apicomplexan-like microalga Chromera is of particular interest due to its greater temperature tolerance. We generated a de novo transcriptome for a Chromera strain isolated from a GBR coral ('GBR Chromera') and compared with those of the reference strain of Chromera ('Sydney Chromera'), and to those of Symbiodiniaceae (Fugacium kawagutii, Cladocopium goreaui and Breviolum minutum), as well as the apicomplexan parasite, Plasmodium falciparum. In contrast to the high sequence divergence amongst representatives of different genera within the family Symbiodiniaceae, the two Chromera strains featured low sequence divergence at orthologous genes, implying that they are likely to be conspecifics. Although KEGG categories provide few criteria by which true coral mutualists might be identified, they do supply a molecular rationalization that explains the ecological dominance of Cladocopium spp. amongst Indo-Pacific reef corals. The presence of HSP20 genes may contribute to the high thermal tolerance of Chromera.

Coral reefs are sustained by symbioses between corals and symbiodiniacean dinoflagellates. These symbioses vary in the extent of their permanence in and specificity to the host. Although dinoflagellates are primarily free-living, Symbiodiniaceae diversified mainly as symbiotic lineages. Their genomes reveal conserved symbiosis-related gene functions and high sequence divergence. However, the evolutionary mechanisms that underpin the transition from the free-living lifestyle to symbiosis remain poorly understood. Here, we discuss the genome evolution of Symbiodiniaceae in diverse ecological niches across the broad spectrum of symbiotic associations, from free-living to putative obligate symbionts. We pose key questions regarding genome evolution vis-à-vis the transition of dinoflagellates from free-living to symbiotic and propose strategies for future research to better understand coral-dinoflagellate and other eukaryote-eukaryote symbioses.

Since the discovery of Chromera velia as a novel coral-associated microalga, this organism has attracted interest because of its unique evolutionary position between the photosynthetic dinoflagellates and the parasitic apicomplexans. The nature of the relationship between Chromera and its coral host is controversial. Is it a mutualism, from which both participants benefit, a parasitic relationship, or a chance association? To better understand the interaction, larvae of the common Indo-Pacific reef-building coral Acropora digitifera were experimentally infected with Chromera, and the impact on the host transcriptome was assessed at 4, 12, and 48 h post-infection using Illumina RNA-Seq technology. The transcriptomic response of the coral to Chromera was complex and implies that host immunity is strongly suppressed, and both phagosome maturation and the apoptotic machinery is modified. These responses differ markedly from those described for infection with a competent strain of the coral mutualist Symbiodinium, instead resembling those of vertebrate hosts to parasites and/or pathogens such as Mycobacterium tuberculosis. Consistent with ecological studies suggesting that the association may be accidental, the transcriptional response of A. digitifera larvae leads us to conclude that Chromera could be a coral parasite, commensal, or accidental bystander, but certainly not a beneficial mutualist., 論文

Despite the ecological significance of the relationship between reef-building corals and intracellular photosynthetic dinoflagellates of the genus Symbiodinium, very little is known about the molecular mechanisms involved in its establishment. Indeed, microarray-based analyses point to the conclusion that host gene expression is largely or completely unresponsive during the establishment of symbiosis with a competent strain of Symbiodinium. In this study, the use of Illumina RNA-Seq technology allowed detection of a transient period of differential expression involving a small number of genes (1073 transcripts

It has been previously demonstrated that dicotyledonous plants perform rhizophagy, a process in which live microbial cells are engulfed by root cells and digested to acquire the nutrients from the microbes. Here we tested the hypothesis that rhizophagy is a mechanism of nutrient acquisition that is not restricted to dicotyledonous plants. We report that the monocotyledonous species sugarcane (Saccharum officinarum x spontaneum), grown in controlled axenic conditions, incorporated yeast cells into root cells. This suggests that rhizophagy is an evolutionarily conserved trait that predates the divergence of dicot and monocot species. To explore the potential relevance and practical application of rhizophagy, we investigated brewers' yeast (Saccharomyces cerevisiae), a waste product of the brewing industry, for its role as biofertilizer. The addition of live or dead yeast to fertilized soil substantially increased the nitrogen (N) and phosphorus (P) content of roots and shoots of tomato (Solanum lycopersicum) and young sugarcane plants. Yeast addition to soil also increased the root-to-shoot ratio in both species and induced species-specific morphological changes that included increased tillering in sugarcane and greater shoot biomass in tomato plants. These findings support the notion that brewers' yeast is a cost-effective biofertilizer that improves not only plant nutrition but also plant vigor during the early growth phase. It remains to be established which yeast-derived substances trigger the observed plant growth effects, and how rhizophagy contributes to plant nutrient acquisition.

