Background Most tailed bacteriophages (phages) feature linear dsDNA genomes. Characterizing novel phages requires an understanding of complete genome sequences, including the definition of genome physical ends. Result We sequenced 48 Bacillus cereus phage isolates and analyzed Next-generation sequencing (NGS) data to resolve the genome configuration of these novel phages. Most assembled contigs featured reads that mapped to both contig ends and formed circularized contigs. Independent assemblies of 31 nearly identical I48-like Bacillus phage isolates allowed us to observe that the assembly programs tended to produce random cleavage on circularized contigs. However, currently available assemblers were not capable of reporting the underlying phage genome configuration from sequence data. To identify the genome configuration of sequenced phage in silico , a terminus prediction method was developed by means of ‘neighboring coverage ratios’ and ‘read edge frequencies’ from read alignment files. Termini were confirmed by primer walking and supported by phylogenetic inference of large DNA terminase protein sequences. Conclusions The Terminus package using phage NGS data along with the contig circularity could efficiently identify the proximal positions of phage genome terminus. Complete phage genome sequences allow a proposed characterization of the potential packaging mechanisms and more precise genome annotation.

The purpose of this study was to evaluate the effects of environmentally relevant dietary MeHg exposures on adult female yellow perch (Perca flavescens) and female zebrafish (Danio rerio) ovarian development and reproduction. Yellow perch were used in the study for their socioeconomic and ecological importance within the Great Lakes basin, and the use of zebrafish allowed for a detailed analysis of the molecular effects of MeHg following a whole life-cycle exposure. Chronic whole life dietary exposure of F1 zebrafish to MeHg mimics realistic wildlife exposure scenarios, and the twenty-week adult yellow perch exposure (where whole life-cycle exposures are difficult) captures early seasonal ovarian development. For both species, target dietary accumulation values were achieved prior to analyses. In zebrafish, several genes involved in reproductive processes were shown to be dysregulated by RNA-sequencing and quantitative real-time polymerase chain reaction (QPCR), but no significant phenotypic changes were observed regarding ovarian staging, fecundity, or embryo mortality. Yellow perch were exposed to dietary MeHg for 12, 16, or 20 weeks. In this species, a set of eight genes were assessed by QPCR in the pituitary, liver, and ovary, and no exposure-related changes were observed. The lack of genomic resources in yellow perch hinders the characterization of subtle molecular impacts. The ovarian somatic index, circulating estradiol and testosterone, and ovarian staging were not significantly altered by MeHg exposure in yellow perch. These results suggest that environmentally relevant MeHg exposures do not drastically reduce the reproductively important endpoints in these fish, but to capture realistic exposure scenarios, whole life-cycle yellow perch exposures are needed.

BACKGROUND:A number of researchers have speculated that neurological disorders are mostly due to the interaction of common susceptibility genes with environmental, epigenetic and stochastic factors. Genetic factors such as mutations, insertions, deletions and copy number variations (CNVs) are responsible for only a small subset of cases, suggesting unknown environmental contaminants play a role in triggering neurological disorders like idiopathic autism. Psychoactive pharmaceuticals have been considered as potential environmental contaminants as they are detected in the drinking water at very low concentrations. Preliminary studies in our laboratory identified gene sets associated with neuronal systems and human neurological disorders that were significantly enriched after treating fish brains with psychoactive pharmaceuticals at environmental concentrations. These gene expression inductions were associated with changes in fish behavior. Here, we tested the hypothesis that similar treatments would alter in vitro gene expression associated with neurological disorders (including autism) in human neuronal cells. We differentiated and treated human SK-N-SH neuroblastoma cells with a mixture (fluoxetine, carbamazepine and venlafaxine) and valproate (used as a positive control to induce autism-associated profiles), followed by transcriptome analysis with RNA-Seq approach.RESULTS:We found that psychoactive pharmaceuticals and valproate significantly altered neuronal gene sets associated with human neurological disorders (including autism-associated sets). Moreover, we observed that altered expression profiles in human cells were similar to gene expression profiles previously identified in fish brains.CONCLUSIONS:Psychoactive pharmaceuticals at environmental concentrations altered in vitro gene expression profiles of neuronal growth, development and regulation. These expression patterns were associated with potential neurological disorders including autism, suggested psychoactive pharmaceuticals at environmental concentrations might mimic, aggravate, or induce neurological disorders.

