Magnetic structure of Yb2Pt2Pb: Ising moments on the Shastry-Sutherland lattice.

Neutron diffraction measurements were carried out on single crystals and powders of Yb2Pt2Pb, where Yb moments form two interpenetrating planar sublattices of orthogonal dimers, a geometry known as Shastry-Sutherland lattice, and are stacked along the c axis in a ladder geometry. Yb2Pt2Pb orders antiferromagnetically at TN=2.07K, and the magnetic structure determined from these measurements features the interleaving of two orthogonal sublattices into a 5×5×1 magnetic supercell that is based on stripes with moments perpendicular to the dimer bonds, which are along (110) and (−110). Magnetic fields applied along (110) or (−110) suppress the antiferromagnetic peaks from an individual sublattice, but leave the orthogonal sublattice unaffected, evidence for the Ising character of the Yb moments in Yb2Pt2Pb that is supported by point charge calculations. Specific heat, magnetic susceptibility, and electrical resistivity measurements concur with neutron elastic scattering results that the longitudinal critical fluctuations are gapped with ΔE≃0.07meV

Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation

Mapping Meaning : Critical Cartographies for Participatory Water Management in Taita Hills, Kenya

Participation of local people is often neglected in natural resource management, which leads to failure to understand the social aspects and historical construction of environmental problems. Participatory mapping can enhance the communication of local spatial knowledge for management processes and challenge the official maps and other spatial representations produced by state authorities and scientists. In this study, we analyze what kind of social meanings can be revealed through a multimethod participatory mapping process focusing on water resources in Taita Hills, Kenya. The participatory mapping clearly complicates the simplified image of the physical science mappings, typically depicting natural water supply, by addressing the impacts of contamination, inadequate infrastructure, poverty, distance to the sources, and restrictions in their uses on people's access to water. Moreover, this shared exercise is able to trigger discussion on issues that cannot always be localized but still contribute to place making. Local historical accounts reveal the social and political drivers of the current water-related problems, making explicit the political ecology dynamics in the area.Peer reviewe

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

<p>Abstract</p> <p>Background</p> <p>Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.</p> <p>Results</p> <p>To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (<it>e.g</it>., for biomolecular sequences, alignments, structures) and functionality (<it>e.g</it>., to parse/write standard file formats).</p> <p>Conclusions</p> <p>PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at <url>http://muralab.org/PaPy</url>, and includes extensive documentation and annotated usage examples.</p

A Case Study for Large-Scale Human Microbiome Analysis Using JCVI’s Metagenomics Reports (METAREP)

As metagenomic studies continue to increase in their number, sequence volume and complexity, the scalability of biological analysis frameworks has become a rate-limiting factor to meaningful data interpretation. To address this issue, we have developed JCVI Metagenomics Reports (METAREP) as an open source tool to query, browse, and compare extremely large volumes of metagenomic annotations. Here we present improvements to this software including the implementation of a dynamic weighting of taxonomic and functional annotation, support for distributed searches, advanced clustering routines, and integration of additional annotation input formats. The utility of these improvements to data interpretation are demonstrated through the application of multiple comparative analysis strategies to shotgun metagenomic data produced by the National Institutes of Health Roadmap for Biomedical Research Human Microbiome Project (HMP) (http://nihroadmap.nih.gov). Specifically, the scalability of the dynamic weighting feature is evaluated and established by its application to the analysis of over 400 million weighted gene annotations derived from 14 billion short reads as predicted by the HMP Unified Metabolic Analysis Network (HUMAnN) pipeline. Further, the capacity of METAREP to facilitate the identification and simultaneous comparison of taxonomic and functional annotations including biological pathway and individual enzyme abundances from hundreds of community samples is demonstrated by providing scenarios that describe how these data can be mined to answer biological questions related to the human microbiome. These strategies provide users with a reference of how to conduct similar large-scale metagenomic analyses using METAREP with their own sequence data, while in this study they reveal insights into the nature and extent of variation in taxonomic and functional profiles across body habitats and individuals. Over one thousand HMP WGS datasets and the latest open source code are available at http://www.jcvi.org/hmp-metarep