Motivation: Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models. Results: Here, we construct Hopfield networks from static gene-expression data and demonstrate that cancer subtypes can be characterized by different attractors of the Hopfield network. We evaluate the clustering performance of the network and find that it is comparable with traditional methods but offers additional advantages including a dynamic model of the energy landscape and a unification of clustering, feature selection and network inference. We visualize the Hopfield attractor landscape and propose a pruning method to generate sparse networks for feature selection and improved understanding of feature relationships. Availability: Software and datasets are available at http://acb.qfab.org/acb/hclust/ Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Background Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset.

Recent advances in molecular technologies have opened up unprecedented opportunities for molecular ecologists to better understand the molecular basis of traits of ecological and evolutionary importance in almost any organism. Nevertheless, reliable and systematic inference of functionally relevant information from these masses of data remains challenging. The aim of this review is to highlight how the Gene Ontology (GO) database can be of use in resolving this challenge. The GO provides a largely species-neutral source of information on the molecular function, biological role and cellular location of tens of thousands of gene products. As it is designed to be species-neutral, the GO is well suited for cross-species use, meaning that, functional annotation derived from model organisms can be transferred to inferred orthologues in newly sequenced species. In other words, the GO can provide gene annotation information for species with nonannotated genomes. In this review, we describe the GO database, how functional information is linked with genes/gene products in model organisms, and how molecular ecologists can utilize this information to annotate their own data. Then, we outline various applications of GO for enhancing the understanding of molecular basis of traits in ecologically relevant species. We also highlight potential pitfalls, provide step-by-step recommendations for conducting a sound study in nonmodel organisms, suggest avenues for future research and outline a strategy for maximizing the benefits of a more ecological and evolutionary genomics-oriented ontology by ensuring its compatibility with the GO.

Bacteria and archaea can exchange genetic material across lineages through processes of lateral genetic transfer (LGT). Collectively, these exchange relationships can be modeled as a network and analyzed using concepts from graph theory. In particular, densely connected regions within an LGT network have been defined as genetic exchange communities (GECs). However, it has been problematic to construct networks in which edges solely represent LGT. Here we apply term frequency-inverse document frequency (TF-IDF), an alignment-free method originating from document analysis, to infer regions of lateral origin in bacterial genomes. We examine four empirical datasets of different size (number of genomes) and phyletic breadth, varying a key parameter (word length k) within bounds established in previous work. We map the inferred lateral regions to genes in recipient genomes, and construct networks in which the nodes are groups of genomes, and the edges natively represent LGT. We then extract maximum and maximal cliques (i.e., GECs) from these graphs, and identify nodes that belong to GECs across a wide range of k. Most surviving lateral transfer has happened within these GECs. Using Gene Ontology enrichment tests we demonstrate that biological processes associated with metabolism, regulation and transport are often over-represented among the genes affected by LGT within these communities. These enrichments are largely robust to change of k.

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.

