ViPR: an open bioinformatics database and analysis resource for virology research

The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal ‘Workbenches’ for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Pre-Bed Casein Protein Supplementation Does Not Enhance Acute Functional Recovery in Physically Active Males and Females When Exercise is Performed in the Morning

This study examined whether consuming casein protein (CP) pre-sleep could accelerate acute recovery following muscle-damaging exercise. Thirty-nine active males and females performed 100 drop jumps in the morning, consumed their habitual diet during the day, and then within 30 min pre-bed consumed either ~40 g of CP (n = 19) or ~40 g of a carbohydrate-only control (CON) (n = 20). Maximal isometric voluntary contractions (MIVC), countermovement jumps (CMJ), pressure-pain threshold (PPT), subjective muscle soreness and the brief assessment of mood adapted (BAM+) were measured pre, 24 and 48 h following the drop jumps. MIVC decreased in CP and CON post-exercise, peaking at 24 h post (CP: −8.5 ± 3.5 vs. CON: −13.0 ± 2.9%, respectively); however, no between-group differences were observed (p = 0.486; ηp2 =0.02). There were also no group differences in the recovery of CMJ height, PPT and BAM+ (p > 0.05). Subjective muscle soreness increased post-exercise, but no group differences were present at 24 h (CP: 92 ± 31 mm vs. CON: 90 ± 46 mm) or 48 h (CP: 90 ± 44 mm vs. CON: 80 ± 58 mm) (p > 0.05). These data suggest that pre-bed supplementation with ~40 g of CP is no more beneficial than CON for accelerating the recovery following muscle-damaging exercise

Yeast glucose pathways converge on the transcriptional regulation of trehalose biosynthesis

<p>Abstract</p> <p>Background</p> <p>Cellular glucose availability is crucial for the functioning of most biological processes. Our understanding of the glucose regulatory system has been greatly advanced by studying the model organism <it>Saccharomyces cerevisiae</it>, but many aspects of this system remain elusive. To understand the organisation of the glucose regulatory system, we analysed 91 deletion mutants of the different glucose signalling and metabolic pathways in <it>Saccharomyces cerevisiae</it> using DNA microarrays.</p> <p>Results</p> <p>In general, the mutations do not induce pathway-specific transcriptional responses. Instead, one main transcriptional response is discerned, which varies in direction to mimic either a high or a low glucose response. Detailed analysis uncovers established and new relationships within and between individual pathways and their members. In contrast to signalling components, metabolic components of the glucose regulatory system are transcriptionally more frequently affected. A new network approach is applied that exposes the hierarchical organisation of the glucose regulatory system.</p> <p>Conclusions</p> <p>The tight interconnection between the different pathways of the glucose regulatory system is reflected by the main transcriptional response observed. Tps2 and Tsl1, two enzymes involved in the biosynthesis of the storage carbohydrate trehalose, are predicted to be the most downstream transcriptional components. Epistasis analysis of <it>tps2</it>Δ double mutants supports this prediction. Although based on transcriptional changes only, these results suggest that all changes in perceived glucose levels ultimately lead to a shift in trehalose biosynthesis.</p

A consensus of core protein complex compositions for Saccharomyces cerevisiae

Analyses of biological processes would benefit from accurate definitions of protein complexes. High-throughput mass spectrometry data offer the possibility of systematically defining protein complexes; however, the predicted compositions vary substantially depending on the algorithm applied. We determine consensus compositions for 409 core protein complexes from Saccharomyces cerevisiae by merging previous predictions with a new approach. Various analyses indicate that the consensus is comprehensive and of high quality. For 85 out of 259 complexes not recorded in GO, literature search revealed strong support in the form of coprecipitation. New complexes were verified by an independent interaction assay and by gene expression profiling of strains with deleted subunits, often revealing which cellular processes are affected. The consensus complexes are available in various formats, including a merge with GO, resulting in 518 protein complex compositions. The utility is further demonstrated by comparison with binary interaction data to reveal interactions between core complexe

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

BACKGROUND: Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease. RESULTS: To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression". CONCLUSIONS: These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships

Additional file 3: of A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

Contribution of selection criteria to genetic interaction types. (A) Hierarchical clustering of GSTF pairs selected on growth-based genetic interaction scores, represented as in Fig. 3. Clustering was performed on the epistatic effects. GSTF pairs marked with a solid circle were also selected based on similarity in DNA binding. Colored branches depict example groups described in the text. (B) Hierarchical clustering of GSTF pairs selected based on similarity in DNA binding, represented as in A. GSTF pairs marked with a solid circle also exhibit a genetic interaction as derived by growth on agar plates [11]. (PDF 339 kb

Additional file 1: of A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

List of putative GSTFs and strains used. (XLSX 27 kb