The integrated microbial genomes (IMG) system in 2007: data content and analysis tool extensions

The integrated microbial genomes (IMG) system is a data management, analysis and annotation platform for all publicly available genomes. IMG contains both draft and complete JGI microbial genomes integrated with all other publicly available genomes from all three domains of life, together with a large number of plasmids and viruses. IMG provides tools and viewers for analyzing and annotating genomes, genes and functions, individually or in a comparative context. Since its first release in 2005, IMG's data content and analytical capabilities have been constantly expanded through quarterly releases. IMG is provided by the DOE-Joint Genome Institute (JGI) and is available from http://img.jgi.doe.gov

IMG/M: a data management and analysis system for metagenomes

IMG/M is a data management and analysis system for microbial community genomes (metagenomes) hosted at the Department of Energy's (DOE) Joint Genome Institute (JGI). IMG/M consists of metagenome data integrated with isolate microbial genomes from the Integrated Microbial Genomes (IMG) system. IMG/M provides IMG's comparative data analysis tools extended to handle metagenome data, together with metagenome-specific analysis tools. IMG/M is available at http://img.jgi.doe.gov/

IMG/M: the integrated metagenome data management and comparative analysis system

The integrated microbial genomes and metagenomes (IMG/M) system provides support for comparative analysis of microbial community aggregate genomes (metagenomes) in a comprehensive integrated context. IMG/M integrates metagenome data sets with isolate microbial genomes from the IMG system. IMG/M's data content and analytical capabilities have been extended through regular updates since its first release in 2007. IMG/M is available at http://img.jgi.doe.gov/m. A companion IMG/M systems provide support for annotation and expert review of unpublished metagenomic data sets (IMG/M ER: http://img.jgi.doe.gov/mer)

Measuring the effects through time of the influence of visuomotor and visuotactile synchronous stimulation on a virtual body ownership illusion

Previous studies have examined the experience of owning a virtual surrogate body or body part through specific combinations of cross-modal multisensory stimulation. Both visuomotor (VM) and visuotactile (VT) synchronous stimulation have been shown to be important for inducing a body ownership illusion, each tested separately or both in combination. In this study we compared the relative importance of these two cross-modal correlations, when both are provided in the same immersive virtual reality setup and the same experiment. We systematically manipulated VT and VM contingencies in order to assess their relative role and mutual interaction. Moreover, we present a new method for measuring the induced body ownership illusion through time, by recording reports of breaks in the illusion of ownership ("breaks") throughout the experimental phase. The balance of the evidence, from both questionnaires and analysis of the breaks, suggests that while VM synchronous stimulation contributes the greatest to the attainment of the illusion, a disruption of either (through asynchronous stimulation) contributes equally to the probability of a break in the illusion

Altered visual feedback from an embodied avatar unconsciously influences movement amplitude and muscle activity

Evidence suggests that the sense of the position of our body parts can be surreptitiously deceived, for instance through illusory visual inputs. However, whether altered visual feedback during limb movement can induce substantial unconscious motor and muscular adjustments is not known. To address this question, we covertly manipulated virtual body movements in immersive virtual reality. Participants were instructed to flex their elbow to 90° while tensing an elastic band, as their virtual arm reproduced the same, a reduced (75°), or an amplified (105°) movement. We recorded muscle activity using electromyography, and assessed body ownership, agency and proprioception of the arm. Our results not only show that participants compensated for the avatar’s manipulated arm movement while being completely unaware of it, but also that it is possible to induce unconscious motor adaptations requiring significant changes in muscular activity. Altered visual feedback through body ownership illusions can influence motor performance in a process that bypasses awareness

Genomic Analysis of the Hydrocarbon-Producing, Cellulolytic, Endophytic Fungus Ascocoryne sarcoides

The microbial conversion of solid cellulosic biomass to liquid biofuels may provide a renewable energy source for transportation fuels. Endophytes represent a promising group of organisms, as they are a mostly untapped reservoir of metabolic diversity. They are often able to degrade cellulose, and they can produce an extraordinary diversity of metabolites. The filamentous fungal endophyte Ascocoryne sarcoides was shown to produce potential-biofuel metabolites when grown on a cellulose-based medium; however, the genetic pathways needed for this production are unknown and the lack of genetic tools makes traditional reverse genetics difficult. We present the genomic characterization of A. sarcoides and use transcriptomic and metabolomic data to describe the genes involved in cellulose degradation and to provide hypotheses for the biofuel production pathways. In total, almost 80 biosynthetic clusters were identified, including several previously found only in plants. Additionally, many transcriptionally active regions outside of genes showed condition-specific expression, offering more evidence for the role of long non-coding RNA in gene regulation. This is one of the highest quality fungal genomes and, to our knowledge, the only thoroughly annotated and transcriptionally profiled fungal endophyte genome currently available. The analyses and datasets contribute to the study of cellulose degradation and biofuel production and provide the genomic foundation for the study of a model endophyte system

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Methods for Evaluating Depth Perception in a Large-Screen Immersive Display

We perform an experiment on distance perception in a large-screen display immersive virtual environment. Large-screen displays typically make direct blind walking tasks impossible, despite them being a popular distance response measure in the real world and in head-mounted displays. We use a movable large-screen display to compare direct blind walking and indirect triangulated pointing with monoscopic viewing. We find that participants judged distances to be 89.4% ± 28.7% and 108.5% ± 44.9% of their actual distances in the direct blind walking and triangulated pointing conditions, respectively. However, we find no statistically significant difference between these approaches. This work adds to the limited number of research studies on egocentric distance judgments with a large display wall for distances of 3-5 meters. It is the first, to our knowledge, to perform direct blind walking with a large display

MPW: the Metabolic Pathways Database.

The Metabolic Pathwasy Database (MPW) (www.biobase.com/emphome.html/homepage. html.pags/pathways.html) a derivative of EMP (www.biobase.com/EMP) plays a fundamental role in the technology of metabolic reconstructions from sequenced genomes under the PUMA (www.mcs.anl.gov/home/compbio/PUMA/Production/ ReconstructedMetabolism/reconstruction.html), WIT (www.mcs.anl.gov/home/compbio/WIT/wit.html ) and WIT2 (beauty.isdn.msc.anl.gov/WIT2.pub/CGI/user.cgi) systems. In October 1997, it included some 2800 pathway diagrams covering primary and secondary metabolism, membrane transport, signal transduction pathways, intracellular traffic, translation and transcription. In the current public release of MPW (beauty.isdn.mcs.anl.gov/MPW), the encoding is based on the logical structure of the pathways and is represented by the objects commonly used in electronic circuit design. This facilitates drawing and editing the diagrams and makes possible automation of the basic simulation operations such as deriving stoichiometric matrices, rate laws, and, ultimately, dynamic models of metabolic pathways. Individual pathway diagrams, automatically derived from the original ASCII records, are stored as SGML instances supplemented by relational indices. An auxiliary database of compound names and structures, encoded in the SMILES format, is maintained to unambiguously connect the pathways to the chemical structures of their intermediates