Many microbes can acquire genetic material from their environment and incorporate it into their genome, a process known as lateral genetic transfer (LGT). Computational approaches have been developed to detect genomic regions of lateral origin, but typically lack sensitivity, ability to distinguish donor from recipient and scalability to very large datasets. To address these issues we have introduced an alignment-free method based on ideas from document analysis, term frequency-inverse document frequency (TF-IDF). Here we examine the performance of TF-IDF on three empirical datasets: 27 genomes of Escherichia coli and Shigella, 110 genomes of enteric bacteria and 143 genomes across 12 bacterial and three archaeal phyla. We investigate the effect of k-mer size, gap size and delineation of groups on the inference of genomic regions of lateral origin, finding an interplay among these parameters and sequence divergence. Because TF-IDF identifies donor groups and delineates regions of lateral origin within recipient genomes, aggregating these regions by gene enables us to explore, for the first time, the mosaic nature of lateral genes including the multiplicity of biological sources, ancestry of transfer and over-writing by subsequent transfers. We carry out Gene Ontology enrichment tests to investigate which biological processes are potentially affected by LGT.

Alignment-free (AF) approaches have recently been highlighted as alternatives to methods based on multiple sequence alignment in phylogenetic inference. However, the sensitivity of AF methods to genome-scale evolutionary scenarios is little known. Here, using simulated microbial genome data we systematically assess the sensitivity of nine AF methods to three important evolutionary scenarios: sequence divergence, lateral genetic transfer (LGT) and genome rearrangement. Among these, AF methods are most sensitive to the extent of sequence divergence, less sensitive to low and moderate frequencies of LGT and most robust against genome rearrangement. We describe the application of AF methods to three well-studied empirical genome datasets and introduce a new application of the jackknife to assess node support. Our results demonstrate that AF phylogenomics is computationally scalable to multi-genome data and can generate biologically meaningful phylogenies and insights into microbial evolution.

Small RNAs (sRNAs) are widespread in bacteria and play critical roles in regulating physiological processes. They are best characterized in Escherichia coli K-12 MG1655, where 83 sRNAs constitute nearly 2% of the gene complement. Most sRNAs act by base pairing with a target mRNA, modulating its translation and/or stability; many of these RNAs share only limited complementarity to their mRNA target, and require the chaperone Hfq to facilitate base pairing. Little is known about the evolutionary dynamics of bacterial sRNAs. Here, we apply phylogenetic and network analyses to investigate the evolutionary processes and principles that govern sRNA gene distribution in 27 E. coli and Shigella genomes. We identify core (encoded in all 27 genomes) and variable sRNAs; more than two-thirds of the E. coli K-12 MG1655 sRNAs are core, whereas the others show patterns of presence and absence that are principally due to genetic loss, not duplication or lateral genetic transfer. We present evidence that variable sRNAs are less tightly integrated into cellular genetic regulatory networks than are the core sRNAs, and that Hfq facilitates posttranscriptional cross talk between the E. coli-Shigella core and variable genomes. Finally, we present evidence that more than 80% of genes targeted by Hfq-associated core sRNAs have been transferred within the E. coli-Shigella clade, and that most of these genes have been transferred intact. These results suggest that Hfq and sRNAs help integrate laterally acquired genes into established regulatory networks.

The widespread presence of antibiotic resistance and virulence among Staphylococcus isolates has been attributed in part to lateral genetic transfer (LGT), but little is known about the broader extent of LGT within this genus. Here we report the first systematic study of the modularity of genetic transfer among 13 Staphylococcus genomes covering four distinct named species. Using a topology-based phylogenetic approach, we found, among 1,354 sets of homologous genes examined, strong evidence of LGT in 368 (27.1%) gene sets, and weaker evidence in another 259 (19.1%). Within-gene and whole-gene transfer contribute almost equally to the topological discordance of these gene sets against a reference phylogeny. Comparing genetic transfer in single-copy and in multicopy gene sets, we observed a higher frequency of LGT in the latter, and a substantial functional bias in cases of whole-gene transfer (little such bias was observed in cases of fragmentary genetic transfer). We found evidence that lateral transfer, particularly of entire genes, impacts not only functions related to antibiotic, drug, and heavy-metal resistance, as well as membrane transport, but also core informational and metabolic functions not associated with mobile elements. Although patterns of sequence similarity support the cohesion of recognized species, LGT within S. aureus appears frequently to disrupt clonal complexes. Our results demonstrate that LGT and gene duplication play important parts in functional innovation in staphylococcal genomes.

A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.

Protein-protein interactions (PPIs) underpin key biological processes in cells. Describing and interpreting this network of interactions is a key focus of computational systems biology. While mouse is commonly used as a model system for mammalian biology, the description of mouse PPIs available in public interaction databases is remarkably poor. Collectively, public resources such as BIND, IntACT and MINT contain only a few thousand mouse PPIs, far behind the many tens or hundreds of thousands likely to exist. To supplement this lack and to take advantage of other high-throughput omic data sets in mouse, here we identify that portion of the human interactome with orthologs in mouse, and from that infer a mouse interolog network. By inferring interactions in mouse based on only the most closely related species with abundant PPI data (human), we create a view of mouse interactions enriched for shared mammalian biological processes. We also demonstrate that available methods for determining orthologs between even closely related species produce distinctly different results, and we propose an integrated view of mouse-human orthology from which to infer a broader interolog network.

Lateral genetic transfer (LGT) is an important adaptive force in evolution, contributing to metabolic, physiological and ecological innovation in most prokaryotes and some eukaryotes. Genomic sequences and other data have begun to illuminate the processes, mechanisms, quantitative extent and impact of LGT in diverse organisms, populations, taxa and environments; deep questions are being posed, and the provisional answers sometimes challenge existing paradigms. At the same time, there is an enhanced appreciation of the imperfections, biases and blind spots in the data and in analytical approaches. Here we identify and consider significant open questions concerning the role of LGT in genome evolution.

Pairwise local sequence alignment methods have been the prevailing technique to identify homologous nucleotides between related species. However, existing methods that identify and align all homologous nucleotides in one or more genomes have suffered from poor scalability and limited accuracy. We propose a novel method that couples a gapped extension heuristic with an efficient filtration method for identifying interspersed repeats in genome sequences. During gapped extension, we use the MUSCLE implementation of progressive global multiple alignment with iterative refinement. The resulting gapped extensions potentially contain alignments of unrelated sequence. We detect and remove such undesirable alignments using a hidden Markov model (HMM) to predict the posterior probability of homology. The HMM emission frequencies for nucleotide substitutions can be derived from any time-reversible nucleotide substitution matrix. We evaluate the performance of our method and previous approaches on a hybrid data set of real genomic DNA with simulated interspersed repeats. Our method outperforms a related method in terms of sensitivity, positive predictive value, and localizing boundaries of homology. The described methods have been implemented in freely available software, Repeatoire, available from: http://wwwabi.snv.jussieu.fr/public/Repeatoire. © 2006 IEEE.

Lateral genetic transfer (LGT) involves the movement of genetic material from one lineage into another and its subsequent incorporation into the new host genome via genetic recombination. Studies in individual taxa have indicated lateral origins for stretches of DNA of greatly varying length, from a few nucleotides to chromosome size. Here we analyze 1,462 sets of single-copy, putatively orthologous genes from 144 fully sequenced prokaryote genomes, asking to what extent complete genes and fragments of genes have been transferred and recombined in LGT. Using a rigorous phylogenetic approach, we find evidence for LGT in at least 476 (32.6%) of these 1,462 gene sets: 286 (19.6%) clearly show one or more "observable recombination breakpoints" within the boundaries of the open reading frame, while a further 190 (13.0%) yield trees that are topologically incongruent with the reference tree but do not contain a recombination breakpoint within the open reading frame. We refer to these gene sets as observable recombination breakpoint positive (ORB(+)) and negative (ORB(-)) respectively. The latter are prima facie instances of lateral transfer of an entire gene or beyond. We observe little functional bias between ORB(+) and ORB(-) gene sets, but find that incorporation of entire genes is potentially more frequent in pathogens than in nonpathogens. As ORB(+) gene sets are about 50% more common than ORB(-) sets in our data, the transfer of gene fragments has been relatively frequent, and the frequency of LGT may have been systematically underestimated in phylogenetic studies.