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.https://doi.org/10.1186/1471-2105-12-35

From proteomic analysis to potential therapeutic targets: functional profile of two lung cancer cell lines, A549 and SW900, widely studied in pre-clinical research

Lung cancer is a serious health problem and the leading cause of cancer death worldwide. The standard use of cell lines as in vitro pre-clinical models to study the molecular mechanisms that drive tumorigenesis and access drug sensitivity/effectiveness is of undisputable importance. Label-free mass spectrometry and bioinformatics were employed to study the proteomic profiles of two representative lung cancer cell lines and to unravel the specific biological processes. Adenocarcinoma A549 cells were enriched in proteins related to cellular respiration, ubiquitination, apoptosis and response to drug/hypoxia/oxidative stress. In turn, squamous carcinoma SW900 cells were enriched in proteins related to translation, apoptosis, response to inorganic/organic substances and cytoskeleton organization. Several proteins with differential expression were related to cancer transformation, tumor resistance, proliferation, migration, invasion and metastasis. Combined analysis of proteome and interactome data highlighted key proteins and suggested that adenocarcinoma might be more prone to PI3K/Akt/mTOR and topoisomerase IIα inhibitors, and squamous carcinoma to Ck2 inhibitors. Moreover, ILF3 overexpression in adenocarcinoma, and PCNA and NEDD8 in squamous carcinoma shows them as promising candidates for therapeutic purposes. This study highlights the functional proteomic differences of two main subtypes of lung cancer models and hints several targeted therapies that might assist in this type of cancer.publishe

Horizontal gene transfer in Histophilus somni and its role in the evolution of pathogenic strain 2336, as determined by comparative genomic analyses

<p>Abstract</p> <p>Background</p> <p>Pneumonia and myocarditis are the most commonly reported diseases due to <it>Histophilus somni</it>, an opportunistic pathogen of the reproductive and respiratory tracts of cattle. Thus far only a few genes involved in metabolic and virulence functions have been identified and characterized in <it>H. somni </it>using traditional methods. Analyses of the genome sequences of several <it>Pasteurellaceae </it>species have provided insights into their biology and evolution. In view of the economic and ecological importance of <it>H. somni</it>, the genome sequence of pneumonia strain 2336 has been determined and compared to that of commensal strain 129Pt and other members of the <it>Pasteurellaceae</it>.</p> <p>Results</p> <p>The chromosome of strain 2336 (2,263,857 bp) contained 1,980 protein coding genes, whereas the chromosome of strain 129Pt (2,007,700 bp) contained only 1,792 protein coding genes. Although the chromosomes of the two strains differ in size, their average GC content, gene density (total number of genes predicted on the chromosome), and percentage of sequence (number of genes) that encodes proteins were similar. The chromosomes of these strains also contained a number of discrete prophage regions and genomic islands. One of the genomic islands in strain 2336 contained genes putatively involved in copper, zinc, and tetracycline resistance. Using the genome sequence data and comparative analyses with other members of the <it>Pasteurellaceae</it>, several <it>H. somni </it>genes that may encode proteins involved in virulence (<it>e.g</it>., filamentous haemaggutinins, adhesins, and polysaccharide biosynthesis/modification enzymes) were identified. The two strains contained a total of 17 ORFs that encode putative glycosyltransferases and some of these ORFs had characteristic simple sequence repeats within them. Most of the genes/loci common to both the strains were located in different regions of the two chromosomes and occurred in opposite orientations, indicating genome rearrangement since their divergence from a common ancestor.</p> <p>Conclusions</p> <p>Since the genome of strain 129Pt was ~256,000 bp smaller than that of strain 2336, these genomes provide yet another paradigm for studying evolutionary gene loss and/or gain in regard to virulence repertoire and pathogenic ability. Analyses of the complete genome sequences revealed that bacteriophage- and transposon-mediated horizontal gene transfer had occurred at several loci in the chromosomes of strains 2336 and 129Pt. It appears that these mobile genetic elements have played a major role in creating genomic diversity and phenotypic variability among the two <it>H. somni </it>strains.</p

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies