Distributed, High-Performance and Grid Computing in Computational Biology

Distributed, High-Performance and Grid Computing in Computational Biology

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This volume of the Springer Lecture Notes in Computer Science series contains the contributionspresentedatthe InternationalWorkshoponDistributed, High- PerformanceandGridComputinginComputationalBiology2006(GCCB2006) heldinEilat, January21, 2007inconjunctionwiththe?fthEuropeanConference on Computational Biology (ECCB 2006). Modern computational biology and bioinformatics are characterized by large and complex-structured data and by applications requiring considerable c- puting resources, such as processing units, storage elements and software p- grams. In addition, these disciplines are intrinsically geographically distributed in terms of their instruments, communities and computing resources. Tackling the computational challenges in computational biology and bioinformatics - creasingly requires high-end and distributed computing infrastructures, systems and tools. The main objective of this workshop is to bring together researchers andpractitioners fromthese areasto discuss ideas andexperiences in developing and applying distributed, high-performance and grid computing technology to problems in computational biology and bioinformatics. The challenges in distributed, high-performance and grid computing in - ology and biotechnology are inherently more complicated than those in such domainsasphysics, engineering and conventional business areas. Some of the added complexities arise from the: a€“ Conceptual complexity of biological knowledge and the methodologies used in biology and biotechnology a€“ Need to understand biological systems and processes at a detailed mec- nistic, systemic and quantitative level across several levels of organization (ranging from molecules to cells, populations, and the environment) a€“ Growingavailabilityofhigh-throughputdatafromgenomics, transcriptomics, proteomics, metabolomics and other high-throughput methods a€“ Widespread use of image data in biological research and development (- croscopy, NMR, MRI, PET, X-ray, CT, etc.First, it requires a compression ratio of about 25:1 to keep the transmission time below 10 msec for most of the network traffic. ... compression cost and the reduction of transmission time. Table 2. Compression ratio for four genomic data analysis applications Differencing ... Ziv-Lempel Total (zlib) compression To measure the compression ratios the Varg system can achieve, we have used four 15-minuteanbsp;...

Title:Distributed, High-Performance and Grid Computing in Computational Biology
Author:Werner Dubitzky
Publisher:Springer Science & Business Media - 2007-01-03


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