The genome sequence is an icon of early twenty-first century biology. Genomes of nearly 2000 cellular organisms, and from many thousands of organelles and viruses, are now in the public domain. For biological research in individual species, the genome sequence increasingly provides the common

The process of inferring phylogenetic trees from molecular sequences almost always starts with a multiple alignment of these sequences but can also be based on methods that do not involve multiple sequence alignment. Very little is known about the accuracy with which such alignment-free methods recover the correct phylogeny or about the potential for increasing their accuracy. We conducted a large-scale comparison of ten alignment-free methods, among them one new approach that does not calculate distances and a faster variant of our pattern-based approach; all distance-based alignment-free methods are freely available from http://www.bioinformatics.org.au (as Python package decaf+py). We show that most methods exhibit a higher overall reconstruction accuracy in the presence of high among-site rate variation. Under all conditions that we considered, variants of the pattern-based approach were significantly better than the other alignment-free methods. The new pattern-based variant achieved a speed-up of an order of magnitude in the distance calculation step, accompanied by a small loss of tree reconstruction accuracy. A method of Bayesian inference from k-mers did not improve on classical alignment-free (and distance-based) methods but may still offer other advantages due to its Bayesian nature. We found the optimal word length k of word-based methods to be stable across various data sets, and we provide parameter ranges for two different alphabets. The influence of these alphabets was analyzed to reveal a trade-off in reconstruction accuracy between long and short branches. We have mapped the phylogenetic accuracy for many alignment-free methods, among them several recently introduced ones, and increased our understanding of their behavior in response to biologically important parameters. In all experiments, the pattern-based approach emerged as superior, at the expense of higher resource consumption. Nonetheless, no alignment-free method that we examined recovers the correct phylogeny as accurately as does an approach based on maximum-likelihood distance estimates of multiply aligned sequences.

The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.

Motivation: Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. Results: Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. Availability: On request from the authors. Contact: m.ragan@imb.uq.edu.au

We describe a new method for using neural networks to predict residue contact pairs in a protein. The main inputs to the neural network are a set of 25 measures of correlated mutation between all pairs of residues in two "windows" of size 5 centered on the residues of interest. While the individual pair-wise correlations are a relatively weak predictor of contact, by training the network on windows of correlation the accuracy of prediction is significantly improved. The neural network is trained on a set of 100 proteins and then tested on a disjoint set of 1033 proteins of known structure. An average predictive accuracy of 21.7% is obtained taking the best L/2 predictions for each protein, where L is the sequence length. Taking the best L/10 predictions gives an average accuracy of 30.7%. The predictor is also tested on a set of 59 proteins from the CASP5 experiment. The accuracy is found to be relatively consistent across different sequence lengths, but to vary widely according to the secondary structure. Predictive accuracy is also found to improve by using multiple sequence alignments containing many sequences to calculate the correlations.

Fungi play important roles as decomposers, plant symbionts and pathogens in soils. The structure of fungal communities in the rhizosphere is the result of complex interactions among selection factors that may favour beneficial or detrimental relationships. Using culture-independent fungal community profiling, we have investigated the effects of nitrogen fertilizer dosage on fungal communities in soil and rhizosphere of field-grown sugarcane. The results show that the concentration of nitrogen fertilizer strongly modifies the composition but not the taxon richness of fungal communities in soil and rhizosphere. Increased nitrogen fertilizer dosage has a potential negative impact on carbon cycling in soil and promotes fungal genera with known pathogenic traits, uncovering a negative effect of intensive fertilization.

Summary: New high-resolution approaches for mapping ultrastructure of cells in 3D are leading to unprecedented quantities of spatial data. Here we present Illoura, a software tool for the integrated management, analysis and visualization of these data within a semantic context, and illustrate its capability by analysis of spatial relationships in mammalian beta cells. Availability: http://www.visiblecell.com/illoura Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

The metabolism of D-galactose is a major feature of red-algal physiology. We have cloned and sequenced a gene from the red alga Gracilaria gracilis that encodes a key enzyme of D-galactose metabolism, galactose-1-phosphate uridylyltransferase (GALT). This gene, designated GgGALT1, is apparently devoid of introns. A potential TATA box, four potential CAAT boxes, and a repeated sequence occur in the 5'-flanking region. The predicted 369-aa peptide shares significant sequence similarity with GALTs from other organisms (human, 47%; Saccharomyces cerevisiae, 49%; Solanum tuberosum, 49%). Southern-hybridization analysis reveals two related, but apparently not identical, GALT genes in the nuclear genome of G. gracilis. Sequence analysis indicates that the GgGALT1 enzyme lacks a rubredoxin "knuckle" motif, which in bacterial and fungal GALTs is involved in binding zinc. An open reading frame encoding a potential peptidyl tRNA hydrolase occurs 179 bp downstream from the GgGALT1 gene.

The Sulfolobus solfataricus P2 genome collaborators are poised to sequence the entire 3-Mbp genome of this crenarchaeote archaeon. About 80% of the genome has been sequenced to date, with the rest of the sequence being assembled fast. In this publication we introduce the genomic sequencing and automated analysis strategy and present intial data derived from the sequence analysis. After an overview of the general sequence features, metabolic pathway studies are explained, using sugar metabolism as an example. The paper closes with an overview of repetitive elements in S. solfataricus.

Motivation: Deciphering the modus operandi of dysregulated cellular mechanisms in cancer is critical to implicate novel cancer genes and develop effective anti-cancer therapies. Fundamental to this is meticulous tracking of the behavior of core modules, including complexes and pathways across specific conditions in cancer. Results: Here, we performed a straightforward yet systematic identification and comparison of modules across pancreatic normal and cancer tissue conditions by integrating PPI, gene-expression and mutation data. Our analysis revealed interesting change-patterns in gene composition and expression correlation particularly affecting modules responsible for genome stability. Although in most cases these changes indicated impairment of essential functions (e.g. of DNA damage repair), in several other cases we noticed strengthening of modules possibly abetting cancer. Some of these compensatory modules showed switches in transcription regulation and recruitment of tumor inducers (e.g. SOX2 through overexpression). In-depth analysis revealed novel genes in pancreatic cancer, which showed susceptibility to copy-number alterations (e.g. for USP15 in 17 of 67 cases), supported by literature evidence for their involvement in other tumors (e.g. USP15 in glioblastoma). Two of the identified genes, YWHAE and DISC1, further supported the nexus between neural genes and pancreatic carcinogenesis. Extension of this assessment to BRCA1 and BRCA2 breast tumors showed specific differences even across the two sub-types and revealed novel genes involved therein (e.g. TRIM5 and NCOA6). Availability: Our software CONTOURv1 is available at: http://bioinformatics.org.au/tools-data/. Contact: m.ragan@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

We have determined the nucleotide sequence of the nuclear gene encoding small-subunit ribosomal ribonucleic acid of the ciliate Anophryoides haemophila, a parasite of the American lobster Homarus americanus. The gene is 1763 bp in length, and has a guanosine-plus-cytosine content of 43.9%. Inferred phylogenetic frameworks strongly support the monophyly of the scuticociliates, and suggest that order Scuticociliatida should be elevated to at least subclass rank. Oligonucleotide probes based on A. haemophila ssurDNA can discriminate between DNAs of A. haemophila and other investigated hymenostome ciliates, and effectively prime polymerase chain reaction-based detection of A. haemophila deoxyribonucleic acid against at least a 1600-fold excess of total deoxyribonucleic acid from H. americanus. GENBANK/U51554

We have cloned and sequenced the single-copy nuclear gene (GapC) encoding the complete 335-amino acid cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPC) from the red alga Gracilaria verrucosa. The proline residue which contributes to the specificity of NAD+ binding in other GAPC-like proteins is present. Putative regulatory regions, including GC-rich regions, a GATA element, and 11-base T- and T/G-clusters, but excluding TATA- and CCAAT-boxes, were identified upstream. Two types of GapC cDNAs differing in polyadenylation site were characterized. An 80-bp phase-two spliceosomal intron was identified in a novel position interrupting the highly conserved cofactor-coding region I. The G. verrucosa GAPC was easily aligned with other known GAPC-type sequences. Inferred phylogenetic trees place red algae among the eukaryote crown taxa, although with modest bootstrap support and without stable resolution among related GAPC lineages.

We have cloned a nuclear gene from the marine red alga Gracilaria verrucosa that encodes the complete 779 amino-acid mitochondrial aconitase (m-ACN), the first characterized from a photosynthetic organism. The N-terminal 28 deduced amino acids are predicted to constitute the mitochondrial transit peptide, the first described from a red alga. Putative transcriptional cis-acting elements were identified in the upstream untranslated region. The G. verrucosa m-ACN gene (m-ACN) is present in a single copy and is located ca. 1.5 kb upstream from the single-copy polyubiquitin gene. The single spliceosomal intron is located near the 5' end of the region encoding the mature m-ACN in precisely the same location and phase as intron 2 in Caenorhabditis elegans m-ACN; sequences at its 3' and 5' splice junctions and at the predicted lariat branch point conform well to the eukaryote consensus sequences. Multiple protein-sequence alignment of m-ACN, bacterial aconitase (b-ACN) and iron-responsive element-binding protein (IRE-BP), and phylogenetic analyses, revealed that m-ACN does not share a recent common ancestry with either b-ACN or IRE-BP.

SUMMARY We investigated phylogenetic relationships among red algae of the order Bangiales by analysis of sequences of the nuclear gene encoding cytosolic small-subunit ribosomal RNA in Bangia atropurpurea (Roth) C. Ag. and eight samples representing seven species of Porphyra. The ssu-rDNA range from 1818 to 1845 nucleotides in length, with guanosine plus cytosine ratios between 47.0% and 48.6%. A group IC1 intron occurs in the B atropurpurea ssu-rDNAs at the same position as in P. spiralis var. amplifolia Oliveira Filho et Coll and several other eukaryote ssu-rDNAs. The nine sequences form a stable monophyletic group upon phylogenetic analysis. The ssu-rDNA from B. atropurpurea nests stably within the Porphyra group and is closely related to P. amplissima (Kjellm.) Setchell et Hus in Hus, making the genus Porphyra paraphyletic. No correlation is seen between phylogenetic position and number of cell layers in the Porphyra thallus. We discuss possible taxonomic and evolutionary implications of these observations.

The intergenic spacers (IGS) separating the 23S-like and 16S-like rDNAs of the two varieties of the human pathogenic fungus Cryptococcus neoformans were amplified, cloned and sequenced. The C. neoformans var. neoformans IGS was 2421 nt with 5S rRNA at positions 1228-1345 3' of the 23S-like rRNA. The C. neoformans var. gattii IGS was 2480 nt with 5S rRNA at positions 1268-1385 3' of the 23S-like rRNA. For both varieties the 5S rDNA genes were in the same orientation as the 16S-5.8-23S genes and encode a 118 nt molecule of identical sequence. Phylogenetic comparison of C. neoformans 5S rDNA with that of other fungi placed this fungus in close relationship with other basidiomycetes including Tremella mesenterica, Bullera alba, and Cryptococcus laurentii. A secondary structure model for the deduced 5S rRNA was constructed by comparative sequence analysis. Polymerase chain reaction-amplified IGS of 12 C. neoformans var. neoformans strains revealed extensive size variation ranging from 100 to 300 nt. Size variation between strains in the length of the IGS may be useful for distinguishing strains. Structurally, the IGS were characterized by the presence of occasional short direct GC-rich 19-nt repeats. Overall IGS sequence identity between the C. neoformans varieties was only 78.5%, in sharp contrast to the identical or nearly identical sequences for the rDNA genes, and suggests rapid evolution for IGS sequences.