Compared to Beowulf clusters and shared-memory machines, GPU and FPGA are emerging alternative architectures that provide massive parallelism and great computational capabilities. These architectures can be utilized to run compute-intensive algorithms to analyze ever-enlarging datasets and provide scalability. In this paper, we present four implementations of K-means data clustering algorithm for different high performance computing platforms. These four implementations include a CUDA implementation for GPUs, a Mitrion C implementation for FPGAs, an MPI implementation for Beowulf compute clusters, and an OpenMP implementation for shared-memory machines. The comparative analyses of the cost of each platform, difficulty level of programming for each platform, and the performance of each implementation are presented.

Bioinformatics is an exciting new interdiscipline. This book introduces how computer hardware platforms (computer cluster, FPGA, and GPU) can be used to accelerate time-consuming bioinformatics applications and also introduces two bioinformatics data analysis pipelines to extract knowledge from large amount of biological data. This book has the following contents: A parallel phylogenetic bootstrap analysis program that can greatly reduce the computational time when running one a computer cluster, a parallel k-means data clustering algorithm that can run on a Field Programmable Gate Array (FPGA), a parallel k-means data clustering algorithm that can run on a modern Graphics Processing Unit (GPU), a data analysis pipeline that can apply gene set enrichment analysis on non-human organisms to predict human health consequences of a given treatment based on the responses in non-human organisms, and a data analysis pipeline to detect differentially expressed genes in organisms without a reference genome sequence using transcriptome analysis by sequencing (RNA-seq)

BACKGROUND:Methods for gene-class testing, such as Gene Set Enrichment Analysis (GSEA), incorporate biological knowledge into the analysis and interpretation of microarray data by comparing gene expression patterns to pathways, systems and emergent phenotypes. However, to use GSEA to its full capability with non-mammalian model organisms, a microarray platform must be annotated with human gene symbols. Doing so enables the ability to relate a model organism's gene expression, in response to a given treatment, to potential human health consequences of that treatment. We enhanced the annotation of a microarray platform from a non-mammalian model organism, and then used the GSEA approach in a reanalysis of a study examining the biological significance of acute and chronic methylmercury exposure on liver tissue of fathead minnow (Pimephales promelas). Using GSEA, we tested the hypothesis that fathead livers, in response to methylmercury exposure, would exhibit gene expression patterns similar to diseased human livers.RESULTS:We describe an enhanced annotation of the fathead minnow microarray platform with human gene symbols. This resource is now compatible with the GSEA approach for gene-class testing. We confirmed that GSEA, using this enhanced microarray platform, is able to recover results consistent with a previous analysis of fathead minnow exposure to methylmercury using standard analytical approaches. Using GSEA to compare fathead gene expression profiles to human phenotypes, we also found that fathead methylmercury-treated livers exhibited expression profiles that are homologous to human systems & pathways and results in damage that is similar to those of human liver damage associated with hepatocellular carcinoma and hepatitis B.CONCLUSIONS:This study describes a powerful resource for enabling the use of non-mammalian model organisms in the study of human health significance. Results of microarray gene expression studies involving fathead minnow, typically used for aquatic ecological toxicology studies, can now be used to generate hypotheses regarding consequences of contaminants and other stressors on humans. The same approach can be used with other model organisms with microarray platforms annotated in a similar manner.

A marine propulsion auxiliary cooling system is provided by an electric auxiliary water pump (36) pumping sea water to cool the engine (4) and/or fuel line (14) after turn off of the engine to prevent vaporization of the fuel, or in response to another given engine condition.

This review will introduce areas of evolutionary research that require substantial computing resources using the examples of phylogenetic reconstruction and homology searching. We will discuss the commonly used analytical approaches and computational tools. We will discuss two computing environments employed by academic evolutionary researchers. We present a simple empirical demonstration of scalable cluster computing using the Apple Xserve solution for phylogenetic reconstruction and homology searching. We conclude with comments about tool development for evolutionary biology and Open Source strategies to promote scientific inquiry.