288 research outputs found

    Sustainability Evaluation of Resident Building in Bosnia and Herzegovina

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    The energy system of resident building requires to be seen as the complex system with defined respective indicators including: economic, environmental and social indicators with respective sub-indicators. In our analysis, we will assumed that the energy system is a complex system which may interact with its surrounding by utilizing resources, exchange conversion system products, utilize economic benefits from conversion process and absorb the social consequences of conversion process. This evaluation will be based on the selection of a number of resident buildings as the potential options appropriate for the geographic, climate and cultural region. With multi-criteria method based on the selected number of indicators the sustainability index will be determined. In this evaluation attention is focused on the following resident buildings: Bosnian family house, Modern architecture dwelling, Traditional family house, Best choice of local family house. The finale result of this study will be presented in Sustainability Index rating for the options under consideration. It can be noticed that the quality of the selected objects is defined in relation to the Sustainability Index

    Bioinformatics on the Cloud Computing Platform Azure

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    We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development. Ā© 2014 Shanahan et al

    Grinder: a versatile amplicon and shotgun sequence simulator

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    We introduce Grinder (http://sourceforge.net/ projects/biogrinder/), an open-source bioinformatic tool to simulate amplicon and shotgun (genomic, metagenomic, transcriptomic and metatranscriptomic) datasets from reference sequences. This is the first tool to simulate amplicon datasets (e.g. 16S rRNA) widely used by microbial ecologists. Grinder can create sequence libraries with a specific community structure, Ī± and Ī² diversities and experimental biases (e.g. chimeras, gene copy number variation) for commonly used sequencing platforms. This versatility allows the creation of simple to complex read datasets necessary for hypothesis testing when developing bioinformatic software, benchmarking existing tools or designing sequence-based experiments. Grinder is particularly useful for simulating clinical or environmental microbial communities and complements the use of in vitro mock communities

    CGAT-core: a python framework for building scalable, reproducible computational biology workflows

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    In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment
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