We have used molecular characteristics to clarify the taxonomic status of three problematic gracilarioid algae, by comparison with existing data for the Gracilariaceae. Nucleotide sequences of nuclear-encoded 18S rDNA were determined for an unidentified species of Gracilariopsis from North Carolina, and two infertile strains of gracilarioid algae from Namibia, tentatively assigned to Gracilariopsis. The sequence from the North Carolinian alga was intermediate between those established previously for Gracilariopsis lemaneiformis and European Gracilariopsis sp., and represents a different, probably undescribed, species. Sequences from the Namibian plants were characteristic of Gracilaria, and very similar to those from Gracilaria verrucosa and two closely related congeners. In parsimony, distance and maximum-likelihood analyses of these sequences and others already published for the Gracilariaceae, the North Carolinian Gracilariopsis sp. consistently grouped with its congeners, and the two Namibian strains ...

The single-copy nuclear gene (GapA), encoding the plastid-localized glyceraldehyde-3-phosphate dehydrogenase (GAPDH) of the marine red alga Gracilaria verrucosa, has been cloned and sequenced. The GapA transcriptional initiation site was located 49 bp upstream of the start codon, and a putative TATA box was found 54 bp farther upstream. A spliceosomal intron was identified in the transit-peptide-encoding region in a position very similar to intron 1 of GapA and GapB of higher plants; no introns occur in the region encoding the mature protein. These observations provisionally suggest that both red algae and higher plants descend from a single ancestral photosynthetic eukaryote, i.e. that a single endosymbiotic event gave rise to red algal and higher-plant plastids.

Nuclear-encoded small-subunit (18S) ribosomal RNA genes of Chara australis R. Brown (C. corallina var. nobilis f. nobilis R.D.W.) and Nitella flexilis (L.) Ag. were amplified by polymerase chain reaction, cloned, and completely sequenced. Using structural criteria, the sequences were aligned with 18S ribosomal DNAs (rDNAs) from 11 other chlorophyll b-containing algae and six higher plants (embryophytes). Phylogenetic trees were inferred by distance, neighbor-joining, parsimony, and maximum-likelihood approaches; confidence intervals were estimated by bootstrapping, and nonrandomness of tree structure was confirmed by permutation tests. 18S rDNAs of c. australis and two Nitella species were found to be specifically related and, together with 18S rDNAs of Chlorokybus atmophyticus Geitler, Klebsormidium flaccidum (A. Br.) Silva, Mattox, et Blackwell, and two Coleochaete species, support a robust monophyletic group (Charaphyceae). Although most trees favored a specific sister-group relationship between Charophyceae and embryophytes, statistical tests revealed that a sister-group relationship between Charophyceae and Chlorophyceae could not be ruled out. Additional complete sequences from 18S rDNAs of lower land plants may be required to resolve phylogenetic relationships among these organisms.

Lateral genetic transfer (LGT) is a major source of phenotypic innovation among bacteria. Determinants for antibiotic resistance and other adaptive traits can spread rapidly, particularly by conjugative plasmids, but also phages and natural transformation. Each successive step from the uptake of foreign DNA, its genetic recombination and regulatory integration, to its establishment in the host population presents differential barriers and opportunities. The emergence of successive multidrug-resistant strains of Staphylococcus aureus illustrates the ongoing role of LGT in the combinatorial assembly of pathogens. The dynamic interplay among hosts, vectors, DNA elements, combinations of genetic determinants and environments constructs communities of genetic exchange. These relations can be abstracted as a graph, within which an exchange community might correspond to a path, transitively closed set, clique or near-clique. We provide a set-based definition, and review the features of actual genetic exchange communities (GECs), adopting first a knowledge-driven approach based on literature, and then a synoptic data-centric bioinformatic approach. GECs are diverse, but share some common features. Differential opportunity and barriers to lateral genetic transfer create bacterial communities of exchange.

Background The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual. Results Here we present an objective framework for generating customised ontology slims for specific annotated datasets, exploiting information latent in the structure of the ontology graph and in the annotation data. This framework combines ontology engineering approaches, and a data-driven algorithm that draws on graph and information theory. We illustrate this method by application to GO, generating GO slims at different information thresholds, characterising their depth of semantics and demonstrating the resulting gains in statistical power. Conclusions Our GO slim creation pipeline is available for use in conjunction with any GO-annotated dataset, and creates dataset-specific, objectively defined slims. This method is fast and scalable for application to other biomedical ontologies.